Feasibility of expanding traffic monitoring systems with floating car data technology

Trajectory information reported by certain vehicles (Floating Car Data or FCD) can be applied to monitor the road network. Policy makers face difficulties when deciding to invest in the expansion of their infrastructure based on inductive loops and cameras, or to invest in a FCD system. This paper targets this decision. The provided FCD functionality is investigated, minimum requirements are determined and reliability issues are researched. The communication cost is derived and combined with other elements to assess the total costs for different scenarios. The outcome is to target a penetration rate of 1%, a sample interval of 10 seconds and a transmission interval of 30 seconds. Such a deployment can accurately determine the locations of incidents and traffic jams. It can also estimate travel times accurately for highways, for urban roads this is limited to a binary categorization into normal or congested traffic. No reliability issues are expected. The most cost efficient scenario when deploying a new FCD system is to launch a smartphone application. For Belgium, this costs 13 million EUR for 10 years. However, it is estimated that purchasing data from companies already acquiring FCD data through their own product could reduce costs with a factor 10.

[1]  Tingting Zhao,et al.  Evaluating the Performance of Link Travel Time Estimation Based on Floating Car Data , 2010, 2010 International Conference on Optoelectronics and Image Processing.

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

[3]  M Neuherz,et al.  TRAFFIC INFORMATION POTENTIAL AND NECESSARY PENETRATION RATES , 2004 .

[4]  Sofie Verbrugge,et al.  Realistic cost estimation of an intelligent transportation system roll-out , 2010 .

[5]  Nick Cohn Real-Time Traffic Information and Navigation , 2009 .

[6]  Der-Horng Lee,et al.  Probe Vehicle Population and Sample Size for Arterial Speed Estimation , 2002 .

[7]  Longhui Gang,et al.  Impact of Probe Vehicles Sample Size on Link Travel Time Estimation , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[8]  Yanbing Liu,et al.  Real-time urban traffic monitoring with global positioning system-equipped vehicles , 2010 .

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

[10]  P. Demeester,et al.  Impact of introducing road charging on supporting mobile data networks , 2009, 2009 9th International Conference on Intelligent Transport Systems Telecommunications, (ITST).

[11]  鈴木 宏典,et al.  DYNAMIC ESTIMATION OF TRAFFIC STATES ON A FREEWAY USING PROBE VEHICLE DATA , 2003 .

[12]  Darcy M. Bullock,et al.  Travel time studies with global positioning and geographic information systems: an integrated methodology , 1998 .

[13]  Steven I-Jy Chien,et al.  Dynamic freeway travel time prediction using probe vehicle data: Link-based vs , 2001 .

[14]  Serge P. Hoogendoorn,et al.  The Technical and Economic Benefits of Data Fusion for Real-Time Monitoring of Freeway Traffic: Preliminary Results and Implications of a Study with Simulated Data , 2007 .

[15]  Gaetano Valenti,et al.  Traffic Estimation And Prediction Based On Real Time Floating Car Data , 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.

[16]  Xu Li,et al.  Performance Evaluation of Vehicle-Based Mobile Sensor Networks for Traffic Monitoring , 2009, IEEE Transactions on Vehicular Technology.

[17]  Hillel Bar-Gera,et al.  Evaluation of a Cellular Phone-Based System for Measurements of Traffic Speeds and Travel Times: A Case Study from Israel , 2007 .

[18]  Zhongya Wei,et al.  Spatial and Temporal Analysis of Probe Vehicle-based Sampling for Real-time Traffic Information System , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[19]  Brian L. Smith,et al.  Investigation of the Performance of Wireless Location Technology-Based Traffic Monitoring Systems , 2007 .

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