Trip Chain Based Traffic Network Equilibrium For Electric Vehicles

This paper proposes and analyzes a distance-constrained traffic assignment problem with trip chains embedded in equilibrium network flows.The purpose of studying this problem is to develop an appropriate modeling tool for characterizing traffic flow patterns in emerging transportation networks that serve a massive adoption of plug-in electric vehicles.This need arises,given that electric vehicles suffer from the “range anxiety” issue caused by the unavailability of public electricitycharging infrastructures and the far-below-expectation battery capacity.We argue that if range anxiety makes any impact on travel behaviors,it more likely occurs on the tour or trip chain level rather than the trip level.The focus of this paper is thus on the development of the modeling and solution methods for the proposed traffic assignment problem with trip chains.In this modeling paradigm,given that trip chains are the basic modeling unit for individual decision making,any traveler's combined travel route and activity location choices under the distance limit results in a distanceconstrained,node-sequenced shortest path problem.A cascading labeling algorithm is developed for this shortest path problem and embedded into a linear approximation framework for equilibrium network solutions.The numerical result derived from an illustrative example clearly shows the mechanism and magnitude of the distance limit and trip chain settings in reshaping network flows from the simple case characterized merely by user equilibrium.