Abstract
Traffic impact assessment is a key step in the process of work zone planning and scheduling for transportation agencies. Microscopic traffic simulation models enable transportation agencies to conduct detailed analyses of work zone mobility performance measures during the planning and scheduling process. However, traffic simulation results are valid only when the simulation model is calibrated to replicate driver behavior that is observed in the field. Few studies have provided guidance on the calibration of traffic simulation models at work zones and have offered driver behavior parameters that reproduce capacity values that are observed in the field. This paper contributes to existing knowledge of work zone simulation by providing separate driver behavior model parameters for heavy vehicles and passenger vehicles. The driver behavior parameters replicate the flow and speed at the work zone taper and at roadway segments upstream of the work zone. A particle swarm optimization framework is proposed to improve the efficiency of the calibration process. The desired time headway was found to be 2.31 seconds for heavy vehicles and 1.53 seconds for passenger cars. The longitudinal following threshold was found to be 17.64 meters for heavy vehicles and 11.70 meters for passenger cars. The proposed parameters were tested against field data that had not previously been used in the calibration of driver behavior models. The average absolute relative error for flow rate at the taper was 10% and the mean absolute error was 54 veh/h/ln. The GEH statistic for the validation dataset was 1.48.
Highlights
Work zones intrinsically reduce roadway capacity and contribute to travel delay and congestion on urban and rural roadways [1]
It is worth mentioning that traffic simulation results are only valid when the model is calibrated to replicate traffic patterns that are observed in the field; the Federal Highway Administration (FHWA) traffic analysis toolbox [8] and other researchers [9,10,11,12,13] have provided general guidelines for such calibration of traffic simulation models
The results showed that CC1 and CC2 were the two driver behavior parameters in the Wiedemann 99 model that were pivotal in replicating the work zones in the simulation model
Summary
Work zones intrinsically reduce roadway capacity and contribute to travel delay and congestion on urban and rural roadways [1]. To the best of the authors’ knowledge, this is the first time that heavy vehicle driver behavior models have been provided for work zones. Driver behavior models are calibrated with two goals in mind: (1) to replicate capacity at work zone taper, and (2) to replicate traffic conditions upstream of the work zone. This paper proposes a framework based on the particle swarm optimization (PSO) algorithm to calibrate car-following and lane-changing model parameters in a work zone. The Wiedemann 99 model is implemented in VISSIM traffic simulation software It is the responsibility of the software user to determine the driving behavior model parameters to represent driver behavior in any transportation facility and geographical area.
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