Detecting Home and Work Locations Using Multiday Transit Smart Card Data: Comparison of Three Methods
Publication: Journal of Urban Planning and Development
Volume 149, Issue 4
Abstract
The identification of commuters’ home and work locations is crucial for urban and transport planning because it enables a better understanding of the urban spatial structure and commuting flow. Various methods have been developed for home and job location identification; however, the accuracy, reliability, and sensitivity of these methods have not been thoroughly examined. This study aimed to compare three commonly used approaches—the staying time method, the trip frequency method, and the hidden Markov chain method (HMM)—in terms of their adaptability and sensitivity to different scales of data, advantages and disadvantages by using smart card data of Beijing in 5 weeks of 2016. Our results showed significant differences among the three methods in identifying actual commuters. The staying time method had the largest error, while HMM was more intelligent in the recognition result due to its combination with historical inbound and outbound passenger flow rules. Although the staying time method was simple and easy to implement, it was unable to fully reflect the data’s characteristics. For larger amounts of sample data, the trip frequency method demonstrated faster processing efficiency; however, missing data had a significant impact on the results. Finally, the machine learning method was able to identify locations more intelligently than the other two approaches, although its algorithm’s time complexity and resource consumption were very high. These findings provided new insights into the application of big data in urban spatial research and offered suggestions for selecting the most appropriate identification method based on data and scenarios.
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Data Availability Statement
Some or all data, models, or codes used during the study were provided by a third party. Direct requests for these materials may be made to the provider as indicated in the “Acknowledgments.”
Acknowledgments
The data come from Beijing Intelligent Transportation Systems and Development Center. This work was supported by the Fundamental Research Funds for the Central Universities (2022JBZY039) and the National Natural Science Foundation of China (Grant No.: 42171190). The authors also thank the editor and the two anonymous reviewers for their valuable comments. The analysis and interpretation and any errors are solely those of the authors.
Notation
The following symbols are used in this paper:
- A
- transition probability set;
- transition probability;
- ay
- proportional coefficient describing the activity level of station y;
- c
- land-use feature vector;
- d
- activity duration;
- fσk
- probability of visible state corresponding to observation parameters;
- H
- number of time periods;
- k
- visible state;
- l
- number of activities;
- M
- parameter of multiple distributions;
- n
- number of trip chains, corresponding to the number of passengers;
- p
- output probability set;
- p(xik)
- output probability;
- s
- activity start time;
- wd
- parameter of the binomial distribution;
- xi
- hidden state;
- inbound passenger flow of station y in period i on the nth day;
- outbound passenger flow of station y in period i on the nth day;
- λzwh
- fluctuation level of passenger flow in and out of station y with time under land-use type z and date type w;
- μk
- mean of Gaussian distribution;
- σ
- observable parameters; and
- σk
- variance of Gaussian distribution.
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Information & Authors
Information
Published In
Journal of Urban Planning and Development
Volume 149 • Issue 4 • December 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Nov 3, 2022
Accepted: Aug 3, 2023
Published online: Sep 29, 2023
Published in print: Dec 1, 2023
Discussion open until: Feb 29, 2024
ASCE Technical Topics:
- Analysis (by type)
- Commute
- Comparative studies
- Data analysis
- Detection methods
- Engineering fundamentals
- Infrastructure
- Methodology (by type)
- Public transportation
- Research methods (by type)
- Spatial analysis
- Spatial data
- Traffic engineering
- Transportation engineering
- Transportation management
- Urban and regional development
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