Microscopic Simulation-Based High Occupancy Vehicle Lane Safety and Operation Assessment: A Case Study

Abstract

This study proposes two general alternative designs to enhance the operation and safety of High Occupancy Vehicle (HOV) lanes at junctions with bus terminals or parking lots. A series of analysis tools, including microscopic simulation, video-based vehicle tracking technique, and Surrogate Safety Assessment Model (SSAM), are applied to model and test the safety and operational efficiency of an HOV road segment near a bus terminal in Québec as a case study. A metaheuristic optimization algorithm (i.e., Whale Optimization Algorithm) is employed to calibrate the microscopic model while deviation from the observed headway distribution is considered as a cost function. The results indicate that this type of HOV configurations exhibits significant safety problems (high number of crossing conflicts) and operational issues (high value of total delay) due to the terminal-bound buses that frequently need to travel across the main road. It is shown that the proposed alternative geometry design efficiently ameliorates the traffic conflicts issues. In addition, the alternative control design scheme significantly reduces the public transit delay. It is expected that this methodology can be applied to other reserved lane configurations similar to the investigated case study.

Two‐fold calibration approach for microscopic traffic simulation models

“…Moreover, the deviation of the number of conflicts at different sections of the road as well as the time series of the speed at different points of the road from their true values are considered as two distinct objectives. Based on previous studies [4, 5, 30], the five most sensitive model parameters are chosen to be calibrated in our study.…”

“…This data together with the geometric alignment of the road section are processed by VISSIM to generate vehicular traffic in the network. The reviewed literature, [4, 5, 30], show that the five most sensitive parameters of VISSIM which have the most impact on calibrating the model are: the ASSD, the AFSD, the dimensionless multiplicative factor for safety distance (MFSD), the safety distance reduction factor (SRF), and minimum headway (MH). Each parameter is assigned values within a specified range, and three simulation runs of VISSIM were conducted using different random seeds.…”

“…Moreover, the process of evaluating the versatility of a calibrated model to capture similar real‐world conditions is called validation [3]. Owing to the stochastic nature of the microscopic traffic models, all simulation platforms require calibration of various parameters in order to capture the real‐world vehicular interactions [4, 5]. However, finding the best parameter set values is a time‐consuming process of searching the whole landscape of parameters.…”

“…Most calibration studies have focused on minimising the objectives related to traffic operation (e.g. [4–9]). While there is no guarantee that the calibrated simulation model can successfully capture the traffic safety conditions as well.…”

On the Effects of Various Measures of Performance Selections on Simulation Model Calibration Performance

“…In order to develop a reliable microscopic simulation model, model calibration is considered as the most critical step that matches simulated traffic conditions with actual environment. To calibrate a simulation model, a measure of performance (MOP) usually needs to be selected as the calibration objective, such as average queue length [1], volume [2], speed [3], travel time (or time passing the intersection) [4], control delay [5], and headway distribution [6]. However, there seems to be no benchmark MOP for microscopic simulation model calibration.…”