Abstract
Bangkok, the capital city of Thailand, is one of the most developed and expansive cities. Due to the ongoing development and expansion of Bangkok, urbanization has continued to expand into adjacent provinces, creating the Bangkok Metropolitan Region (BMR). Continuous monitoring of human mobility in BMR aids in public transport planning and design, and efficient performance assessment. The purpose of this study is to design and develop a process to derive human mobility patterns from the real movement of people who use both fixed-route and non-fixed-route public transport modes, including taxis, vans, and electric rail. Taxi GPS open data were collected by the Intelligent Traffic Information Center Foundation (iTIC) from all GPS-equipped taxis of one operator in BMR. GPS probe data of all operating GPS-equipped vans were collected by the Ministry of Transport’s Department of Land Transport for daily speed and driving behavior monitoring. Finally, the ridership data of all electric rail lines were collected from smartcards by the Automated Fare Collection (AFC). None of the previous works on human mobility extraction from multi-sourced big data have used van data; therefore, it is a challenge to use this data with other sources in the study of human mobility. Each public transport mode has traveling characteristics unique to its passengers and, therefore, specific analytical tools. Firstly, the taxi trip extraction process was developed using Hadoop Hive to process a large quantity of data spanning a one-month period to derive the origin and destination (OD) of each trip. Secondly, for van data, a Java program was used to construct the ODs of van trips. Thirdly, another Java program was used to create the ODs of the electric rail lines. All OD locations of these three modes were aggregated into transportation analysis zones (TAZ). The major taxi trip destinations were found to be international airports and provincial bus terminals. The significant trip destinations of vans were provincial bus terminals in Bangkok, electric rail stations, and the industrial estates in other provinces of BMR. In contrast, electric rail destinations were electric rail line interchange stations, the central business district (CBD), and commercial office areas. Therefore, these significant destinations of taxis and vans should be considered in electric rail planning to reduce the air pollution from gasoline vehicles (taxis and vans). Using the designed procedures, the up-to-date dataset of public transport can be processed to derive a time series of human mobility as an input into continuous and sustainable public transport planning and performance assessment. Based on the results of the study, the procedures can benefit other cities in Thailand and other countries.
Highlights
IntroductionNonthaburi, Pathum Thani, Nakhon Pathom, Samut Sakhon, Ayutthaya, and creativecommons.org/licenses/by/ 4.0/)
Modern and wealthy work areas are still located in the central business district (CBD), residential zones have recently expanded into the suburban area of Bangkok and surrounding provinces
The longer-distance tripsin of vans, in indicate that van mode supports people commuting from the surrounding provinces and cyan, indicate that van mode supports people commuting from the surrounding provsuburban areas of Bangkok to the CBD
Summary
Nonthaburi, Pathum Thani, Nakhon Pathom, Samut Sakhon, Ayutthaya, and creativecommons.org/licenses/by/ 4.0/). Urbanization has expanded outside of central Bangkok to the outer zones of BMR. Modern and wealthy work areas are still located in the central business district (CBD), residential zones have recently expanded into the suburban area of Bangkok and surrounding provinces. Understanding the human mobility patterns between areas helps planners to design efficient and appropriate public transport for people commuting among BMR areas. Human mobility is the movement of people from one location to others based on their needs. The study of human mobility patterns can reflect the urban structure, the distribution of facilities, and the required transport services [1]. The common form of human mobility is the origin–destination matrix, or OD matrix for short, which indicates the travel demand between places. The OD matrix is generated from surveys, such as household travel surveys and roadside interviews
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call