Probe vehicle-based traffic state estimation method with spacing information and conservation law

This paper proposes a method of estimating a traffic state based on probe vehicle data that contain spacing and position of probe vehicles. The probe vehicles were assumed to observe spacing by utilizing an advanced driver assistance system, that has been implemented in practice and is expected to spread in the near future. The proposed method relies on the conservation law of the traffic flow but is independent of a fundamental diagram. The conservation law is utilized for reasonable aggregation of the spacing data to acquire the traffic state, i.e., a flow, density and speed. Its independence from a fundamental diagram means that the proposed method does not require predetermined nor exogenous assumptions with regard to the traffic flow model parameters. The proposed method was validated through a simulation experiment under ideal conditions and a field experiment conducted under actual traffic conditions; and empirical characteristics of the proposed method were investigated.

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

[2]  Ludovic Leclercq,et al.  The Lagrangian Coordinates and What it Means for First Order Traffic Flow Models , 2007 .

[3]  Ludovic Leclercq,et al.  The Hamilton-Jacobi Partial Differential Equation and the Three Representations of Traffic Flow , 2013 .

[4]  Alexandre M. Bayen,et al.  Incorporation of Lagrangian measurements in freeway traffic state estimation , 2010 .

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

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

[7]  N. Geroliminis,et al.  An analytical approximation for the macropscopic fundamental diagram of urban traffic , 2008 .

[8]  N. Geroliminis,et al.  Existence of urban-scale macroscopic fundamental diagrams: Some experimental findings - eScholarship , 2007 .

[9]  Serge P. Hoogendoorn,et al.  Discontinuities in the Lagrangian formulation of the kinematic wave model , 2013 .

[10]  P. G. Gipps,et al.  A behavioural car-following model for computer simulation , 1981 .

[11]  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 .

[12]  Alexandre M. Bayen,et al.  Evaluation of traffic data obtained via GPS-enabled mobile phones: The Mobile Century field experiment , 2009 .

[13]  Carlos F. Daganzo,et al.  Fundamentals of Transportation and Traffic Operations , 1997 .

[14]  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.

[15]  Jorge A. Laval.  Hysteresis in traffic flow revisited: An improved measurement method , 2011 .

[16]  G. F. Newell,et al.  Three-Dimensional Representation of Traffic Flow , 1971 .

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