Volume List  / Volume 12 (3)

Article

DETERMINATION OF POTENTIAL FEEDER BUS STOPS FROM SMART CARD DATA AND CAPACITATED CLUSTERING ALGORITHM

DOI: 10.7708/ijtte2022.12(3).06


12 / 3 / 361-372 Pages

Author(s)

H S Abdulrahman - Civil Engineering Department, Federal University of Technology, Minna, PMB 65 Niger State, Nigeria -


Abstract

Most of the literature that deals with feeder bus route design simply assumes the feeder bus stops are already established. The Feeder bus stops are established to connect some known origins to a known destination unlike the conventional bus stop location. In this study smart card data together with geo location of existing bus stops are used to obtain the origin (feeder bus stops) and destination (train station). The first step is to identify the conventional bus stops by isolating smart card users who use these stops to a particular destination. These conventional stops are numerous and hence a demand-constrained clustering method was developed to convert these stops into feeder bus stops. This method was then applied to data extracted from Izmir's database management system. This methodology was compared to well-known clustering algorithms and it performed reasonably well. This study can serve as a foundation in the application of smart data in the planning of feeder bus routes.


Download Article

Number of downloads: 66


References:

Bachok, S.; Ponrohono, Z.; Osman, M.M.; Bohari, Z.A. 2013. Gps/Gis Identification of Potential Bus Stop Locations and Passenger’s Access and Egress Points. In Third International Conference on Geotechnique, Construction Materials and Environment, 13-15.

 

Chen, J.; Wang, S.; Liu, Z.; Chen, X. 2018. Network-level optimization of bus stop placement in urban areas, KSCE Journal of Civil Engineering 22(4): 1446-1453. doi.org/10.1007/s12205-017-0075-2.

 

Cui, Y.; Chen, S.K.; Liu, J.F.; Jia, W.Z. 2015. Optimal locations of bus stop connecting subways near urban intersections, Mathematical Problems in Engineering 2015: 537049. doi.org/10.1155/2015/537049.

 

Deri, A.; Kalpakci, A. 2014. Efficient Usage of Transfer-based System in Intracity Bus Transit Operation: Sample of Izmir, Procedia-Social and Behavioral Sciences 111: 311-319. doi.org/10.1016/j.sbspro.2014.01.064.

 

Dragan, D.; Kramberger, T.; Lipičnik, M. 2011. Monte Carlo simulation-based approach to optimal bus stops allocation in the municipality of Laško, PROMET-Traffic&Transportation 23(4): 265-278. doi.org/10.7307/ptt.v23i4.129.

 

Ge, Y.; Zhao, J.J.; Bian, Y.; Rong, J. 2011. Feeder bus stop selection within an integrated feeder bus planning framework. In ICCTP 2011: Towards Sustainable Transportation Systems, 2854-2865. doi.org/10.1061/41186(421)284.

 

Jahani, M.; Mehdi, H.S.; Ghatee, M.; Jahanshahi, M. 2013. A novel model for bus stop location appropriate for Public Transit Network Design: The case of Central Business Districts (CBD) of Tehran, International journal of smart electrical engineering 2(3): 133-141. Available from Internet: https://www.sid.ir/en/journal/ViewPaper.aspx?id=380586.

 

Leksakul, K.; Smutkupt, U.; Jintawiwat, R.; Phongmoo, S. 2017. A heuristic approach for solving employee bus routes in a large-scale industrial factory, Advanced Engineering Informatics 32: 176-187. doi.org/10.1016/j.aei.2017.02.006.

 

Li, H.G.; Chen, Y.S. 2016. Study on enterprise shuttle bus location and route optimization: An integrated approach, J. Univ. Electron. Sci. Technol. China 18: 68–73. doi:10.14071/j.1008-8105(2016)04-0068-06.

 

Liu, Y.; Jia, G.; Tao, X.; Xu, X. 2014. A stop planning method over big traffic data for airport bus shuttle, In IEEE Fourth International Conference on Big Data and Cloud Computing, 63-70.

 

Perugia, A.; Moccia, L.; Cordeau, J.F.; Laporte, G. 2011. Designing a home-to-work bus service in a metropolitan area, Transportation Research Part B: Methodological 45(10): 1710-1726. doi: 10.1016/j.trb.2011.05.025.

 

Prah, K.; Keshavarzsaleh, A.; Kramberger, T.; Jereb, B.; Dragan, D. 2018. Optimal bus stops' allocation: a school bus routing problem with respect to terrain elevation, Logistics and sustainable transport 9(2): 1-15. doi: 10.2478/jlst-2018-0006.

 

Ren, G.; Yu, Z.G.; Yuan, C.Q.; Xue, H.; Jiang, Q.Y. 2018. Optimal Bus Stop Location to Coordinate Transfer between Urban Rail Transit and Feeder Bus near Urban Road Intersection. In CICTP 2017: Transportation Reform and Change—Equity, Inclusiveness, Sharing, and Innovation, 2847-2855.

 

Reston, VA: American Society of Civil Engineers, doi.org/10.1061/9780784480915.299.

 

Shatnawi, N.; Al-Omari, A.A.; Al-Qudah, H. 2020. Optimization of Bus Stops Locations Using GIS Techniques and Artificial Intelligence, Procedia Manufacturing 44: 52-59. doi.org/10.1016/j.promfg.2020.02.204.

 

Stat, T. 2014. Information Society Statistics. Turkish statistical institute. Available from Internet: http://www.

 

Tuik. Gov. Tr/PreIstatistikTablo. Do. Taplin, J.H.; Sun, Y. 2020. Optimizing bus stop locations for walking access: Stops-first design of a feeder route to enhance a residential plan, Environment and Planning B: Urban Analytics and City Science 47(7): 1237-1259. doi.org/10.1177/2399808318824108.

 

Wei, M.; Liu, T.; Sun, B. 2021. Optimal Routing Design of Feeder Transit with Stop Selection Using Aggregated Cell Phone Data and Open Source GIS Tool, IEEE Transactions on Intelligent Transportation Systems 22(4): 2452-2463. doi: 10.1109/TITS.2020.3042014.

 

Xiong, J.; Guan, W.; Song, L.; Huang, A.; Shao, C. 2013. Optimal routing design of a community shuttle for metro stations, Journal of Transportation Engineering 139(12): 1211-1223. doi.org/10.1061/(ASCE)TE.1943-5436.0000608.

 

Zhang, X.; Zhang, Q.; Sun, T.; Zou, Y.; Chen, H. 2018. Evaluation of urban public transport priority performance based on the improved TOPSIS method: A case study of Wuhan, Sustainable Cities and Society 43: 357–365. doi.org/10.1016/j.scs.2018.08.013.


Quoted IJTTE Works



Related Keywords