By Topic

Modeling and analyzing departure time slots allocation to optimize dynamic network capacity —the case of A15-motorway to rotterdam port

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $31
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Yinyi Ma ; Dept. of Decision & Inf. Sci., Erasmus Univ. Rotterdam, Rotterdam, Netherlands ; van Zuylen, H.J. ; Yusen Chen ; van Dalen, J.

Congestion of motorway is a serious problem, causing inefficient road usage, increased cost of transport and more intense CO2 emission. The worldwide economic crisis has somewhat lessons the congestion problem locally, but still there is an urge to develop methodologies to effectively reduce congestion. In this paper, we propose a new concept of departure time slot allocation to analyze road transport congestion. The paper aims to redistribute the demand over departure time-slots at on-ramps to alleviate congestion in the network with minimized system travel time and optimized network utilization. The mechanism of time slot allocation follows the first-in-first-out (FIFO) principle. The objective is thus to minimize the cost difference between cumulative desired demand (current demand) and cumulative optimum demand, taking network capacity as a constraint. The situation of both early and late departure with costs is mathematically modelled as a non-linear programming. The motorway A15 in the Netherlands is taken as a case study with the corresponding simulation. It is interesting to note that the global-searching algorithm, genetic algorithm, offers a solution that more early departure occurs for the short journeys, and late departure for the long journeys with the average distribution of optimum demand. Other algorithms are also tested. Moreover, traffic state improves greatly with 7.0% of reduction on total travel time and 8.9% (5 out of 56 combinations of 8 time-slots and 7 locations) of reduction in time-space locations of congestion, from the original 19.6% (11 out of 56). Practically with respect to the socio-economic impacts, travellers' departure profiles can be linked to their value of travel time savings. In this perspective, authorities may take measures or design policies to promote time-slot allocation, such as providing rewards for late departure and early departure outside the peak period o

Published in:

Advanced Forum on Transportation of China (AFTC 2009), 5th

Date of Conference:

17-17 Oct. 2009