Congested Freeway Microsimulation Model Using VISSIM

Abstract

A procedure for constructing and calibrating a detailed model of a freeway by using VISSIM is presented and applied to a 15-mi stretch of I-210 West in Pasadena, California. This test site provides several challenges for microscopic modeling: a high-occupancy vehicle (HOV) lane with an intermittent barrier, a heavy freeway connector, 20 metered on-ramps with and without HOV bypass lanes, and three interacting bottlenecks. Field data used as input to the model were compiled from two separate sources: loop detectors on the on-ramps and main line (PeMS) and a manual survey of on-ramps and off-ramps. Gaps in both sources made it necessary to use a composite data set, constructed from several typical days. FREQ was used as an intermediate tool to generate a set of origindestination matrices from the assembled boundary flows. The model construction procedure consists of (1) identification of important geometric features, (2) collection and processing of traffic data, (3) analysis of the main-line data to identify recurring bottlenecks, (4) VISSIM coding, and (5) calibration based on observations fromStep 3. A qualitative set of goals was established for the calibration. These were met with relatively few modifications to VISSIM's driver behavior parameters.

Operational Assessment of Joint and Conventional Lane Merge Configurations for Freeway Work Zones

“…In this study, the first two parameters of the model were selected for calibration: (1) CC0, standstill distance, defined as the desired distance between vehicles when stopped, and (2) CC1, headway time, defined as the desired headway between vehicles in seconds. According to previous studies (e.g., Chitturi & Benekohal, 2008b;Gomes, May, & Horowitz, 2004;Lownes & Machemehl, 1988), the effect of these two parameters on capacity was found to be statistically significant, and therefore, they were both selected for calibration of VISSIM to the driving behavior in Louisiana.…”

A Dynamic Spatiotemporal Analysis Model for Traffic Incident Influence Prediction on Urban Road Networks

“…Traffic flow was simulated through dynamic assignment [42] that determined the amount and routing of the simulated vehicles. Real traffic monitoring data of both cases were collected by traffic cameras on surrounding road networks at that time.…”

Optimizing schedule for improving the traffic impact of work zone on roads

“…Several default vehicle types (e.g., Car, HGV, and Bus), are applied in the simulation model together with their default parameters (e.g., Length and Width). Generator (SSG), which is programmable through the VAP interface and a separate module from the traffic simulation module [34].…”