High -occupancy toll (HOT) lanes are in operation, under constr uction, and planned for in several major metropolitan areas. The premise behind HOT lanes is to allow single occupan t vehicles (SOVs) to access high occupancy vehicle (HOV) lanes (and, a higher level of service) if they are willing to pay a toll. To main tain a high level of service in the HOT lanes, the toll rate is set dynamically to restrict the number of SOVs which access the facility as it nears capacity . Thus, HOT facilities provide operators of transportation systems with a new operations tool: pricing. In order to effectively use pricing , it is critical to understand driver behavior when faced with a set of traffic conditions and toll levels. This paper presents the results of an empirical investigation into the relationship between toll rate , traffic conditions, and SOV driver behavior, based on data from the dynamically -tolled I -394 HOT facility in Minneap olis, Minnesota . Analysis of the empirical data indicated that a large percentage of SOV drivers use the HOT lanes at different, yet predicta ble rates throughout the AM peak period, even when there is no clear travel time advantage. After accounting for these “regular” users, the remaining SOV drivers utilize the HOT lanes at greater rates when the cost per hour of commute time saved is lowest . A model was developed that incorporates both of these findings, predict ing HOT lane usage rates based on time savings, time of day, and toll rates with an R2 value of 0.684. Key words: Intelligent transportation systems, HOT , dynamic tolling Goodall and Smith 2 INTROD UCTION High occupancy toll (HOT) lanes are a form of managed lanes that offer free or reduced - toll travel to high occupancy vehicles (HOVs), while allowing single occupan t vehicles (SOVs) that pay a toll (1). Some HOT lanes employ dynamic pricing strategi es to control congestion and maintain high speeds , by changing toll rates in response to congestion . These HOT facilities are an example of the emerging approach known as “active traffic management” (ATM), in which infrastructure providers seek to manage recurrent and non-recurrent congestion based on real time traffic conditions ( 2). Currently, HOT lanes exist in several U.S. cities, and more are under construction or planned. The HOT lanes concept relies on the assumption that SOVs will be discouraged from entering the HOT lanes as toll rates increase , thus preventing the HOT lanes from becoming congested. Most willingness -to-pay research has focused either on revealed preference (RP) surveys ( 3), or has investigated the median cost per hour of commut e time saved ( 4). Little research has been conducted to analyze and model drivers’ responses to minute -to-minute changes in toll rates and traffic conditions using empirical data from an operational HOT lane system . This paper attempts to understand and model the impact of toll rates and traffic conditions on single occupant driver behavior in a HOT lane system, through analysis of data from an operating HOT lane facility in Minneapolis, Minnesota . This will be necessary in order to make maximum use of t his ATM strategy – allowing operators to fully understand the impact of toll increases on traffic assignment. HOT lane usage is measured from a combination of toll transaction records and detector data from the I -394 MnPASS Express Lane system. The study period was the AM peak period between November 7, 2006, and March 31, 2008, along the 2.7 mile barrier -separated reversible section of I -394 near downtown Minneapolis. The study ignores the possibility of SOVs us ing the system illegally, based on the ver y low 3% non - compliance rate found on the study section in a 2006 study (5). MNPASS FACILITY The I -394 MnPASS is a n 11-mile HOT lane facility in Minneapolis, Minnesota operated by the Minnesota Department of Transportation (MnDOT) . The facility , which has been in operation since May 2005, is one of the few HOT lanes that dynamically adjusts toll rates in response to traffic conditions. Between Central Avenue and Trunk Highway 100 (TH 100), the HOT lanes are former HOV lanes in both directions, separated from general purpose (GP) lanes over 8 miles by double white lines. The double striped lanes, often referred to as “diamond” lanes, operate Monday through Friday, from 6 AM to 10 AM in the eastbound direction, and 2 PM to 7 PM in the westbound direction. From TH 100 to I -94, the facility operates as a two lane reversible facility 2.7 miles in length. The reversible HOT lanes are barrier separated from the GP lanes. The reversible section is operational at all times, including weekends, except when the l anes’ directions are being switched. The HOT lanes are operated in the eastbound direction from 6 AM to 1 PM, and in the westbound direction from 2 PM to 5 AM. A map of the facility is provided in Figure 1 (6). Goodall and Smith 3 FIGURE 1 I -394 MnPASS HOT Lanes (6). Tolls for SOVs are collected electronically at five locations. SOVs using the HOT lanes must be equipped with MnPASS transponders. Transponders can be leased for $1.50 per month. The d iamond and reversible sections have separate toll rates . Rates are automatically adjusted every three minutes in order to maintain high speeds in the HOT lanes. The total toll for a vehicle using the entire system range s between $0. 50 and $8.00 per trip . ANALYSIS To focus on the periods of highest congestion and toll rate s (when SOVs are most likely motivated to utilize HOT lanes ), the study focused on the reversible section during the AM peak period from 6 AM to 10 AM, Monday through Friday. Data was obtained from MnDOT covering the period from November 2006 to March 200 8 at three -minute intervals. The following data was collected from MnDOT: • Toll rates and their start times. • Individual toll transactions by vehicle, time -stamped and identified by each electronic toll reader passed. • Detector data from five detectors in th e HOT lanes and 5 detectors in the GP lanes, with speeds, volumes, and occupancies recorded at three -minute intervals. Detector data was validated using data quality standards developed by the Texas Transportation Institute (7). This data was used to de termine the cost per hour of travel time saved by employing the following set of equations shown in Table 1 . Goodall and Smith 4 TABLE 1 Equations Used in Calculating Cost per Hour Travel Time Saved HOT Lane Speed (speed limit = 65 mi/hr) GP Lane Speed (speed limit = 55 mi /hr) Equation Used  65 mi/hr  55 mi/hr No advantage, value is null  65 mi/hr  55 mi/hr HOT GP LD SDTc −=  65 mi/hr  55 mi/hr No advantage, value is null  65 mi/hr  55 mi/hr HOT GP SD SDTc −= Where c = cost per hour of travel ti me saved ($/hr) T = toll rate ($) D = distance of the highway segment (mi), in this case 2.7 miles S = speed of the HOT or GP lanes (mi/hr) L = speed limit of the HOT or GP lanes , 65 and 55 respectively (mi/hr) RESULTS When one first examines the data, it appears that the cost per hour of travel time saved has little influence on SOV behavior. As seen in Figure 2, which summarizes the data over all time intervals in the AM peak, regardless of the cost per hour of travel time saved, roughly 14 -15% of SOVs choose to travel in the HOT lanes. 00.020.040.060.080.10.120.140.160.18 0 20 40 60 80 100 120 140 160 Cost per Hour Saved ($)Portion of SOVs Utilizing HOT Lanes FIGURE 2 Average portion of SOVs utilizing HOT lanes vs. cost per hour saved . Goodall and Smith 5 However, when plotting the time series of the percentage of SOVs utilizing the HOT lanes during the AM peak period, one will notice a clear fluctuation in HOT lane utilization, regardless of traffic conditions or toll rate . For example, Figure 3 shows the average HOT lane utilization when GP speeds are above 55 miles per hour, the speed limit on this facility . Even in these conditions, with very little apparent incentive to utilize the HOT lanes, drivers are almost six times more likely to use the HOT lanes at mid-peak than during early peak. 00.020.040.060.080.10.120.140.16 6:00 AM 6:30 AM 7:00 AM 7:30 AM 8:00 AM 8:30 AM 9:00 AM 9:30 AM 10:00 AM TimePortion of SOVs Utilizing HOT Lanes FIGURE 3 Time series of the average portion of SOVs utilizing HOT lanes , when GP speeds are gr eater than 55 miles per hour . There are several possible explanations for this phenomenon. First, because MnDOT was careful not to over -market the MnPASS transponders (8), it is likely that most of the MnPASS users are drivers who routinely experience h eavier than average congestion. It is possible that a greater percentage of drivers during the mid-peak period have transponders, and thus explains their greater proportion in the HOT lanes. Small et al. (9) have suggested that travel time reliability is also an important factor in driver behavior. In their analysis of the I -15 dynamically -priced HOT lanes in San Diego, Brownstone et al. assumed travel time reliability to be a major decision factor for drivers (4). Because the GP lane speeds are less re liable during mid -peak, drivers may be using the HOT lanes as insurance against congestion , regardless of the evidence of any congestion upstream. Assuming that the drivers who use the HOT lanes even when there is no apparent benefit in travel time are in sensitive to price, these drivers can be considered to be “every day” HOT lane users . In other words, these SOV users will choose the HOT lanes Goodall and Smith 6 largely for their reliability as opposed to an improvement in current conditions. Therefore, it is reasonable to “remove” these SOVs from the data in an attempt to model behavior for the set of drivers that are sensitive to costs associated with real -time travel time savings. By removing the average portion of “every day” drivers from each individual data recor d based on its time -of-day, one can directly consider drivers who are more sensitive to travel time savings and conditions. Figure 4 shows the average portion of SOVs using the HOT lanes at various prices for travel time savings, with the “every day” driv ers removed from the data. The relationship here is much clearer, as SOV drivers use the HOT lanes at greater rates when the relative cost is lowest. 00.010.020.030.040.050.060.070.080.090.1 0 20 40 60 80 100 120 140 160 Cost per Hour Saved ($)Portion of SOVs Utilizing HOT Lanes FIGURE 4 Average portion of SOVs utilizing HOT lanes vs. cost per hour saved, after removing the “ever y day” HOT lane SOVs. The research team chose to model the relationship illustrated in Figure 4, with an exponential model, as shown below . += )ln( )( c cP Where P(c) is the average percentage of SOVs using the HOT lanes at the cost per hour of travel time saved c. Based on a regression of average HOT lane utilizations, values of α and  were found to be -0.0107 and 0.078 respectively. The model had an R2 value of 0.7265. Goodall and Smith 7 Development of SOV HOT Lane Model Based on the results presented in th e previous section, the research team is proposing the following two component model of SOV behavior to account for the “every day” and price sensitive drivers. U(t,c) = E(t) + P(c) Where U(t,c) = Percentage of SOVs using the HOT lanes in time interva l t at cost per hour of travel time saved c E(t) = Average percentage of SOVs using the HOT lanes in time interval t when speed in the GP lanes is above 55 mi/hr (speed limit) P(c) = Average percentage of SOVs using the HOT lanes at the cost per hour of travel time saved c When the model was applied to the entire MnPass data set, the multi -component model has an R2 value of 0.68 4. Figure 5 provides an example of the model as applied to a typical day. 0.000.050.100.150.200.25 6:00 AM 6:30 AM 7:00 AM 7:30 AM 8:00 AM 8:30 AM 9:00 AM 9:30 AM 10:00 AM Time of DayPortion of SOVs Utilizing HOT Lanes Actual HOT Utilization Predicted HOT Utilization FIGURE 5 Comparison of predicted and actual HOT lane utilization on March 28, 2008 . Goodall and Smith 8 As the reader will note, the performance of the multi -component model is driven largely by the everyday user component, E(t). When only the historical average percentage of SOVs using HOT, under any traffic cond itions (i.e. E(t) without the restriction of GP lanes operating at 55 mph or higher) is used to model U, an R2 value of 0.675 results. Thus, the pricing has negligible influence. This is a very important finding in terms of future operation of HOT facilities. In this case, it is clear that the current pricing structure and population of eligible SOVs result in a situation where pricing is ineffective in preventing SOVs from using the HOT lanes when deemed necessary. CONCLUSIONS Based on an analysis of the dat a from the MnPASS HOT facility, SOVs utilize the HOT lanes at different rates throughout the AM peak period, even when GP lanes are operating above the speed limit and there is no strong advantage to using the HOT lanes. These drivers appear to place grea ter value on protection from possible future congestion than immediately visible travel time savings. Assuming these drivers use the HOT lanes as an “insurance” against unanticipated congestion, this portion of vehicles is can be ignored in the analysis o f vehicles that are influenced by cost per hour of commute time saved. The remaining drivers show a clear relationship between value of travel time and usage of the HOT lanes. Accounting for both groups of drivers separately, then adding together their combined usage rates, creates a model which may be useful as DOTs attempt to predict traffic assignment in HOT facilities. An equally important finding of this research, if not more important, is that m uch of the variation in HOT lane usage may be explained not by cost or current conditions, but by expected traffic conditions. This is likely because most drivers do not typically have access to real-time travel time estimates , and thus must estimate the traffic conditions based on a combination of sporadic traffic reports, HOT lane toll rates, and historical congestion at that particular time of day. The HOT lanes experience an increase in usage during the middle of the AM peak period , even when there is little immediately apparent advantage to using HOT lan es. There are two possible explanations. First, drivers may be anticipating greater congestion during the middle of the peak period based on congestion in the past , and may be using HOT lanes to avoid potential rather than actual congestion . Second, the re may be a greater proportion of drivers with HOT lane transponders during this period, as drivers using the facility during this time period are the most likely to encounter congestion, and therefore have the greatest incentive to participate in the MnPA SS program. Previous analysis of HOT lane usage assumed drivers had knowledge only of historic average travel times, not real time traffic conditions ( 4). As traveler information becomes more widely available through 511, IntelliDrive applications, sma rtphones, and in-vehicle navigation systems, drivers will be able to make more informed decisions. As these devices and applications become more widely available, and as travel time data become more accurate and sophisticated, drivers may begin to make de cisions based more on real -time conditions rather than perceived conditions or past experiences. This indicates the need for on -going analysis of HOT usage data to allow for most effective operation of the facilities. Goodall and Smith 9 ACKNOWLEDGEMENT S This research would have not been possible without the d ata regarding the performance of the MnPASS system provided by MnDOT and Cofiroute, the MnDOT contracted administrator of MnPASS operations . The authors acknowledge Mr. Nick Thomspon’s and Mr. David Zinser ’s cooperation in providing the data used in this research.