A data fusion approach for real-time traffic state estimation in urban signalized links

Abstract Real-time estimation of the traffic state in urban signalized links is valuable information for modern traffic control and management. In recent years, with the development of in-vehicle and communication technologies, connected vehicle data has been increasingly used in literature and practice. In this work, a novel data fusion approach is proposed for the high-resolution (second-by-second) estimation of queue length, vehicle accumulation, and outflow in urban signalized links. Required data includes input flow from a fixed detector at the upstream end of the link as well as location and speed of the connected vehicles. A probability-based approach is derived to compensate the error associated with low penetration rates while estimating the queue tail location, which renders the proposed methodology more robust to varying penetration rates of connected vehicles. A well-defined nonlinear function based on traffic flow theory is developed to attain the number of vehicles inside the queue based on queue tail location and average speed of connected vehicles. The overall scheme is thoroughly tested and demonstrated in a realistic microscopic simulation environment for three types of links with different penetration rates of connected vehicles. In order to test the efficiency of the proposed methodology in case that data are available at higher sampling times, the estimation procedure is also demonstrated for different time resolutions. The results demonstrate the efficiency and accuracy of the approach for high-resolution estimation, even in the presence of measurement noise.

[1]  Xuegang Ban,et al.  Privacy Protection Method for Fine-Grained Urban Traffic Modeling Using Mobile Sensors , 2013 .

[2]  Bin Ran,et al.  Cycle-by-Cycle Queue Length Estimation for Signalized Intersections Using Sampled Trajectory Data , 2011 .

[3]  William H. K. Lam,et al.  Distributions of queue lengths at fixed time traffic signals , 1996 .

[4]  D. Heidemann.  Queue length and delay distributions at traffic signals , 1994 .

[5]  Jianfeng Zheng,et al.  Estimating traffic volumes for signalized intersections using connected vehicle data , 2017 .

[6]  Xinkai Wu,et al.  A shockwave profile model for traffic flow on congested urban arterials , 2011 .

[7]  Bin Ran,et al.  An Exploratory Shockwave Approach to Estimating Queue Length Using Probe Trajectories , 2012, J. Intell. Transp. Syst..

[8]  Wang,et al.  Review of road traffic control strategies , 2003, Proceedings of the IEEE.

[9]  Mohamad Talas,et al.  Simple Methodology for Estimating Queue Lengths at Signalized Intersections Using Detector Data , 2013 .

[10]  Mecit Cetin,et al.  Queue length estimation from probe vehicle location and the impacts of sample size , 2009, Eur. J. Oper. Res..

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

[12]  Gunnar Flötteröd,et al.  Stochastic network link transmission model , 2017 .

[13]  Jordi Casas,et al.  Dynamic Network Simulation with AIMSUN , 2005 .

[14]  Markos Papageorgiou,et al.  Store-and-forward based methods for the signal control problem in large-scale congested urban road networks , 2009 .

[15]  Peng Hao,et al.  Cycle-by-cycle intersection queue length distribution estimation using sample travel times , 2014 .

[16]  Markos Papageorgiou,et al.  Real-time estimation of vehicle-count within signalized links , 2008 .

[17]  Markos Papageorgiou,et al.  SOME REMARKS ON MACROSCOPIC TRAFFIC FLOW MODELLING , 1998 .

[18]  Nikolas Geroliminis,et al.  Queue Profile Estimation in Congested Urban Networks with Probe Data , 2015, Comput. Aided Civ. Infrastructure Eng..

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

[20]  Ashish Bhaskar,et al.  Fusing Loop Detector and Probe Vehicle Data to Estimate Travel Time Statistics on Signalized Urban Networks , 2011, Comput. Aided Civ. Infrastructure Eng..

[21]  Markos Papageorgiou,et al.  Extensions and New Applications of the Traffic-Responsive Urban Control Strategy: Coordinated Signal Control for Urban Networks , 2003 .

[22]  D. N. Rewadkar,et al.  Review of Different Methods Used for Large-Scale Urban Road Networks Traffic State Estimation , 2013 .

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

[24]  Markos Papageorgiou,et al.  A Simplified Estimation Scheme for the Number of Vehicles in Signalized Links , 2010, IEEE Transactions on Intelligent Transportation Systems.

[25]  Fuliang Li,et al.  Cycle-by-Cycle Estimation of Signal Timing and Queue Length at Signalized Intersections Based on Probe Data , 2017 .

[26]  Carlos F. Daganzo,et al.  THE CELL TRANSMISSION MODEL, PART II: NETWORK TRAFFIC , 1995 .

[27]  Xuegang Ban,et al.  Long queue estimation for signalized intersections using mobile data , 2015 .

[28]  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.

[29]  Xiubin Bruce Wang,et al.  Queue Length Estimation Using Connected Vehicle Technology for Adaptive Signal Control , 2015, IEEE Transactions on Intelligent Transportation Systems.

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

[31]  Pravin Varaiya,et al.  Max pressure control of a network of signalized intersections , 2013 .

[32]  Isaak Yperman,et al.  The Link Transmission Model for dynamic network loading , 2007 .

[33]  Pravin Varaiya,et al.  Real-Time Measurement of Link Vehicle Count and Travel Time in a Road Network , 2010, IEEE Transactions on Intelligent Transportation Systems.

[34]  Francesco Viti,et al.  Probabilistic models for queues at fixed control signals , 2010 .