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ISPRS Int. J. Geo-Inf. 2018, 7(1), 35; doi:10.3390/ijgi7010035

Short-Range Prediction of the Zone of Moving Vehicles in Arterial Networks

1
School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 1439957131, Iran
2
Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 1996715433, Iran
3
School of Civil Engineering, College of Engineering, University of Tehran, Tehran 1417613131, Iran
*
Author to whom correspondence should be addressed.
Received: 29 October 2017 / Revised: 15 January 2018 / Accepted: 18 January 2018 / Published: 22 January 2018
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Abstract

In many moving object databases, future locations of vehicles in arterial networks are predicted. While most of studies apply the frequent behavior of historical trajectories or vehicles’ recent kinematics as the basis of predictions, consideration of the dynamics of the intersections is mostly neglected. Signalized intersections make vehicles experience different delays, which vary from zero to some minutes based on the traffic state at intersections. In the absence of traffic signal information (red and green times of traffic signal phases, the queue lengths, approaching traffic volume, turning volumes to each intersection leg, etc.), the experienced delays in traffic signals are random variables. In this paper, we model the probability distribution function (PDF) and cumulative distribution function (CDF) of the delay for any point in the arterial networks based on a spatiotemporal model of the queue at the intersection. The probability of the presence of a vehicle in a zone is determined based on the modeled probability function of the delay. A comparison between the results of the proposed method and a well-known kinematic-based method indicates a significant improvement in the precisions of the predictions. View Full-Text
Keywords: moving objects prediction; probability distribution function of delay; spatiotemporal models; arterial transportation networks; traffic signals moving objects prediction; probability distribution function of delay; spatiotemporal models; arterial transportation networks; traffic signals
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Forouzandeh Jonaghani, R.; Honarparvar, S.; Khademi, N. Short-Range Prediction of the Zone of Moving Vehicles in Arterial Networks. ISPRS Int. J. Geo-Inf. 2018, 7, 35.

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