Traffic State Estimation with the Advanced Probe Vehicles Using Data Assimilation

This paper proposes a method for estimating traffic state from data collected by the advanced probe vehicles, namely, probe vehicles with spacing measurement equipment. The probe vehicle data are assumed to include spacing information, in addition to conventional position information. The spacing information is collected as secondary products from advanced vehicle technologies, such as automated vehicles. Traffic states and a fundamental diagram are derived from the probe vehicle data. Then, a traffic state estimator based on a data assimilation technique and a traffic flow model is formulated. This procedure is intended to mitigate negative effects in traffic state estimation caused by high fluctuations in microscopic vehicular traffic. The validation results with a simulation experiment suggested that the proposed method works reasonably, for example, the proposed method was able to estimate precise traffic state compared with the previous methods. Therefore, we expect that the proposed method can estimate precise traffic states in wide area where the advanced probe vehicles are penetrated, without depending on fixed sensor infrastructures nor careful parameter calibration.

[1]  Fabrizio Granelli,et al.  Intelligent extended floating car data collection , 2009, Expert Syst. Appl..

[2]  Bart De Schutter,et al.  A comparison of filter configurations for freeway traffic state estimation , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[3]  Toshio Sato,et al.  A Camera-Based Probe Car System for Traffic Condition Estimation , 2013 .

[4]  Maëlle Nodet,et al.  Introduction to Data Assimilation , 2011, Numerical Weather Prediction and Data Assimilation.

[5]  C. Daganzo.  THE CELL TRANSMISSION MODEL.. , 1994 .

[6]  Masao Kuwahara,et al.  Traffic Measurements on Signalized Arterials from Vehicle Trajectories , 2015 .

[7]  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.

[8]  Markos Papageorgiou,et al.  Overview and analysis of Vehicle Automation and Communication Systems from a motorway traffic management perspective , 2015 .

[9]  David Fernández Llorca,et al.  Extended Floating Car Data System: Experimental Results and Application for a Hybrid Route Level of Service , 2012, IEEE Transactions on Intelligent Transportation Systems.

[10]  G. Evensen.  Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

[11]  Markos Papageorgiou,et al.  An adaptive freeway traffic state estimator , 2009, Autom..

[12]  Takahiko Kusakabe,et al.  Probe vehicle-based traffic state estimation method with spacing information and conservation law , 2015 .

[13]  Mohan M. Trivedi,et al.  Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis , 2013, IEEE Transactions on Intelligent Transportation Systems.

[14]  Markos Papageorgiou,et al.  Real-time freeway traffic state estimation based on extended Kalman filter: a general approach , 2005 .

[15]  Hironori Suzuki,et al.  Application of Probe-Vehicle Data for Real-Time Traffic-State Estimation and Short-Term Travel-Time Prediction on a Freeway , 2003 .

[16]  Yasuo Asakura,et al.  Estimation of flow and density using probe vehicles with spacing measurement equipment , 2015 .

[17]  P. I. Richards.  Shock Waves on the Highway , 1956 .

[18]  M. Cassidy.  BIVARIATE RELATIONS IN NEARLY STATIONARY HIGHWAY TRAFFIC , 1998 .

[19]  Serge P. Hoogendoorn,et al.  Real-Time Lagrangian Traffic State Estimator for Freeways , 2012, IEEE Transactions on Intelligent Transportation Systems.