Nonlinearities and threshold points in the effect of contextual features on the spatial and temporal variability of bus use in Beijing using explainable machine learning
Predictable or uncertain trips
Bibliographic Data
| ID | 12296352 |
|---|---|
| Authors | Sui Tao (0000-0001-5485-2266, Beijing Normal University), Francisco Rowe (0000-0003-4137-0246, University of Liverpool, corresponding author), Hongyu Shan (0000-0003-1213-4690, Beijing Normal University) |
| Year | 2025 |
| Volume | 123 |
| Pages | 104126-104126 |
| Publication date | 2025-01-23 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Transport Geography (JOURNAL) |
| Journal identifiers | ISSN: 0966-6923 • E-ISSN: 1873-1236 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.jtrangeo.2025.104126 |
| OpenAlex | W4406749609 |
| Language | EN |
| Citations received | 1 |
| References cited | 59 |
In pursuing sustainable transport, understanding the dynamics of transit passengers' travel demand is necessary for establishing more attractive public transport relative to cars. However, to what extent daily transit use displays geographic and temporal variabilities or predictability, and identifying what are the contributing factors explaining these patterns have not been fully addressed. Drawing on smart card data in Beijing, China, this study adopts new indices to capture the spatial and temporal variability of bus use during peak hours and investigates their associations with relevant contextual features. Using explainable machine learning, our findings reveal non-linearities and threshold points in the spatial and temporal variability of bus trips as a function of trip frequency. Greater distance to the urban centres (>10 km) is associated with increased spatial variability of bus use, while greater separation of trip origins and destinations from the subcentres reduces both spatial and temporal variability reflecting highly predictable of trips. Higher availability of bus routes is linked to higher spatial variability but lower temporal variability. Meanwhile, both lower and higher road density is associated with higher spatial variability of bus use especially in morning times. These findings indicate that different built environment features moderate the flexibility of choosing travel time and locations influencing the predictability of trips. Understanding highly predictable trips is key to develop more effective planning and operation of public transport
Beijing · China · Geography · Transport engineering · TRIPS architecture · Computer Science · Engineering · Human Mobility and Location-Based Analysis · Transportation Planning and Optimization · Urban Transport and Accessibility · Artificial Intelligence
Climate Change 2014: Mitigation of Climate Change
The promises of big data and small data for travel behavior (aka human mobility) analysis
A city of cities
Smart card data use in public transit
Greedy function approximation
Developing two-dimensional indicators of transport demand and supply to promote sustainable transportation equity
Urban experiments with public transport for low carbon mobility transitions in cities
Incorporating polycentric development and neighborhood life-circle planning for reducing driving in Beijing
New evidence on walking distances to transit stops
Subjective well-being in China
Repetitions in individual daily activity–travel–location patterns
Multidimensional visualization of transit smartcard data using space–time plots and data cubes
Systematic variability in repetitious travel
Spatiotemporal variation in travel regularity through transit user profiling
An examination of the determinants of day-to-day variability in individuals' urban travel behavior
Exploring spatial variety in patterns of activity-travel behaviour
Urban exodus? Understanding human mobility in Britain during the Covid‐19 pandemic using Meta‐Facebook data
Segmenting travellers based on day-to-day variability in work-related travel behaviour
Understanding the travel behaviors and activity patterns of the vulnerable population using smart card data
Examining the spatial–temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap
Examining public transport usage by older adults with smart card data
Examining the influence of stop level infrastructure and built environment on bus ridership in Montreal
Segregation through space
Using metro smart card data to model location choice of after-work activities
Examining the non-linear effects of transit accessibility on daily trip duration
Investigating commuting flexibility with GPS data and 3D geovisualization
Revisiting car dependency
Employment centers change faster than expected
Urban rhythms and travel behaviour
Travel and the Built Environment
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 1 |