Measuring Cross-Border Travel Times for 

Freight: 

Otay Mesa International Border Crossing 

Final Report 

September 2010 

 
. 

QUALITY ASSURANCE STATEMENT 

The Federal Highway Administration (FHWA) provides high-quality information to serve 

Government, industry, and the public in a manner that promotes public understanding. 

Standards and policies are used to ensure and maximize the quality, objectivity, utility, and 

integrity of its information. FHWA periodically reviews quality issues and adjusts its programs 

and processes to ensure continuous quality improvement. 
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FOREWORD 

In October 2008 The Federal Highway Administration contracted a study team to conduct a two-part 

study to assess technology for the measurement of travel times for trucks through the Otay Mesa 

international border crossing into the US. The primary goal was to evaluate the ability of one of two 

technologies to accurately record travel times through the border zone. 

The specific objectives of the project were to: 

 Assess the effectiveness of a technology for automated capture of travel time for vehicles 

crossing the border. 

 Gather historical travel time data over the period of one year. 

The results of the work are summarized in this Final Report. 

Electronic copies of these documents are available from FHWA. 
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Contents 

Executive Summary ..................................................................................................... vi

1 Project Background................................................................................................ 1

1.1 Study Part I – Comparative Technology Examination...................................................2

1.2 Study Part II – Technology Viability Assessment..........................................................3

1.3 Study Part III – Compilation of Historical Travel Time Data ..........................................4

2 Project Approach.................................................................................................... 5

2.1 Technology Assessment ...............................................................................................5

2.2 Stakeholder Coordination..............................................................................................5

2.3 Technology Deployment................................................................................................6

2.4 Technology Testing.......................................................................................................7

2.5 Data Archiving...............................................................................................................7

3 Results and Findings ............................................................................................. 8

3.1 Crossing Characteristics ...............................................................................................8

3.2 Stakeholder Session Results ......................................................................................16

3.3 Technology Selection..................................................................................................18

ALPR................................................................................................................................. 18

GPS................................................................................................................................... 20

3.4 Technology Deployment..............................................................................................29

3.5 Trip Statistics...............................................................................................................31

3.6 Border Travel Time Data.............................................................................................37

3.7 Motor Carrier Feedback ..............................................................................................55

4 Conclusions.......................................................................................................... 57

4.1 Data Validity ................................................................................................................57

4.2 Data Applicability.........................................................................................................59

4.3 Data Accessibility ........................................................................................................62

4.4 Data Value...................................................................................................................62

4.5 Data Business Model Options.....................................................................................63

5 Recommendations ............................................................................................... 67
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Table of Figures

Figure ES-1. Otay Mesa Data Trips – January 2009 through March 2010.................................. xi

Figure ES-2. Calculated Travel Time Values by Day of the Week ..............................................xii

Figure 1. Satellite Image of Otay Mesa Border Crossing Location ...............................................8

Figure 2. Otay Mesa Truck Crossings – January 2009 – February 2010 .....................................9

Figure 3. Google Earth Map of Northbound Access to Otay Crossing - MX...............................10

Figure 4. Google Earth Map of Northbound Access to Otay Crossing (MX/US Customs Facilities 

and CHP Inspection Facility........................................................................................................11

Figure 5. Satellite Image of Trucks Passing Through the Mexican Customs Export Compound..1

Figure 6. Satellite Image of Trucks Passing Through the US Customs Entry Compound..........14

Figure 7. Satellite Image of the CHP Screening Facility .............................................................15

Figure 8. Otay Mesa Data Trips – January 2009 through March 2010.......................................34

Figure 9. Illustration of Geofence Used to Identify FAST Trips...................................................35

Figure 10. Otay Mesa Data Trips by Day of the Week ...............................................................36

Figure 11. Otay Mesa Data Trips by Time of Day.......................................................................37

Figure 12. Sample Otay Mesa GPS Data in Districts .................................................................38

Figure 13. Otay Mesa Travel Time Measurement Districts.........................................................41

Figure 14. Calculated Travel Time Values by Day of the Week (Dist 1 – 10)............................42

Figure 15. Calculated Travel Time Values by Month of the Year (Dist 1 – 10)...........................44

Figure 16. Calculated Travel Time Values by Time of Day (Dist 1 – 10) ....................................46

Figure 17. Calculated Travel Time Values by Day of the Week (Dist 1 – 6)..............................48

Figure 18. Calculated Travel Time Values by Month of the Year (Dist 1 – 6)............................49

Figure 19. Calculated Travel Time Values by Time of Day (Dist 1 – 6) .....................................50

Figure 20. Cumulative Distribution of Travel Times (Dist 1 – 10) ...............................................53

Figure 21. Distribution of Travel Times (Dist 1 – 10) ..................................................................53

Figure 22. Cumulative Distribution of Travel Times (Dist 1 – 6) .................................................54

Figure 23. Distribution of Travel Times (Dist 1 – 6) ....................................................................54
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Executive Summary 

Background 

Cross border movement of people and goods is a vital part of the North American economy. 

Accurate real-time data on travel times along the US-Mexico border can help generate a range 

of tangible benefits covering improved operations and security, lower costs for travelers (both 

personal and commercial, reduced environmental impacts, reduced negative impacts on 

adjacent communities, and, of course, increased economic activity for both countries. 

Congestion creates delays that result in wasted fuel and exacerbate noise and air pollution for 

communities adjacent to the crossings. Accurate travel time data can provide considerable 

benefits to the people that use the crossing, and the entities that are charged with managing it 

and the adjacent transportation network. It can: 

 Help travelers make more informed decisions regarding which crossing to use and when 

to use it, thus saving time and fuel and making it less likely they will encounter 

congestion delays; 

 Help operators, including transportation agencies, customs and immigration agencies, 

and private crossing operators, make more informed operating decisions such as the 

assignment of staff (e.g., from secondary inspection to primary inspection), or the 

opening of additional toll or inspection lanes to accommodate surges in demand; 

 Support improved planning both along the border and for the transport network that 

provides access through Mexico and the US. Planning decisions would include facility 

design or layout modification that would ease the congestion at a particular crossing; 

 More convincingly make the case for needed improvements in areas such as 

infrastructure upgrades and increased staffing, it can be a powerful planning aid for 

transportation and compliance agencies, as well as businesses; 

 Be a very helpful means to educate border crossing users of the benefits of engaging in 

better planning, and those associated with expedited crossing programs. 

The current quality of data on border crossing delays is mixed. Border agencies often rely on 

visual observations of the vehicle queue or a manual data collection process (driver surveys or 

time-stamped cards) to provide an estimate of total wait time. Unfortunately, these approaches 
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are costly, inconsistent over time and from location to location, and do not take advantage of 

existing technology to monitor traffic movement. 

In October 2007 The Federal Highway Administration initiated a three-part assignment to 

assess technology for the measurement of travel times for trucks through the Otay Mesa 

international border crossing into the US. The primary goal was to evaluate the ability of one of 

two technologies to accurately record travel times through the border zone. The three parts of 

the study were to: 

 Examine two candidate technologies for installation and testing at the Otay Mesa 

crossing, and recommend one for testing; 

 Conduct an assessment of the suitability of the selected technology to provide accurate 

border crossing travel time data; and 

 Gather one-year’s worth of travel time data at the crossing to establish a historic 

database for future use. 

The Otay Mesa crossing is a truck-only crossing between the US and Mexico south of San 

Diego, California. At the onset of the project the port served an average of 2,500 vehicles per 

day inbound into the US. Over the last several decades, the crossing has seen an increase in 

the amount of traffic, and as a result, the amount of time necessary to cross into the US has 

been perceived to have increased for the average trucker. Rather than assess the accuracy of 

that perception, this project was focused on evaluating the ability of a technology solution to 

accurately measure the time it takes a truck to pass through the border. 

The first part of the project involved the comparative examination of two candidate technologies. 

These technologies were: 

 Automatic License Plate Recognition (ALPR). ALPR applications seek to identify, 

catalog, and track individual truck movements using optical image capture and reidentification. ALPR systems record a combination of registered license plates and 

USDOT numbers, use optical character recognition (OCR) to resolve images into strings 

of characters, store these character strings in a central database, and compare them to 

subsequently captures and resolved character strings in order to re-identify individual 

vehicles. 

 Global Positioning System (GPS). GPS applications, particularly in the form of 

commercial feet management systems, are becoming more commonplace. Motor 
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carriers typically rely upon such systems to locate individual trucks in order to more 

effectively manage dispatch operations, and to provide “visibility” regarding shipment 

status to customers. GPS systems rely upon a long-used navigational technique called 

triangulation to locate assets. 

Based on a careful examination of functional characteristics, deployment requirements, longterm potential for viability, and options related to business models for gaining access to the 

data, it was determined that GPS technology would be used for this project. 

The second part of the project was to capture data over an extended period in order to 

determine whether it was of sufficient quantity and quality to determine travel time. This task 

consisted of ongoing statistical analysis of weekly data files provided by a third-party data 

provider. As additional data was received, it was processed and analyzed, and stored both raw 

and processed data in a database for future use. 

The final component of the project was the archiving for future use of one year’s worth of GPS 

data and associated travel time calculations. 

Stakeholder input was an essential component of the success of the project during both the 

design and execution of the technology assessment. Because it was important to gain national, 

international and local insight into the operations at the Otay crossing, the study team 

conducted stakeholder sessions in the San Diego / Otay Mesa area. The sessions were 

designed to communicate the objectives of the study, to discuss the various information needs 

of the stakeholders, and to provide information regarding the progress of the study. These 

stakeholders included motor carriers, and Federal, State, and local agencies from both sides of 

the border. 

The stakeholder sessions offered the study team some valuable insights into the challenges 

faced by users and administrators at the border. They allowed for a much more comprehensive 

understanding of not only the conditions as they exist, but also regarding what might be done to 

improve them. 

Stakeholders provided some useful ideas regarding what would be a potential set of functional 

requirements for any border travel time measurement solution. Table ES-1 contains a brief 

summary of important input. Most notably, the stakeholders offered information regarding their 

needs, and the functional requirements that a system that provides travel time information might 

be expected to meet. 
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Table ES-1. Stakeholder Input Summary 

Stakeholder Group User Needs (Northbound) 

FHWA Provide leadership in the area of automated cross 

border travel time measurement by testing a 

technology to provide better quality data, to facilitate 

better transportation operations in the border region 

Motor Carriers Gain access to real-time cross-border total travel time 

to assist in dispatching and routing decisions and in 

providing information to customers 

State and Local Transportation Agencies Historic travel time data for the assessment of overall 

system performance and real-time data for use in 

traffic operations and transportation system 

management 

US/MX Customs Historic and real-time data to assess needs for 

modifications to crossing infrastructure and to assist in 

managing resources and facilities during daily 

operations 

Findings 

Technology Assessment 

Based on a combination of stakeholder preferences for access to real-time data, as well as the 

need to establish a focused localized data source, the study team recommended that the best 

GPS-based alternative for this test was one that included a third-party provider. Stakeholders 

strongly supported this approach, and the study team procured the services of Calmar 

Telematics to capture, process, and deliver fleet GPS data for the study. 

Data acquisition requirements were broadly prescribed to the data provider. The key 

requirements were that the sample size be sufficient to adequately characterize travel times and 

that the participating carriers allow unrestricted access to all available GPS data generated by 

trucks passing through the Otay Mesa crossing into the US. 

In order to meet the requirement for truck GPS data, the data provider needed to identify and 

recruit motor carriers to participate in the project. The data provider chose to address this 

requirement by recruiting three carriers that regularly travel the Otay crossing. Initially, the 

carriers were chosen because they already possessed vehicles with GPS data systems 

installed. 
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The data initially provided by the carriers that agreed to participate was generated by devices 

affixed to semi-trailers. While the position data provided by these systems has proven reliable 

over a number of years, it became clear as the test progressed that the reporting frequency the 

carriers had negotiated with the data provider was not sufficient for measuring travel time over 

such a short distance. Rather than incur the costs associated with increasing the frequency of 

reports, the participating carriers opted to upgrade their GPS units to devices that are installed 

in the tractor (as opposed to trailers) and provide a higher reporting rate. The carriers purchased 

and installed a total of 175 units in trucks. 

Crossing Statistics 

During the test the study team obtained data from CBP that indicated that the average daily USbound vehicle count between January 2009 and February 2010 was between 1,900 and 2,400. 

In order to assess whether the fleets participating in the project were providing an adequate 

percentage of overall trips, the study team examined the GPS data to identify two different types 

of trips. The first was “total trips.” For this category, any appearance of multiple points from the 

same truck within the dataset for a given two-hour period was considered a trip. This was true 

regardless of where the points were located, as long as they were within the travel time 

measurement zone (defined as the distance between the point at which stakeholders indicated 

that trucks typically begin to queue up to cross and the point where trucks exit the California 

Highway Patrol commercial vehicle inspection station downstream of the US Customs import 

processing facility), and indicated US-bound movement. The second trip type was “crossed 

trips.” The study team considered a trip a crossed trip if a truck was identified as having been on 

both sides of the border during an individual trip, and headed US-bound. Hence, the group of 

total trips included all crossed trips, plus any additional trips for which points could not be 

identified on one side of the border or the other. 

Figure ES-1 contains a plot of the data gathered during the test from January 2009 through 

March 2010. It illustrates the number of trips for which data was captured during each month. 
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Figure ES-1. Otay Mesa Data Trips – January 2009 through March 2010 

 

As the data indicate, the number of crossed trips consistently fell between three and five percent 

of all trips through the border. Based on these figures, the study team felt confident it could rely 

upon the data from crossed trips to characterize travel time through the crossing. 

Border Travel Time Findings 

To facilitate calculating border crossing travel time across the full measurement zone and for 

portions of the trip, the trip length was segregated into a series of consecutive travel zones, or 

districts, ten in all. The districts were utilized for determining the portion of the total trip travel 

time that was accumulated on the approach to the US customs primary inspection facility. 

Figure ES-2 shows the calculated travel times through the measurement zone by the day of the 

week (DoW) for all trips, trips conducted by trucks participating in the FAST (Free and Secure 

Trade)1

 expedited processing program, and trucks that were not required to pass through the 

US Customs secondary inspection facility, over the period of a year. 

 

1

 The Free and Secure Trade (FAST) program was established and is operated by US Customs and Border 

Protection, Mexican Customs, and Canada Border Services Agency as a means to expedite the movement of safe, 

legal trade movements among the three North American countries. It relies on the comprehensive screening and 

validation of supply chain security. 
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Figure ES-2. Calculated Travel Time Values by Day of the Week 

The travel time values calculated from the data indicate that the mean measured travel time 

across the entire measurement zone for Mondays through Saturdays was approximately 67 

minutes. Sundays, which see very limited traffic, showed a mean travel time value of just over 

43 minutes. The data suggests that travel times are longest on Thursdays and Fridays, where 

mean values are approximately 70 minutes. 

The data also indicates that FAST trips—at least those that could be identified definitively—take 

less time for all days except Saturdays. Differences range from values of 15 percent lower on 

Mondays for FAST trips to just over 10 percent lower on Thursdays. 

Among the more notable characteristics of the data is the very large value associated with the 

standard deviation from the mean (illustrated in the figure by the vertical black lines). The values 

calculated from the data indicate that the standard deviation ranged from 61 percent to 81 

percent of the mean. These values suggest that the carriers that participated in the study 

experienced very wide fluctuations in travel times. Fluctuations of this magnitude are indicative 

of low travel time reliability, a characteristic that makes it difficult for border users to anticipate 

how long a border crossing trip might take. 

Because it is less prone to the influence of outliers, the study team also calculated median travel 

time values for each day of the week. The data indicated that the median values were 15 to 30 
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percent lower than the mean values, which would place the median travel time for Mondays 

through Saturdays between 42 and 53 minutes. 

The upward bias in mean travel time values is revealed further through the identification of 25% 

and 75% quartile values. These values form the upper and lower limits of observed travel times 

for the middle 50% of all observed trips. A comparison of the portions of the standard deviation 

range outside the middle 50% to those inside reveals that more of the deviation interval lies to 

the upper side of the range. 

The root cause of the high degree of travel time variability is not known for certain. However, the 

study team suspects that several factors may contribute to it. Fluctuations in demand volume, 

the opening and closing of primary screening lanes, the proportion of trucks participating in the 

FAST expedited screening program, variations in screening actions undertaken by US CBP can 

affect travel time. 

Travel time values were also calculated and reviewed on a month of year (MOY) and time of 

day (TOD) basis. Some of the characteristics of these datasets are summarized in Table ES-2. 

Table ES-2. MOY and TOD Data Characteristics 

Analysis Timeframe Characteristics 

Month of Year (MOY) Mean and median travel time values peaked in the March-April 2009 and 

September-October 2009 timeframes. According to CBP crossing 

statistics, the September-October 2009 period was the busiest period. 

The mean travel times for FAST trips were lower than the overall 

average for all but three of the months: January 2009, April 2009, and 

January 2010. 

The monthly data is consistent with the daily data in that they show that 

the median travel time is consistently lower than the mean, and that the 

middle 50% of trips fall to the lower end of the standard deviation range. 

Time of Day (TOD) For the TOD data, because the number of points was exceedingly small, 

all trips identified as having started between 12 AM (when the border 

closes) and 6 AM were excluded from the dataset. The study team did 

this because it did not have confidence in the values provided. 

Because the border does not open until 8 AM local time, the high travel 

time values reported between 6 AM and the opening of the border 

suggest that some vehicles are queuing in advance in order to ensure 

earlier entry into the US. 

The increase in volume seen in the peak afternoon/evening travel period 

does not translate into longer travel times. This is likely due at least in 

part to higher staffing levels during those periods. 
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Data Validation 

At the conclusion of the data collection period, the study team contacted carriers that 

participated in the project to gain some perspective on their operations, and to gather 

information related to their typical crossing experience. The principal objective was to validate 

the border crossing travel time findings. 

The study team first sought feedback regarding the mix of loaded to unloaded trips and FAST to 

non-FAST trips for each carrier. The intent was to assess the ability of the GPS devices to 

discern the various trip types. The participating carriers indicated FAST trip frequencies of 

between 75 and 90 percent of all northbound trips. This is in contrast to the GPS data that 

indicated that approximately 30 percent of trips were FAST trips. While neither carrier offered 

detailed records to indicate that these figures were close to actual numbers, the implication is 

clear that the use of GPS data to isolate FAST trips was not an accurate method at the Otay 

Mesa crossing, where vehicles conducting FAST trips are physically separated by a significant 

distance from non-FAST and empty trips for only a short period. Separation of paths of travel is 

essential if GPS data is to be relied upon for differentiating trip types. 

The study team also sought feedback from the carriers regarding whether their drivers 

encounter a high degree of travel time variability (i.e., a low level of travel time reliability) when 

crossing into the US. Representatives from both carriers concurred that there is, in fact, 

significant variability, and that the degree seen in the data could be accurate. 

Finally, the study team asked the carriers about the value they perceive GPS-based technology 

systems have with respect to their operations. Both carriers indicated that they routinely use the 

tracking and tracing functions as a means to meet business needs of customers, and both use 

the data on a continual basis. 

Conclusions 

Based on the observed results from the full data collection period, the study team is able to 

draw a number of conclusions regarding the use of truck fleet GPS data for the measurement of 

travel time across international border crossings, both in general terms and in terms specific to 

the Otay Mesa Crossing. 

With respect to the primary goal, the devices and the processing methods employed to capture 

and analyze the GPS data succeeded. There are, however, some considerations that must be 

taken into account as border stakeholders contemplate its use on a regular basis. 
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First, the figures reflected in this report are based on a marginally representative sample. While 

the number of trips and their distribution throughout the day consistently exceeded the three to 

five percent threshold commonly required for probe vehicle-based traffic monitoring, the carriers 

that participated are similar to only a portion of those that cross the border. Both carriers are 

highly reputable companies that are C-TPAT2

 qualified, and conduct a significant portion of their 

cross-border trips as FAST trips. As such, the test sample was not random, and may not be 

representative of the general population. 

The second consideration that must be taken into account is the size and placement of the 

measurement zone. At the onset of the project, carriers indicated to the study team that queues 

often extend to the Bellas Artes/Calle Doce intersection. However, the data gathered during the 

test suggests that traffic often flowed quite freely from there to the sorting gate upstream of the 

Mexican Customs Export screening point. 

The implication is that care should be taken in deciding at what point the border crossing travel 

time clock should start. It is worthwhile to consider beginning measurement of an individual trip 

after a vehicle has reached the end of the queue waiting to cross. The time from that point until 

the crossing is completed is probably of more value to a border user. 

Finally, consideration must be given to the implications associated with the large values of 

standard deviation, and the differences between the mean and median values seen in the data. 

This level of variability does present particular challenges in formulating accurate values for 

current travel time, such as might be used in a traveler information system. 

This is important because high levels of variability in the data can make it difficult to accurately 

characterize travel time fluctuations associated with non-recurring congestion, which has a 

profound effect on travel time reliability. A larger number of vehicles, spread across a more 

diverse set of carriers, should improve the data and lower the degree of variability. From a 

statistical standpoint, as the sample size increases, the proportion of outliers and their influence 

on the mean values should diminish. It is not clear at this point the threshold at which that might 

occur to an acceptable degree. 

It is important to remember that some degree of variability will continue to exist simply due to 

factors such as variations in the proportion of vehicles participating in secure supply chain 

 

2

 C-TPAT (Customs-Trade Partnership Against Terrorism) is a supply chain security assessment program, 

participation in which is required in order to access FAST processing lanes. 
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operations (such as the C-TPAT and FAST programs), fluctuations in the total number of 

vehicle crossing during a given period, the number of empty trucks versus loaded trucks 

passing through the crossing, and the application of various enforcement actions. 

The capture and use of real-time—or more accurately, near real time—data presents a set of 

challenges that were not completely addressed during the project, but the experience offers 

some useful insights. The information exchange and processing mechanisms employed for the 

project would not be suitable as deployed solutions. These mechanisms would need to be 

replaced with appropriate communications and processing tools and protocols in order to be 

usable for applications that rely upon timely information processing, such as traveler information 

systems that might inform border users of current conditions. 

The data model that the data provider applied to access motor carrier data—seeking to obtain it 

directly from the carriers—is different from that which is typically seen from other fleet data 

providers. This approach presents both benefits and challenges. One potential benefit is that, 

because the carrier already owns the data for its fleet, and only its fleet, it may be willing to 

grant access to the data for a modest sum. Further, by working directly with the carrier while 

agreeing to protect sensitive information from distribution, a data provider could structure its 

data agreements to allow for the re-use of information for multiple purposes without incurring 

additional cost. 

Perhaps the most significant challenge to this approach is that which was encountered during 

the recruitment of carriers for participation in this project. The data provider was forced to 

endure a lengthy courtship process before the carriers finally agreed to grant access to their 

data. Ultimately, the carriers became more receptive as they recognized that the devices are 

useful for other functions, as well. Nonetheless, the level of time and effort necessary to execute 

agreements with the carriers indicates that this should be factored in whenever GPS fleet data 

is sought. 

Even with the level of variability demonstrated in the data gathered during this project there 

appears to be significant value associate with GPS fleet data. GPS devices can easily provide 

data for other movements, and the data processed to quickly multiply its planning and 

operations value. 

For instance, the data provider already possesses raw GPS data for Mexico-bound movements 

at the Otay crossing. This means that with the completion of some processing logic, travel time 

for southbound trucks during the same period could be calculated without additional data 
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collection. Further, raw GPS data has already been recorded for movements both within the 

areas adjacent to and those beyond the Otay border zone. This data could be used not only for 

travel time calculation, but also for the identification of origins and destinations and route 

selection. This also underscores the importance of carefully considering the terms of any data 

agreement to ensure that the maximum value can be obtained. 

Finally, modern fleet GPS devices are becoming both more affordable and more capable, 

opening up additional opportunities to capture valuable information. One example is the capture 

of device from a vehicle’s engine data bus, which would include such information as throttle 

position, travel speed and fuel use by location. This has potential value for the identification of 

locations where excessive idling is occurring—important data for carriers to establish 

mechanisms to improve fleet fuel efficiency and for agencies to examine the potential 

environmental value of infrastructure upgrades. 

Recommendations 

While a significant amount of information was captured during the project, the study team 

recommends that a series of actions be undertaken to establish a complete service for the 

various potential users of GPS data. These actions will increase the likelihood that the data 

obtained from a GPS-based system offers the appropriate value and usability to enable the cost 

of deployment to be spread over a broad set of stakeholders. The team’s recommendations are 

offered below, along with an explanation of the value associated with exercising each of them 

Recommendation #1: Use a Larger Number of Reports. 

While the rule of thumb threshold of three to five percent of the total population was successfully 

reached during the project, the data suggests that a larger population of GPS-equipped trucks 

could significantly enhance the dataset by reducing the effect of outlier trips on the mean travel 

time value. The relationship between sample size and variability is not known, but an increase in 

the number of units to 300 (at the same crossing frequency) should yield definitive evidence of 

it. Of course, such action would need to be balanced against the cost of the additional devices, 

and the expected value of the (presumably) more precise data. 

Recommendation #2: Increase the Variety of Carrier Participants. 

The carriers that participated in the project indicated that the overwhelming majority of their USbound trips are FAST trips. At the very least, that means that the samples gathered for this 

project under-represent non-FAST trips. If a GPS data solution is to be deployed in a production 
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setting, the team suggests that the trucks from which GPS data is collected conduct non-FAST 

trips into the US at a representative frequency. 

Recommendation #3: Examine Secondary Uses for Data. 

The measurement of travel time for vehicles entering the US represents only a small portion of 

the value to be extracted from GPS data. These devices are typically on when the vehicle is 

running, offering the opportunity to examine movements, origins and destinations, idling points, 

and various travel delays throughout a vehicle’s duty cycle. Those entities that adopt GPSbased systems should examine these and other secondary uses for the data in order to realize 

its full value. For instance, a significant volume of data was captured during the project 

regarding southbound movements, but it was not analyzed because it fell outside the scope of 

the project. At this point, the value and usefulness of this data is unknown. 

Recommendation #4: Use Extended Data Collection to Reinforce Calculations & Diminish 

Variability. 

Much in the way that an increase in the number of trucks using the crossing should reduce 

variability and enhance confidence in the data, so too should the capture of data over a long 

period of time. Doing so will likely lend stability and reduce variability in historical travel time 

data, thereby reducing the effects of outliers. Additionally, external factors such as the level of 

economic activity will also affect the figures. Collecting data over an extended period offers the 

opportunity to examine the effects. 

Recommendation #5: Examine and Test Near Real Time Data Use. 

Although GPS-based traffic monitoring solutions are being used in other locations on a real-time 

basis (e.g., I-95 Corridor Coalition), the usefulness and usability of similar data in the border 

environment is still largely unknown. The approach used for this project offers an opportunity to 

examine whether enough value can be extracted to affect agency and carrier decisions during 

the course of everyday operations. This could be accomplished through a continuation of data 

acquisition and analysis activities, with the participation of and interaction with users who have 

access to a near real time data feed. 

Recommendation #6: Pursue Transfer of Data Acquisition Oversight and Funding to 

State/Local Agencies, Customs Agencies, or Others. 
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It is understood that FHWA does not have an ongoing operations function at international border 

crossings, and that its primary role is the conduct of research and support for deployment of 

solutions that enhance the nation’s transportation system. Hence, the funding and use of 

systems that measure border travel time are the purview of border management agencies, 

agencies with responsibility over local transportation infrastructure, and the border user 

community. The results of this project establish the viability of GPS technology for travel time 

measurement. They also present an opportunity to explore how a transition to a production 

solution might take place through the leveraging of carrier relationships established during the 

test, the expansion of data coverage to a comprehensive geographic scale, and the examination 

of opportunities for secondary data uses. 
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1 Project Background 

Cross border movement of people and goods is a vital part of the North American economy. 

Accurate real-time data on travel times along the US-Mexico border can help generate a range 

of tangible benefits covering improved operations and security, lower costs for travelers (both 

personal and commercial, reduced environmental impacts, reduced negative impacts on 

adjacent communities, and, of course, increased economic activity for both countries. 

Congestion creates delays that result in wasted fuel and exacerbate noise and air pollution for 

communities adjacent to the crossings. Accurate travel time data can help travelers make more 

informed decisions regarding which crossing to use and when to use it, thus saving time and 

fuel and making it less likely they will encounter congestion delays. Just as travelers save time 

and fuel, these savings will be passed on to local communities. 

Accurate travel time data can also help operators, including transportation agencies, customs 

and immigration agencies, and private crossing operators, make more informed operating 

decisions such as the assignment of staff (e.g., from secondary inspection to primary 

inspection), or the opening of additional toll or inspection lanes to accommodate surges in 

demand. Such data also can support improved planning both along the border and for the 

transport network that provides access through Mexico and the US. Planning decisions would 

include facility design or layout modification that would ease the congestion at a particular 

crossing. 

Further, accurate and timely delay information is a useful public policy tool. Not only can it more 

convincingly make the case for needed improvements in areas such as infrastructure upgrades 

and increased staffing, it can be a powerful planning aid for transportation and compliance 

agencies, as well as businesses. These data can also be a very helpful means to educate 

border crossing users of the benefits of engaging in better planning, and those associated with 

expedited crossing programs. 

The current quality of data on border crossing delays is mixed. Border agencies often rely on 

visual observations of the vehicle queue or a manual data collection process (driver surveys or 

time-stamped cards) to provide an estimate of total wait time. Unfortunately, these approaches 

are costly, inconsistent over time and from location to location, and do not take advantage of 

existing technology to monitor traffic movement. 
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In October 2007 The Federal Highway Administration tasked the team of SAIC and Delcan to 

conduct a three-part assignment to assess technology for the measurement of travel times for 

trucks through the Otay Mesa international border crossing into the US. The primary goal was to 

evaluate the ability of one of two technologies to accurately record travel times through the 

border zone. 

The ultimate objective for the project was to put in place an automated system that had the 

potential to provide data of sufficient value and reliability to become a long-term solution for 

border stakeholders, and replace the labor-intensive and imprecise manual methods currently in 

use. By facilitating the deployment of a system at Otay Mesa, FHWA hopes to provide 

leadership in the effort to produce information that can support improvements in operations at 

border crossings, and to demonstrate the viability of such systems for implementation at other 

locations. 

The three parts of the study were to: 

 Examine two candidate technologies for installation and testing at the Otay Mesa 

crossing, and recommend one for testing; 

 Conduct an assessment of the suitability of the selected technology to provide accurate 

border crossing travel time data; and 

 Gather one year’s worth of travel time data at the crossing to establish a historic 

database for future use. 

The Otay Mesa crossing is a truck-only crossing between the US and Mexico south of San 

Diego, California. At the onset of the project the port served an average of 2,500 vehicles per 

day inbound into the US. Over the last several decades, the crossing has seen an increase in 

the amount of traffic, and as a result, the amount of time necessary to cross into the US has 

been perceived to have increased for the average trucker. Rather than assess the accuracy of 

that perception, this project was focused on evaluating the ability of a technology solution to 

accurately measure the time it takes a truck to pass through the border. 

1.1 Study Part I – Comparative Technology Examination 

The first part of the project involved the comparative examination of two candidate technologies. 

The FHWA directed the study team to catalog the relative capabilities and potential 

shortcomings of each, and recommend one for implementation and use during the test. These 

technologies were: 
FHWA Cross-border Travel Times - Otay Mesa International Border Crossing: Final Report 

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Automatic License Plate Recognition (ALPR). ALPR applications seek to identify, catalog, 

and track individual truck movements using optical image capture and re-identification. 

ALPR systems record a combination of registered license plates and USDOT numbers, use 

optical character recognition (OCR) to resolve images into strings of characters, store these 

character strings in a central database, and compare them to subsequently captured and 

resolved character strings in order to re-identify individual vehicles. Travel time can then be 

calculated directly for individual vehicles using the time elapsed between sequential 

successful “reads.” ALPR systems typically consist of cameras mounted either along the 

roadside or on elevated structures (a pole, for example), tied to a central processor through 

various communications media. Each camera requires a power source and data 

transmission capability, and depending upon the configuration, OCR processing may take 

place at the camera location or at the central processing system. 

Global Positioning System (GPS). GPS applications, particularly in the form of commercial 

feet management systems, are becoming more commonplace. Motor carriers typically rely 

upon such systems to locate individual trucks in order to more effectively manage dispatch 

operations, and to provide “visibility” regarding shipment status to customers. GPS systems 

rely upon a long-used navigational technique called triangulation to locate assets. GPSbased systems come in different forms. Among the most common types used for 

commercial trucking are transceivers that transmit data through the cell phone network or 

via satellite to report location and time information. The location and time stamp information 

from successive system reports can then be used to travel time. 

These technologies and the methodology used to compare their characteristics for use at Otay 

Mesa are discussed in detail in Section 2.1. During this process, the study team consulted with 

local and national stakeholders to examine key functional characteristics and the operating 

environment at the crossing. These activities are described in detail in Section 2.2. 

1.2 Study Part II – Technology Viability Assessment 

Once the technology was selected, the study team oversaw its deployment at the Otay Mesa 

crossing and captured travel time data at volumes significant enough to provide the opportunity 

to examine the output of the selected system to determine its viability as a travel time data 

source. 
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This critically important portion of the project involved several significant activities, which are 

detailed in Sections 2.3 and 2.4 of this report. 

1.3 Study Part III – Compilation of Historical Travel Time Data 

Once the technology was selected and deployed, the study team captured data to catalogue 

average travel times for a period of one year. This was done to capture historical data that could 

be used for a variety of purposes, including as a basis of comparison for assessing future 

delays. Detailed information regarding how the study team accomplished this activity is provided 

in Section 2.5. 

The remainder of this report details the approach, findings, conclusions, and recommendations 

formulated during the project. 
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2 Project Approach 

As discussed in the previous section, the project consisted of three major parts: data 

comparative technology examination, a technology viability assessment, and the compilation of 

historical travel time data. These three parts were accomplished through the execution of five 

activities, as described below. 

2.1 Technology Assessment 

The primary focus of the technology assessment was to determine which of the two technology 

options, ALPR or GPS, was best suited for testing at the Otay Mesa crossing. The intent of the 

assessment was not to imply that one technology was superior to the other with respect to the 

capture of border crossing travel time. Rather, it was intended to determine whether either 

technology would function adequately under the conditions as they exist at the Otay crossing, 

and whether there was a significant opportunity to learn about the capabilities of a particular 

technology. 

This activity was accomplished by comparing functional characteristics, and assessing the 

degree to which these characteristics might affect the ability of the technology to function in the 

Otay Mesa crossing environment. The assessment also included a review of potential business 

models for gaining access to the travel time data, and the implications of choosing between a 

capital investment approach and a data purchase agreement approach. 

In completing this task, the study team examined documentation regarding the two 

technologies, and conferred with representatives from providers of both types of technologies. 

Additionally, the study team solicited cost estimates from both providers for the instrumentation 

and support services associated with establishing a travel time data feed at the crossing. 

The study team then completed a comprehensive examination of options, and provided to 

FHWA a recommendation regarding which technology to deploy. The results of these activities 

are provided in Section 3 of this report. 

2.2 Stakeholder Coordination 

At the onset of the project, FHWA and the study team recognized the importance of gaining 

appropriate national, international and local insight into the operations at the Otay crossing, from 

the perspective of the agencies and users. The team conducted two separate stakeholder 

sessions in the San Diego / Otay Mesa area—one at the start of the project, and one after the 
FHWA Cross-border Travel Times - Otay Mesa International Border Crossing: Final Report 

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system had been selected and data was being gathered. The Study Team designed these 

sessions to: 

 Communicate the objectives of the study and the objectives of the US DOT Federal 

Highway Administration (FHWA) in collecting a baseline, historic, cross-border travel 

time data set; 

 Gather information related to the cross-border journey to better understand how and 

when these data should be collected; 

 Summarize the needs of the stakeholders and potential uses of these data; 

 Provide updates about the study as it progressed; and 

 Assess the importance of providing real-time data to various stakeholders in the future. 

Stakeholders from the following groups were invited to attend the sessions: 

 Cross-border motor carriers, freight forwarders, and logistics companies; 

 State and local transportation agencies from the San Diego and Tijuana, Mexico (MX) 

region, including Caltrans, the San Diego Association of Governments (SANDAG), the 

state of Baja California, and the Instituto Municipal de Investigación y Planeación de 

Ensenada (IMIP); 

 US and MX customs staff from the Otay Mesa port of entry - US Customs and Border 

Protection (CBP) and MX Aduana; 

 US and MX federal transportation agencies - FHWA and MX Secretaría de 

Comunicaciones y Transportes (SCT). 

The input received from stakeholders is detailed in Section 3 of this report. 

2.3 Technology Deployment 

The two different technologies required very different deployment paths and present different 

infrastructure approaches. By nature of its design, ALPR requires the installation of devices at 

fixed locations on both sides of the border. It then relies upon this fixed infrastructure to detect 

and decipher visual images of license plates, which are required for all vehicles operating in the 
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US. Hence, ALPR requires nothing be done on the part of the border user (other than maintain 

a legible license plate). 

In contrast, GPS requires the installation of GPS devices in vehicles, which places some burden 

on the vehicle owner to support the travel time measurement infrastructure. The remainder of 

this infrastructure (typically) consists of the satellite owner (in this case the US Department of 

Defense), and the vendor that receives, processes, and forwards data from the devices. 

Based upon the analysis of technology options, the FHWA concurred with the recommendation 

to deploy GPS for this project. This required the selection of the GPS data provider, Calmar 

Telematics, and the execution of a subcontract to procure their services. Once the contracting 

was completed, the data provider then proceeded to negotiate data access agreements with 

candidate carriers. 

2.4 Technology Testing 

The third, and primary, activity was the capture of data over an extended period in order to 

determine whether it was of sufficient quantity and quality to determine border crossing travel 

time. This task consisted of ongoing statistical analysis of weekly data files provided by Calmar. 

As additional data was received, the study team processed and analyzed it, and stored both raw 

and processed data in a database for future use. 

The study team then examined the data within the context of known border operating conditions 

and conferred with the motor carriers that participated in the project. Sections 3 and 4 of this 

report contain the results of the testing and the conclusions drawn from the travel time data. 

2.5 Data Archiving 

The final component of the project was the archiving for future use of one year’s worth of GPS 

data and associated travel time calculations. The study team has created and will transfer to 

FHWA files containing both the GPS data as provided by the data provider, and the processed 

data as used to calculate border crossing travel time.3

 

 

3

 Interested parties can obtain the data referenced in this report by sending an e-mail to: FreightFeedback@dot.gov. 
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3 Results and Findings 

3.1 Crossing Characteristics 

The Otay Mesa International Border Crossing is a truck-only crossing connecting the San 

Diego, California metro area with the city of Tijuana in the Baja California Peninsula. The Otay 

port of entry (POE), coupled with the adjacent San Ysidro crossing that serves automobile 

traffic, represents a key international trade transportation link. With more than 1.4 million truck 

crossings per year, the Otay Mesa Port of Entry is the largest commercial crossing in the 

California/Mexico border and handles the second highest volume of trucks and third dollar value 

of trade among all U.S./Mexico land border crossings.4

 The satellite image in Figure 1 illustrates 

the location of the crossing. 

Figure 1. Satellite Image of Otay Mesa Border Crossing Location 

At the onset of the project, US Customs and Border Protection (CBP) staff at the Otay Mesa 

commercial crossing reported processing approximately 2,500 trucks per day during peak 

season, which runs from October through December. Figure 2 shows the actual volume of 

 

4

 Otay Mesa Chamber of Commerce, http://www.otaymesa.org/ab_otay/port_of_entry.html. 
FHWA Cross-border Travel Times - Otay Mesa International Border Crossing: Final Report 

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trucks that crossed through Otay Mesa into the US during the period between January 2009 and 

February 2010.5

 As the figure indicates, the 2009 peak occurred in October, during which a total 

of nearly 65,000 trucks crossed from Mexico. 

Figure 2. Otay Mesa Truck Crossings – January 2009 – February 2010 

According to motor carriers, during the peak period each individual truck makes between 1.4 

and 2 cross-border “turns” daily. The current layout of the Northbound crossing (from MX to the 

US) provides 1 FAST (Free and Secure Trade)6

 lane, 2 laden lanes for general cargo, and 1 

lane for empty trucks. This configuration starts at the “sorting gate” on the Mexican side of the 

crossing (see Figure 2), which is approximately 1.55 miles from the MX (Mexican) export facility 

and approximately 2 miles from the entry to the US Customs compound. FAST lanes process 

approximately 15 percent of total Northbound traffic (approximately 375 trucks per day during 

peak season). About one-third of Northbound movements are empty movements that cannot 

use FAST. 

 

5

 Based upon data obtained from CBP at the Otay Mesa Port of Entry. 

6

 The FAST (Free and Secure Trade) program was established by the US CBP to facilitate expedited screening at US 

border POEs. It requires an entire supply chain to apply for and obtain certification under the Customs-Trade 

Partnership Against Terrorism (C-TPAT), a program that seeks to ensure that every portion of a supply chain 

complies with certain minimum security assurance criteria. 
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Otay Mesa is primarily a maquila crossing, where merchandise parts are shipped Southbound 

into the Tijuana area to be assembled and shipped Northbound into the US for delivery to 

distribution centers and retail stores. A substantial portion of peak season movements consist of 

electronics being shipped to the US for the Christmas holiday season. Motor carriers and US 

and MX customs staff note that traffic volume during this period is such that trucks crossing the 

international border often wait up to three hours. The second quarter (April to June) is the least 

busy from a truck volumes perspective, according to CBP staff, who indicate that wait times 

during this period are often 1 hour or less. Quarters 1 and 3 are moderately busy. 

Border stakeholders report that Friday is the often busiest day at the crossing (more so during 

peak season), followed by Thursday, Monday/Tuesday, and Wed (in order of demand). 

Weekend hours and truck movements are more limited and represent some hazardous 

materials and Customs-Trade Partnership Against Terrorism (C-TPAT) movements only. 

Motor carriers attending stakeholder sessions identified Northbound queues and respective 

border crossing travel times to characterize the Northbound movement. Figures 3 and 4 show 

the Northbound travel pattern. Figure 3 shows the approach from Mexico; Figure 4 includes 

customs facilities on both sides of the border as well as the California Highway Patrol (CHP) 

commercial vehicle safety inspection facility. 

Figure 3. Google Earth Map of Northbound Access to Otay Crossing - MX 

The yellow lines indicate the 

direction of northbound travel

 To MX Export Lot
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Mexican and US inspection facilities are shown in Figure 4 and are labeled as follows: 

A – Mexican Export Lot 

 B – US Federal Inspection Facilities 

 C – CHP: State Inspection Facility 

The road connecting the MX sorting gate to MX customs serves is the location of the physical 

border. 

Figure 4. Google Earth Map of Northbound Access to Otay Crossing (MX/US Customs 

Facilities and CHP Inspection Facility 

During the stakeholder sessions, trucking company representatives and state and local 

government officials expressed interest in obtaining border travel time information across 

several distances: 

 From the beginning of the queue at Calle Doce and Bellas Artes, through MX and US 

customs, to the exit of the CHP inspection facility. Motor carriers noted that this 

To MX Sorting Gate 

A 

C 

B 
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characterizes a complete cross-border trip. Additionally, from the intersection of Calle 

Doce and Bellas Artes, the travel time would likely extend beyond 3 hours. 

 From the beginning of the queue to the MX sorting gate. In peak season, the crossborder queue often extends beyond Calle Doce and Bellas Artes. In off-peak, the MX 

sorting gate often marks the end of the queue. 

 From the start of the queue to/through MX customs. A measure from the start of the 

queue to MX customs and then a measure of the time to traverse MX customs was of 

interest to both Aduana and US Customs and Border Protection (CBP) staff. 

 At the exit of MX customs to/through US customs primary, allowing for a measure of wait 

times to enter customs as well as a measure of the time to traverse primary inspection. 

CBP officials noted that waits to traverse secondary inspection should not be included. 

 From the exit of US customs primary to the exit of the CHP facility, with the CHP facility 

marking the end of the cross-border journey. 

Table 1 summarizes the distances between these points of interest. 

Table 1. Distances between points of interest identified by stakeholders 

 Measurement Segment Distance - Miles 

Image 1 Bellas Artes at Calle Doce to MX Sorting Gate 0.5 

Image 2 MX Sorting Gate to MX Customs Primary 1.55 

MX Customs Primary through MX Customs Exit 0.2 

MX Customs Exit to US Customs Primary 0.3 

US Customs Exit, through CHP inspection, to CHP Exit 0.65 

 Total Distance (Estimated) Northbound 3.20 

Generally speaking, the roadway infrastructure between the Bellas Artes/Calle Doce 

intersection and the exit from the CHP safety facility has limited access, and at certain locations, 

has restrictive geometry. At the northern end of Calle Doce, the roadway turns 90 degrees to 

the left, and truck drivers are presented almost immediately with the sorting gate. Immediately 

downstream of the sorting gate the empty and FAST lanes are separated from the standard 

lanes by barriers. Trucks must then traverse the entire length of the roadway between the 
FHWA Cross-border Travel Times - Otay Mesa International Border Crossing: Final Report 

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sorting gate and the MX export compound (a distance of approximately 1.55 miles) in this 

configuration. 

Upon arrival at and processing through the MX export facility, FAST trucks, which are in the 

leftmost lane, are routed south around the MX export secondary facility single-file, following a 

path just inside the perimeter of the compound. Empty trucks and non-FAST loaded trucks 

follow a route directly eastward to the exit at the northwest corner of the compound. The three 

truck types come together at this point, but remain in separate lanes—FAST trucks and bobtails 

(trucks without trailers) stay in a single lane to the left, loaded non-FAST trucks remain in the 

two middle lanes, and empty trucks stay in the lane to the right. An overhead view of the MX 

export compound is provided in Figure 5. 

Figure 5. Satellite Image of Trucks Passing Through the Mexican Customs Export 

Compound 

Once they pass across the border, trucks queue for entry processing into the US. Figure 6 

contains an image of the US CBP entry processing facility. As the image shows, empty trucks 

follow a path parallel to the border from west to east, and do not enter the CBP compound. 

These vehicles are processed at a separate gate, at which CBP has an x-ray portal and a CBP 

officer stationed. 

MX Export Primary

FAST Trucks

Non-FAST Trucks

Empty Trucks 
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All other trucks must pass through the CBP primary screening facility and enter the US 

compound. FAST trucks are directed to the leftmost lanes. The two left lanes are dedicated to 

FAST traffic, and additional lanes can be used for FAST traffic as needed. Loaded trucks and 

bobtails must use one of the seven lanes to the right. 

Figure 6. Satellite Image of Trucks Passing Through the US Customs Entry Compound 

Once inside the US compound, trucks may be directed to do one of four different things: 

 Proceed to the compound exit at the southeast corner of the compound by executing a 

u-turn near the northeast corner of the compound; 

 Proceed to the secondary inspection facility at the center of the compound for further 

examination of cargo, paperwork, or driver credentials; 

 Proceed in a loop around the secondary inspection facility to one of two x-ray screening 

portals on the west side of the compound; or 

 Proceed straight ahead and form lines four abreast (called blocking) and await canine 

screening. 

Which activity a given vehicle may be directed to complete is a function of a combination of 

screening practices employed by US CBP. Some of these actions may be directed as a result of 

FAST Trucks US Import Primary

Non-FAST Trucks

Empty Trucks 
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specific screening outcomes, while others are random in nature. At the conclusion of screening 

activities, all vehicles must merge with other traffic to exit at the southeast corner of the 

compound, and then merge with empty vehicles onto a single-lane access road leading to the 

CHP screening facility. 

The final leg in the US inbound trip is the conclusion of commercial vehicle safety screening at 

the CHP facility. The facility, shown in a satellite image in Figure 7, has low speed and 

stationary vehicle weighing capabilities, and a full commercial vehicle inspection facility. 

Figure 7. Satellite Image of the CHP Screening Facility 

Trucks enter the southern end of the facility, and proceed along its western side through weigh 

scales. Vehicles and drivers that pass weight and credentials screening can exit the compound 

at its northwest corner onto northbound Enrico Fermi Drive. Vehicles that must undergo 

inspections are directed around the inspection facility in the center of the compound, and enter 

the covered inspection bays from the south. Once inspections are completed, vehicles can exit 

at the northeast corner of the compound onto Siempre Viva Road, either eastbound or 

westbound. Most vehicles head west from that exit. 

Once the US bound crossing process is complete, trucks either complete their trip at one of the 

many warehouse facilities in the vicinity of the Otay crossing, or they continue west on Siempre 

Viva Road to Highway 905, which intersects with Interstate 805, Interstate 5, and Highway 125. 
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During the period of the test and data collection, the traffic volume at the Otay Mesa crossing 

dropped significantly from its daily average of 2,500 trucks. According to statistics provided by 

the CBP, the average daily volume ranged from just over 1,600 to approximately 2,000 trucks 

per day, with the average staying between 1,800 and 2,000 per day between June and 

November 2009. This reflects a drop of approximately 20 percent. The implication is that a 

smaller total number of trips by participating trucks would be required to meet the desired three 

to five percent of total truck trips. 

3.2 Stakeholder Session Results 

The stakeholder sessions offered valuable insights into the challenges faced by users and 

administrators at the border. They allowed for a much more comprehensive understanding of 

not only the conditions as they exist, but also regarding what might be done to improve them. 

While each stakeholder session was tailored to the specific groups represented, the general 

format for each session was as follows: 

1. Project Overview: the Study Team delivered basic project information summarizing the Otay 

Mesa test. 

2. Stakeholders were shown a live a Google Earth map to facilitate a discussion among 

participants to gain a “day in the life” overview of border workings from their perspective. The 

discussion was tailored to the stakeholder session attendees, and included a facilitated 

discussion of stakeholder observations regarding travel time through the Otay Mesa crossing, 

including: 

 How often are delays experienced? 

 Where do the delays occur (start and end)? 

 Are there delays in both directions? 

 Are there any patterns worth noting (e.g., changes based on days of week, other 

periods)? 

3. Stakeholders then took part in a facilitated discussion of border travel time information needs 

of the stakeholder group. This included summarizing the needs identified at Sessions 1 and 2 

to state, local, and federal transportation agencies and customs representatives. 

4. The Study Team led a summary of the proposed technologies to be tested: GPS and ALPR. 
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5. Stakeholders were then asked to look forward and offer thoughts on what the future holds for 

the crossing, including increases in trade traffic and how these may be related to information 

needs and/or any facility and technology programs planned or underway that might affect this 

project and potential future deployment. 

6. The sessions closed with a request for each stakeholder group to identify a representative to 

be part of a Technical Working Group. This group was to be utilized for any follow-up questions 

and/or to review proposed technologies or deployment locations. 

In addition to the information presented in earlier sessions regarding the operations at the 

crossing, stakeholders provided some useful ideas regarding what would be a potential set of 

functional requirements for any border travel time measurement solution. These are provided in 

Table 2. 

Table 2. User Needs/Functional Requirements Summary 

Stakeholder 

Group 

User Needs 

(Northbound) 

Characteristics Functional Requirements 

FHWA Measure a total 

cross border travel 

time from the 

beginning to the end 

of the queue; need 

historic data for 

planning 

applications 

Without isolating any 

one agency, 

measure a crossborder travel time 

from beginning to 

end of queue 

End of queue is likely the longest end 

point in peak season; measuring “too 

close” to the border will cause bias; 

travel times can be determined at two 

instrumented points, but load type 

may require additional analysis 

and/or another instrumented location 

Motor Carriers Real-time crossborder total travel 

time is of great 

interest for providing 

information to 

customers; GPS 

technology has 

implications for CTPAT 

Queues often back 

up to Calle 12 and 

Bellas Artes (peak 

season); queues 

usually reach the MX 

sorting gate; the exit 

of the CHP facility 

marks the end of the 

cross-border trip 

Measure from the start of the queue 

at Calle 12 and Bellas Artes to a 

common point outside of CHP 

inspection, at the intersection of 

Enrico Fermi Drive and Siempre Viva 

Road; alternatively, measure at both 

CHP exit gates) 
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Stakeholder 

Group 

User Needs 

(Northbound) 

Characteristics Functional Requirements 

State and 

Local 

Transportation 

Agencies 

For transportation 

systems operations, 

it is unlikely that 

historic or real-time 

data will affect 

procedures; 

however, any data 

of interest to 

customers will be 

incorporated into 

regional ITS 

Real-time data will 

be utilized in the 

regional 511 system; 

data could be 

integrated into 

PeMS1 

Data requirements for PeMS 

integration will be explored; any data 

would be used and no one 

technology is preferred over another 

US/MX 

Customs 

Customs and 

Aduana are 

interested in 

intermediary 

measures; CBP 

notes that a 

measure of exiting 

MX gate through US 

primary is needed; 

must normalize for 

secondary 

inspections 

Aduana is interested 

in measures from 

the beginning of the 

queue to/through 

MX customs; 

US CBP is 

interested in the 

total travel time and 

time to/through MX 

customs, but 

primarily in the time 

from exiting MX 

customs to/through 

US customs primary 

inspection 

Requires multiple measurement 

points; FAST, laden, and empty 

moves should be differentiated 

End of queue (1); MX sorting gate (2) 

to differentiate load type; entry to MX 

export lot (3); exit of MX export lot (4); 

entry to US primary (5); exit of US 

primary (6); common point outside of 

CHP facility (7 or 8) 

1. PeMS – California’s Freeway Performance Measurement System: The PeMS project is conducted and 

maintained by the University of California at Berkeley with cooperation from the California Department of 

Transportation (Caltrans), California Partners fro Advanced Transit and Highways, and Berkeley 

Transportation Systems. This statewide database collects historical and real-time freeway data from 

freeways in the State of California in order to compute freeway performance measures. 

3.3 Technology Selection 

ALPR 

Automatic license plate recognition is a specialized application of digital cameras to detect the 

presence of individual vehicles. License plate recognition is used for traffic management, weighin-motion commercial vehicle inspections, security, parking, and border control, as well as for 

toll collection and violation tracking. London’s congestion pricing program, for example, uses 

license plate recognition to charge a fee to vehicles that enter central London during peak 

hours. Toronto’s Highway 407 uses ALPR applications to toll system users whose vehicles are 

not equipped with radio frequency identification (RFID) toll transponders. The tolling system 
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uses an ALPR system to capture vehicle license plate numbers. From there, optical character 

recognition (OCR) software is used to identify the plate numbers for billing the toll at a higher 

rate.7

ALPR-based travel time measurement systems work by electronically recording the front or rear 

license plates of vehicles as they pass mounted ALPR cameras or “out-stations.” Once a 

vehicle passes an out-station, the system resolves the image into a string of alphanumeric 

characters and transmits the resulting string to an “in-station.” This data transfer may happen 

via wired connection or wirelessly. After a vehicle passes two out-stations, the system then 

calculates the travel time between them. 

ALPR technology is widely deployed in both the US and Mexico. ALPR vendors interviewed for 

this study also confirm the use of ALPR in Mexico for security applications. Intelligent Imaging 

Systems,8

 for example, works closely with the City of Tijuana and states in the Mexican interior; 

Devices provided by imaging system supplier PIPS Technology are also widely deployed in 

Mexico. Both technologies can also detect license plates from various Mexican states. 

ALPR applications for travel time measurement are not as widely deployed as the security and 

enforcement applications; however, ALPR can provide an accurate measurement of travel times 

between fixed locations provided that the cameras have an appropriate sight angle to read the 

license plates and that the license plates themselves are not too damaged or dirty for the 

camera to collect a “good read.” ALPR can work both during the day and at night and is not 

dependent upon carrier participation. ALPR can theoretically collect data from the total vehicle 

population, up to the successful read rate. However, because an unimpeded visual line of sight 

to each plate is required to capture a complete image, sufficient space between commercial 

vehicles is needed. This may be a problem at congested border locations where nose-to-tail 

operations are common.9

 Further, the security of ALPR infrastructure is of concern since 

roadway geometry and other factors may dictate that it must be mounted in areas outside of 

controlled-access facilities. 

 

7

 FHWA Public Roads. May 2005. http://www.tfhrc.gov/pubrds/05may/10.htm. 

8 http://www.intelligentimagingsystems.com/ 

9

 It is important to note that USDOT numbers are not unique to individual trucks, but are typically used as a 

secondary source of verification in the absence of an unobscured license plate number. 
FHWA Cross-border Travel Times - Otay Mesa International Border Crossing: Final Report 

20

The most likely business model for deploying ALPR in Otay Mesa is to purchase ALPR 

equipment from an ALPR vendor and install two out-stations and one in-station to collect a total 

cross-border travel time. These installations would be located at the beginning and end of 

queue as detailed in the Task 1 Stakeholder Summary and would be permanent installations. 

Additional temporary installations could be utilized to collect a sample of information at various 

intermediate points. For example, such an installation would be necessary to capture data 

points at locations such as the exit of MX customs, the entrance to the US primary booths, or 

one for the FAST lanes entering US Customs. This temporary installation would then be used 

to collect representative sample of “average day” data that could be applied to extrapolate travel 

times by type of movement or between various points. This could be accomplished within the 

FHWA’s budget for this study; however, funding for equipment maintenance and replacement 

past the equipment warranty, or to address vandalism would need to be identified and 

programmed for each installation. 

GPS 

GPS data are becoming more commonplace as a solution to collecting travel time information 

where fixed devices (including sensors and ALPR) cannot provide the same levels of area-wide 

coverage. Section 1201 of the Safe Accountable Flexible Transportation Equity Act – A Legacy 

for Users (SAFETEA-LU) has encouraged regional GPS deployments to collect traffic data. 

Section 1201 established the Real-Time System Management Information Program and 

encourages states to “monitor, in real-time, the traffic and travel conditions of the major 

highways” and to share data with local governments and travelers alike.10

Because of deployment, maintenance, and operations expenses associated with fixed travel 

time data collection technologies (such as loop detectors, fixed sensors, and even ALPR), GPS 

and other types of probe data (including cellular probe data) provide a cost-effective solution 

and can cover an entire metropolitan region. 

Large-scale probe-based travel time data are currently being collected in Baltimore, MD; 

Missouri – statewide; San Francisco, CA; New York State; and at the northern border (among 

other locations nationwide). In Baltimore and Missouri, ITIS Holdings Plc. in partnership with 

Delcan is collecting probe data from the cellular phone network and from GPS-equipped 

commercial vehicles. In San Francisco, researchers at the University of California Berkeley 

 

10 SAFETEA-LU Section 1201, as summarized in Federal Register Vol. 72, No. 198, Monday, October 15, 2007. 
FHWA Cross-border Travel Times - Otay Mesa International Border Crossing: Final Report 

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have partnered with Nokia to test using GPS-enabled mobile phones to provide traffic 

information. Perhaps the largest GPS-based deployment to date is on the I-95 Corridor, from 

New Jersey to North Carolina, where INRIX was recently awarded a multi-year, multi-million 

dollar contract to provide travel time and speed data from more than 750,000 GPS-equipped 

vehicles.11

GPS-equipped vehicles are often used to calculate “ground truth” travel time estimates to 

evaluate the accuracy of travel times collected via fixed sensors. This technique is used to 

validate regional congestion information by the Baltimore Metropolitan Council, for example.12

GPS units can provide a continuous source of location information with an infinite number of 

measurement points; however, reporting cycles of equipment and vendors often dictate when a 

measurement is taken. Many carriers, for example, choose to have in-vehicle units send 

location information every 15 to 30 minutes as a means to manage the overall communications 

costs associated with such systems. Vendors transmitting location information on the cellular 

data network (Nextel, for example) may collect location information every 2-5 minutes.13 While 

older GPS units may not provide enough accuracy to detail lane-by-lane travel times, studies by 

the Institute of Transportation Engineers (ITE) and the Texas Transportation Institute (TTI) 

among others validate the accuracy of GPS-based traffic data.14 

The FHWA is also engaged in GPS-based travel time measurement on a national scale. In work 

conducted under a project entitled “Freight Performance Measurement: Travel Time in FreightSignificant Corridors,” the FHWA has been examining the use of GPS systems to calculate the 

average speed for trucks on specific road segments.15 The speeds of multiple trucks are then 

aggregated to determine average speed on a road segment. These measured speeds are then 

 

11 “Inrix to Deliver Real-Time Traffic Information for I-95 Corridor.” GPS Daily. 

http://www.gpsdaily.com/reports/INRIX_To_Deliver_Real_Time_Traffic_Information_For_I_95_Corridor_999.html 

12 Delcan for the Baltimore Multimodal Traveler Information System Business Model. 

13 Delcan for the Baltimore Multimodal Traveler Information System Business Model. 

14 Addressing Issues with GPS Data Accuracy and Position Update Rate for Field Traffic Studies, Institute of 

Transportation Engineers, ITE Journal. Feb 2003; Travel Time Studies with Global Positioning and Geographic 

Information Systems: An Integrated Methodology, Texas Transportation Institute. 1998. 

15 http://ops.fhwa.dot.gov/freight/freight_analysis/perform_meas/fpmtraveltime/index.htm#travel 
FHWA Cross-border Travel Times - Otay Mesa International Border Crossing: Final Report 

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used to calculate travel time reliability along various routes. This same GPS data is being used 

to assess border crossing time at 15 US/Canada ports of entry. 

At the Otay Mesa border, a number of different deployment options were considered for crossborder travel time measurement using GPS data. A key study objective was to utilize a 

deployment option and business model that could be sustained over time and have a high 

likelihood of adoption after the test was completed. These options are shown in more detail in 

Table 3, which discusses the characteristics of and the risks associated with each of four 

different deployment models. 
FHWA Cross-border Travel Times - Otay Mesa International Border Crossing: Final Repor

Table 3. GPS Data Collection – Deployment Model vs. Risk 

Deployment 

Model 

Time to 

Implement 

Historic Data 

Collection 

Real-Time 

Data 

Collection 

Contractual 

Agreements 

Hardware Software Ownership Future 

Operatio

Collect GPS data by deploying units in trucks 

6 months for deployment followed by 1 year for data collection 

Baseline data will be analyzed over time 

Real-time data demonstration may require additional funding 

Will require contracts with carriers; if carriers are paid for participation; contract oversight will be timeconsuming and costly 

Will require hard-mount GPS units in trucks; handheld units may not provide accurate data, but could be considered 

Will require basic algorithms to analyze data 

FHWA will own baseline data and equipment after test conclusion 

A continuatof this modunlikely to bsustainable over time dto time spefacilitating arrangemewith carrierand operatmaintenancequipment 

Purchase GPS data from vendors (no carrier involvement) 

6-12 months for agreements followed by 1 year for data collection 

Baseline data will be analyzed over time 

Real-time data demonstration may require additional funding 

Contracts with multiple vendors will be difficult to arrange; vendors may be unwilling to participate 

No additional hardware; will use systems already deployed 

Will require basic algorithms to analyze data 

Baseline dataset will be public, but additional data use will require renegotiation with vendor 

Likely to be sustainable over time if vendors are willing to participate; privacy concerns arisk 

Carrier request GPS data from vendor to be exported to Study Team 

6 months for data stream followed by 1 year for data collection 

Baseline data will be analyzed over time 

Real-time data demonstration may require additional funding

Will require contracts with carriers; if carriers are paid for participation; contract oversight will be timeconsuming and costly

No additional hardware; will use systems already deployed

Will require basic algorithms to analyze data

Baseline dataset will be public, but additional data use will require renegotiation with carriers and vendors

Likely to be sustainable over time; however, maintaining contracts wcarriers wiltime consuming
FHWA Cross-border Travel Times - Otay Mesa International Border Crossing: Final Repor

Deployment 

Model 

Time to 

Implement 

Historic Data 

Collection 

Real-Time 

Data 

Collection 

Contractual 

Agreements 

Hardware Software Ownership Future 

Operatio

Collect travel time information from third-party provider (e.g. Calmar, Inrix) 

3-6 months for initial data stream, followed by 1 year of data collection 

Baseline data will be analyzed over time 

Real-time data demonstration may require additional funding

Will require agreement with third-party provider 

No additional hardware; will use systems already deployed

No additional software for baseline data 

Baseline dataset will be public, but additional data will require third-party provider

Sustainablethird-party providers sin businesscost may initially risethen adjust market conditions 
FHWA Cross-border Travel Times - Otay Mesa International Border Crossing: Final Report 

25

The options for collecting GPS data from commercial vehicles crossing the international border 

at Otay Mesa are highlighted in Table 3 and summarized below. Because GPS equipment is 

largely a carrier-based asset, the business models must involve cross-border motor carriers in a 

way that is acceptable for their businesses. While the following deployment models are all 

potential methods of collecting GPS-based data, some make more sense than others in the 

case of the Otay Mesa deployment, where options with lower risk and greater likelihood of 

adoption are preferable. 

Option 1: Deploy GPS units in trucks to collect baseline travel time data and validate 

technology. This would require purchasing GPS units and software to collect and 

aggregate these data as well as coordination with carriers for participation agreements. 

While this model is technically possible, its feasibility is doubtful. In addition to the expenses 

associated with device purchase and use, many larger carriers already have GPS-equipped 

vehicles and may be unwilling to participate. Additionally, the validity of the data may be in 

question, since the GPS equipment could be easily disconnected if it is deemed not valuable 

to business operations, if a driver feels it is intrusive, or if external parties become aware of 

its presence in vehicles. Government-owned GPS devices would also require FHWAsponsored operations and maintenance, which would significantly complicate the 

contractual process. Table 3 summarizes this model as: Low feasibility, high risk.

Option 2: Purchase GPS data directly from vendors. Acquiring data directly from vendors 

provides a means to streamline GPS data collection. This method does not include 

purchasing any propriety information about the carriers themselves, only access to 

aggregated GPS data not segmented by carrier or carrier type. While some third-party 

providers have had success in purchasing data from GPS vendors, Delcan’s experience in 

the US suggests that probe data that are provided from vendors or cellular phone carriers, 

while robust, are difficult to procure due to contracting and privacy issues. While carrier 

privacy would not be violated, carriers may perceive a privacy breach and question vendor 

rights in selling data. Table 3 summarizes this model as: Low feasibility, high risk for the 

purposes of this study; however, other initiatives may find success for this model with larger 

budgets and longer timeframes. 

Option 3: Purchase GPS data directly from carriers and/or carriers request data feed from 

vendor. This business model will likely provide more success than working with the vendor. 

Three large carriers attending the Otay Mesa sessions have already agreed to provide GPS 
FHWA Cross-border Travel Times - Otay Mesa International Border Crossing: Final Report 

26

data for the test. These carriers, by agreeing to participate, would also likely share business 

characteristics (fleet size, FAST/CTPAT certified, empty movements, etc), which could be 

used to help statistically differentiate the travel times of the laden, FAST, and empty 

movements traversing the border. At the same time, contracting and data access issues 

will be more time consuming not only for the test, but for the FHWA over the long-term if the 

data stream is to be maintained. Finally, there is some concern that outside parties may not 

trust the data obtained directly from motor carriers, since carriers may be reticent to provide 

complete datasets directly to a government entity. Table 3 summarizes this model as: 

Medium feasibility, but high risk due to the high involvement of the FHWA in contracting with 

carriers. 

Option 4: Purchase GPS data from a third party provider. Calmar Telematics and Inrix 

provide these services, among others. As third-party providers, they contract with the 

carriers and/or owners to collect GPS data either directly from the fleet’s IT department or 

from the vendor. Fleets will ask vendors to push data to the third party provider, and the 

third party will then provide data for that specific carrier. Third party providers may pay 

between $20 and $100 per vehicle per year directly to the carriers. It should be noted that 

the payments are based on market conditions, where heavily congested urban areas often 

demand higher payments to carriers as third parties have more success reselling data in 

these areas. Calmar is currently providing data on the New York State Thruway and at the 

northern border Peace Bridge. Inrix was recently awarded a contract to provide data along 

the I-95 Corridor and provides data in cities nationwide. 

To meet the project objectives, a GPS deployment strategy that utilizes a third-party provider 

(Option 4) was selected. 

Acquiring GPS data from a third-party provider is not without risk, however, and data 

“ownership” issues must be addressed. The dissemination of raw GPS data is more likely to be 

restricted by the carrier or by the third party because of the issues associated with collecting, 

using and maintaining these data. In this case, the customer agency might have full rights to 

review the analyzed data and travel times and to disseminate summary information, but the raw 

data would likely have certain limitations for use outside of agency activities and may not be 

available directly to the agency. This was not a significant concern during the test, but may 

introduce more complex contracting language under deployed conditions. 
FHWA Cross-border Travel Times - Otay Mesa International Border Crossing: Final Report 

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Additionally, third-party providers may provide GPS data, which they purchase directly from 

motor carriers, for a greatly reduced cost for the purposes of the test. However, the cost of 

these data may increase (or decrease) based on coverage and market conditions. Delcan’s 

experience with probe data shows that deployment costs may initially increase after the initial 

test period until they adjust for market conditions and/or a larger business to the third party is 

realized.16

Both GPS and ALPR were considered for deployment at Otay Mesa and were evaluated against 

the following User Requirements for technology deployment as detailed in the Task 1 

Stakeholder Sessions. A summary comparison of ALPR and GPS for each User Requirement 

is shown in Table 4, with the primary User Requirements of: 

 Total cross-border travel times (historic data); 

 Total cross-border travel time with FAST, empty, and laden movements differentiated; 

 Real-time information on delay; and 

 Measures of travel times between multiple points within the US and MX Customs 

compounds. 

It should be noted that not all user requirements could be addressed with this initial technology 

deployment; however, each technology and its potential to provide these capabilities was 

addressed in detail as shown in shown in Table 4. 

 

16 Delcan for the Baltimore Multimodal Traveler Information System Business Model. 
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Table 4. Functional Requirements / Technology Implications Summary 

User Needs (Northbound) GPS ALPR 

Total cross border travel time 

(historic) from the beginning to 

the end of the queue 

GPS data can be used to provide a total cross-border 

measurement of travel times within FHWA’s budget. 

GPS risks are related to carrier commitments to 

participate; however, carriers were eager to participate 

in the stakeholder session conducted in January 2008 

and in follow-up contacts with stakeholders. 

ALPR can be used to provide a total crossborder measurement of travel times within 

FHWA’s budget. ALPR risks include safety 

and security of the ALPR equipment. The 

specific location of ALPR camera at beginning 

of queue has yet to be determined 

Total cross-border travel time 

with FAST, laden, and empty 

moves differentiated 

GPS data for each movement type may be 

differentiated using fleet characteristics and geofencing. This requires additional analysis and 

estimation using statistics. 

Travel times will be estimated based on 

statistical distributions of trip types within the 

total sample. CBP crossing data, if provided, 

can enhance this analysis. 

Real-time wait information: total 

travel time, FAST, laden, and 

empty 

GPS data can be used to collect data on the approach 

to, as well as within, the customs compound and can 

provide real-time travel time information. 

The total travel time using ALPR can be 

reported in real-time, but only after the truck 

passes both the first and last reader locations. 

Travel time information between 

multiple points within the MX and 

US customs compounds 

GPS data for multiple measurement points can be 

differentiated using geofencing, provided the location 

sampling frequency is high enough to capture a truck 

as it passes through a geofenced area (e.g., CBP 

secondary). This requires additional statistical analysis. 

ALPR data for multiple measurement points 

can be collected using portable ALPR stations 

at temporary points. This requires additional 

analysis and estimation for segments not 

measured. Alternatively, could relate plate 

reads to CBP records showing crossings by 

individual trucks, if access to such data is 

permissible. 

Data/ infrastructure ownership GPS equipment will not be owned by the FHWA; raw 

GPS data may not be owned or even accessible to the 

FHWA. As a risk, carriers can choose to terminate data 

access at any time, but would lose revenue 

FHWA will own both the infrastructure and the 

raw data, but will also be responsible for 

maintenance of the physical assets, which 

include at least two camera/antenna/power 

“out station” assemblies, and one “in station” 

to receive transmitted data. 

Safety, security, and 

maintenance 

No issues associated with safety and security - many 

GPS deployments by carriers support C-TPAT 

programs; however, maintenance of contracts with 

carriers is required. 

Safety and security of ALPR infrastructure is 

primary concern. Life span of equipment is 3-

5 years; more information is needed to 

assess useful life under rugged border 

conditions. Potential security risk from theft 

or damage to fixed infrastructure. 

Long-term viability Carrier commitments will likely change over time. For 

long-term viability, a third party can best address carrier 

contracts, where business models provide incentives to 

add more carriers and data-related service offerings. 

Historical precedent exists regarding ability of 

agencies to support long-term maintenance, 

security, and life-span of equipment. Trends 

in state-of-practice suggest in the long-term, 

fixed infrastructure for travel time 

measurements will likely be replaced by 

probe technologies. 

Stakeholder support Stakeholders support sharing or selling of GPS data 

based on feedback obtained during January 2008 

stakeholder sessions and follow-up phone calls/ emails. 

Initial cost of infrastructure along with 

maintenance and security issues are sources 

of high stakeholder concern. Cameras near 

border may add additional privacy concerns 

for carriers. 

Information beyond borders Could potentially track trip origin, destination, and 

routes for more robust analysis. Could provide data for 

FHWA’s freight travel time data base. Southbound data 

can be added at no additional deployment cost. 

Could provide travel times with other ALPR 

sites from same vendor with additional 

software. Extension of geographic 

measurement window or addition of 

southbound data would require additional 

installations. 
FHWA Cross-border Travel Times - Otay Mesa International Border Crossing: Final Report 

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Additionally, the following “other considerations” for deployment are documented in Table 4: 

 Data and/or infrastructure ownership; 

 Safety, security, and maintenance; 

 Long-term viability of the technology; 

 Stakeholder support for deployment; and 

 Potential for a richer dataset “beyond” the border and/or on the southbound approach. 

In summary, GPS data provide a robust stream of multiple data points. The continuous nature 

of the data allows for analysis of travel times for different segments of the cross border trip. 

Southbound information can also be added without additional deployment costs. The study 

team recommended a third-party provider approach to help mitigate risks from carrier 

involvement and data contracts; however, the provision of the data from the carriers was still the 

largest risk for GPS deployment. The January stakeholder sessions helped identify carriers 

who were willing to participate in this task. Stakeholders strongly supported GPS applications. 

ALPR deployment can provide a concrete measure of the total cross-border travel time provided 

that the infrastructure can be mounted in a safe and secure location that is within range to read 

commercial license plates. Temporary ALPR stations can provide additional estimates of travel 

times for segments of the trip within the MX and US Customs compounds. Because the FHWA 

would be responsible for maintenance of ALPR infrastructure, safety and security is a significant 

risk. 

3.4 Technology Deployment 

Once the decision was made to go forward with a GPS-based deployment, data acquisition 

requirements were broadly described to the data provider. The key requirements were that the 

sample size be sufficient to provide the required volume of data to adequately characterize 

travel times and that carriers would need to allow unrestricted access to all available GPS data 

generated by trucks passing through the Otay Mesa crossing. 

Prior research indicates that, in order to accurately characterize travel times on a roadway 

network, between three and five percent of the total vehicle population must provide GPS data 
FHWA Cross-border Travel Times - Otay Mesa International Border Crossing: Final Report 

30

on a regular basis.17 With a typical daily volume of 2,500 truck trips at the Otay crossing, this 

equates to between 75 and 125 truck trips daily. 

In order to meet these requirements for truck GPS data, the data provider needed to identify 

and recruit motor carriers to participate in the project. The data provider’s operating model 

involves the acquisition of GPS data directly from the carriers, as opposed to other vendors that 

obtain data from fleet management system providers. Though for business confidentiality 

reasons the data provider did not fully disclose the reasons for adopting this model, they did 

indicate a preference for dealing directly with the owners of the data, as opposed to negotiating 

agreements with system or service providers. 

The data provider chose to address this requirement by recruiting three carriers with trucks that 

regularly travel the Otay crossing. Initially, the carriers were chosen because they already 

possessed vehicles with GPS data systems installed. This approach is taken for two primary 

reasons. First, because the carrier has ultimate ownership rights to the data, they can grant 

access directly, without requiring the approval of any additional party. Second, carriers are 

typically open to receiving modest compensation, since they incur no additional expense and 

can recoup a portion of their investment in fleet management systems and services. 

The primary drawback of this approach is that individual agreements must be negotiated and 

maintained with each carrier. This proved especially challenging for this project. While the 

carriers from whom the data provider acquires GPS data are primarily US-based fleets—many 

that are located in the northeast US—many of those that use the Otay crossing are co-owned 

by families that have operations on both sides of the border. Hence, the nature of negotiations 

was somewhat unique, as compared to that which the data provider is accustomed. The result 

was that it took somewhat longer to negotiate agreements than might otherwise be required in a 

non-border environment. 

Initial data received from the equipped trucks indicated that many of the trucks that appeared in 

the measurement zone did so only once or twice during a given trip, often on the Mexican side 

of the border before reaching US CBP primary inspection. This level of data was deemed by the 

team to be insufficient to reliably measure travel time in the Otay border zone. 

 

17 Ferman, Martin A., Dennis E. Blumenfeld, Xiaowen Dai, “An Analytical Evaluation of a Real-Time Traffic 

Information System using Probe Vehicles,” Journal of Intelligent Transportation Systems, 9:1, 23-34. Available at: 

http://dx.doi.org/10.1080/15472450590912547. 
FHWA Cross-border Travel Times - Otay Mesa International Border Crossing: Final Report 

31

Initially, the GPS data provided by the carriers that agreed to participate was generated with a 

trailer-tracking system called SkyBitz. While the position data provided by these systems has 

proven reliable over a number of years, it became clear as the test progressed that the reporting 

frequency was not sufficient for measuring travel time over such a short distance. Skybitz 

systems typically report position every 30 minutes, plus provide additional reports when vehicles 

begin moving, or stop for a predetermined period of time. 

Because the existing carrier agreements with SkyBitz did not generate data with enough 

frequency to provide a sufficient number of data points within the border travel time 

measurement zone, and because the participating carriers often move trailers that are not 

equipped with SkyBitz devices, To address this, the participating carriers opted to upgrade their 

GPS units. These units, provided by LocJack, are designed to be installed in the tractor. The 

units capture position data every two to three minutes, and forward reports within 10 minutes. 

Going this route meant that the opportunity existed to also capture the movement of unequipped 

trailers and bobtail trucks (trucks without trailers attached). 

Carriers utilized the compensation provided to them through their data agreement with the data 

provider to purchase and install a total of 175 units in trucks. The devices were installed and 

brought on line during the February to April timeframe in 2009. The carriers purchased these 

units at a price of $350 per unit, and subscribed to data services at a cost of $17 per month. 

Once the units were installed, the data provider was granted access to data feeds by the 

carriers. 

While the study team was not a party to the specific terms and conditions of the agreements 

between the data provider and the carriers, the terms included a provision to provide 

anonymous data to the study team, and by extension to the FHWA, for all US-bound truck trips 

occurring within the area in the immediate vicinity of the crossing, and all data points falling 

within the measurement zone, for a period of one year. This was agreed upon by the carriers in 

return for the provision of data from the GPS units. 

3.5 Trip Statistics 

As previously discussed, the average daily truck traffic volume at the beginning of the test was 

expected to be approximately 2,500 trips per day. Using the general rule of thumb that between 

three and five percent of the vehicle total needs to provide probe data in order to accurately 

characterize travel times, this places the desired number of daily trips by equipped vehicles 

between 75 and 125. 
FHWA Cross-border Travel Times - Otay Mesa International Border Crossing: Final Report 

32

In actuality, during the test the total vehicle counts were consistently lower than the 2,500 figure. 

CBP provided data that indicated that the average daily US-bound vehicle count. Table 5 shows 

the total (including loaded and empty) truck counts by month, plus the daily averages for the 

period from January 2009 through February 2010. 
FHWA Cross-border Travel Times - Otay Mesa International Border Crossing: Final Report 

33

Table 5. Truck Counts at Otay Mesa – US Inbound 

Truck Counts 

Month 

Total Daily* Loaded Empty 

January 2009 51,766 1,922 31,787 20,099 

February 2009 48,205 2,040 30,501 18,465 

March 2009 55,285 2,126 35,758 19,527 

April 2009 56,709 2,181 37,242 19,467 

May 2009 54,790 2,107 36,778 18,012 

June 2009 60,191 2,314 38,851 21,321 

July 2009 60,550 2,047 39,560 21,862 

August 2009 57,821 2,224 36,682 21,141 

September 2009 59,743 2,298 39,094 20,649 

October 2009 64,848 2,402 43,236 21,612 

November 2009 58,879 2,355 39,545 19,334 

December 2009 55,638 2,059 37,207 18,389 

January 2010 51,020 1,962 32,504 18,516 

February 2010 50,816 2,201 33,884 18,945 

*Based on Mon-Sat. Sundays were excluded because trip counts are very small. 

According to port officials at Otay Mesa, approximately one third of the total laden shipments 

are FAST trips. 

In order to assess whether the fleets participating in the project were providing an adequate 

percentage of overall trips, the study team examined the GPS data to identify two different types 

of trips. The first was “total trips.” For this category, any appearance of multiple points from the 

same truck within the dataset for a given two-hour period was considered a trip. This was true 

regardless of where the points were located, as long as they were within the travel time 

measurement zone, and indicated US-bound movement. The second trip type was “crossed 

trips.” The study team considered a trip a crossed trip if a truck reported its position at least 

once on each side of the border during an individual trip. Hence, the group of total trips included 

all crossed trips, plus any additional trips for which points could not be identified on one side of 

the border or the other. 

Figure 8 contains a plot of the data gathered during the test from January 2009 through March 

2010. It illustrates the number of trips for which data was captured during each month. The 

acronym MOY represents month of the year. 
FHWA Cross-border Travel Times - Otay Mesa International Border Crossing: Final Report 

34

Figure 8. Otay Mesa Data Trips – January 2009 through March 2010 

 

As the data indicate, the number of crossed trips for which GPS data was collected consistently 

fell between three and five percent of the total trips through the border as reported by CBP.18

Based on these figures, the study team was confident it could rely upon the data from crossed 

trips to characterize travel time through the crossing. The alternative would have been to 

attempt to construct composite travel times from the totality of the captured trips. The team 

considered this unnecessary and therefore did not attempt it. 

As the graph shows, the number of trips that the team was able to identify as FAST trips was 

very small. The FAST trips generally equated to between 10 and 18 percent of the total crossed 

trips on a monthly basis, with just under 13 percent over the duration of data collection. The 

team arrived at these figures by creating a “geofence”19 around the portions of the MX customs 

compound through which only FAST trucks travel. This was accomplished using TransCAD, 

overlaying all of the points on the map grid for the crossing, and identifying individual truck GPS 

data points that fell within the area. A graphical depiction of this geofenced area is provided in 

Figure 9. 

 

18 CBP had not released statistics for March 2010 as of the writing of this report, so the 5 percent value could not be 

determined for that period. 

19 A Geofence is a virtual perimeter on a geographic area using a location-based service, so that when a telemetry 

device enters or exits the area a notification is generated. (Source: Wikipedia - http://en.wikipedia.org/wiki/Geofence). 
FHWA Cross-border Travel Times - Otay Mesa International Border Crossing: Final Report 

35

Figure 9. Illustration of Geofence Used to Identify FAST Trips 

The study team designated the area indicated in red as the most likely opportunity to isolate 

trucks completing FAST trips. The small black dots superimposed over the image represent 

individual GPS data points received from participating trucks. As the image indicates, there is a 

distinct separation in traffic flow. However, based upon the figures cited by the carriers 

regarding the percentage of trips conducted as FAST trips, the method clearly resulted in underreporting. 

By contrast, the study team was confident that the method was appropriate for the identification 

of vehicles that passed through secondary inspection, which includes the screening of vehicles 

by x-ray units on the western end of the US customs compound. Vehicles directed to secondary 

for x-ray screening pass along the upper perimeter of the compound from east to west, then turn 

south to pass through one of two x-ray units. Travel speeds are typically low through this area, 

and every truck must come to a stop during the x-ray screening process. The result is that 

vehicles directed to complete this screening can be expected to report multiple times while in 

secondary. 
FHWA Cross-border Travel Times - Otay Mesa International Border Crossing: Final Report 

36

The team also analyzed crossing data by day of the week (DOW) and time of day (TOD). 

Figures 10 and 11 illustrate the distribution of trips for these parameters. 

Figure 10. Otay Mesa Data Trips by Day of the Week 

In Figure 10, the days of the week start with Sunday, which is designated as day 1, and end 

with Saturday, which is day 7. As the data indicate, Wednesdays and Thursdays were the 

busiest days, while very little traffic crossed on Sundays. With the exception of Sundays, the 

number of crossed trips captured for each day type was consistently above 500, with values 

approaching or exceeding 1000 trips for all days from Tuesday through Friday. 

The data illustrated in Figure 11 requires more explanation. The crossing itself is closed 

between midnight and 8 AM. However, there are two phenomena that result in the appearance 

of trips during those hours. The first is that trips that begin late at night, and include processing 

through US customs primary prior to the midnight closure must still complete the trip through 

customs and the CHP inspection facility. This phenomenon likely accounts for most of the trips 

reported until the 4 AM low point. The second phenomenon, which likely accounts for most of 

the trips between 5 and 8 AM, is the queuing of trucks awaiting the opening of the border. In any 

case, the data for the period from 3 AM up until 6 AM is very sparse, and are not considered 

reliable. 
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Figure 11. Otay Mesa Data Trips by Time of Day 

3.6 Border Travel Time Data 

The accumulation and processing of individual GPS data points is a multi-step process. GPS 

data from the units used during this test is similar to data that would be forwarded from other 

GPS devices. The units transmit numerical values for latitude and longitude, along with a time 

stamp (in Greenwich Mean Time – GMT) and a unit identifier. Each of these points is then 

translated into a position on a roadway grid (see Figure 9 above), along with a direction of 

travel, and from that data vehicle speed and travel time can be calculated. 

For this project, both the data provider and the study team ran calculations on the data, with the 

data provider conducting initial filtering and mapping, the forwarding it to the study team for 

analytical computation. At the onset of the data collection effort, the data provider created a set 

of geo-registered spaces that represented segments of the trip through the Otay crossing. 

These spaces, which are referred to as “districts,” are shown as segments outlined in green 

polygons in the illustration in Figure 12, which also contains data from one empty vehicle trip 

through the border (dark blue dots) and one FAST truck trip (light blue dots). 

The purpose of this was to allow for the consideration of partial trips in the overall calculation of 

travel time through the measurement zone. It was initially intended to accommodate the 

infrequent reporting intervals of the Skybitz devices. When the LocJack devices were brought 
FHWA Cross-border Travel Times - Otay Mesa International Border Crossing: Final Report 

38

on line, these districts simply became convenient mechanisms for observing partial-trip travel 

times, and to identify whether trucks were completing FAST trips or were unloaded. 

Figure 12. Sample Otay Mesa GPS Data in Districts 

For the purposes of understanding the translation of individual GPS points into travel time 

calculation parameters, it is useful to review the data manipulation sequence used during the 

project. The following sequence of actions took place for the data captured during the test: 

Data Processing Sequence 

1. Data is collected from units: 

a. If the carrier has a database, Calmar applies a tool (created by them) that polls 

the carrier database, translates the GPS data into XML20 format, and then sends 

it to Calmar using encrypted TCP/IP.21

 

20 Extensible Markup Language (XML) is a simple, very flexible text format derived from SGML (ISO 8879). Originally 

designed to meet the challenges of large-scale electronic publishing, XML is also playing an increasingly important 

role in the exchange of a wide variety of data on the Web and elsewhere. (Source: W3C - http://www.w3.org/XML/) 

21 The set of communications protocols used for the Internet and other similar networks. It is named from two of the 

most important protocols in it: the Transmission Control Protocol (TCP) and the Internet Protocol (IP), which were the 

first two networking protocols defined in this standard. (Source: Wikipedia - 

http://en.wikipedia.org/wiki/Internet_Protocol_Suite) 
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39

b. If a carrier uses the provider website (rather than its own database), Calmar 

accesses the XML or .csv22 feeds through the website with a tool they created 

2. GPS data is fed into a “threshold database,” which contains all raw data 

3. Calmar applies routines to process the GPS data and store in a “national database.” 

a. Assigns a token ID to each GPS point. 

b. Strips specific vehicle identifiers, replacing them with a weeklong artificial 

identifier 

c. Snaps individual GPS points to the roadway grid using a routine written by 

Calmar,23 and the Navteq database shape file 

4. Snapping results – the results of the snapping routine are rows in a database file with 

the following headings: 

a. roadID – node to node breakdowns 

b. linkNum – segment of road 

c. DistToSeg-Ft – perpendicular distance from an individual GPS point to roadway 

segment 

d. DistFrom_Ft – distance from projected point (point location on link) to beginning 

of segment 

e. Method – internal reference identifier that helps to sort dataset and understand 

characteristics of subsets of the dataset 

5. Labels are applied to different SensorStatus points (also labeled as “Reason”), based on 

what is and isn’t known (start, stop, breadcrumb,24 etc.) 

6. GPS data is picked from the database using a specific area of interest defined as a box 

defined by lat/long corner points. For this project, this is defined as a rectangular area 

that fully encompasses all of the roadway segments that constitute the measurement 

zone. 

7. Outliers are identified and excluded by analyzing for unreasonable speeds between 

successive points. 

 

22 csv – Comma Separated Values – a file type often used to transfer data among various applications where 

individual values are separated by commas. 

23 Calmar uses its own proprietary geo-location tuning logic to improve upon the precision found in standard 

commercial mapping products, which Calmar considers insufficient for accurate travel time measurement. This was 

the case with the telemetry data acquired during the project. 

24 “Breadcrumb” is a term that is often used to identify points that are recorded at regular intervals by a telemetry unit 

as a vehicle is moving 
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8. GPS data is fed into a GIS25 where it is associated with a selected area or district 

bounded by a geo-registered space. Then a geospatial overlay is superimposed over the 

dataset to bind the area/district identifier to the GPS data. 

9. A series of geospatial transforms is applied: 

a. The district of interest in which an individual point resides is associated with the 

underlying link structure as well as the GPS – i.e., a district ID is assigned. If the 

point is not in a district, the district identifier is a null. 

b. Export GIS data that has been snapped to a district of interest into a statistical 

analysis package.26

c. Within the statistical analysis package, data parsing modifies the dataset to add 

useful characteristics (e.g., day of week, month, etc.), and to sort by different 

criteria. 

d. The system then tries to identify logical trip chain endpoints using elapsed time 

between consecutive points from an individual vehicle. Elapsed time clock (time 

between consecutive GPS points from the same vehicle) is reset to zero when a 

vehicle reappears in the dataset after having been absent for more than 6-1/2 

hours. 

e. Single points are tossed out if there is not another data point within the districts 

within the 6-1/2 hour window. 

10. The refined output is placed into a data file to facilitate the calculation of travel times. 

Many of the individual actions within this sequence required manual triggering of calculations or 

processing. As such, it is not likely to be useful for anything other than examination of data after 

the fact. 

For the purposes of analyzing the data generated during the test, the study team fed the data 

into Excel and compiled statistical summaries of travel time. Because the travel time 

measurement zone was defined as the full distance between the Bellas Artes/Calle Doce 

intersection to the exit of the CHP facility (see Figures 3 and 4), the travel times were initially 

calculated to reflect that distance. 

As described above, to facilitate calculating travel time across the full measurement zone and 

for portions of the trip, the trip length was segregated into a series of consecutive travel zones, 

or districts. The data provider formulated a set of eleven travel districts and provided the GIS 

 

25 GIS – Geographic Information System. A GIS is a system that allows for spatial placement and analysis of 

telemetry points. For this project, Calmar utilized Manifold, while Delcan used TransCAD. 

26 For this project, Calmar used SAS, while Delcan used a SQL database and a set of analytical algorithms. 
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41

shapefile to the study team for use during data collection and analysis. The image in Figure 13 

shows all of the districts. 

Figure 13. Otay Mesa Travel Time Measurement Districts 

This was initially done to accommodate the compilation of travel time using portions of trips as 

reported by vehicles using the Skybitz solution, which, as was indicated previously, reports 

relatively infrequently. Ultimately, this became unnecessary. However, the districts were still 

utilized for determining the portion of the total trip travel time that was accumulated on the 

approach to the US customs primary inspection facility. 

Based on the original definition of the measurement zone, the travel time calculation consisted 

of the total time elapsed between the first appearance of a truck GPS data report within district 1 

and the last report within district 10. Using this approach, the study team concluded that there 

was a risk that the calculated travel time would be underreported, since conceptually speaking, 

a truck GPS unit could travel two to three minutes into the trip before issuing an initial report, 

and could then issue its last report two to three minutes before leaving the measurement zone 

on the US side. Ultimately, the team concluded that little could be done to correct for this and 

that given the typical travel times calculated, it would have a minimal effect. 

The charts in Figures 14, 15, and 16 show the calculated border travel time values for the trips 

observed during the test period, by day of the week, month of the year, and time of day. In each 
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42

of the figures, travel time is presented in seconds, and the data is segregated to show all trips, 

and FAST trips. 

Figure 14. Calculated Travel Time Values by Day of the Week (Dist 1 – 10) 

The vertical red, blue and green bars indicate mean travel time for all crossed trips, all FAST 

trips, and all trips for which the truck did not pass through secondary inspection, respectively. 

The vertical black interval lines represent the calculated standard deviation values for each 

mean value. The green line with intervals designated by triangles represents the median travel 

time for all trips, and the purple line with intervals designated by x’s represents the median 

travel time value for FAST trips. Finally, the remaining lines represent the 25% and 75% quartile 

values for all trips and FAST trips. 

The values presented in Figure 14 indicate that the mean measured travel time across the 

entire measurement zone for Mondays through Saturdays was between 60 and 70 minutes. 

Sundays, which as stated previously see very limited traffic, showed a mean travel time value of 

approximately 43 minutes. The data suggests that average travel times are longest on 

Thursdays and Fridays. The data corresponding to Figure 14 is provided in Table 6, which 

shows mean border travel time values and standard deviation values by day of the week. 
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Table 6. Mean District 1 – 10 Travel Times by DOW 

All Trips FAST Trips Non‐Secondary Trips Day of

Week Mean Travel

Time 1‐10 Stdev

Mean Travel

Time 1‐10 Stdev

Mean Travel

Time 1‐10 Stdev

Day 1   Sun 44 34 33 24 37 27

Day 2 Mon 60 47 52 43 55 43

Day 3 Tue 65 49 56 41 61 45

Day 4 Wed 67 47 62 42 62 44

Day 5 Thu 70 51 63 46 65 47

Day 6 Fri 67 48 57 34 62 45

Day 7 Sat 57 42 58 38 52 38

One notable characteristic in the dataset is that FAST trips—at least those that could be 

identified definitively—take less time for all days except Saturdays. Differences range from 

values of 15 percent lower on Mondays for FAST trips to just over 10 percent lower on 

Thursdays. This does not include Sundays, where differences are negligible and crossing 

numbers are very low. Saturday data suggests that FAST trips take an average of 2 percent 

longer. However, given the low level of confidence associated with the identification of FAST 

trips using the GPS data, this value cannot be considered significant. 

Also notable within the data is the very large value associated with the standard deviation from 

the mean. The values calculated from the data indicate that the standard deviation ranged from 

61 percent to 81 percent of the mean. These values suggest that the carriers that participated in 

the study experienced very wide fluctuations in travel times. 

Because the standard deviation values were so high, the study team was concerned that a 

small number of trips with inordinately high or low travel times were potentially influencing the 

values to a high degree. This is not uncommon for datasets where the sample is non-random, 

and where there are extenuating circumstances that may influence the outcome, such as 

variability in enforcement methods. In the case of travel times, vehicles that are delayed for 

extended periods, or that queue in advance of the opening of the border, may influence the data 

to such an extent that the mean values are biased to the high side. Because it is less prone to 

the influence of outliers, the study team also calculated median travel time values for each day 

of the week. As Figure 13 shows, the median values were measurably lower than the mean 

values. The data indicated that the median values were 15 to 30 percent lower than the mean 
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44

values, which would place the median travel time for Mondays through Saturdays between 42 

and 53 minutes. 

The upward bias in mean travel time values is revealed further through the identification of 25% 

and 75% quartile values. These values form the upper and lower limits of observed travel times 

for the middle 50% of all observed trips. A comparison of the portions of the standard deviation 

range outside the middle 50% to those inside reveals that more of the deviation interval lies to 

the upper side of the range. 

Figure 15 shows similar values for the crossing by month of the year between January 2009 and 

March 2010. 

Figure 15. Calculated Travel Time Values by Month of the Year (Dist 1 – 10) 

The travel time data characteristics for the months shown in the figure are consistent with those 

in Figure 14, with a few notable unique characteristics and observable differences. The first 

notable characteristic is that the mean and median travel time values peaked in the March-April 

2009 and September-October 2009 timeframes. 

According to CBP crossing statistics, the September-October 2009 period was the busiest 

period (see Table 5). This would seem to correlate with the increases in mean and median 

travel times. Interestingly, this is not the case with the March-April peak travel time values. The 

CBP crossing volume data indicates that the June-July 2009 timeframe was busier than the 

March-April 2009 time period. Unfortunately this is not explainable directly from the data. 
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45

The monthly data also indicates that the mean travel times for FAST trips were lower than the 

overall average for all but three of the months: January 2009, April 2009, and January 2010. 

Finally, the monthly data is consistent with the daily data in that they show that the median 

travel time is consistently lower than the mean, and that the middle 50% of trips fall to the lower 

end of the standard deviation range. Table 7 contains a summary of the mean and standard 

deviation values by month. 

Table 7. Mean District 1 – 10 Travel Times by MOY 

Month All Trips Fast Trips No US Secondary Trips

and

Year

Mean Travel

Time 1‐10 Stdev

Mean Travel

Time 1‐10 Stdev

Mean Travel

Time 1‐10 Stdev

Month 1 Jan‐09 60 43 62 32 55 39

Month 2 Feb‐09 62 49 60 34 59 47

Month 3 Mar‐09 68 43 65 28 62 39

Month 4 Apr‐09 69 50 79 58 63 45

Month 5 May‐09 63 45 54 35 61 43

Month 6 Jun‐09 54 39 53 35 50 37

Month 7 Jul‐09 63 46 54 42 60 42

Month 8 Aug‐09 67 48 54 39 62 46

Month 9 Sep‐09 75 55 58 38 71 50

Month 10 Oct‐09 76 56 61 39 72 53

Month 11 Nov‐09 70 50 62 44 66 45

Month 12 Dec‐09 68 48 62 41 62 45

Month 13 Jan‐10 53 42 59 46 48 41

Month 14 Feb‐10 54 42 49 35 48 36

Month 15 Mar‐10 65 46 61 47 60 41

Finally, Figure 16 illustrates the travel time data broken out by time of day. 
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Figure 16. Calculated Travel Time Values by Time of Day (Dist 1 – 10) 

 

The time of day (TOD) dataset reflects the total travel time experienced by vehicles based upon 

the hour of the day when they first appeared in the system. For example, the value shown for 

hour 19 indicates that the mean travel time is approximately 62 minutes for trucks that first 

entered the measurement zone at 7 PM local time. 

The more notable characteristics of the TOD dataset are as follows. First, because the number 

of points was exceedingly small, all trips identified as having started between 12 AM (when the 

border closes) and 6 AM were excluded from the dataset. The study team did this because it did 

not have confidence in the values provided. Second because the border does not open until 8 

AM local time, the high travel time values reported between 6 AM and the opening of the border 

suggest that some vehicles are queuing in advance in order to ensure earlier entry into the US. 

This would explain why travel times are longer for those vehicles, despite the fact that lower 

numbers of vehicles are processed in the early hours than in the peak volume hours between 4 

PM and 7 PM (see Figure 11). Interestingly, the increase in volume seen in the peak 

afternoon/evening travel period does not translate into longer travel times. This is likely due at 

least in part to higher staffing levels during those periods. 

Table 8 contains a summary of the mean and standard deviation values by time of day. 

As was indicated previously, the travel time values presented to this point included the entire trip 

through the measurement zone from the Bellas Artes/Calle Doce intersection all the way 
FHWA Cross-border Travel Times - Otay Mesa International Border Crossing: Final Report 

47

through the exit of the CHP inspection facility. The study team and FHWA selected this as the 

measurement zone because it reflects the total border crossing as experienced by the truckers. 

While this is likely to be the most useful measurement zone, the nature of the GPS data 

suggests that it may be useful for the examination of portions of the trip, as well. This might be 

useful to isolate factors that may contribute to overall travel time, such as infrastructure 

geometry and overall facility capacity. 

Table 8. Mean District 1 – 10 Travel Times by TOD 

All Trips Fast Trips No US Secondary Trips

Time Mean Travel

Time 1‐10 Stdev

Mean Travel

Time 1‐10 Stdev

Mean Travel

Time 1‐10 Stdev

Hour 0 12:00 AM 60 48 54 36 53 39

Hour 1 1:00 AM 53 32 56 30 47 26

Hour 2 2:00 AM 48 38 43 24 45 37

Hour 3 3:00 AM 87 0 41 0 81 64

Hour 4 4:00 AM 123 0 91 0 123 65

Hour 5 5:00 AM 124 0 35 0 120 52

Hour 6 6:00 AM 106 50 96 46 105 50

Hour 7 7:00 AM 99 59 85 67 93 49

Hour 8 8:00 AM 72 50 60 45 69 48

Hour 9 9:00 AM 65 55 54 48 59 47

Hour 10 10:00 AM 71 58 67 52 62 51

Hour 11 11:00 AM 65 49 64 38 60 46

Hour 12 12:00 PM 63 45 61 35 58 41

Hour 13 1:00 PM 67 44 59 30 63 43

Hour 14 2:00 PM 69 39 59 31 66 36

Hour 15 3:00 PM 67 43 59 45 65 42

Hour 16 4:00 PM 60 46 57 44 57 46

Hour 17 5:00 PM 55 40 56 42 52 39

Hour 18 6:00 PM 55 42 55 37 50 38

Hour 19 7:00 PM 61 46 64 41 57 40

Hour 20 8:00 PM 63 51 57 47 56 43

Hour 21 9:00 PM 59 44 55 36 52 37

Hour 22 10:00 PM 60 46 66 51 54 39

Hour 23 11:00 PM 59 44 59 37 53 35

In order to examine the efficacy of partial trip travel time measurement, the study team also 

analyzed the data to calculate the travel time from the beginning of the measurement zone to 

the exit from the US Customs primary inspection facility. This facility is represented as zone 6 in 
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48

the dataset (see Figure 11). Hence, the travel time for this measurement zone would include the 

time elapsed between the first time a vehicle reported in zone 1 through the last time it reported 

in zone 6. The result of this analysis is illustrated in Figures 17 through 19. 

Figure 17. Calculated Travel Time Values by Day of the Week (Dist 1 – 6) 

The most immediately identifiable characteristic is that the mean and median travel time values 

for the district 1 – 6 dataset are consistently approximately 75 to 80 percent of the district 1 – 10 

values. This indicates that approximately three-quarters of the total travel time is spent queuing 

for and processing through US Customs primary inspection. The second notable characteristic 

is that the standard deviation is a slightly higher percentage of the mean, averaging from three 

to ten percentage points higher than those for the district 1 – 10 values. This suggests that 

travel time variability is more dependent upon the portion of the trip between the beginning of 

the measurement zone and the exit of the US compound than those portions afterwards. This is 

a reasonable result given the variety of screening actions that may be taken by CBP. 

The data corresponding to Figure 17 is provided in Table 9, which shows mean border travel 

time values and standard deviation values by day of the week. 
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Table 9. Mean District 1 – 6 Travel Times by DOW 

All Trips FAST Trips Non‐Secondary Trips

Day

of the

Week

Mean

Travel

Time 1‐6

Stdev Mean

Travel Time

1‐6

Stdev Mean

Travel

Time 1‐6

Stdev

Day 1   Sun 35 30 33 24 30       25  

Day 2 Mon 45 37 37 31 42       35  

Day 3 Tue 52 40 47 35 49       37  

Day 4 Wed 52 40 48 34 49       38  

Day 5 Thu 54 41 49 32 52       39  

Day 6 Fri 52 39 48 32 48       37  

Day 7 Sat 46 37 55 38 43       33  

The data shown in Figure 18 indicates these findings are consistent when the travel times are 

examined by month of the year. 

Figure 18. Calculated Travel Time Values by Month of the Year (Dist 1 – 6) 

The data corresponding to Figure 18 is provided in Table 10, which shows mean border travel 

time values and standard deviation values by day of the week. 

The data shown in Figure 19 indicates these findings are consistent when the travel times are 

examined by time of day. 
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Table 10. Mean District 1 – 6 Travel Times by MOY 

Month All Trips Fast Trips No US Secondary Trips

and

Year

Mean Travel

Time 1‐6

Stdev Mean Travel

Time 1‐6

Stdev Mean Travel

Time 1‐6

Stdev

Month 1 Jan‐09 38 26 42 23 37 25

Month 2 Feb‐09 40 28 45 29 36 22

Month 3 Mar‐09 49 34 57 27 48 33

Month 4 Apr‐09 54 43 60 37 52 42

Month 5 May‐09 46 38 44 29 43 35

Month 6 Jun‐09 37 28 35 23 36 27

Month 7 Jul‐09 45 34 40 25 44 34

Month 8 Aug‐09 51 41 38 27 47 38

Month 9 Sep‐09 56 41 47 29 54 38

Month 10 Oct‐09 57 47 49 32 56 46

Month 11 Nov‐09 58 44 54 41 55 41

Month 12 Dec‐09 58 44 53 39 54 41

Month 13 Jan‐10 43 35 51 45 40 29

Month 14 Feb‐10 45 36 40 30 40 32

Month 15 Mar‐10 55 38 49 33 51 34

Figure 19. Calculated Travel Time Values by Time of Day (Dist 1 – 6) 

The data corresponding to Figure 19 is provided in Table 11, which shows mean border travel 

time values and standard deviation values by day of the week. 
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Table 11. Mean District 1 – 6 Travel Times by TOD 

All Trips Fast Trips No US Secondary Trips

Time Mean Travel

Time 1‐6 Stdev

Mean Travel

Time 1‐6 Stdev

Mean Travel

Time 1‐6 Stdev

Hour 0 12:00 AM 46 35 43 28 42 31

Hour 1 1:00 AM 44 31 47 29 41 28

Hour 2 2:00 AM 38 31 36 24 35 29

Hour 3 3:00 AM 64 0 30 0 66 64

Hour 4 4:00 AM 108 0 61 0 110 59

Hour 5 5:00 AM 99 0 44 0 97 50

Hour 6 6:00 AM 86 45 81 49 84 43

Hour 7 7:00 AM 79 44 57 35 77 43

Hour 8 8:00 AM 55 43 45 30 54 43

Hour 9 9:00 AM 45 38 40 25 42 34

Hour 10 10:00 AM 49 43 50 33 46 38

Hour 11 11:00 AM 46 34 48 26 45 33

Hour 12 12:00 PM 47 36 51 30 45 34

Hour 13 1:00 PM 49 35 47 25 46 33

Hour 14 2:00 PM 54 39 49 38 51 35

Hour 15 3:00 PM 53 40 49 40 51 38

Hour 16 4:00 PM 47 37 45 41 44 35

Hour 17 5:00 PM 43 36 46 38 40 33

Hour 18 6:00 PM 44 36 47 37 40 32

Hour 19 7:00 PM 50 39 53 36 47 35

Hour 20 8:00 PM 50 38 46 32 46 34

Hour 21 9:00 PM 47 36 45 29 44 33

Hour 22 10:00 PM 50 39 53 40 47 36

Hour 23 11:00 PM 50 38 51 34 46 33

The root cause of the high degree of travel time variability is not known for certain. However, the 

study team suspects that several factors may contribute to it. Fluctuations in demand volume, 

the opening and closing of primary screening lanes, the proportion of trucks participating in the 

FAST expedited screening program, variations in screening actions undertaken by US CBP can 

affect travel time. For example, during the team members’ visit to the crossing, they observed 

an operation termed “blocking” by CBP staff. In this operation, trucks that have passed through 

the US Customs primary inspection booth are directed to form several northbound lines inside 

the compound, and are held stationary until the lines are filled. Once the lines are filled, CBP 

staff performs canine screening of all of the stationary trucks by walking around all of the trucks. 
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52

Once the screening is complete the trucks are released in unison, whereupon they complete the 

u-turn to head south back through the exit of the compound and proceed on to the CHP 

inspection facility. 

This activity is conducted on a periodic basis, at different times, and for obvious reasons is not 

announced in advance. Hence, while initial screening at the primary inspection booth may take 

no more than a minute, the trucks that are subjected to blocking are likely to encounter travel 

time increases of five to fifteen minutes, depending upon their position in line and the speed with 

which the canine screening can be completed. Given a mean travel time in the 60-minute range, 

such activities could easily result in measurable variations. 

In order to gain a more complete perspective on the characteristics of the data set—and more 

importantly, develop a better understanding of the effects of outliers on the mean and median 

travel times—the team examined the frequency of occurrence of travel times within various 

“bands” or ranges. The results of this analysis are illuminating, and could have a significant 

effect on the methodology that should be applied when analyzing travel time data, particularly 

under near real time conditions. 

The graph in Figure 20 depicts the cumulative distribution of travel times for trips from district 1 

through district 10. As the image shows, 90 percent of all trips incurred a travel time of less than 

133 minutes, and 90 percent of FAST trips took less than 100 minutes to complete. Figure 21 

offers a slightly different view of the same data. It depicts the distribution of individual trips 

across the measurement zone by range of travel time. 

These results suggest that is may be advantageous to examine the use of an arithmetic filtering 

process by which the trips with the longest travel times are removed from mean and median 

travel time calculations, thus yielding more meaningful values for the vast majority of crossing 

users. 
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Figure 20. Cumulative Distribution of Travel Times (Dist 1 – 10) 

Figure 21. Distribution of Travel Times (Dist 1 – 10) 

For completeness, the study team conducted similar analyses on the travel time data for trips 

from district 1 through district 6. The results of these analyses are presented in Figures 22 and 

23. As can be seen in these graphs, the data profiles for the cumulative and individual trip travel 
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54

times follow a similar pattern, suggesting that trimming of higher travel times should also be 

possible for the trip from the beginning of the measurement zone to the exit of US primary. 

Figure 22. Cumulative Distribution of Travel Times (Dist 1 – 6) 

Figure 23. Distribution of Travel Times (Dist 1 – 6) 
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55

3.7 Motor Carrier Feedback 

At the conclusion of the data collection period, the study team contacted the two carriers that 

participated in the project to gain some perspective on their operations, and to gather 

information related to their typical crossing experience. The principal objective was to ascertain 

whether the data accurately reflected the conditions they encounter when crossing into the US. 

The study team first asked questions regarding the mix of loaded to unloaded trips and FAST to 

non-FAST trips for each carrier. The intent was to assess the ability of the GPS devices to 

discern the various trip types. As stated previously, trucks conducting FAST trips follow a path 

around the southern perimeter of the MX export compound before reaching the point where they 

actually cross the border and queue for entry into the US. 

According to operations staff at the two carriers, the majority of US-bound trips are FAST trips. 

One carrier indicated that more than 90 percent of traffic was FAST trips, while the second 

carrier said approximately 75 percent of its US-bound trips were FAST trips. This is in contrast 

to the GPS data that indicated that approximately 13 percent of trips were FAST trips. While 

neither carrier offered detailed records to indicate that these figures were close to actual 

numbers, the implication is clear that the use of GPS data to isolate FAST trips is not an 

accurate method, at least as it was applied during the project. Further, because both carriers 

indicated that trucks completing FAST trips typically do not encounter queuing between MX 

export primary and the physical border, it is logical to conclude that the travel time within the MX 

export compound (district 4) is too brief to allow a large portion of the trucks to report. 

The second major question the team asked the carriers was whether their drivers encounter a 

high degree of travel time variability when crossing into the US. As indicated previously, the 

recorded travel times showed a very high degree of variability, whether observing the data by 

time of day, day of week, or month of the year. 

Representatives from both carriers concurred that there is, in fact, significant variability, and that 

the degree seen in the data could be accurate.27 This feedback was, of course, anecdotal in 

nature. As such, it does not alone validate the travel time data. It does, however, lend credibility 

to them. The study team asked the carriers if they had an insight into why the level of variability 

might be so large. They both indicated that there are several factors that can contribute to it. 

 

27 The carrier representatives were not shown specific numbers regarding variability. They were told, however, that 

the travel time might deviate by as much as 100 percent or more from the mean on a regular basis. 
FHWA Cross-border Travel Times - Otay Mesa International Border Crossing: Final Report 

56

They suggested that travel time on days just before and just after holidays is typically 

unpredictable. They also stated that other factors such as significant law enforcement events—

major drug interdictions, for example—can have a profound effect on screening practices, at 

least for a period of time. 

Carriers also cited manufacturing-related events as contributors to travel time variability. 

Because the participating carriers do a significant amount of hauling for maquiladoras, 

production schedules and factory cutoffs can create spikes in volume. Finally, the carriers stated 

that systems problems—most notably those associated with managing the flow of 

documentation for US import—can slow things down. These may be systems operated by 

customs brokers and banks. 

Both carriers cited all of the above conditions as contributing factors that complicate predicting 

the amount of time needed to cross. 

Finally, the study team asked the carriers about the value they perceive GPS-based GPS 

systems have with respect to their operations. Both carriers indicated that they routinely use the 

tracking and tracing functions as a means to meet business needs of customers, and to satisfy 

shipment visibility requirements associated with compliance with C-TPAT. Both use the data on 

a continual basis. 
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4 Conclusions 

Based on the observed results from the full data collection period, the study team is able to 

draw a number of conclusions regarding the use of truck fleet GPS data for the measurement of 

travel time across international border crossings, both in general terms and in terms specific to 

the Otay Mesa Crossing. These conclusions are offered in the sections that follow. 

4.1 Data Validity 

As discussed earlier in this report, GPS-based fleet tracking systems have been in commercial 

use for some time. They have gained the trust of users as an accurate means for locating 

assets for the purposes of managing operations and providing customers information about 

shipment status. As such, the team did not consider it necessary to examine the accuracy or 

measurement precision of the device used for the test. It is a commercially available solution 

that relies upon standard components and communications methods. 

This test focused on the degree to which such devices can adequately characterize travel time 

on a specific roadway network through a commercial vehicle border crossing. The secondary 

goal was to assess the viability of GPS-generated travel time data as a means to support realtime traveler information. 

With respect to the primary goal, the devices and the processing methods employed to capture 

and analyze the GPS data succeeded. The combination of reverse geo-coding, which is a 

commonly employed practice in the use of GPS-based roadway speed monitoring, and the 

segregation of the border trip into geo-coded segments has proven to be a viable method. 

There are, however, some considerations that must be taken into account as border 

stakeholders contemplate its use on a regular basis. 

First, the figures reflected in this report are based on a marginally representative sample. While 

the number of trips and their distribution throughout the day consistently exceeded the three to 

five percent threshold commonly required for probe vehicle-based traffic monitoring, the carriers 

that participated are similar to only a portion of those that cross the border. Both carriers are 

highly reputable companies that are C-TPAT qualified, and conduct a significant portion of their 

cross-border trips as FAST trips. As such, the test sample was not random, and may not be 

representative of the general population. However, it is important to note that all FAST trips 

conducted by all carriers experience the same travel patterns, and occupy the same portions of 
FHWA Cross-border Travel Times - Otay Mesa International Border Crossing: Final Report 

58

the infrastructure. In that regard, the figures shown here can be considered representative for all 

carriers with similar profiles. 

The second consideration that must be taken into account is the location and length of the travel 

time measurement zone. At the onset of the project, carriers indicated to the study team that 

queues often extend to the Bellas Artes/Calle Doce intersection. However, the data gathered 

during the test suggests that traffic often flowed quite freely from there to the sorting gate 

upstream of the Mexican Customs Export screening point. 

The implication is that care should be taken in deciding at what point the border crossing travel 

time clock should start. It is worthwhile to consider beginning measurement of an individual trip 

after a vehicle has reached the end of the queue waiting to cross. The time from that point until 

the crossing is completed is probably of more value to a border user. 

In theory, if the first district is too short or is positioned at a point so far upstream that vehicles 

most often pass quickly through it then it is probably of little value. This was not an issue for the 

calculation of border travel time during this project, since the number of trips during which 

vehicles reported in District 1 was nearly identical to the number of reports recorded in District 2. 

This suggests that the size of District 1 was appropriate. As the number of vehicles providing 

GPS data increases, this becomes less of an issue, since those not reporting in the first district 

can be excluded without significantly compromising the dataset. 

Alternately, if a lack of reports in the first district occurs with great frequency, it may be 

appropriate to forego the use of districts altogether and use a combination of vehicle speed data 

and data that shows the linear distance to defined landmarks—both of which can be obtained 

using GPS technology—to characterize the operating profile of each trip, rather than use a fixed 

measurement zone. 

Finally, consideration must be given to the implications associated with the large values of 

standard deviation, and the differences between the mean and median values seen in the data. 

This level of variability does present particular challenges in formulating accurate values for 

current travel time, such as might be used in a traveler information system. 

This is important because high levels of variability in the data can make it difficult to accurately 

characterize travel time fluctuations associated with non-recurring congestion, which has a 

profound effect on travel time reliability. 

A larger number of vehicles, spread across a more diverse set of carriers, should improve the 

data and lower the degree of variability in much the same way. From a statistical standpoint, as 
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59

the sample size increases, the proportion of outliers and their influence on the mean values 

should diminish. 

It is not clear at this point the threshold at which that might occur to an acceptable degree. The 

border environment, while similar to the greater roadway network in many ways, does have 

some unique characteristics. Most notably, it is very small in comparison to a freeway network 

around a metropolitan area, for example. In a border zone such as the Otay Mesa crossing, 

where the trip length is short, travel speeds are typically low, and there is a great deal of 

commercial clutter in the immediate vicinity, extra care must be taken when defining algorithms 

for assignment of points to roadway links, and in defining measurement zones that are 

appropriately sized. On an open roadway network, where speeds are generally higher under 

normal conditions and monitored roadways are more easily segregated in a GIS overlay, the 

same level of precision is not required. 

It is important to remember that some degree of variability will continue to exist simply due to 

factors such as variations in the proportion of vehicles participating in secure supply chain 

operations (such as the C-TPAT and FAST programs), fluctuations in the total number of 

vehicle crossing during a given period, the number of empty trucks versus loaded trucks 

passing through the crossing, and the application of various enforcement actions, such as the 

blocking procedure discussed earlier. 

As for the GPS device installed in the trucks of the participating carriers, it is expected that the 

performance was similar to that of a number of devices currently available commercially. The 

key performance specifications of the unit were the reporting accuracy and the recording and 

reporting frequency (3 minutes and less than 10 minutes, respectively). Any device that offers 

these specifications should suffice for the purposes of travel time reporting. It should be noted 

that the Otay crossing is located in an area that is largely devoid of features that could interfere 

with GPS signals, such as closely-spaced tall buildings. For that reason, the performance 

achieved at Otay may not be similarly achieved in locations where such conditions exist. 

4.2 Data Applicability 

As discussed above, the results of this project indicate that the applicability of the GPS data for 

historical analysis is clear. The current dataset has significant planning and analytical value. 

Using queue modeling techniques, border and transportation agencies should be able to easily 

examine and model the effects of various practices and configurations. They should also be 

able to examine real-world effects of such changes through before-after analysis of the data. 
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60

This should be of particular value for assessing infrastructure modifications and changes in 

staffing practices. 

The capture and use of real-time—or more accurately, near real time—data presents a set of 

challenges that were not completely addressed during the project, but the experience offers 

some useful insights. First, the information exchange and processing mechanisms employed for 

the project would not be suitable as deployed solutions. The study team received the data via 

weekly FTP file exchanges, processed the data manually through TransCAD GIS software, and 

ran algorithms against a static database to derive travel time values. This would not be an 

acceptable solution for use of the data in near real time. 

These mechanisms would need to be replaced with appropriate communications and 

processing tools and protocols in order to be usable for applications that rely upon timely 

information processing, such as traveler information systems that might inform border users of 

current conditions. These sorts of solutions—which use GPS data—are presently in use by 

various traffic data information providers. For example, INRIX uses GPS data as a component 

of its travel speed information services under a program it is conducting for the I-95 Corridor 

Coalition.28 

In order for the Otay data to be made available for such purposes, three major actions would be 

necessary. First, the data from the fleets would need to be received as part of a live data feed. 

One popular method is the use of Extensible Markup Language (XML) connections through a 

web services environment. These sorts of feeds are relatively easy to establish, and offer 

flexibility for modification as necessary. Second, the association of individual points received 

from units with an underlying GIS would need to be automated. Again, this activity is becoming 

more commonplace. In fact, this is being done as part of a separate project with FHWA.29 It 

involves the assignment of individual GPS data points (i.e., latitude, longitude, time stamp, 

travel heading, travel speed) to pre-defined roadway links, as designated by geo-coded base 

maps that are typically in use by state departments of transportation. Finally, the transfer of data 

back to a user (e.g., a DOT) would need to be automated. This could be accomplished in much 

the same way as the GPS data feed, using an XML data stream exchanged using web services. 

 

28 Information available on I-95 Corridor Coalition website at: 

http://www.i95coalition.org/i95/Projects/ProjectDatabase/tabid/120/agentType/View/PropertyID/107/Default.aspx 

29 The project is the Real Time Traffic Monitoring (RTTM) component of the Cross-Town Improvement Project (CTIP). More information is available at: http://www.ctip-us.com/Ctip/home.htm 
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Each of these actions, as cited above, is relatively commonplace and none require extensive 

programming. The investment necessary to accomplish this would be modest, though it would 

certainly be contingent upon the technical sophistication of the entities performing the 

development and the end users. 

The second major challenge associated with the use of the GPS data for real time or near real 

time operations is the interpretation and use of the data for decision-making purposes. As 

indicated previously, the data gathered for the Otay crossing over a one year period indicates a 

high degree of variability for travel time. The implication is that the establishment of a reliable 

baseline value for the “typical” travel time for a defined period is difficult. It is this baseline value 

that forms the basis upon which a determination can be made by a user whether current 

conditions warrant a change in border usage (e.g., a change in departure time or crossing 

location). 

For instance, a user might determine that in order to affect their crossing decision the current 

travel time must exceed the baseline (“normal”) value by 15 percent. That means that if the 

expected travel time is 60 minutes, the current travel time would need to reach 75 minutes for 

that user to decide to delay a trip until a later time period. Consequently, the user must trust that 

the values are accurate and reliable in order to have confidence in his/her decision. The level of 

variability in the data captured during the test suggests that this user scenario would be difficult 

to accommodate without some means to mitigate the effects of the variability. 

Given this outcome, it is important to remember two things. First, there is no evidence to 

suggest that the data gathered during the project is not completely accurate. The data simply 

indicates that the identification of a reliable, precisely-defined mean (or median) value at the 

Otay crossing is difficult. Carrier input supports the conclusion that the degree of variability is 

accurate. Hence, this is not an indictment of the validity of GPS data. To the contrary, it appears 

to be evidence of its accuracy. As indicated above, this may become less of a concern as the 

volume of baseline data increases, and more stringent filtering can be applied to reduce the 

affect of outliers on the mean and median values. 

Second, it is important to remember that there are few options for local carriers that use the 

crossing to apply the information in its current state. Carriers must use the Otay crossing since 

commercial vehicles are not permitted at San Ysidro, and the Calexico crossing is too far to the 

east to be of use for the local maquiladora traffic. Further, their delivery schedules—at least 

those of the carriers that participated in the project—are dictated by customers. Where real time 

or near real time data might be more useful is for the management of agency staff at border 
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62

inspection points, or for long-haul truckers that use the port while they are at points distant 

enough that allow them to re-route or re-schedule to avoid delays. 

Ultimately, a larger penetration level of trucks, from a broader cross-section of the user 

population at the crossing, may have a beneficial effect on the level of variability by driving down 

the standard deviation. 

4.3 Data Accessibility 

The data model that was used to access motor carrier data for this project is one of several 

different approaches employed by GPS data providers. Many data providers obtain GPS data in 

use for roadway monitoring from the provider of the GPS device and/or service. The data 

provider for this project chose to seek it directly from the carriers. Ultimately, regardless of the 

source of the data, if it is generated by a carrier’s vehicles, then it is the property of the carrier. 

As such, the carrier can do with it whatever it desires. Fleet GPS device and service providers 

may have to obtain permission from their carrier customers in order to re-use the data, as was 

the case for this project. 

This approach presents both benefits and challenges. One potential benefit is that, because the 

carrier already owns the data for its fleet, and only its fleet, it may be willing to grant access to 

the data for a modest sum. Further, by working directly with the carrier while agreeing to protect 

sensitive information from distribution, a data provider could structure its data agreements to 

allow for the re-use of information for multiple purposes without incurring additional cost. 

Perhaps the most significant challenge to this approach is that which was encountered during 

the recruitment of carriers for participation in this project. The data provider was forced to 

endure a lengthy courtship process before the carriers finally agreed to grant access to their 

data. Ultimately, the carriers became more receptive as they recognized that the devices are 

useful for other functions, as well. Nonetheless, the level of time and effort necessary to execute 

agreements with the carriers indicates that this should be factored in whenever GPS fleet data 

is sought. 

4.4 Data Value 

Even with the level of variability demonstrated in the data gathered during this project there 

appears to be significant value associated with GPS fleet data. It is important to remember that 

the nature of the GPS system is such that it continuously generates data, regardless of time or 
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63

location. Hence, the very same devices could easily be monitored for other movements, and the 

data processed to quickly multiply its planning and operations value. 

For instance, the data provider already possesses raw GPS data for Mexico-bound movements 

at the Otay crossing. This means that with the completion of some processing logic, travel time 

for southbound trucks during the same period could be calculated without additional data 

collection. Further, raw GPS data has already been recorded for movements both within the 

areas adjacent to and those beyond the Otay border zone. This data could be used not only for 

travel time calculation, but also for the identification of origins and destinations and route 

selection. This also underscores the importance of carefully considering the terms of any data 

agreement to ensure that the maximum value can be obtained. 

Finally, modern fleet GPS devices are becoming both more affordable and more capable, 

opening up additional opportunities to capture valuable information. One example is the capture 

of device from a vehicle’s engine data bus, which would include such information as throttle 

position, travel speed and fuel use by location. This has potential value for the identification of 

locations where excessive idling is occurring—important data for carriers to establish 

mechanisms to improve fleet fuel efficiency and for agencies to examine the potential 

environmental value of infrastructure upgrades. 

The key to accessing such data requires the definition of the appropriate levels of data access 

and use rights with the carriers or with service providers. This will require that significant 

attention is paid to the protection of carrier and driver identity, and restrictions pertaining to the 

use of the data for ancillary purposes, such as public sector analyses. 

4.5 Data Business Model Options 

The previous section discussed some of the conclusions associated with the various options for 

collection and use of GPS data. Obviously, this project was focused on the collection and 

analysis of a narrow segment of GPS data for the purposes of establishing the usefulness of 

GPS as a travel time measurement technology. It was also configured and priced accordingly, 

rather than might otherwise be appropriate for what might be considered a “production” solution. 

In order to establish a more meaningful representation of what a customer body might incur for 

the implementation and operation of a GPS-based solution, the study team worked with Calmar 

to define and establish potential price ranges for a series of data packages. These are provided 

in Table 6 below. 
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The table highlights three different data packages, each containing different types and amounts 

of data, processed to different levels. Each package is described according to eight different 

characteristics: 

 Content – this describes, at a high level, the data that will be made available by the 

vendor. 

 Data Set Size – this reflects the approximate number of vehicles from which GPS data 

would be acquired. 

 Geographic Scope – this defines the geographic area within which data will be collected. 

 Potential Uses – this indicates a brief summary of some of the uses of the data, and 

what activities it might support. 

 Potential Users – this contains a simplified list of potential consumers of the data, by 

type. 

 Output – this defines the proposed format and delivery method for the dissemination of 

the data. 

 Cost Range – this reflects an estimate of initial and ongoing costs associated with 

establishing and maintaining the proposed data feed. 

 Time to Deploy – this offers an estimate of the time necessary to complete carrier 

recruitment, development of the data feed, and testing prior to deployment. 
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65

Table 6. Business Model Options 

Data Package Type Data Package 

Characteristics Basic Enhanced Full 

Content Mildly processed data 

including basic historical 

travel time, and raw GPS 

data with filtering to remove 

outliers 

Fully processed travel time 

data, plus data traces and 

files indicating origins and 

destinations 

Fully processed travel time 

data, plus data traces and 

files indicating origins and 

destinations 

Data Set Size Approx. 200 trucks Approx. 500 trucks Approx. 1000 trucks 

Geographic Scope Area similar to project area, 

which includes a limited 

rectangular are that 

encompasses length of 

measurement zone, in both 

directions across border, 

along the entire distance of 

the California border with 

Mexico 

Area that extends from the 

border, along the entire 

distance of the California 

border with Mexico, to the 

Los Angeles basin to the 

north, and (to the extent 

available) to the Ensenada 

area in Baja California 

Area that extends from the 

border, along the entire 

distance of the California 

border with Mexico, to the 

Los Angeles basin to the 

north, and (to the extent 

available) to the Ensenada 

area in Baja California 

Potential Uses Establishment and 

refinement of historical travel 

time dataset to monitor 

changes over time due to 

various factors (e.g., facility 

alterations, operational 

procedure modification, 

affect of trade fluctuations, 

etc.) 

Establishment and 

refinement of historical travel 

time dataset to monitor 

changes over time, plus 

information for route choice 

modeling and infrastructure 

investment planning, plus 

near real time data for 

integration with existing 

traveler information systems, 

for user decisions regarding 

facility staffing, traffic control, 

and trip planning 

Establishment and 

refinement of historical travel 

time dataset to monitor 

changes over time, plus 

information for route choice 

modeling and infrastructure 

investment planning, plus 

near real time data for 

integration with existing 

traveler information systems, 

for user decisions regarding 

facility staffing, traffic control, 

and trip planning, provided 

via web site and/or wireless 

device notification 

Potential Users Planning organizations, 

customs agencies, shippers, 

carriers 

Transportation system 

managers, planning 

organizations, customs 

agencies, shippers, carriers 

Transportation system 

managers, planning 

organizations, customs 

agencies, shippers, carriers, 

border users 

Output Database file, updated 

monthly via ftp, plus basic 

graphical output similar to 

that produced for this project 

Live XML (or similar) data 

feed suitable for supporting 

near real time operations and 

analysis, plus graphical 

summaries at regular 

intervals 

Live XML (or similar) data 

feed suitable for supporting 

near real time operations and 

analysis, plus a fully 

functional user interface for 

interactive access to data 

Cost Range $10K to 12K per month $40K to $60K plus $14K to 

$20K per month 

$80K to $120K plus $16K to 

$25K per month 

Time to Deploy 3 months 6-9 months 8-12 months 
FHWA Cross-border Travel Times - Otay Mesa International Border Crossing: Final Report 

66

When viewing this information it is important to remember that the figures provided for price 

ranges are intentionally broad. This is necessary for two reasons. First, until detailed 

specifications are identified, precise estimates are impossible to make. Only after working with 

the potential customer can a data provider accurately establish pricing. Second, because this is 

a commercial venture for Calmar and other similar providers, sensitivity exists regarding the 

provision of specific pricing data in a public document such as this. 

A few assumptions must be considered with regard to the data outlined in the table. First, all 

data feeds would include basic quality control/quality assurance (QA/QC) provisions (i.e., 

erroneous and redundant points would be removed), data would be tagged with and trip begin 

and end identifiers where GPS data patterns suggests such a case exists. Second, the data 

would primarily consist of latitude/longitude, date/time stamp, speed, link identification, and 

vehicle identification. Vehicle identification would be a sequentially applied anonymous number 

that would change daily, or upon some other user-defined criteria. 

Finally, two limiting conditions would be placed on the data. Specifically, location information for 

carrier yards and other confidential locations (i.e., customer locations) would be removed, and 

activity within ports and rail yards would be excluded using geo-fencing. All locations would be 

designated as general originations/destinations. 

As the information in the table implies, there appear to be ample opportunities to recoup the 

costs associated with the establishment and maintenance of fleet GPS data beyond that of 

simply generation of travel time values. Among the most immediately promising applications is 

the capture of transportation origin/destination data, which is of significant value for 

transportation planning purposes. Such data can be of use for establishing and validating travel 

demand models popular with planning staff, and can also offer significant insight into overall 

roadway usage patterns. Coupled with capabilities to connect to and extract information from 

engine control units (ECUs), this data could also be used to spatially examine truck operating 

speeds, idling, and emissions on the roadway network, allowing agencies and carriers to identify 

trouble spots and adjust investments and operations accordingly. 
FHWA Cross-border Travel Times - Otay Mesa International Border Crossing: Final Report 

67

5 Recommendations 

GPS and ALPR both provide potential value in applying the technologies at Otay Mesa; 

however the study team recommended that the FHWA choose GPS as the primary means of 

collecting travel time information at the crossing. FHWA’s limited budget made it possible to 

deploy only one technology and the study team concluded that GPS data would yield the most 

robust data set with less risk than that associated with ALPR deployment. 

While a significant amount of information was captured during the project, the study team 

recommends that a series of actions be undertaken to establish a complete service for the 

various potential users of GPS data. These actions will increase the likelihood that the data 

obtained from a GPS-based system offers the appropriate value and usability to enable the cost 

of deployment to be spread over a broad set of stakeholders. The team’s recommendations are 

offered below, along with an explanation of the value associated with exercising each of them. 

Recommendation #1: Use a Larger Number of Reports. 

While the rule of thumb threshold of three to five percent of the total population was successfully 

reached during the project, the data suggests that a larger population of GPS-equipped trucks 

could significantly enhance the dataset by reducing the effect of outlier trips on the mean travel 

time value. The relationship between sample size and variability is not known, but an increase in 

the number of units to 300 (at the same crossing frequency) should yield definitive evidence of 

it. Of course, such action would need to be balanced against the cost of the additional devices, 

and the expected value of the (presumably) more precise data. 

Recommendation #2: Increase the Variety of Carrier Participants. 

The carriers that participated in the project indicated that the overwhelming majority of their USbound trips are FAST trips. At the very least, that means that the samples gathered for this 

project under-represent non-FAST trips. If a GPS data solution is to be deployed in a production 

setting, the team suggests that the trucks from which GPS data is collected conduct non-FAST 

trips into the US at a representative frequency. 

Recommendation #3: Examine Secondary Uses for Data. 

The measurement of travel time for vehicles entering the US represents only a small portion of 

the value to be extracted from GPS data. These devices are typically on when the vehicle is 

running, offering the opportunity to examine movements, origins and destinations, idling points, 

and various travel delays throughout a vehicle’s duty cycle. Those entities that adopt GPS-
FHWA Cross-border Travel Times - Otay Mesa International Border Crossing: Final Report 

68

based systems should examine these and other secondary uses for the data in order to realize 

its full value. For instance, a significant volume of data was captured during the project 

regarding southbound movements, but it was not analyzed because it fell outside the scope of 

the project. At this point, the value and usefulness of this data is unknown. 

Recommendation #4: Use Extended Data Collection to Reinforce Calculations & Diminish 

Variability. 

Much in the way that an increase in the number of trucks using the crossing should reduce 

variability and enhance confidence in the data, so too should the capture of data over a long 

period of time. Doing so will likely lend stability and reduce variability in historical travel time 

data, thereby reducing the effects of outliers. Additionally, external factors such as the level of 

economic activity will also affect the figures. Collecting data over an extended period offers the 

opportunity to examine the effects. 

Recommendation #5: Examine and Test Near Real Time Data Use. 

Although GPS-based traffic monitoring solutions are being used in other locations on a real-time 

basis (e.g., I-95 Corridor Coalition), the usefulness and usability of similar data in the border 

environment is still largely unknown. The approach used for this project offers an opportunity to 

examine whether enough value can be extracted to affect agency and carrier decisions during 

the course of everyday operations. This could be accomplished through a continuation of data 

acquisition and analysis activities, with the participation of and interaction with users who have 

access to a near real time data feed. 

Recommendation #6: Pursue Transfer of Data Acquisition Oversight and Funding to 

State/Local Agencies, Customs Agencies, or Others. 

It is understood that FHWA does not have an ongoing operations function at international border 

crossings, and that its primary role is the conduct of research and support for deployment of 

solutions that enhance the nation’s transportation system. Hence, the funding and use of 

systems that measure border travel time are the purview of border management agencies, 

agencies with responsibility over local transportation infrastructure, and the border user 

community. The results of this project establish the viability of GPS technology for travel time 

measurement. They also present an opportunity to explore how a transition to a production 

solution might take place through the leveraging of carrier relationships established during the 

test, the expansion of data coverage to a comprehensive geographic scale, and the examination 

of opportunities for secondary data uses. 
FHWA Cross-border Travel Times - Otay Mesa International Border Crossing: Final Report 

Technical Report Documentation Page 

1. Report No. 

FHWA-HOP-10-051 

2. Government Accession No. 3. Recipient's Catalog No. 

5. Report Date 

September 2010 

4. Title and Subtitle 

Measuring Cross-Border Travel Times for Freight: Otay Mesa 

International Border Crossing 

Final Report 6. Performing Organization Code 

7. Author(s) 

Paul Belella (Delcan), Sumala Tirumalachetty (Delcan), Justin 

DiStefano (Delcan), Tau Hou (Delcan) 

8. Performing Organization Report 

No. 

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) 

Delcan Corporation 

8618 Westwood Center Drive, Vienna, VA 22182 

(in cooperation with Science Applications International Corporation) 11. Contract or Grant No. 

DTFH61-06-D-00005 

13. Type of Report and Period 

Covered 

Final Report, 10/2008 – 5/2010 

12. Sponsoring Agency Name and Address 

United States Department of Transportation 

Federal Highway Administration 

Office of Freight Management and Operations 

1200 New Jersey Ave., SE 

Washington, DC 20590 

14. Sponsoring Agency Code 

15. Supplementary Notes 

This program was administered through the Federal Highway Administration (FHWA). The FHWA Program 

Manager is Ms. Crystal Jones. 

16. Abstract 

The Federal Highway Administration (FHWA) sought to examine the use of technology to support the automated 

collection of border crossing travel time data at the Otay Mesa international port of entry (POE), with the intent of 

gaining an understanding of the degree to which it might be usable for improving operations and planning at 

border crossings throughout the US. The study involved the deployment and use of Global Positioning System 

(GPS) devices to capture the movements of a sample of trucks passing through the Otay Mesa POE. The Otay 

Mesa POE is a truck-only crossing located between the US and Mexico south of San Diego, CA. The study 

examined the viability of the GPS technology approach, and resulted in the capture of historical travel time data 

over the period of one year. 

17. Key Words 

International Border, Travel Time, Freight, 

Technology, Global Positioning System, Research 

18. Distribution Statement 

No restrictions 

19. Security Classif. (of this 

report) 

Unclassified 

20. Security Classif. (of this 

page) 

Unclassified 

21. No. of Pages 

90 

22. Price 

N/A 

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized. 
Measuring Cross-Border Travel Times for Freight: 

Otay Mesa International Border Crossing 

Final Report 

U.S. Department of Transportation 

Federal Highway Administration 

Office of Freight Management and Operations 

1200 New Jersey Ave., SE 

Washington, DC 20590 

Phone: 202-366-9210 

Fax: 202-366-3225 

http://www.ops.fhwa.dot.gov/freight

September 2010 

FHWA-HOP-10-051