Optimization of Transit Ridership Using Parallel Negative Algorithm

DOI

10.2991/icmeit-16.2016.55How to use a DOI?

Keywords

Macroscopic models, transit vehicles, optimizer.

Abstract

Macroscopic models are in general not suit-able for modeling adaptive ridership control since they do not consider individual vehicle arrivals, which are a necessary input to adaptive traffic ridership. The use of a global weighting factor also unavoidably assumes that the passengers in a transit vehicle enter an intersection one at a time, rather than all at once, thus tends to overstate the impact of transit vehicles. Although microscopic simulation models based on vehicle dynamics have the ability to track the behavior and status of individual vehicles, the computation required to track, the simulation results show that the PGA-based optimizer for adaptive TRP outperformed the fully actuated NEMA control in all test cases. The results also show that the PGA-based optimizer can produce TSP timing plans that benefit the transit vehicles while minimizing the impact of TSP on the general vehicles.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Caixia Han
PY  - 2016/08
DA  - 2016/08
TI  - Optimization of Transit Ridership Using Parallel Negative Algorithm
BT  - Proceedings of the 2016 International Conference on Mechatronics Engineering and Information Technology
PB  - Atlantis Press
SP  - 289
EP  - 292
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmeit-16.2016.55
DO  - 10.2991/icmeit-16.2016.55
ID  - Han2016/08
ER  -