Kriging-Based Approach for Estimation of Vehicular Speed and Passenger Car Units on an Urban Arterial
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 143, Issue 3
Abstract
Use of speed-prediction models sometimes appears as a feasible alternative to laborious field measurements, particularly when field data cannot fulfill designers’ requirements. However, developing speed models is a challenging task, especially in the context of developing countries like India where vehicles with diverse static and dynamic characteristics use the same right of way. Here, the traffic composition plays a significant role in determining vehicular speed. Determination of passenger car units (PCU), which is required to convert heterogeneous traffic flow to its homogeneous equivalent, also requires speed of different types of vehicles at varying traffic conditions and traffic volumes on the road. In this context, the present research is carried out to examine the effects of traffic volume and its composition on speed and PCU factors for individual types of vehicles under mixed traffic conditions. Classified traffic-volume and speed data were collected at six-lane divided arterial sections in New Delhi and categorized speed models developed adopting a kriging approximation technique, an alternative for commonly used regression. A novel algorithm for selecting the optimal correlation function in kriging has also been proposed in this paper. Developed speed models are validated with the data set of another location kept aside for this purpose, and the predicted speeds show a good agreement to the observed ones. Predicted speeds are then used to estimate PCU factors for each vehicle category. Finally, the proposed models are utilized to evaluate the effects of traffic volume and its composition on speed and PCU of different vehicle categories.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 143 • Issue 3 • March 2017
Copyright
©2016 American Society of Civil Engineers.
History
Received: May 15, 2016
Accepted: Oct 26, 2016
Published online: Nov 30, 2016
Published in print: Mar 1, 2017
Discussion open until: Apr 30, 2017
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