Chapter
Jan 13, 2020
Sixth International Conference on Transportation Engineering

Feeder Bus Transit Design for Trunk Rail System: Case Study of Purple Line Corridor in Maryland

Publication: ICTE 2019

ABSTRACT

This study developed an approximate analytical model to design feeder bus transit (e.g. shuttle bus, minivan) services along a trunk line (e.g. metro rail and light rail) whose stations and line alignment are pre-determined. Specifically, an optimization model is developed to identify the density and frequency of feeder bus services that minimizes the total cost of the trunk-feeder system (sum of operating cost and user cost) subject to vehicle capacity constraints. The model is applied using the data from Purple Line Light Rail (LRT) system in Maryland, U.S.A. The results demonstrate that the developed model provides a useful tool to generate the optimal number of feeder bus lines and the best frequency of feeder service along the Purple Line corridor.

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ACKNOWLEDGEMENT

This research is supported by the National Key Research and Development Program of China (Project No. 2017YFB1200701), the National Natural Science Foundation of China (Project No. 51608455), the Research on Complete Technology of Integrated Transportation Hub in Complex Urban Environment (Grant cstc2014yykfb30003) and the Research on Functional Layout and Planning of Shapingba Integrated Transportation Hub (Grant 2015H01372). We are also grateful to project partners and WANG Dian, LAI Qingying for fruitful discussions and help.

REFERENCES

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SUN, Y., GUO, Q., Schonfeld, P., Li, Z. (2017). “Evolution of public transit modes in a commuter corridor.” Transportation Research Part C, 75, 84-102.
The Maryland Transit Administration (MTA), (2013). “Travel Forecasts Results Technical Report.”
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Information & Authors

Information

Published In

Go to ICTE 2019
ICTE 2019
Pages: 813 - 820
Editors: Xiaobo Liu, Ph.D., Southwest Jiaotong University, Qiyuan Peng, Ph.D., Southwest Jiaotong University, and Kelvin C. P. Wang, Ph.D., Oklahoma State University
ISBN (Online): 978-0-7844-8274-2

History

Published online: Jan 13, 2020

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Authors

Affiliations

School of Transportation and Logistics and National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong Univ., Chengdu 611756; National Center for Smart Growth Research and Education, Univ. of Maryland, College Park, MD 20740. E-mail: [email protected]
Sevgi Erdogan [email protected]
National Center for Smart Growth Research and Education, Univ. of Maryland, College Park, MD 20740 (corresponding author). E-mail: [email protected]
School of Transportation and Logistics, National United Engineering Laboratory of Integrated and Intelligent Transportation, and National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong Univ., Chengdu 611756. E-mail: [email protected]
Qiyuan Peng [email protected]
School of Transportation and Logistics, National United Engineering Laboratory of Integrated and Intelligent Transportation, and National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong Univ., Chengdu 611756. E-mail: [email protected]

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