Latest revision as of 00:20, 29 January 2021

Abstract

Emission of airborne pollutants and climate gasses from the transport sector is a growing problem, both in indus- trialised and developing countries. Planning of urban transport system is essential to minimise the environmental, health and economic impact of congestion in the transport system. To get accurate and timely information on traffic congestion, and by extension information on air pollution, near real time traffic models are needed. We present in this paper an implementation of the Restricted Stochastic User equilibrium model, that is capable to model congestions for very large Urban traffic systems, in less than an hour. The model is implemented in an open source database system, for easy interface with GIS resources and crowd sensed transportation data. Emission of airborne pollutants and climate gasses from the transport sector is a growing problem, both in indus- trialised and developing countries. Planning of urban transport system is essential to minimise the environmental, health and economic impact of congestion in the transport system. To get accurate and timely information on traffic congestion, and by extension information on air pollution, near real time traffic models are needed. We present in this paper an implementation of the Restricted Stochastic User equilibrium model, that is capable to model congestions for very large Urban traffic systems, in less than an hour. The model is implemented in an open source database system, for easy interface with GIS resources and crowd sensed transportation data.

Original document

The different versions of the original document can be found in:

• https://pure.au.dk/ws/files/108946065/CASPER16.pdf

• http://xplorestaging.ieee.org/ielx7/7452853/7457013/7457105.pdf?arnumber=7457105,

http://dx.doi.org/10.1109/percomw.2016.7457105

• https://dblp.uni-trier.de/db/conf/percom/percomw2016.html#LehmannG16,

https://academic.microsoft.com/#/detail/2338590772

• https://pure.au.dk/portal/da/publications/using-crowd-sensed-data-as-input-to-congestion-model(aa310726-fa7c-4916-a481-7b3a22b68bb0).html,

https://doi.org/10.1109/PERCOMW.2016.7457105,

https://pure.au.dk/ws/files/108946065/CASPER16.pdf