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Evaluating and Diagnosing Road Intersection Operation Performance Using Floating Car Data

1
MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
2
Transportation Research Institute (IMOB), Hasselt University, Wetenschapspark 5, bus 6, B-3590 Diepenbeek, Belgium
3
Civil Engineering Department, University of Central Florida, Orlando, FL 32816-2450, USA
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(10), 2256; https://doi.org/10.3390/s19102256
Received: 9 April 2019 / Revised: 13 May 2019 / Accepted: 13 May 2019 / Published: 15 May 2019
(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transport)
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Abstract

Urban road intersections play an important role in deciding the total travel time and the overall travel efficiency. In this paper, an innovative traffic grid model has been proposed, which evaluates and diagnoses the traffic status and the time delay at intersections across whole urban road networks. This method is grounded on a massive amount of floating car data sampled at a rate of 3 s, and it is composed of three major parts. (1) A grid model is built to transform intersections into discrete cells, and the floating car data are matched to the grids through a simple assignment process. (2) Based on the grid model, a set of key traffic parameters (e.g., the total time delay of all the directions of the intersection and the average speed of each direction) is derived. (3) Using these parameters, intersections are evaluated and the ones with the longest traffic delays are identified. The obtained intersections are further examined in terms of the traffic flow ratio and the green time ratio as well as the difference between these two variables. Using the central area of Beijing as the case study, the potential and feasibility of the proposed method are demonstrated and the unreasonable signal timing phases are detected. The developed method can be easily transferred to other cities, making it a useful and practical tool for traffic managers to evaluate and diagnose urban signal intersections as well as to design optimal measures for reducing traffic delay and increase operation efficiency at the intersections. View Full-Text
Keywords: intersections; operational state evaluation; grid model; floating car data intersections; operational state evaluation; grid model; floating car data
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Chen, D.; Yan, X.; Liu, F.; Liu, X.; Wang, L.; Zhang, J. Evaluating and Diagnosing Road Intersection Operation Performance Using Floating Car Data. Sensors 2019, 19, 2256.

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