Previous Article in Journal
High-Resolution Human Keypoint Detection: A Unified Framework for Single and Multi-Person Settings
Previous Article in Special Issue
Estimation of Residential Vacancy Rate in Underdeveloped Areas of China Based on Baidu Street View Residential Exterior Images: A Case Study of Nanning, Guangxi
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Algorithms
Volume 18
Issue 8
10.3390/a18080534
algorithms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Research on Commuting Mode Split Model Based on Dominant Transportation Distance

by
Jinhui Tan
1,*,
Shuai Teng
2,*,
Zongchao Liu
1,
Wei Mao
3 and
Minghui Chen
3
1
Guangzhou Railway Polytechnic, Guangdong-Hong Kong-Macao Greater Bay Area Rail Transit Industry Technology Research Institute, Guangzhou 511300, China
2
School of Intelligent Construction and Civil Engineering, Zhongyuan University of Technology, Zhengzhou 450007, China
3
Dongguan Geographic Information and Planning Research Centre, Dongguan 523808, China
*
Authors to whom correspondence should be addressed.
Algorithms 2025, 18(8), 534; https://doi.org/10.3390/a18080534
Submission received: 6 July 2025 / Revised: 13 August 2025 / Accepted: 15 August 2025 / Published: 21 August 2025
(This article belongs to the Special Issue Algorithms for Smart Cities (2nd Edition))
Versions Notes

Abstract

Conventional commuting mode split models are characterized by inherent limitations in dynamic adaptability, primarily due to persistent dependence on periodic survey data with significant temporal gaps. A dominant transportation distance-based modeling framework for commuting mode choice is proposed, formalizing a generalized cost function. Through the application of random utility theory, probability density curves are generated to quantify mode-specific dominant distance ranges across three demographic groups: car-owning households, non-car households, and collective households. Empirical validation was conducted using Dongguan as a case study, with model parameters calibrated against 2015 resident travel survey data. Parameter updates are dynamically executed through the integration of big data sources (e.g., mobile signaling and LBS). Successful implementation has been achieved in maintaining Dongguan’s transportation models during the 2021 and 2023 iterations.
Keywords: commuting; mode split models; dominant transportation distance; probability density curves commuting; mode split models; dominant transportation distance; probability density curves

Share and Cite

MDPI and ACS Style

Tan, J.; Teng, S.; Liu, Z.; Mao, W.; Chen, M. Research on Commuting Mode Split Model Based on Dominant Transportation Distance. Algorithms 2025, 18, 534. https://doi.org/10.3390/a18080534

AMA Style

Tan J, Teng S, Liu Z, Mao W, Chen M. Research on Commuting Mode Split Model Based on Dominant Transportation Distance. Algorithms. 2025; 18(8):534. https://doi.org/10.3390/a18080534

Chicago/Turabian Style

Tan, Jinhui, Shuai Teng, Zongchao Liu, Wei Mao, and Minghui Chen. 2025. "Research on Commuting Mode Split Model Based on Dominant Transportation Distance" Algorithms 18, no. 8: 534. https://doi.org/10.3390/a18080534

APA Style

Tan, J., Teng, S., Liu, Z., Mao, W., & Chen, M. (2025). Research on Commuting Mode Split Model Based on Dominant Transportation Distance. Algorithms, 18(8), 534. https://doi.org/10.3390/a18080534

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop