A dynamic origin-destination (O-D) demand estimation model is presented that uses turning movement counts as observations. Based on an iterative bilevel estimation framework, the upper-level problem is to minimize a weighted objective function of the deviation between simulated link flows and real-time link counts and the deviation between estimated time-dependent demand and an a priori historical O-D table, where the weighting value is determined by an interactive approach to obtain the best compromise solution. A case study was performed on the US-29 network in Maryland to compare the estimated tables of this approach with the one obtained from the traditional method, which uses only approach link volume counts. The application illustrates considerable benefits of using turning movements instead of approach volumes in matching observed counts.
Abstract. Contraflow or lane reversal is an efficient way for increasing the outbound capacity of a network by reversing the direction of in-bound roads during evacuations. Hence, it can be considered as a potential remedy for solving congestion problems during evacuation in the context of homeland security, natural disasters and urban evacuations, especially in response to an expected disaster. Most of the contraflow studies are performed offline, thus strategies are generated beforehand for future implementation. Online contraflow models, however, would be often computationally demanding and time-consuming. This study contributes to the state of the art of contraflow modelling in two regards. First, it focuses on the calibration of a Logit choice model which predicts the online contraflow directions of strategic lanes based on the set of directions obtained from offline scenarios. This is the first effort to adjust offline results to be applied for an online case. The second contribution of this paper is the generation of calibration data set from a novel approach through simulation. The calibrated Logit model is then tested for the network of the City of Fort Worth, Texas. The results show a high performance of this approach to generating beneficial strategies, including an increase in up to 16% in throughput compared to no contraflow case.
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