Impact on Network Performance of Probe Vehicle Data Usage: An Experimental Design for Simulation Assessment

Probe-based technologies are proliferating as a means of inferring traffic states. Technological companies are interested in traffic data for computing the best routes in a traffic-aware manner and they also provide real-time traffic information with certain temporal accuracy. This paper analyses and evaluates how data provided by a fleet of probe cars can be used to develop a navigation service and how the penetration rate of this service affects a set of city-scale KPIs (Key Performance Indicators) and driver KPIs. The case study adopts a model-driven approach in which microscopic simulation emulates real-size fleets of probe vehicles that provide positions and speed data. What is noteworthy about the modelling behaviour is that drivers are segmented according to their knowledge of network conditions for selected trips: experts, regular drivers, and tourists. The paper presents and discusses the modelling approach and the results obtained from an experimental Barcelona CBD model designed to evaluate the penetration rates of probe vehicles and route guidance. An analysis of the simulation experiments reveals remarkable links among city-scale KPIs, which—from a multivariate point of view—is a novelty. A simulation-based framework for results analysis and visualization is also introduced in order to simplify the simulation results analysis and easily visualize OD paths for driver segments.

[1]  Qian Zhang,et al.  Impact of connected vehicle guidance information on network-wide average travel time , 2016 .

[2]  Yu Liu,et al.  The promises of big data and small data for travel behavior (aka human mobility) analysis , 2016, Transportation research. Part C, Emerging technologies.

[3]  Marcus P. Enoch,et al.  Estimating Link Travel Time from Low-Frequency GPS Data , 2013 .

[4]  Joyoung Lee,et al.  Evaluation of Route Guidance Strategies Based on Vehicle-Infrastructure Integration under Incident Conditions , 2008 .

[5]  Haris N. Koutsopoulos,et al.  Travel Time Estimation for Urban Road Networks Using Low-Frequency GPS Probes , 2012 .

[6]  Mario Gerla,et al.  A survey of urban vehicular sensing platforms , 2010, Comput. Networks.

[7]  Karine Zeitouni,et al.  Proactive Vehicle Re-routing Strategies for Congestion Avoidance , 2012, 2012 IEEE 8th International Conference on Distributed Computing in Sensor Systems.

[8]  Francesco Paolo Deflorio,et al.  Evaluation of a reactive dynamic route guidance strategy , 2003 .

[9]  Bo Du,et al.  Artificial Neural Network Model for Estimating Temporal and Spatial Freeway Work Zone Delay Using Probe-Vehicle Data , 2016 .

[10]  R. Jayakrishnan,et al.  Emergence of Private Advanced Traveler Information System Providers and Their Effect on Traffic Network Performance , 2002 .

[11]  Chao Wang,et al.  Dynamic Route Choice Prediction Model Based on Connected Vehicle Guidance Characteristics , 2017 .

[12]  Saiedeh Razavi,et al.  Evaluation of connected vehicle impact on mobility and mode choice , 2015 .

[13]  Jaume Barceló,et al.  A Kalman Filter Approach for Exploiting Bluetooth Traffic Data When Estimating Time-Dependent OD Matrices , 2013, J. Intell. Transp. Syst..

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

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

[16]  Alexandre M. Bayen,et al.  Real-Time Traffic Modeling and Estimation with Streaming Probe Data using Machine Learning , 2010 .

[17]  Haris N. Koutsopoulos,et al.  Path inference from sparse floating car data for urban networks , 2013 .

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

[19]  Carlos Bento,et al.  Intelligent road traffic status detection system through cellular networks handover information: An exploratory study , 2013 .

[20]  Yang Lu,et al.  Origin-Destination Estimation Using Probe Vehicle Trajectory and Link Counts , 2017 .

[21]  Jaume Barceló,et al.  Case Study on Cooperative Car Data for Estimating Traffic States in an Urban Network , 2016 .

[22]  Baher Abdulhai,et al.  Assessing the Potential Impacts of Connected Vehicles: Mobility, Environmental, and Safety Perspectives , 2016, J. Intell. Transp. Syst..

[23]  Yang Zhang,et al.  CarTel: a distributed mobile sensor computing system , 2006, SenSys '06.

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