Abstract

  Traffic congestion has been a major problem in big cities around the world, not to mention several large cities in Indonesia. Bandung is the second largest metropolitan area after Jakarta in Indonesia which suffers from extreme levels of congestion. With a high number of motorcycles and large private car population, congestion in this city is ever growing worsening the environment. While the local authorities struggle to find resources to fund capital intensive capacity expansion projects, this research explores the use of cost effective demand management policy measures to reduce the congestion and pollution. This study aims at assessing two relatively under-researched demand management policy measures that restrict vehicle flows viz., car-free day and odd-even plate schemes to investigate the effect on traffic congestion and the environment. SATURN traffic network modelling software has been used to predict the route choices of vehicles. Bandung city road network and origin destination matrix have been adapted to simulate the two measures during the peak hour. As well as providing the necessary inputs to a pollutant emission estimation model, traffic network modelling output forms the basis for assessing the congestion levels. Results show that both car-free day and odd-even plate measures have unintended consequences that undermine their effectiveness which if addressed could make them highly beneficial solutions. Car-free day scheme reduces the traffic flow levels in the vicinity of scheme but diverts the vehicle flow elsewhere to other routes which may adversely affect the congestion/pollution. Odd-even plate scheme is very effective at the beginning of its implementation but the performance gradually diminishes as drivers start to adapt by buying a second vehicle or even using fake number plates.

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https://api.elsevier.com/content/article/PII:S2213624X17301451?httpAccept=text/plain,
http://dx.doi.org/10.1016/j.cstp.2018.07.008 under the license http://creativecommons.org/licenses/by-nc-nd/4.0
http://eprints.whiterose.ac.uk/133481,
https://academic.microsoft.com/#/detail/2884298310 under the license https://www.elsevier.com/tdm/userlicense/1.0/
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Published on 01/01/2018

Volume 2018, 2018
DOI: 10.1016/j.cstp.2018.07.008
Licence: Other

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