Towards a better management of urban traffic pollution using a Pareto max flow approach - Kamishetty, Sreeja; Vadlamannati, Soumya; Paruchuri, Praveen (Transportation research. Part D, Transport and environment, 2020)
•Continuous exposure to high levels of pollution have adverse effects on human health.•In this work, we build an algorithm for better management of urban traffic pollution.•We develop a Pareto-optimal Max Flow Algorithm (PMFA) to suggest multiple max flow solutions.•Extending PMFA we introduce Pareto k-optimal Max Flow Algorithm (k-PMFA) to ensure that the suggested pareto solutions are sufficiently distinct.•Through SUMO experiments, we show that our solution distributes traffic more evenly across the city. Rising levels of air pollution is a major concern across many parts of the world. In this article, we develop a transportation policy to handle air pollution caused by the heavy flow of traffic in urban areas. In particular, we aim to distribute the traffic flow more evenly through a city, by developing a flow algorithm that computes multiple solutions, each of which accommodates the maximum flow. The paper makes the following contributions to build such a transportation policy: (a) Develops a Pareto-optimal Max Flow Algorithm (PMFA) to suggest multiple max flow solutions. (b) Introduces the notion of k-optimality into PMFA to ensure that the suggested pareto solutions are sufficiently distinct from each other – referred to as Pareto-k-optimal Max Flow Algorithm (k-PMFA). (c) Through a series of experiments performed using the well-known traffic simulator SUMO and by doing emission modeling on the New York map, we could show that our policy distributes the air pollution more uniformly across locations.