Probe vehicle data sampled by time or space: Consistent travel time allocation and estimation

A characteristic of low frequency probe vehicle data is that vehicles traverse multiple network components (e.g., links) between consecutive position samplings, creating challenges for (i) the allocation of the measured travel time to the traversed components, and (ii) the consistent estimation of component travel time distribution parameters. This paper shows that the solution to these problems depends on whether sampling is based on time (e.g., one report every minute) or space (e.g., one every 500 m). For the special case of segments with uniform space-mean speeds, explicit formulae are derived under both sampling principles for the likelihood of the measurements and the allocation of travel time. It is shown that time-based sampling is biased towards measurements where a disproportionally long time is spent on the last segment. Numerical experiments show that an incorrect likelihood formulation can lead to significantly biased parameter estimates depending on the shapes of the travel time distributions. The analysis reveals that the sampling protocol needs to be considered in travel time estimation using probe vehicle data.

[1]  Guillaume Leduc,et al.  Road Traffic Data: Collection Methods and Applications , 2008 .

[2]  Liping Fu,et al.  Decomposing Travel Times Measured by Probe-based Traffic Monitoring Systems to Individual Road Segments , 2008 .

[3]  P. Abbeel,et al.  Path and travel time inference from GPS probe vehicle data , 2009 .

[4]  Fumitaka Kurauchi,et al.  Using Bus Probe Data for Analysis of Travel Time Variability , 2009, J. Intell. Transp. Syst..

[5]  Haris N. Koutsopoulos,et al.  Travel time estimation for urban road networks using low frequency probe vehicle data , 2013, Transportation Research Part B: Methodological.

[6]  Edward A. McBean,et al.  VARIABILITY OF INDIVIDUAL TRAVEL TIME COMPONENTS , 1984 .

[7]  Kai Liu COMPARISON OF TIME / SPACE POLLING SCHEMES FOR A PROBE VEHICLE SYSTEM , 2007 .

[8]  Fangfang Zheng,et al.  Urban link travel time estimation based on sparse probe vehicle data , 2013 .

[9]  Abishai Polus,et al.  A study of travel time and reliability on arterial routes , 1979 .

[10]  Frank A Haight,et al.  A PRACTICAL METHOD FOR IMPROVING THE ACCURACY OF VEHICULAR SPEED DISTRIBUTION MEASUREMENTS , 1962 .

[11]  Alexandre M. Bayen,et al.  This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 1 Learning the Dynamics of Arterial Traffic From Probe , 2022 .

[12]  A J Richardson,et al.  TRAVEL TIME VARIABILITY ON COMMUTER JOURNEYS , 1978 .

[13]  Marcus P. Enoch,et al.  Estimating Link Travel Time from Low-Frequency GPS Data , 2013 .

[14]  Haris N. Koutsopoulos,et al.  A Synthesis of emerging data collection technologies and their impact on traffic management applications , 2011 .

[15]  Shane G. Henderson,et al.  Travel time estimation for ambulances using Bayesian data augmentation , 2013, 1312.1873.