Moving Array Traffic Probes

This paper explores the potential of moving array “probes” to collect traffic data. This application simulates the prospect of mining environmental data on traffic conditions to present an inexpensive and potentially widespread source of traffic conditions. Based on three different simulations, we measure the magnitude and trends of probe error (comparing the probe's “subjective” or time-weighted perception with an “objective” observer) in density, speed, and flow in order to validate the proposed model and compare the results with loop detectors. From these simulations, several conclusions were reached. A single probe's error follows a double hump trend due to an interplay between the factors of traffic heterogeneity and shockwaves. Reduced visibility of the single probe does not proportionately increase the error. Multiple probes do not tend to increase accuracy significantly, which suggests that the data will be still useful even if probes are sparsely distributed. Finally, probes can measure the conditions of oncoming traffic more accurately than concurrent traffic. Further research is expected to consider more complex road networks and develop methods to improve the accuracy of moving array samples.

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

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

[3]  Erik Jenelius,et al.  Non-parametric estimation of route travel time distributions from low-frequency floating car data , 2015 .

[4]  Matthew J. Barth,et al.  Mobile traffic surveillance system for dynamic roadway and vehicle traffic data integration , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[5]  M. Cremer.  Flow Variables: Estimation , 1991 .

[6]  Yasuo Asakura,et al.  Traffic State Estimation with the Advanced Probe Vehicles Using Data Assimilation , 2015, 2015 IEEE 18th International Conference on Intelligent Transportation Systems.

[7]  Juan-Carlos Cano,et al.  Sensing Traffic Density Combining V2V and V2I Wireless Communications , 2015, Sensors.

[8]  Xiaoliang Ma,et al.  Estimation of Driver Reaction Time from Car-Following Data: Application in Evaluation of General Motor–Type Model , 2006 .

[9]  Jingqiu Guo,et al.  Characteristic analysis of mixed traffic flow of regular and autonomous vehicles using cellular automata , 2017 .

[10]  Benjamin Coifman,et al.  Identifying Lane-Change Maneuvers with Probe Vehicle Data and an Observed Asymmetry in Driver Accommodation , 2012 .

[11]  Serge P. Hoogendoorn,et al.  Macroscopic traffic state estimation using relative flows from stationary and moving observers , 2018, Transportation Research Part B: Methodological.

[12]  Daniel J. Fagnant,et al.  Preparing a Nation for Autonomous Vehicles: Opportunities, Barriers and Policy Recommendations , 2015 .

[13]  Alexander Schaub.  Robust Perception from Optical Sensors for Reactive Behaviors in Autonomous Robotic Vehicles , 2017 .

[14]  Xiaoyan Hong,et al.  A distributed framework for network-wide traffic monitoring and platoon information aggregation using V2V communications , 2016 .

[15]  Markos Papageorgiou,et al.  Highway Traffic State Estimation With Mixed Connected and Conventional Vehicles , 2015, IEEE Transactions on Intelligent Transportation Systems.

[16]  Alexandre M. Bayen,et al.  Traffic state estimation on highway: A comprehensive survey , 2017, Annu. Rev. Control..

[17]  Long T. Truong,et al.  Studying the Safety Impact of Autonomous Vehicles Using Simulation-Based Surrogate Safety Measures , 2018 .

[18]  Samer H. Hamdar,et al.  Measuring the safety impact of road infrastructure systems on driver behavior: Vehicle instrumentation and real world driving experiment , 2017, J. Intell. Transp. Syst..

[19]  Wilhelm Leutzbach,et al.  Introduction to the Theory of Traffic Flow , 1987 .

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

[21]  Benjamin Coifman,et al.  Estimating density and lane inflow on a freeway segment , 2003 .

[22]  Peng Peng,et al.  GE-GAN: A novel deep learning framework for road traffic state estimation , 2020 .

[23]  Siyu Huang,et al.  Cyber-physical system enabled nearby traffic flow modelling for autonomous vehicles , 2017, 2017 IEEE 36th International Performance Computing and Communications Conference (IPCCC).

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

[25]  Tony Z. Qiu,et al.  Estimation of Freeway Traffic Density with Loop Detector and Probe Vehicle Data , 2010 .

[26]  Alexander Skabardonis,et al.  Detecting Errors and Imputing Missing Data for Single-Loop Surveillance Systems , 2003 .

[27]  Livia Mannini,et al.  Dynamic demand estimation and prediction for traffic urban networks adopting new data sources , 2017 .

[28]  Keith Redmill,et al.  Collecting ambient vehicle trajectories from an instrumented probe vehicle: High quality data for microscopic traffic flow studies , 2016 .

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

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

[31]  Peter Davidson,et al.  AUTONOMOUS VEHICLES - WHAT COULD THIS MEAN FOR THE FUTURE OF TRANSPORT? , 2015 .

[32]  A. Bayen,et al.  A traffic model for velocity data assimilation , 2010 .

[33]  Markos Papageorgiou,et al.  Highway traffic state estimation with mixed connected and conventional vehicles: Microscopic simulation-based testing , 2016, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).

[34]  Zhengbing He,et al.  Visualizing Traffic Dynamics Based on Floating Car Data , 2017 .

[35]  D. Levinson.  Perspectives on efficiency in transportation , 2003 .

[36]  Alexandra Kondyli,et al.  Assessment of car-following models by driver type and under different traffic, weather conditions using data from an instrumented vehicle , 2014, Simul. Model. Pract. Theory.

[37]  Haris N. Koutsopoulos,et al.  Probe vehicle data sampled by time or space: Consistent travel time allocation and estimation , 2015 .

[38]  R. Nezafat,et al.  Exploring the use of LIDAR data from Autonomous Cars for Estimating Traffic Flow Parameters and Vehicle Trajectories , 2018 .

[39]  Li Li,et al.  Pattern Sensitive Prediction of Traffic Flow Based on Generative Adversarial Framework , 2019, IEEE Transactions on Intelligent Transportation Systems.

[40]  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 .