Travel Time Estimation Between Loop Detectors and Fcd: A Compatibility Study on the Lille Network, France☆

Abstract The availability of floating car data (FCD) enables operators to use novel methods in travel time estimation. A first step towards combining traffic data from loops and FCD is to check the compatibility between the two types of travel time estimates. We perform an in-depth statistical analysis that allows us to compare various travel time estimates using data collected from the peri-urban highways in the region of Lille, in north France. The comparison is performed separately for light and heavy vehicles and for various settings: peak hour, off-peak hours, working day, holiday, rain, and so on. The results show that the two estimates are linearly correlated and a specific function can be calibrated for each site for itineraries of variable length. Overall, this paper provides evidence that different flow regimes necessitate differentiated a priori treatment in order to enhance the reliability of estimates made on data coming from different sources.

[1]  FCD in the Real World– System Capabilities and Applications , 2012 .

[2]  Pravin Varaiya,et al.  Probe Vehicle Runs or Loop Detectors? , 2007 .

[3]  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 .

[4]  Alexandre M. Bayen,et al.  Trade-offs Between Inductive Loops and GPS Probe Vehicles for Travel Time Estimation: Mobile Century Case Study , 2012 .

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

[6]  Fergyanto E. Gunawan,et al.  Optimal Averaging Time for Predicting Traffic Velocity Using Floating Car Data Technique for Advanced Traveler Information System , 2014 .

[7]  Wenxin Qiao,et al.  Short-Term Travel Time Prediction considering the Effects of Weather , 2012 .

[8]  Alexander Sohr,et al.  Self evaluation of floating car data based on travel times from actual vehicle trajectories , 2011, 2011 IEEE Forum on Integrated and Sustainable Transportation Systems.

[9]  Joseph G. Eisenhauer Regression through the Origin , 2003 .

[10]  Stefan Lorkowski,et al.  BENEFITS AND LIMITS OF RECENT FLOATING CAR DATA TECHNOLOGY – AN EVALUATION STUDY , 2007 .

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

[12]  E. Purson,et al.  Simultaneous Assessments of Innovative Traffic Data Collection Technologies for Travel Times Calculation on the East Ring Road of Lyon , 2015 .

[13]  José Eugenio Naranjo,et al.  Floating Car Data Augmentation Based on Infrastructure Sensors and Neural Networks , 2012, IEEE Transactions on Intelligent Transportation Systems.

[14]  Kitae Kim,et al.  Evaluation of floating car technologies for travel time estimation , 2012 .

[15]  Geoff Rose,et al.  Mobile Phones as Traffic Probes: Practices, Prospects and Issues , 2006 .

[16]  Fangfang Zheng,et al.  Urban link travel time estimation based on sparse probe vehicle data , 2013 .