Bottleneck Identification and Calibration for Corridor Management Planning

Abstract

Corridor mobility improvements require a new approach to corridor management planning and operations. Recent investigations are aimed at improving the safety and efficiency of existing transportation systems by integrating state-of-the-art operational analysis (such as microsimulation) into more traditional corridor planning. One of the important elements in developing corridor management improvements is better bottleneck analysis. Such analyses play a crucial role in corridor management planning for both performance assessment and simulation model calibration. New approaches are proposed for bottleneck identification and calibration in simulation. Identification is conducted with percentile speeds based on data from multiple days. It turns out that this method is more appropriate for urban congested freeways than use of single-day data. The algorithm for bottleneck calibration represents the first attempt to rigorously calibrate bottlenecks in microsimulation. It is a three-step process–including visual assessment, bottleneck area matching, and detailed speed calibration–aiming to calibrate bottlenecks in three levels of detail. With the I-880 corridor network in the San Francisco Bay Area of California, it has been shown that the identification method can adequately identify corridor bottlenecks; the calibration procedure complements and improves the current practice of simulation calibration.

Visualization Tools for Traffic Bottleneck Analysis

“…Lund et al [8] developed a vehicle probe based algorithm to estimate the relative impacts of bottleneck for the purpose of bottleneck ranking. Ban et al, [9] proposed a bottleneck identification method based on binary speed contour maps (BSCM) and a three step method for bottleneck calibration in traffic simulation software. The related study by Hale et al, [10] developed methods for identifying and quantifying traffic bottlenecks using estimated vehicle hours of delay and assessed low cost solutions for congestion mitigation.…”

An ensemble Kalman filtering approach to highway traffic estimation using GPS enabled mobile devices

“…The 1880 calibrated model produces individual trajectories on the highway for each vehicle in the simulation, and it has previously been used for bottleneck identification in [39]. For this experiment, a subset of the vehicles are randomly selected as vehicles which are equipped with GPS phones.…”

An automated statistically-principled bottleneck identification algorithm (ASBIA)

“…The bottleneck identification algorithms were further used to predict the propagation of congestion using archived data. In addition, new approaches were developed in [5] to identify bottlenecks and calibration of traffic simulation software. The identification process was conducted using percentile speeds from archived data over multiple days.…”