Allocating exclusive and intermittent transit lanes in dynamic traffic networks with connected and automated vehicles

Abstract

Exclusive bus lanes (XBLs) have been widely discussed and implemented worldwide in recent years. An improved and more flexible transit lane management strategy—intermittent bus lanes (IBLs)—prove to be potentially more efficient and car-friendly than XBLs. The common benefit of XBLs and IBLs arises from the fact that they separate the bus and car traffic and hence eliminate the impacts of slowly moving buses on the car traffic. Compared to XBLs, the application of IBLs has not been broadly adopted in the real world so far except very few cases, because managing an urban IBL segment or area typically involves extra traffic signals and message signs and the switch between its regular and protected statuses could potentially cause unexpected traffic chaos and safety issues. The recent emergence of connected and automated vehicle (CAV) technologies, however, favors relieving these concerns and bring the dawn to a future application of this theoretically sound traffic control strategy. This paper is dedicated to evaluating the network performance of XBLs and IBLs and optimizing the networkwide configurations of these two strategies for future urban traffic networks where CAVs prevail. For this purpose, we formulate a system-optimal dynamic traffic assignment (SO-DTA) problem with car-exclusive lane segments, on the basis of a cell transmission model for separate car and bus traffic (CTM-SCB). By limiting its applicable context to morning commute networks and emergency evacuation networks, this model is set with only one real or virtual destination, which greatly eases its modeling and solution complexity. On the basis of the single-destination SO-DTA model, an XBL-based network design problem and an IBL-based network design problem are then both formulated into mixed integer programming models and solved by a discrete optimization solver, where the former problem statically configures bus lanes while the later one allows a dynamic allocation of bus lanes. Insightful findings obtained by applying the models and solver to a synthetic corridor network and a real commute network illustrate the great promise of these two lane-based strategies in mixed car and bus traffic management.