Eco-reliable path finding in time-variant and stochastic networks

Abstract

This paper addresses a route guidance problem for finding the most eco-reliable path in timevariant and stochastic networks such that travelers can arrive at the destination with the maximum on-time probability while meeting vehicle emission standards imposed by government regulators. To characterize the dynamics and randomness of transportation networks, the link travel times and emissions are assumed to be time-variant random variables correlated over the entire network. A 0-1 integer mathematical programming model is formulated to minimize the probability of late arrival by simultaneously considering the least expected emission constraint. Using the Lagrangian relaxation approach, the primal model is relaxed into a dualized model which is further decomposed into two simple sub-problems. A sub-gradient method is developed to reduce gaps between upper and lower bounds. Three sets of numerical experiments are tested to demonstrate the efficiency and performance of our proposed model and algorithm.

Stochastic Transportation Network Considering ATIS with the Information of Environmental Cost

“…The objectives of minimizing travel time and vehicle emissions are often conflicting, because the rates of vehicle emissions do not change monotonically with average travel speed [18]. Through incorporating measures of environmental cost into the shortest path problem, some methods were proposed to find the so-called environment-friendly or green paths [19,20]. The aims of these researches are to help people choose more environment-friendly routes to reduce the environmental cost.…”