Using Bus Probe Data for Analysis of Travel Time Variability

Abstract

The rapid progress of information technology (IT) may provide us with new insights into understanding traffic phenomena, and could help mitigate traffic problems. One of the key applications of IT to traffic and transport analysis is the identification of the location of moving objects using the Global Positioning System (GPS). It is expected that detailed traffic analysis could be carried out using these data. In this article, we first summarize the various applications of probe data in transport analysis. GPS data are merely a sequence of locations, and further data transformation such as map-matching, data-reduction, processing, and reporting is needed to use them effectively. We then discuss the application of bus probe data to evaluating travel time variability and the level of service (LOS) of roads. A methodology for evaluating the road network from the viewpoint of travel time stability and reliability using busThe rapid progress of IT may provide us new insights into understanding traffic phenomena, and could help mitigate traffic problems. One of the key applications of IT to traffic and transport analysis is the identification of the location of moving objects using GPS. It is expected that detailed traffic analysis could be carried out using these data.GPS consists of a series of satellites that orbit the earth, broadcasting signals to receivers on the ground. A receiver can determine its location using data from at least four satellites. GPS was developed by the U.S. Department of Defense for military purposes, and was opened for public use in 1984. There are now 31 GPS satellites available. The performance of the GPS had, This research was supported by the Innovation and Integration of Technologies for a New Society project funded by the Japanese Ministry of Land, Infrastructure, and Management. We wish to thank the Institute of Systems Science Research and our former students, Yusuke Nagahiro and Yusuke Yamawaki, for supporting the analysis of bus probe data.Address correspondence to Nobuhiro Uno, Department of Urban Management, Kyoto University, Kyoto, Japan. E-mail: uno@trans.kuciv.kyotou.ac.jp in the past, been limited by artificial degradation of the signal through the process of selective availability (SA), but the US Government removed that on May 1, 2000. Ochieng and Sauer (2002) reported that the 74% of data are accurate within 10 m without SA, compared to only 24% with SA. Furthermore, no significant difference is apparent between the level of accuracy achievable with differential positioning and post-SA stand-alone navigation.Zito and colleagues (1995) were the first to address the use of GPS data for traffic engineering. They discussed the accuracy of the GPS and its potential for traffic analysis, and suggested that geographical information systems (GIS) could be used efficiently for managing the data obtained by the GPS. They demonstrated that, whenever possible, the direct GPS speed should be used because calculating a speed using the time between two locations gives poor results....

On the estimation of arterial route travel time distribution with Markov chains

“…A similar approach is investigated, utilizing mixtures of normal distributions to estimate mean travel times for arterial routes with Next Generation Simulation (NGSIM) data (Feng et al, 2011). Uno et al (2009) discuss route travel time variability using bus probe data. In pre-processing stage of their method, a map matching procedure and a data filtering for dwell time elimination are performed.…”

Method for Analysis and Prediction of Dwell Times at Stops in Local Bus Transportation

“…The key findings: - Uno et al (2009) suggested investigation of several factors: weather condition, design of road, land use along road, etc. We have also identified the important influencing factors as preliminary hypotheses, and then revealed their effects.…”

“…Uno et al (2009) determined the level of service of a certain route by the journey times. Mean values and deviations of the journey times have been considered.…”

Route Performance Evaluation of a Closed Bus Rapid Transit System Using GPS Data

“…The initial studies used GPS data to estimate travel time from the bus location data 5 . After developing techniques for travel-time estimation from GPS data, historical data were utilized for predicting travel time using artificial neural network and Kalman filter method 6,7 .…”

“…Using the travel-time estimation, average commercial speed of the buses was also estimated 8 . After the travel-time estimation and prediction studies, the travel-time variability (TTV) and reliability studies were reported 5,[9][10][11][12] . Day-to-day and period-toperiod travel-time reliability analysis was also carried out earlier 13 .…”