MATEC Web Conf.
Volume 270, 2019The 2nd Conference for Civil Engineering Research Networks (ConCERN-2 2018)
|Number of page(s)||7|
|Section||Transportation Engineering and Planning|
|Published online||22 February 2019|
Developing model of toll road traffic forecasting during ramp-up period
Doctoral Study Program of Civil Engineering, Faculty of Civil and Environmental Engineering Institut Teknologi Bandung, Bandung, Indonesia
2 Transportation Engineering Research Group, Faculty of Civil and Environmental Engineering, Institut Teknologi Bandung, Bandung, Indonesia
* Corresponding author: email@example.com
The Feasibility of Toll Road project investment that uses Public Private Partnership (PPP) scheme is largely determined by the accuracy of traffic forecasting as a reflection of revenue streams. The accuracy level of traffic forecasting is needed to get a description of risks and uncertainty of Toll Road projects to be invested. Unfortunately, the international studies of forecasting show to trend of overestimate, particularly occurred in the early years of the new Toll Road operation. It is the acute problem in the short term Toll Road investment or known as ‘ramp-up period’. The conventional model of aggregation based on socio-economic and demographic growth has not been able to anticipate the problem, since the ramp-up period is a process of learning and adaptation for regional travellers due to changes in travel behaviour after the new Toll Road infrastructure began to operate. Accordingly, the disaggregation model is considered the most realistic used to predict the potential traffic that occur during the ramp-up period. This paper provides a review of several studies dealing with traffic forecasting model for Toll Road projects during the rump-up period.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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