Traffic Density Estimation: A Mobile Sensing Approach

Traffic density is one of the fundamental traffic variables used in modeling road traffic dynamics. It measures how packed the vehicles are on the observed road space. Typically, traffic density is estimated indirectly from the data collected by fixed sensors such as inductive loop detectors. However, using fixed sensors has limitations in terms of cost and coverage. It is more effective and less expensive to use vehicles as mobile sensors. With the wide adoption of smartphones, mobile traffic sensing has become more realizable. In this article, we explore the possibility of using only the built-in sensors of off-the-shelf smartphones for traffic density estimation.

[1]  Quan Yu Chaos Research of Vehicle Spacing in Signalized Intersection , 2007 .

[2]  Markos Papageorgiou,et al.  Highway Traffic State Estimation with Mixed Connected and Conventional Vehicles , 2016 .

[3]  Shrey H. Majmudar Federal Motor Vehicle Safety Standards No . 150 ; V 2 V Communications ( Final Standards ) , 2019 .

[4]  Radu Danescu,et al.  An efficient obstacle awareness application for Android mobile devices , 2014, 2014 IEEE 10th International Conference on Intelligent Computer Communication and Processing (ICCP).

[5]  Markos Papageorgiou,et al.  Highway Traffic State Estimation With Mixed Connected and Conventional Vehicles , 2015, IEEE Transactions on Intelligent Transportation Systems.

[6]  Alexandre M. Bayen,et al.  Traffic state estimation on highway: A comprehensive survey , 2017, Annu. Rev. Control..

[7]  Denos C. Gazis,et al.  Application of Kalman Filtering to the Surveillance and Control of Traffic Systems , 1972 .

[8]  M. Tekalp,et al.  Automatic Vehicle Counting from Video for Traffic Flow Analysis , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[9]  Puttipong Leakkaw,et al.  Traffic Sensing Through Accelerometers , 2016, IEEE Transactions on Vehicular Technology.

[10]  Hwang Soo Lee,et al.  A survey on vehicle density estimation in vehicular safety communications and its challenging issues , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[11]  Haris N. Koutsopoulos,et al.  Requirements and potential of GPS-based floating car data for traffic management: Stockholm case study , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[12]  Maen Artimy,et al.  Local Density Estimation and Dynamic Transmission-Range Assignment in Vehicular Ad Hoc Networks , 2007, IEEE Transactions on Intelligent Transportation Systems.