Online Web query system for various frequency distributions of bus passengers in Taichung city of Taiwan. Issue 3 (4th September 2020)

• Record Type: Journal Article
• Authors: Wang, Jing‐Doo; Pan, Shin‐Hung; Ho, Cheng‐Yuan; Lien, Yao‐Nan; Liao, Shu‐chuan; Nurmandi, Achmad
• Is Part Of: IET smart cities. Volume 2:Issue 3(2020)
• Page Start: 135
• Page End: 145
• Publication Date: 2020-09-04
• Subjects: traffic engineering computing -- Internet -- public transport -- parallel processing -- data handling -- scheduling -- transportation; online Web query system -- frequency distributions -- bus passengers -- city residents -- public transportation system -- web‐based information system -- Taichung City Bus Riding Records -- similar bus transportation system -- passenger trips -- passenger volume -- bus companies -- flexible bus schedules -- passenger demand; Smart cities -- Periodicals; City planning -- Technological innovations -- Periodicals; Cities and towns -- Growth -- Periodicals; Sustainable urban development -- Periodicals; Sustainable development; City planning -- Technological innovations; Cities and towns -- Growth; Periodicals; 307.76
• DOI: 10.1049/iet-smc.2020.0017
• Languages: English
• ISSNs: 2631-7680

Abstract

Abstract : It is highly desirable that traffic controllers or city residents can discern regular patterns and promptly detect irregularities or abnormal events in a public transportation system. This study proposes a web‐based information system that allows users to study the travel behaviour of bus passengers from various perspectives. The system uses data from the comprehensive set of Taichung City Bus Riding Records between 2015 and 2016. However, it can provide the same functionality to any other similar bus transportation system by using the appropriate data. It should be emphasised that the system can provide the frequency distributions not only of passenger trips between two stops but also of the passenger volume for a given segment of any route. Owing to the increased computational and storage‐capacity requirements of the proposed system, the scalable Hadoop MapReduce programming model was used. Furthermore, bus companies can use the system to design better service plans, such as more flexible bus schedules and more convenient routes, to meet passenger demand as well as reduce operation cost and energy consumption. The authors believe that the proposed system can make a valuable contribution to public welfare.