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Designing Environment-Oriented Pricing and Traffic Rationing Schemes for Travel Demand Management


Optimization-based approaches are presented for the design of environment-oriented road pricing and traffic rationing schemes, particularly with the objective of curbing human exposure to motor vehicle generated air pollutants. In addition, surrogate-based solution algorithms are developed to accelerate the search of good solutions for the problems considered.

A toll design problem is proposed for selecting tolling locations and levels that minimize environmental inequality and human exposure to pollutants, subject to budget constraints and pollutant concentration constraints at receptor points. 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 proposed algorithms are promising solution methods for transportation network design problems.

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 utilizes 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 surrogate-assisted differential evolution algorithm for multiobjective continuous optimization problems with constraints is proposed.

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