Uncovering quasi-periodicity of transit behavior based on smart card data

Zhong, Jiaming; He, Zhaocheng; Tian, Chenyu

doi:10.1016/j.jtrangeo.2019.102466

Cited by 1 publication

(2 citation statements)

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“…Through the research on bus passengers in previous literature (9,14,15), it can be found that some passenger trips have the same travel pattern, which means these kinds of trips have similar boarding time and similar boarding and alighting stations. Therefore, this paper introduces the concept of travel pattern into the prediction of individual travel.…”

Section: Algorithm Of Travel Pattern Identificationmentioning

confidence: 99%

“…The concept of travel pattern, which refers to the trip with similar boarding time and similar boarding and alighting stations, is usually used in the study of the regularity of bus passengers. Zhong et al identified the quasi-periodicity of transit behavior ( 14 ); Goulet-Langlois et al concluded that the sequence of travel events is an important part of travel pattern ( 9 ); Medina used a week’s IC card data and 1% of the residents’ travel survey data to obtain the main activity patterns of passengers ( 15 ). Through the study of travel pattern, it can be found that the travel of bus passengers is implied imply with regularity.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Clustering-Based Travel Pattern for Individual Travel Prediction of Frequent Passengers by Using Transit Smart Card

2022

Transportation Research Record: Journal of the Transportation R

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Individual travel prediction is very important for the construction of intelligent urban transportation systems. Previous studies mainly focus on the improvement of algorithms, but pay little attention to the mining of data information. In this paper, the concept of the travel pattern is introduced into the field of individual travel prediction of frequent bus passengers. The travel pattern of passengers refers to the trip with similar boarding time and similar boarding and alighting stations of the same person. Through clustering the travel pattern by DBSCAN algorithm, the regularity of passenger travel can be better exploited and travel information can be integrated into a unified unit as well. In the process of prediction, we first predict whether the passenger will travel, and then, if so, predict the probability distribution of the next trip conditional on the previous one. The proposed method is tested using the Automatic Fare Collection data of Chengdu’s frequent bus passengers in May 2019. Based on travel pattern, the average accuracy of travel information prediction is about 41%, which is 13% higher than the method without using travel pattern. Furthermore, this paper also discusses the influence of spatial threshold in clustering on the prediction results.

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Section: Algorithm Of Travel Pattern Identificationmentioning

confidence: 99%