Use of Probe Vehicle Data to Determine Joint Probability Distributions of Vehicle Location and Speed on an Arterial Road

Monitoring traffic conditions over large road networks is a significant challenge for many transportation authorities. However, the rich data from probe vehicles that have become available in recent years can assist with such traffic monitoring. Hydrodynamic theory and horizontal queuing theory are used to derive the joint probability distribution function of vehicle location and speed on an arterial road for undersaturated and congested traffic conditions. Specifically considered are the effects of signal controllers (including deceleration, acceleration, and queuing) on the distributions of location and speed. This probabilistic model is parameterized by link parameters (red signal time, cycle time, and critical flow density), driving behavior (average deceleration and acceleration, average speed, and variation of arrival flow and dissipating flow), and traffic state (arrival flow density) obtained from historical probe data (for location and speed) with maximum likelihood estimates. In a numerical experiment with data collected from private cars in Toyota City, Japan, for the 2011 Green Project, the Kolmogorov–Smirnov test validates the use of the proposed distributions of vehicle location and speed.

[1]  Henry X. Liu,et al.  Real-time queue length estimation for congested signalized intersections , 2009 .

[2]  G. M.,et al.  Partial Differential Equations I , 2023, Applied Mathematical Sciences.

[3]  Matthew G. Karlaftis,et al.  A multivariate state space approach for urban traffic flow modeling and prediction , 2003 .

[4]  Hesham Rakha,et al.  VEHICLE PROBES AS REAL-TIME ATMS SOURCES OF DYNAMIC O-D AND TRAVEL TIME DATA , 1993 .

[5]  Michel Bierlaire,et al.  A Probabilistic Map Matching Method for Smartphone GPS data , 2013 .

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

[7]  Toshiyuki Yamamoto,et al.  Bilevel Generalized Least Squares Estimation of Dynamic Origin–Destination Matrix for Urban Network with Probe Vehicle Data , 2013 .

[8]  Xuegang Jeff Ban,et al.  Real time queue length estimation for signalized intersections using travel times from mobile sensors , 2011 .

[9]  Alexandre M. Bayen,et al.  The Path Inference Filter: Model-Based Low-Latency Map Matching of Probe Vehicle Data , 2011, IEEE Transactions on Intelligent Transportation Systems.

[10]  T H Witte,et al.  Accuracy of non-differential GPS for the determination of speed over ground. , 2004, Journal of biomechanics.

[11]  R. Herring,et al.  A hydrodynamic theory based statistical model of arterial traffic , 2010 .

[12]  Alexander Skabardonis,et al.  Real-Time Estimation of Travel Times on Signalized Arterials , 2005 .

[13]  Bart De Schutter,et al.  Efficient network-wide model-based predictive control for urban traffic networks , 2012 .

[14]  Y. Xi,et al.  An Efficient Model for Urban Traffic Network Control , 2008 .

[15]  Alexandre M. Bayen,et al.  Arterial travel time forecast with streaming data: A hybrid approach of flow modeling and machine learning , 2012 .

[16]  Masao Kuwahara,et al.  Implementing kinematic wave theory to reconstruct vehicle trajectories from fixed and probe sensor data , 2011 .

[17]  H S Mahmassani.  Transportation and Traffic Theory: Flow, Dynamics and Human Interaction , 2005 .

[18]  Hillel Bar-Gera,et al.  Evaluation of a Cellular Phone-Based System for Measurements of Traffic Speeds and Travel Times: A Case Study from Israel , 2007 .

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

[20]  A. Bayen,et al.  Three-Stream Model for Arterial Traffic , 2012 .

[21]  Tomio Miwa,et al.  Route Identification and Travel Time Prediction Using Probe-Car Data , 2004 .

[22]  Alexandre M. Bayen,et al.  Delay Pattern Estimation for Signalized Intersections Using Sampled Travel Times , 2009 .

[23]  Alexandre M. Bayen,et al.  The Path Inference Filter: Model-Based Low-Latency Map Matching of Probe Vehicle Data , 2014, WAFR.

[24]  F. Massey.  The Kolmogorov-Smirnov Test for Goodness of Fit , 1951 .

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

[26]  M J Lighthill,et al.  On kinematic waves II. A theory of traffic flow on long crowded roads , 1955, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[27]  Chengyang Zhang,et al.  Map-matching for low-sampling-rate GPS trajectories , 2009, GIS.

[28]  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 .

[29]  Qiang Ji,et al.  Signal Timing Estimation Using Sample Intersection Travel Times , 2012, IEEE Transactions on Intelligent Transportation Systems.