Vehicles' emissions consideration in transportation network design using Markov chain traffic assignment

Sinan Salman, Zayed UniversityFollow
Suzan Alaswad, Zayed University

Document Type

Conference Proceeding

Source of Publication

Proceedings of the International Conference on Industrial Engineering and Operations Management

Publication Date

1-1-2020

Abstract

© IEOM Society International. Markov chain traffic assignment (MCTA) is a relatively recent approach for modeling traffic conditions in a road network. It results in traffic density measurements that express congestion levels in the network at the road level. While the stochastic approach is powerful analytically, the resulting traffic density does not readily lend itself to emissions estimation. Typically, average-speed emission models are utilized to estimate emissions on an aggregate network level. However, while this is easily done in user equilibrium (UE) models using the Bureau of Public Roads (BPR) relationship to derive vehicles@@ average speed from traffic flow, no such relationship exists for traffic density. We highlight the mathematical challenge here and propose an approach to estimate vehicles@@ average speed based on traffic density obtained from MCTA. The average speeds are then used to estimate vehicles@@ Greenhouse Gas emissions using average-speed emission estimation models. This approach can be applied on a road level or aggregated to a network level. We utilize the BPR relationship to build a linear piece-wise approximation function and illustrate its use through two widely used average-speed emission estimation models: TRANSYT-7F and COPERT v5.

ISBN

9781532359491

ISSN

2169-8767

Publisher

IEOM Society

Issue

March

First Page

762

Last Page

771

Disciplines

Computer Sciences

Keywords

Markov Chain traffic assessment, Network design, Vehicles emissions

Scopus ID

85088992923

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Bronze: This publication is openly available on the publisher’s website but without an open license

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