Abstract
Freight trucks are known to be a major source of air pollutants as well as greenhouse gas emissions in U.S. metropolitan areas, and they have significant effects on air quality and global climate change. Emissions from freight trucks during their deliveries should be considered by the trucking service sector when they make routing decisions. This study proposes a model that incorporates total delivery time, various emissions including CO2, VOC, NOX, and PM from freight truck activities, and a penalty for late or early arrival into the total cost objective of a stochastic shortest path problem. We focus on urban transportation networks in which random congestion states on each link follows an independent probability distribution. Our model finds the best truck routing on a given network so as to minimize the expected total cost. This problem is formulated into a mathematical model, and two solution algorithms including a dynamic programming approach and a deterministic shortest path heuristic are proposed. Numerical examples show that the proposed approach performs very well even for the large-size U.S. urban networks.
Highlights
Traffic congestion in large urban areas is responsible for a significant portion of air pollution and various related human health problems [1]
This paper explicitly considers the environmental cost caused by truck activities in a stochastic shortest path problem setting
We focus on urban transportation networks which can be represented by graphs composed of node sets and directed link sets in a time period whose length is comparable to what is needed for making a local delivery
Summary
Traffic congestion in large urban areas is responsible for a significant portion of air pollution and various related human health problems [1]. The optimal routing decision can be addressed as a shortest path problem in a stochastic network Such studies considered travel delay as the only travel cost component and focused on minimizing the expected total travel time [7,8,9,10]. This paper explicitly considers the environmental cost caused by truck activities in a stochastic shortest path problem setting. The model can be directly incorporated into dynamic route guidance systems to help truck drivers and freight carriers make better decisions This effort will be useful to help transportation planners and policy makers in both public and private sectors reduce adverse social impacts caused by freight truck emissions in metropolitan areas.
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