Analysis of Day-to-Day Variations of Travel Time Using GPS and GIS
Nobuaki Ohmori1, Yasunori Muromachi2, Noboru Harata3 and Katsutoshi Ohta4

Abstract
This paper presents an analysis of day-to-day variations of travel time on road
network using Global Positioning System (GPS) and Geographic Information
System (GIS). Data collection system was developed using GPS connected withnotebook PC, which can capture vehicle movement of multiple days. One-week
vehicle travel surveys using the system were conducted for commuting drivers inlocal cities. Methods for handling GPS data on GIS platform were summarized, andday-to-day variations of travel time and speed were analyzed in the study area.
Although positional data are very promising, usefulness in travel time studies
depends on the process of data handling and the quality of GIS database.
Introduction
In urban transportation planning, it is the most basic and important element tounderstand people’s travel behavior. The existence of day-to-day variations of travel
behavior and road network performance has been recognized. In order to evaluate
the effects of transportation demand management (TDM) measures and intelligent
transport system (ITS) technologies on travel behavior, more detailed and accurate
travel data have to be collected considering day-to-day variations. Although travel
surveys of multiple days are needed to capture these variations, it is difficult to
conduct that kind of survey by traditional survey techniques.
There is strong possibility for advanced positioning technologies, like
Global Positioning System (GPS) and Personal Handyphone System (PHS), greatly
contributing to travel data collection. Data on position and speed collected atone-second intervals by GPS would give effective information, when analyzing the-----------------1

Research associate, Institute of Environmental Studies, The University of Tokyo
Associate professor, Engineering Research Institute, The University of Tokyo
3
Professor, Institute of Environmental Studies, The University of Tokyo
4
Professor, Department of Urban Engineering, The University of Tokyo
7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan; nobuaki@ut.t.u-tokyo.ac.jp
2

1

Ohmori, Muromachi, Harata and Ohtachanges of travel behavior (start time, route and travel time, etc.) and performancemeasures (link’s travel time, link’s travel speed, stopped time, etc.) after introductionof TDM and ITS. More detailed evaluations would be possible for investigating
vehicle emissions such as CO2 and NOx. PHS location tracking systems have been
available since 1998 in Japan. Although positional accuracy is lower and theminimum logging intervals are longer than GPS, PHS is portable for people in theirpocket or bag, and it can also collect positional data in the buildings and subway
stations where PHS service is available. Recently an innovative device combined
PHS with acceleration sensor was developed in order to estimate travel mode (forexample, car, bus, bike or walk) from acceleration data (Okamoto et al., 2000).
Many empirical studies have been reported in recent years, which examined
the applicability of GPS, PHS and GIS as a tool for travel behavior surveys and thepossibility of traffic monitoring techniques. Zito et al. (1995) examined basic
characteristics of vehicle position and speed data using GPS and demonstrated its
usefulness for collecting travel data. Murakami and Wagner (1999) compared
computer-assisted self-interviewing using GPS with retrospective trip reporting
using telephone interviews. They showed the differences of travel distances andtravel times comparing both data, and gave examples of breakdown of travel andspeed by roadway functional class. Quiroga and Bullock (1998) developed theintegrated system analyzing travel time using GPS and GIS, and analyzed
appropriate road segment length to detect traffic conditions and sampling period for
GPS data collection. Schütte et al. (1999) analyzed travel time using “probevehicles” equipped with GPS and aerial photograph in München ring road. The
characteristics of PHS data and its applicability to travel behavior surveys have beenstudied. Asakura et al. (2000) developed algorithms dividing PHS tracking data on
time-space path of a whole day into travels and activities, and proposed the method
for estimating travel route using the data. Asakura and Hato (2001) analyzed thedifferences of the number of trips and travel time between PHS data andself-reported diary data. Ohmori et al. (2000) compared PHS data and self-reported
diary data for the elderly and non-elderly people. These studies have shown theusefulness of positioning technologies for travel data collection and monitoring
traffic conditions.
This paper focuses on development of GPS data collection system, its
application to one-week travel surveys of drivers commuting the same work place
and analysis of day-to-day variations of travel time and speed on GIS platform.
Development of GPS Data Collection System
Data collection system was developed using low-priced GPS connected withnotebook PC, which can collect GPS data on vehicle movement of multiple days byeasy operation. We also paid attention to the cost of the devices, the way of power
supply, capacity of data logger and the ease of transferability from vehicle to vehicle(Ohmori, et al., 2000). The equipment is set in a vehicle, so that GPS
antenna/receiver is on the dashboard and a power cord is connected to a vehicle
cigarette lighter for supplying power to both GPS and PC. A driver starts travelingby starting the engine and pushing a switch button of PC. After PC working, the2

Ohmori, Muromachi, Harata and Ohtabatch file programmed by “WinBatchEh” starts automatically and “HyperTerminal”
for both communications to GPS and data logging begins to run in about 30 seconds(depending on the performance of PC). A-data file of which the name is travel start
time, for example “03150805.txt” for start time of 8:05 on March 15, is created. The
driver arrives at a destination and immediately pushes two buttons of PC, so that thebatch program restarts and save the GPS data file. Lastly, s/he just turns off theengine.
This system was applied to collect data for commuting drivers in two local
cities (Ohmori, et al., 2000). After these surveys, the system was enhanced to display
vehicle’s current position and travel tracks on a digital map like a car navigation
system using “ProAtlas” software (Figure 1). The enhanced system was applied totouring behavior surveys in mountain areas and has successfully collected GPS data,
where drivers were much interested in real-time view in their traveling (Fujiwara, etal., 2001). Since May 1, 2000, when United States had stopped Selective Availability(SA) that degrades GPS accuracy deliberately, GPS accuracy has dramatically
improved. Now, positional accuracy is several meters not using differential GPS(D-GPS) (Assistant Secretary of Defense, 2001). The usefulness of the low cost
system developed in this study has increased.
One-Week Travel Data Collection Surveys
One-week vehicle travel surveys for commuting drivers were conducted in two local
cities, Tochigi City (about 80,000 population) and Toyota City (about 340,000
population), using this system (Ohmori, et al., 2000). These surveys were conductedin January and March in 1999. When SA had been working, the positional accuracyof standard positioning system (SPS) was tens of meters. Participants were asked toequip their vehicle, which is used in daily commuting, with the GPS data collection
system, and to record other information about trips (travel purposes, the name ofdestination site, etc.) on a questionnaire sheet. All participants in two surveys

Current position andtravel tracks on a map

Information by GPS(time, longitude/latitude,
speed, etc.)

GPS antenna/receiverPower cord

Figure 1. GPS data collection system
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Ohmori, Muromachi, Harata and Ohta

commuted to the same working places located at the center of the city; especially allparticipants in Toyota City living in the same direction (southwest) from the working
place. The number of participants is 15 in Tochigi and 28 in Toyota. PCs were rented
from a rental office. The PC sets were available at low price. The Garmin GPS 35
was used as low-priced GPS receiver.
Obtained Information According to Data Available in Analyses
Accurate and detailed travel data for an individual travel, such as travel start/end
time, start/end location, travel time, travel distance and travel route are very usefulto understand travel behavior and to develop more detailed travel behavioral models.
Another usage of GPS data is traffic monitoring of road network, “probevehicles”. Quiroga (2000) and Taylor et al. (2000) summarized performancemeasures and congestion indices captured by GPS. Obtained information differs
according to additional data as shown in table 1 that summarizes what kinds ofinformation are obtained according to data available in analyses. Only GPS data ontime and position can provide trip start/end times and start/end positions, and thentotal travel time and travel distance can be also calculated. Duration of stopped time
and the number of stops are calculated using data on speed of each position. Total
vehicle emissions such as CO2 and NOx could be also estimated more accurately byconsidering acceleration calculated by speed profiles. Road network data are needed
for providing information on travel movement (e.g. travel route) in urban spatialenvironment. Travel time and travel speed at each road segment can be also
calculated by combination of GPS data and GIS road network data. If data on
various attributes of road segments are available, performance measures arecalculated by road characteristics (functional class, width, the number of lane, etc.).
Land/property data enable us to specify buildings and parking places where trip
starts and ends. Monitoring data on traffic conditions at fixed points and video
recorded data with the vehicle contribute to understand the events during travel.
Although more information can be collected if probe vehicles are equipped withpositioning devices and also other instruments (e.g. engine management system),
only GPS provides useful information about travel and road performance measures.
Table 1. Obtained information according to data available in analyses
Data

Obtained Information
trip start/end time, start/end position, total travel time, travel distance,
GPS data
the number of stops, stopped time, vehicle emissions
trip start/end location, travel route, link travel time, link travel speed,+ road network data(calculated by road characteristics)+ land/property data buildings and parking places where trip starts and ends
+ monitoring/video
information about travel environment (vehicle volume, events, etc.)
recorded data

Data Handling Process for Selecting Congestion Points in Road Network
GPS data on vehicle travel need to be converted into link information for analyzingroad network performance. First step is to filter GPS data for excluding
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Ohmori, Muromachi, Harata and Ohta

extraordinary points and transforming them into GIS format data. Then, each GPS
point should be matched with GIS road link, because we cannot know which linkeach GPS point was collected at. This procedure is actually very difficult but several
methods have been proposed (Asakura and Hato, 2001). Although the simplest
matching method is that each GPS point is considered to be on the closest link, thismethod often doesn’t specify a reasonable travel route. For that reason, it is better toestimate a travel route before each point is matched to a road link. In this study, eachtravel route was specified manually and GPS data were matched with each link of
the specified route. When matching GPS data with GIS road links, distance between
each GPS position and links, and topological angles between successive positions
and links were considered.
Recently, a car navigation system is available, which can record data on
vehicle movement. This car navigation system can collect more accurate positional
data and be used in urban canyon and tunnel, since it uses GPS but also
gyrocompass, pulse code and real-time map matching technologies. Yet, this system
is much expensive and is not so easy to install to vehicle in comparison with thesystem developed in this study. Experiments of probe vehicles (taxis and trucks withthe car navigation system) have been conducted at Tokyo Metropolitan Area by
Ministry of Land, Infrastructure and Transport in Japan (Makimura et al., 2002).
After each GPS point is matched with road network links, it would be betterto estimate driving mode because low speed do not necessarily mean the uniquetravel condition, for example congestion. It is difficult to distinguish congestions
from stopping at red signal by only one point speed data. Travel mode estimation
models or algorithms should be developed to specify congestions. The best way
would be using video recording methods on vehicles but expensive. Finally,
congestion points can be selected through a series of processes.
Digital Road Map (DRM), which is the most standard road network data inJapan, is used as GIS road network data in the study. MapInfo is used for GIS
platform and a series of data handling programs are written in MapBasic and C.
Analysis of Day-to-Day Variations of Travel Time and Speed
As mentioned in previous sections, GPS data provide information about trip
start/end times, start/end positions and total travel times. Figure 2 shows thevariations of commuting travel times traveled on weekdays during the survey periodin two local cities. The analysis uses data on trips which drivers traveled using
almost the same route and their start times are also the same (within 10 minutes).
Travel times of each individual range from 3 to 11 minutes (average of the range is
6.1 min.) in the Tochigi survey and from 2 to 21 minutes (average is 6.3 min.) in the
Toyota survey.
After each GPS point is labeled link code, average link travel speed as oneof road performance measures can be calculated as:mik

u ik =

∑u
j =1

k
ij

(1)

mik
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Ohmori, Muromachi, Harata and Ohta

Travel Time (min.)
80

80

70

70

60

60

50

50

40

40

30

30

20

20

10

Travel Time (min.)10

Tochigi City

0

Toyota City

0

0

10

20

30

40

50

60

70

0

80

10

Average Travel Time (min.)

20

30

40

50

60

70

80

Average Travel Time (min.)Figure 2. Variations of travel time of commuting trips
u : the average link travel speed of link i on date kkiuijk : j th speed record associated with link i on date k
mik : the number of records of link i on date k (the number of participants’ vehicles
traveled through link i on date k)
Figures 3 and 4 show the examples of analyses of day-to-day variations oftravel speed using GPS data on 117 commuting travel (from home to the working
place) of 28 participants in the Toyota survey. All trips were traveled from 7:00 to
9:00 and these figures showed road network conditions at morning peak period on
weekdays. The number of total links is 1,058 and total length of all links is 176,528
meters. Figure 3 shows low speed links considering its variation. “Max [uik] < 20km/h” in the figure means that the maximum average link speed is under 20 km/h,
so it took longer time to run through these links on weekdays during the survey
period. Total length of these links is 15,342 m. “Min [uik] < 20 km/h” means theminimum average link speed is under 20 km/h and the maximum speed is over 20

max [uik] < 20km/h
min [uik] < 20km/h
min [uik] >= 20km/h

01.5

Working place
Participants’ home
Travel Direction

3

Kilometers

Figure 3. Low speed links at morning peak periods on weekdays
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Ohmori, Muromachi, Harata and Ohta

0 - 10km/h
10 - 20
20 <

0

1.5

Working place
Participants’ home
Travel Direction

3

Kilometers

Figure 4. The range of link travel speeds at morning peak periods on weekdays
km/h. Total length of these links is 43,121 m. “Min [uik] = 20 km/h” means theminimum average link speed is over 20 km/h. Figure 4 shows the range of linkspeeds (max [uik] – min [uik]). The analyses in the study do not distinguishcongestion and stopping at signals or intersections, but suggest the existence of
day-to-day variations of traffic conditions in road network. Effects of a policy
measures to road network performance should be evaluated by travel times andspeeds of multiple days, considering day-to-day variations.
Travel time between every two nodes can be also calculated using
information about each link travel time calculated by link speed and link length. In
this calculation, much attention should be paid to vehicle turning movement atintersections, because travel time crossing intersections differ between going straightand turning left and right.
Conclusions
This paper presented development of GPS data collection system, its application to
one-week travel surveys and analysis of day-to-day variations of travel time andspeed on GIS platform. Variations of travel times of commuting drivers who started
traveling at a regular time were identified using GPS data collected in the one-week
travel surveys. Low speed links at morning peak period and its day-to-day variations
were analyzed using GPS data and GIS road network data.
Positional data are very promising for tracking vehicle movements.
However, usefulness in travel time studies depends on the process of data handlingand the quality of background GIS database. Further research concerns thefollowings:- More appropriate algorithms should be developed for matching positional datawith GIS road network.
- Methods for estimating driving mode should be developed for distinguishingcongestion from other conditions.
- Sample sizes required for statistically significant results should be examined for
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Ohmori, Muromachi, Harata and Ohta--

analysis of network performance measures in the study area.
Detailed GIS road network data with various attributes should be prepared andupdated periodically, because some road links are not included in the DRM
database especially in local cities. In this study, we added road links of which
total length is about 7,000 meters. Since not all road links have useful attributes(width, the number of lane, etc.), analyses by road characteristics are limited tomajor roads.
Comparative analysis of drivers’ commuting travel routes would be interesting.

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