DESIGNING ENVIRONMENTALLY ORIENTED PRICING AND TRAFFIC RATIONING SCHEMES FOR TRAVEL DEMAND MANAGEMENT

*PhD Defense*
Time
July 29 2015 10:00–12:00
Location
4080 AIR Building
Daniel Rodriguez Roman
Daniel Rodriguez Roman
TSE PhD Candidate
Abstract

Optimization-based approaches are presented for the design of environmentally oriented road pricing and traffic rationing schemes, particularly with the objective of curbing human exposure to motor vehicle generated air pollutants. The focus on human exposure to pollutants advances previous road pricing and traffic rationing problems which primarily account for congestion minimization, emission minimization, or emissions constraints. Practical utilization of the proposed problems is hindered by their time-consuming nature, so surrogate-based algorithms are developed to accelerate the search for good problem solutions. Given that the algorithms are derivative-free, they can be applied to various types of computationally expensive transportation network design problems.

A toll design problem is proposed for selecting tolling locations and levels that minimize environmental inequality and human exposure to pollutants. A mixed-integer variant of the metric stochastic response surface algorithm and a hybrid genetic algorithm-metric stochastic heuristic are presented to solve the mixed integer toll design problem. Numerical tests suggest that the surrogate-based algorithms have superior performance relative to previous genetic algorithm-based methods.

In addition, an optimization problem is presented for the design of cordon and area-based road pricing schemes subject to environmental constraints. Flexible problem formulations are considered which can be easily utilized with state-of-the-practice transportation planning models. A surrogate-based solution algorithm that uses a geometric representation of the charging area boundary is proposed to solve cordon and area pricing problems.

Lastly, a bi-objective traffic rationing problem is considered where the planner attempts to maximize auto usage while minimizing pollutant exposure inequality, subject to constraints on the levels of greenhouse gas emissions and pollutant concentration levels. A personal pollutant exposure methodology is integrated with standard models used in transportation planning to simulate person-level pollutant intake. To solve this problem a surrogate-assisted differential evolution algorithm for multiobjective continuous optimization problems with constraints is proposed. A sample application illustrates a possible implementation of the traffic rationing problem and the ability of the proposed algorithm to find diverse feasible solutions