Potential of vision-enhanced floating car data for urban traffic estimation

Abstract

Floating car data (FCD) have recently become a popular tool of urban traffic engineering. Conventional FCD contains a series of probe cars’ timestamped locations and are used to estimate traffic speeds and travel times and identify congestions. In this study, we propose the enhancement of conventional FCD with car vision information: traffic measurements, collected by video cameras or lidars, installed on the probe cars. Given the limited penetration rates of probe cars, such vision information can significantly improve the accuracy of traffic estimation. Our experimental study is based on two simulated vision-enhanced FCD data sets: sensor-based real-world traffic data set with simulated observability and a simulation model with vision-equipped probe cars. We estimate the potential of vision-enhanced FCD for urban traffic flow estimation for different probe car penetration rates. A recently proposed temporal geometric matrix completion algorithm is utilized for traffic speed estimation given incomplete spatiotemporal traffic flow information. Empirical results show that the availability of vision-enhanced FCD leads to significant improvement: the coverage of 6% of spatiotemporal slots by probe cars gives reasonable, and the coverage of 9% – nearly optimal accuracy of traffic speed estimation. Thus, obtained experimental results support our hypothesis about the utility of vision-enhanced FCD to improve traffic estimation accuracy and discover related problems and limitations.