Planning Strategies for Lane Reversals in Transportation Networks

TL;DR

This paper presents a novel optimization method for lane reversal planning in transportation networks, enabling efficient solutions and cost-benefit analysis, with empirical results showing up to 40% travel time reductions.

Contribution

We reformulate the lane reversal problem using a piecewise affine approximation and ILP relaxation, allowing efficient linear programming solutions and analysis of reversal costs.

Findings

Travel times reduced by up to 40% in case study

Method effectively compares original and optimized lane configurations

Provides a framework for cost-benefit analysis of lane reversals

Abstract

This paper studies strategies to optimize the lane configuration of a transportation network for a given set of Origin-Destination demands using a planning macroscopic network flow model. The lane reversal problem is, in general, NP-hard since the optimization is made over integer variables. To overcome this burden, we reformulate the problem using a piecewise affine approximation of the travel latency function which allows us to exploit the total unimodularity property of Integer Linear Programming (ILP). Consequently, we transform the ILP problem to a linear program by relaxing the integer variables. In addition, our method is capable of solving the problem for a desired number of lane reversals which serves to perform cost-benefit analysis. We perform a case study using the transportation network of Eastern Massachusetts (EMA) and we test our method against the original lane configuration and a projected lower bound solution. Our empirical results quantify the travel time savings for different levels of demand intensity. We observe reduction in travel times up to 40% for certain links in the network.