Project Description
Integrated urban transportation models have several benefits over sequential models including consistent solutions, quicker convergence, and more realistic representation of behavior. Static models have been integrated using the concept of Supernetworks. However integrated dynamic transport models are less common. In this paper, activity location, time of participation, duration, and route choice decisions are jointly modeled in a single unified dynamic framework referred to as Activity-Travel Networks (ATNs). ATNs is a type of Supernetwork where virtual links representing activity choices are added to augment the travel network to represent additional choice dimensions. Each route in the augmented network represents a set of travel and activity arcs. Therefore, choosing a route is analogous to choosing an activity location, duration, time of participation, and travel route. A cell-based transmission model (CTM) is embedded to capture the traffic flow dynamics. The dynamic user equilibrium (DUE) behavior requires that all used routes (activity-travel sequences) provide equal and greater utility compared to unused routes. An equivalent variational inequality problem is obtained. A solution method based on route-swapping algorithm is tested on a hypothetical network under different demand levels and parameter assumptions.
Project Overview
Urban transportation planning models have been studied along two approaches: one, descriptive statistical and econometric models of travel choice and, the other, network equilibrium models based on mathematical programming formulations and prescriptive behavior. Only recently have researchers world-wide realized that to obtain better estimates of the future from transportation planning process, we need to develop integrated models which account for both supply-demand interactions.
The goal of this work is to incorporate the different dimensions of travel behavior including activity participation, location, time of participation, duration, mode choice, route choice etc. into the participation of activities in the transportation system (which are time varying based on the network conditions). To address this goal we will specifically explore the following questions: (a) how to capture transportation demand-supply dynamics by jointly modeling activity location, time of participation, duration, and route choice, (b) how to capture activity demand-supply dynamics in addition to transportation demand-supply dynamics, and (c) how to develop a framework for testing alternative behavioral mechanisms for urban transport models. These questions will be answered by developing new mathematical optimization formulations and solution approaches. The developed formulations will be solved on test networks available to the PI.