Towards a better management of urban traffic pollution using a Pareto max flow approach
•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 Paret...
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| Published in | Transportation research. Part D, Transport and environment Vol. 79; p. 102194 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.02.2020
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1361-9209 1879-2340 |
| DOI | 10.1016/j.trd.2019.11.023 |
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| Abstract | •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. |
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| AbstractList | •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. |
| ArticleNumber | 102194 |
| Author | Vadlamannati, Soumya Paruchuri, Praveen Kamishetty, Sreeja |
| Author_xml | – sequence: 1 givenname: Sreeja surname: Kamishetty fullname: Kamishetty, Sreeja email: kamishetty.sreeja@research.iiit.ac.in organization: Machine Learning Lab, IIIT Hyderabad, Gachibowli, Hyderabad 500032, India – sequence: 2 givenname: Soumya surname: Vadlamannati fullname: Vadlamannati, Soumya email: soumya.vadlamannati@students.iiit.ac.in organization: Center for Visual Information Technology, IIIT Hyderabad, Gachibowli, Hyderabad 500032, India – sequence: 3 givenname: Praveen surname: Paruchuri fullname: Paruchuri, Praveen email: praveen.p@iiit.ac.in organization: Machine Learning Lab, IIIT Hyderabad, Gachibowli, Hyderabad 500032, India |
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| Keywords | Pollution management Pareto solutions k-optimality Max Flow Distribution of traffic Transportation policy |
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