New Snapshot Generation Protocol for Travel Time Estimation in a Connected Vehicle Environment

Connected vehicle technology offers great potential to improve the safety and the mobility of a transportation system. Probe data collection is one feature of connected vehicle technology in which vehicles collect information such as their location and speed. Probe data could be used to support various traffic management and traveler information applications. This paper presents the novel R2 protocol, used to collect probe data in a connected vehicle environment. The core principle of R2 protocol is to collect only vehicle snapshots when a significant change occurs in vehicle speed. Data from a connected vehicle simulation test bed in Boise, Idaho, and a real-world test bed in Oakland County, Michigan, were used to evaluate the proposed protocol. An average speed method and a method with its basis in the reconstruction of vehicle time–speed plots were used to estimate link travel time. Linear regression, cubic spline, and piecewise cubic Hermite interpolation were applied to reconstruct time–speed plots. The proposed R2 protocol was compared with three existing protocols: fixed 2-s, fixed 4-s, and SAE J2735. The results from the simulation test bed indicated that the R2 protocol not only outperformed the three protocols in error measurement but also required fewer snapshots to achieve the lower-error value. The snapshots recorded by the R2 protocol were 30%, 26%, and 4% lower than those recorded by the other three J2735 protocols. The Michigan test bed case study showed that the R2 protocol produced fewer errors and needed 11% fewer snapshots than the SAE J2735 protocol.

[1]  R. E. Carlson,et al.  Monotone Piecewise Cubic Interpolation , 1980 .

[2]  Raj Bridgelall,et al.  Connected Vehicle Approach for Pavement Roughness Evaluation , 2014 .

[3]  Darcy M. Bullock,et al.  Real-Time Measurement of Travel Time Delay in Work Zones and Evaluation Metrics Using Bluetooth Probe Tracking , 2010 .

[4]  S. Nash,et al.  Numerical methods and software , 1990 .

[5]  Steven M Click,et al.  Applicability of Bluetooth Data Collection Methods for Collecting Traffic Operations Data on Rural Freeways , 2012 .

[6]  Francois Dion,et al.  Evaluation of Usability of IntelliDrive Probe Vehicle Data for Transportation Systems Performance Analysis , 2011 .

[7]  Praveen Edara,et al.  Placement of Roadside Equipment in Connected Vehicle Environment for Travel Time Estimation , 2013 .

[8]  Eil Kwon,et al.  Vehicle-to-Infrastructure and Vehicle-to-Vehicle Information System in Work Zones , 2012 .

[9]  Alexander Skabardonis,et al.  Freeway Traffic Shockwave Analysis: Exploring NGSIM Trajectory Data , 2007 .

[10]  Jaehyun So,et al.  Sustainability assessments of cooperative vehicle intersection control at an urban corridor , 2013 .

[11]  Steven E Shladover,et al.  Traffic Probe Data Processing for Full-Scale Deployment of Vehicle-Infrastructure Integration , 2008 .

[12]  Romain Billot,et al.  Motorway travel time prediction based on toll data and weather effect integration , 2010 .

[13]  Brian Lee Smith,et al.  Investigating Benefits of IntelliDriveSM in Freeway Operations--Lane Changing Advisory Case Study , 2010 .

[14]  Ilsoo Yun,et al.  Cumulative Travel-Time Responsive Real-Time Intersection Control Algorithm in the Connected Vehicle Environment , 2013 .

[15]  H. Mahmassani,et al.  Travel time estimation based on piecewise truncated quadratic speed trajectory , 2008 .

[16]  Yi Wen,et al.  Coordination of Connected Vehicle and Transit Signal Priority in Transit Evacuations , 2012 .

[17]  Carlo Ratti,et al.  Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome , 2011, IEEE Transactions on Intelligent Transportation Systems.

[18]  Jun-Seok Oh,et al.  Virtual Testbed for Assessing Probe Vehicle Data in IntelliDrive Systems , 2011, IEEE Transactions on Intelligent Transportation Systems.

[19]  Pravin Varaiya,et al.  Arterial travel time estimation based on vehicle re-identification using wireless magnetic sensors , 2009 .

[20]  Taehyung Kim,et al.  Capability-Enhanced Probe Vehicle Surveillance System with Vehicle-to-Vehicle Communications , 2010 .

[21]  Liping Fu,et al.  Automatic Traffic Shockwave Identification Using Vehicles’ Trajectories , 2009 .

[22]  Xiao Qin,et al.  An Exploratory Shockwave Approach for Signalized Intersection Performance Measurements Using Probe Trajectories , 2010 .

[23]  Francois Dion,et al.  Estimating dynamic roadway travel times using automatic vehicle identification data for low sampling rates , 2006 .

[24]  Carl de Boor,et al.  A Practical Guide to Splines , 1978, Applied Mathematical Sciences.

[25]  Michael J. Cassidy,et al.  VEHICLE REIDENTIFICATION AND TRAVEL TIME MEASUREMENT.. , 2001 .

[26]  Cheol Oh,et al.  Estimation of Lane-Level Travel Times in Vehicle-to-Vehicle and Vehicle-to-Infrastructure–Based Traffic Information System , 2011 .

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

[28]  Ashish Bhaskar,et al.  Arterial traffic congestion analysis using Bluetooth Duration data , 2011 .

[29]  윤태영,et al.  Transportation Research Board of the National Academies , 2015 .

[30]  Hyungjun Park,et al.  Integrated Traffic–Communication Simulation Evaluation Environment for IntelliDrive Applications Using SAE J2735 Message Sets , 2011 .