Individual Travel Behavior Modeling of Public Transport Passenger Based on Graph Construction

Quan Liang, Jiancheng Weng, Wei Zhou, Selene Baez Santamaria, Jianming Ma, Jian Rong

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

This paper presents a novel method for mining the individual travel behavior regularity of different public transport passengers through constructing travel behavior graph based model. The individual travel behavior graph is developed to represent spatial positions, time distributions, and travel routes and further forecasts the public transport passenger's behavior choice. The proposed travel behavior graph is composed of macronodes, arcs, and transfer probability. Each macronode corresponds to a travel association map and represents a travel behavior. A travel association map also contains its own nodes. The nodes of a travel association map are created when the processed travel chain data shows significant change. Thus, each node of three layers represents a significant change of spatial travel positions, travel time, and routes, respectively. Since a travel association map represents a travel behavior, the graph can be considered a sequence of travel behaviors. Through integrating travel association map and calculating the probabilities of the arcs, it is possible to construct a unique travel behavior graph for each passenger. The data used in this study are multimode data matched by certain rules based on the data of public transport smart card transactions and network features. The case study results show that graph based method to model the individual travel behavior of public transport passengers is effective and feasible. Travel behavior graphs support customized public transport travel characteristics analysis and demand prediction.

LanguageEnglish
Article number3859830
Pages1-13
Number of pages13
JournalJournal of Advanced Transportation
Volume2018
DOIs
Publication statusPublished - 16 May 2018

Fingerprint

Smart cards
Travel time
Graph
Modeling
Travel behavior
Public transport
Node
Regularity
Rule-based
Smart card
Choice behavior
Prediction

Cite this

Liang, Quan ; Weng, Jiancheng ; Zhou, Wei ; Santamaria, Selene Baez ; Ma, Jianming ; Rong, Jian. / Individual Travel Behavior Modeling of Public Transport Passenger Based on Graph Construction. In: Journal of Advanced Transportation. 2018 ; Vol. 2018. pp. 1-13.
@article{d0e983264c574cce9b7cf69795d2ec0e,
title = "Individual Travel Behavior Modeling of Public Transport Passenger Based on Graph Construction",
abstract = "This paper presents a novel method for mining the individual travel behavior regularity of different public transport passengers through constructing travel behavior graph based model. The individual travel behavior graph is developed to represent spatial positions, time distributions, and travel routes and further forecasts the public transport passenger's behavior choice. The proposed travel behavior graph is composed of macronodes, arcs, and transfer probability. Each macronode corresponds to a travel association map and represents a travel behavior. A travel association map also contains its own nodes. The nodes of a travel association map are created when the processed travel chain data shows significant change. Thus, each node of three layers represents a significant change of spatial travel positions, travel time, and routes, respectively. Since a travel association map represents a travel behavior, the graph can be considered a sequence of travel behaviors. Through integrating travel association map and calculating the probabilities of the arcs, it is possible to construct a unique travel behavior graph for each passenger. The data used in this study are multimode data matched by certain rules based on the data of public transport smart card transactions and network features. The case study results show that graph based method to model the individual travel behavior of public transport passengers is effective and feasible. Travel behavior graphs support customized public transport travel characteristics analysis and demand prediction.",
author = "Quan Liang and Jiancheng Weng and Wei Zhou and Santamaria, {Selene Baez} and Jianming Ma and Jian Rong",
year = "2018",
month = "5",
day = "16",
doi = "10.1155/2018/3859830",
language = "English",
volume = "2018",
pages = "1--13",
journal = "Journal of Advanced Transportation",
issn = "2042-3195",
publisher = "Wiley",

}

Individual Travel Behavior Modeling of Public Transport Passenger Based on Graph Construction. / Liang, Quan; Weng, Jiancheng; Zhou, Wei; Santamaria, Selene Baez; Ma, Jianming; Rong, Jian.

In: Journal of Advanced Transportation, Vol. 2018, 3859830, 16.05.2018, p. 1-13.

Research output: Contribution to JournalArticleAcademicpeer-review

TY - JOUR

T1 - Individual Travel Behavior Modeling of Public Transport Passenger Based on Graph Construction

AU - Liang, Quan

AU - Weng, Jiancheng

AU - Zhou, Wei

AU - Santamaria, Selene Baez

AU - Ma, Jianming

AU - Rong, Jian

PY - 2018/5/16

Y1 - 2018/5/16

N2 - This paper presents a novel method for mining the individual travel behavior regularity of different public transport passengers through constructing travel behavior graph based model. The individual travel behavior graph is developed to represent spatial positions, time distributions, and travel routes and further forecasts the public transport passenger's behavior choice. The proposed travel behavior graph is composed of macronodes, arcs, and transfer probability. Each macronode corresponds to a travel association map and represents a travel behavior. A travel association map also contains its own nodes. The nodes of a travel association map are created when the processed travel chain data shows significant change. Thus, each node of three layers represents a significant change of spatial travel positions, travel time, and routes, respectively. Since a travel association map represents a travel behavior, the graph can be considered a sequence of travel behaviors. Through integrating travel association map and calculating the probabilities of the arcs, it is possible to construct a unique travel behavior graph for each passenger. The data used in this study are multimode data matched by certain rules based on the data of public transport smart card transactions and network features. The case study results show that graph based method to model the individual travel behavior of public transport passengers is effective and feasible. Travel behavior graphs support customized public transport travel characteristics analysis and demand prediction.

AB - This paper presents a novel method for mining the individual travel behavior regularity of different public transport passengers through constructing travel behavior graph based model. The individual travel behavior graph is developed to represent spatial positions, time distributions, and travel routes and further forecasts the public transport passenger's behavior choice. The proposed travel behavior graph is composed of macronodes, arcs, and transfer probability. Each macronode corresponds to a travel association map and represents a travel behavior. A travel association map also contains its own nodes. The nodes of a travel association map are created when the processed travel chain data shows significant change. Thus, each node of three layers represents a significant change of spatial travel positions, travel time, and routes, respectively. Since a travel association map represents a travel behavior, the graph can be considered a sequence of travel behaviors. Through integrating travel association map and calculating the probabilities of the arcs, it is possible to construct a unique travel behavior graph for each passenger. The data used in this study are multimode data matched by certain rules based on the data of public transport smart card transactions and network features. The case study results show that graph based method to model the individual travel behavior of public transport passengers is effective and feasible. Travel behavior graphs support customized public transport travel characteristics analysis and demand prediction.

UR - http://www.scopus.com/inward/record.url?scp=85047859036&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85047859036&partnerID=8YFLogxK

U2 - 10.1155/2018/3859830

DO - 10.1155/2018/3859830

M3 - Article

VL - 2018

SP - 1

EP - 13

JO - Journal of Advanced Transportation

T2 - Journal of Advanced Transportation

JF - Journal of Advanced Transportation

SN - 2042-3195

M1 - 3859830

ER -