Analysis of Bus Passengers’ Travel Behavior based on Easycard Big Data
Date Issued
2015
Date
2015
Author(s)
Hsieh, Wan-Hsing
Abstract
In recent years, Big Data has been applied to transportation field and obtained benefits for governments, operators and passengers. Smartcard data with characteristics of plenty of personal travel data connected to cardholders as well as long-term continuous journey information, it has been considered having high commercial value. Easycard has issued 50 million cards since 2000, while the daily transactions were more than 5 million in 2014. The study aims to understand the behavior and characteristics of bus passengers, to explore spatial-temporal variability among different user groups. The study selected 20 bus routes in Nanjing Corridor for case study in which data from nearly 5 million transaction records in November 2014 were collected and analyzed. Temporal analysis indicated that there had two obvious peaks on weekdays compared to holidays while student group had a third peak from 8PM to 10PM on weekdays. For all card types, adult and student groups had less regularity between weekdays and holidays, while elderly one remained high. It is also shown opening MRT Songshan Line has caused bus passengers in Nanjing corridor fell 14.65 % on weekdays and 8.97 % on holidays, especially for adult and student card types, while the rest card types (Concessionaire, Senior, Charity and Escort) yet increased on Sundays after MRT operation. The results of spatial analysis showed stop selection varies from different card types on morning and afternoon peaks. On associate rule analysis side, Apriori algorithm was applied to conduct data mining on relationships among data fields. It is found that MRT-Bus interchange proportion changed before and after operation of MRT Songshan Line among different card types and corridor bus routes, the entire routes fell from 11.47% to 9.47%. The study selected over 1 million transaction records from November 10 to 14, 2014 to establish commuting time and route selection matrix which corresponded to the first and the last transaction records by matching unique card ID and sequence number of each card. The results showed that 8-9AM and 6-7PM was the highest frequency combination of all and there were 54.96% passengers choosing the same route on their first and the last transaction records. The duration between the two records could also showed the transit characteristics and mobility patterns among different card type users. Overall, the travel behaviour of the all categories follows a certain degree of temporal and spatial universality but also displays unique patterns within their own specialties. The study proposed a systematic process from data pre-processing, spatial-temporal pattern analysis, association rule analysis to commuter journeys analysis. It has shown that the proposed methodology has high applicability and transferability for any bus routes with smart card as payment media. The process can help of evaluating impact of the implementation of the new policy, the introduction of new routes and coordination of feeder bus routes. It can also provide information of travel characteristics of each focused groups and changes on operating performance so that transport authorities on decision-making, operator efficiency and service quality could be all enhanced.
Subjects
Smartcard
Public Transport
Travel Behavior
Spatial-Temporal Heterogeneity
Big Data
Type
thesis
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