2022
Liu, Yang; Feng, Tao; Shi, Zhuangbin; He, Mingwei
Understanding the route choice behaviour of metro-bikeshare users Journal Article
In: Transportation Research Part A: Policy and Practice, vol. 166, pp. 460-475, 2022, ISSN: 0965-8564.
Abstract | Links | BibTeX | Tags: Metro-bikeshare integration, Multinomial logit model, Route choice, Smart card data
@article{LIU2022460,
title = {Understanding the route choice behaviour of metro-bikeshare users},
author = {Yang Liu and Tao Feng and Zhuangbin Shi and Mingwei He},
url = {https://www.sciencedirect.com/science/article/pii/S0965856422002890},
doi = {https://doi.org/10.1016/j.tra.2022.11.006},
issn = {0965-8564},
year = {2022},
date = {2022-01-01},
journal = {Transportation Research Part A: Policy and Practice},
volume = {166},
pages = {460-475},
abstract = {Understanding the determinants of the route choice behaviour on a multi-modal transit network of metro and shared bike is important to improve personalized multimodal travel services. This paper attempts to analyse the route choice behaviour of metro-bikeshare users considering passengers’ socio-economic attributes and perceived congestion which is approximated by load status. An abstract integrated metro-bikeshare network (IMBN) is built with virtual nodes by aggregating shared bike stations within the walkable distance and abstract routes by aggregating optional paths for each OD pair. Using the metro- and shared bike smart- card data from Nanjing, China, the route sets of metro-bikeshare users were extracted from the IMBN. A multinomial Logit model (MNL) was then applied to investigate the determinants of route choice behaviour for two types of users, namely “return-enter” and “exit-lease”, respectively. The results show that the models with the load status attributes have a better performance than the models without these attributes. We found the sensitivity of “exit-lease” users to the train crowding is significantly greater than that of the “return-enter” users. “Return-enter” users have a higher perception of out-of-vehicle travel time (OVTT) than that of in-vehicle travel time (IVT), while the “exit-lease” users have the opposite perception. Besides, the change rate of shared bike inventory, departure time and whether he or she is a regular user also have a significant impact on route choice behaviour. The findings can help policymakers and system operators to improve the services and the efficiency of the multimodal transportation system.},
keywords = {Metro-bikeshare integration, Multinomial logit model, Route choice, Smart card data},
pubstate = {published},
tppubtype = {article}
}
Understanding the determinants of the route choice behaviour on a multi-modal transit network of metro and shared bike is important to improve personalized multimodal travel services. This paper attempts to analyse the route choice behaviour of metro-bikeshare users considering passengers’ socio-economic attributes and perceived congestion which is approximated by load status. An abstract integrated metro-bikeshare network (IMBN) is built with virtual nodes by aggregating shared bike stations within the walkable distance and abstract routes by aggregating optional paths for each OD pair. Using the metro- and shared bike smart- card data from Nanjing, China, the route sets of metro-bikeshare users were extracted from the IMBN. A multinomial Logit model (MNL) was then applied to investigate the determinants of route choice behaviour for two types of users, namely “return-enter” and “exit-lease”, respectively. The results show that the models with the load status attributes have a better performance than the models without these attributes. We found the sensitivity of “exit-lease” users to the train crowding is significantly greater than that of the “return-enter” users. “Return-enter” users have a higher perception of out-of-vehicle travel time (OVTT) than that of in-vehicle travel time (IVT), while the “exit-lease” users have the opposite perception. Besides, the change rate of shared bike inventory, departure time and whether he or she is a regular user also have a significant impact on route choice behaviour. The findings can help policymakers and system operators to improve the services and the efficiency of the multimodal transportation system.
Liu, Yang; Ji, Yanjie; Feng, Tao; Shi, Zhuangbin
A route analysis of metro-bikeshare users using smart card data Journal Article
In: Travel Behaviour and Society, vol. 26, pp. 108-120, 2022, ISSN: 2214-367X.
Abstract | Links | BibTeX | Tags: Metro-bikeshare integration, Smart card data, Spatiotemporal patterns, Travel route
@article{LIU2022108,
title = {A route analysis of metro-bikeshare users using smart card data},
author = {Yang Liu and Yanjie Ji and Tao Feng and Zhuangbin Shi},
url = {https://www.sciencedirect.com/science/article/pii/S2214367X21000880},
doi = {https://doi.org/10.1016/j.tbs.2021.09.006},
issn = {2214-367X},
year = {2022},
date = {2022-01-01},
journal = {Travel Behaviour and Society},
volume = {26},
pages = {108-120},
abstract = {Few studies have analyzed individuals’ travel route characteristics in the integrated metro and bikeshare network. Taking Nanjing, China, as a case study, this paper analyzes the combined travel route of metro-bikeshare users based on three-week metro- and shared bike smart card data. The smart card data provides both boarding/borrowing and alighting/returning location and time, which makes it possible to trace each combined mode user’s actual metro routes. By assuming that the bike routes are the shortest paths between a specific OD pair from smart card records, we extract the combined metro-bikeshare travel routes of each user by using a metro route reconstruction method. The results show that over 60% of the metro-bikeshare users, most of whom are youth adults (18–35 years old), only occasionally use this mode. Further, the spatial and temporal characteristics of the combined metro-bikeshare travel routes are analyzed visually across different age groups. We find that most of the combined metro-bikeshare routes concentrated on Metro Line 2 with different cycling access/egress routes; however, the routes mostly travelled by different age groups are significantly different. Young adults are mainly distributed in suburban and exurban areas. Adults are more likely to be metro-bikeshare commuters, with travel routes usually passing through the central area of the city. Besides, the routes mostly travelled by the elderly are longer than those by other groups, and highly coincide with the routes mostly travelled by adults during the rush hours. Finally, relevant policy implementations are proposed in conjunction with metro-bikeshare users’ travel route characteristics.},
keywords = {Metro-bikeshare integration, Smart card data, Spatiotemporal patterns, Travel route},
pubstate = {published},
tppubtype = {article}
}
Few studies have analyzed individuals’ travel route characteristics in the integrated metro and bikeshare network. Taking Nanjing, China, as a case study, this paper analyzes the combined travel route of metro-bikeshare users based on three-week metro- and shared bike smart card data. The smart card data provides both boarding/borrowing and alighting/returning location and time, which makes it possible to trace each combined mode user’s actual metro routes. By assuming that the bike routes are the shortest paths between a specific OD pair from smart card records, we extract the combined metro-bikeshare travel routes of each user by using a metro route reconstruction method. The results show that over 60% of the metro-bikeshare users, most of whom are youth adults (18–35 years old), only occasionally use this mode. Further, the spatial and temporal characteristics of the combined metro-bikeshare travel routes are analyzed visually across different age groups. We find that most of the combined metro-bikeshare routes concentrated on Metro Line 2 with different cycling access/egress routes; however, the routes mostly travelled by different age groups are significantly different. Young adults are mainly distributed in suburban and exurban areas. Adults are more likely to be metro-bikeshare commuters, with travel routes usually passing through the central area of the city. Besides, the routes mostly travelled by the elderly are longer than those by other groups, and highly coincide with the routes mostly travelled by adults during the rush hours. Finally, relevant policy implementations are proposed in conjunction with metro-bikeshare users’ travel route characteristics.
2020
Liu, Y.; Ji, Y.; Feng, T.; Shi, Z.
Use frequency of metro-bikeshare integration: Evidence from Nanjing, China Journal Article
In: Sustainability (Switzerland), vol. 12, iss. 4, 2020, ISSN: 20711050.
Abstract | Links | BibTeX | Tags: Frequency, GPS data, Metro-bikeshare integration, Ordered logistic regression, Survey
@article{Liu2020b,
title = {Use frequency of metro-bikeshare integration: Evidence from Nanjing, China},
author = {Y. Liu and Y. Ji and T. Feng and Z. Shi},
doi = {10.3390/su12041426},
issn = {20711050},
year = {2020},
date = {2020-01-01},
journal = {Sustainability (Switzerland)},
volume = {12},
issue = {4},
abstract = {Promoting a transition in individuals' travel mode from car to an integrated metro and bikeshare systems is expected to effectively reduce the traffic congestion that results mainly from commute trips performed by individual automobiles. This paper focuses on the use frequency of an integrated metro-bikeshare by individuals, and presents empirical evidence from Nanjing, China. Using one-week GPS data collected from the Mobike company, the spatiotemporal characteristics of origin/destination for cyclists who would likely to use shared bike as a feeder mode to metro are examined. Three areas of travel-related spatiotemporal information were extracted including (1) the distribution of walking distances between metro stations and shared bike parking lots; (2) the distribution of cycling times between origins/destinations and metro stations; and (3) the times when metro-bikeshare users pick up/drop off shared bikes to transfer to/from a metro. Incorporating these three features into a questionnaire design, an intercept survey of possible factors on the use of the combined mode was conducted at seven functional metro stations. An ordered logistic regression model was used to examine the significant factors that influence groupings of metro passengers. Results showed that the high-, medium- and low-frequency groups of metro-bikeshare users accounted for 9.92%, 21.98% and 68.1%, respectively. Education, individual income, travel purpose, travel time on the metro, workplace location and bike lane infrastructure were found to have significant impacts on metro passengers' use frequency of integrated metro-bikeshares. Relevant policies and interventions for metro passengers of Nanjing are proposed to encourage the integration of metro and bikeshare systems.},
keywords = {Frequency, GPS data, Metro-bikeshare integration, Ordered logistic regression, Survey},
pubstate = {published},
tppubtype = {article}
}
Promoting a transition in individuals’ travel mode from car to an integrated metro and bikeshare systems is expected to effectively reduce the traffic congestion that results mainly from commute trips performed by individual automobiles. This paper focuses on the use frequency of an integrated metro-bikeshare by individuals, and presents empirical evidence from Nanjing, China. Using one-week GPS data collected from the Mobike company, the spatiotemporal characteristics of origin/destination for cyclists who would likely to use shared bike as a feeder mode to metro are examined. Three areas of travel-related spatiotemporal information were extracted including (1) the distribution of walking distances between metro stations and shared bike parking lots; (2) the distribution of cycling times between origins/destinations and metro stations; and (3) the times when metro-bikeshare users pick up/drop off shared bikes to transfer to/from a metro. Incorporating these three features into a questionnaire design, an intercept survey of possible factors on the use of the combined mode was conducted at seven functional metro stations. An ordered logistic regression model was used to examine the significant factors that influence groupings of metro passengers. Results showed that the high-, medium- and low-frequency groups of metro-bikeshare users accounted for 9.92%, 21.98% and 68.1%, respectively. Education, individual income, travel purpose, travel time on the metro, workplace location and bike lane infrastructure were found to have significant impacts on metro passengers’ use frequency of integrated metro-bikeshares. Relevant policies and interventions for metro passengers of Nanjing are proposed to encourage the integration of metro and bikeshare systems.
Liu, Y.; Ji, Y.; Feng, T.; Timmermans, H.
Understanding the determinants of young commuters’ metro-bikeshare usage frequency using big data Journal Article
In: Travel Behaviour and Society, vol. 21, 2020, ISSN: 2214367X.
Abstract | Links | BibTeX | Tags: Metro-bikeshare integration, Negative binomial regression, Smart card data, Transfer frequency, Young commuter
@article{Liu2020,
title = {Understanding the determinants of young commuters’ metro-bikeshare usage frequency using big data},
author = {Y. Liu and Y. Ji and T. Feng and H. Timmermans},
doi = {10.1016/j.tbs.2020.06.007},
issn = {2214367X},
year = {2020},
date = {2020-01-01},
journal = {Travel Behaviour and Society},
volume = {21},
abstract = {This paper examines the determinants of young commuters’ frequency of using public bikes as a feeder mode to/from metro. Using three-week metro- and public bike- smart card data from Nanjing, 1,154 metro-bikeshare commuters aged 18–35 were extracted. As possible factors influencing the use of the combined mode, individual and household socio-demographics, travel-related attributes and built environment characteristics were extracted from multi-source data. A negative binomial regression model was used to examine the effects of these factors on usage frequency. We found that young commuters are the biggest group using metro-bikeshare system. They use public bikes frequently to transfer to/from metro when the cycling time is less than 10 min and the transfer happens during the morning peak. Built environment characteristics also influence usage frequencies, with high-density bike facilities being related to higher cycling rates in inner areas, and residential /employment locations related to lower rates of cycling in the core areas. This suggests that different measures and policies designed to encourage the integrated use of metro-bikeshare should be put forward for different regions.},
keywords = {Metro-bikeshare integration, Negative binomial regression, Smart card data, Transfer frequency, Young commuter},
pubstate = {published},
tppubtype = {article}
}
This paper examines the determinants of young commuters’ frequency of using public bikes as a feeder mode to/from metro. Using three-week metro- and public bike- smart card data from Nanjing, 1,154 metro-bikeshare commuters aged 18–35 were extracted. As possible factors influencing the use of the combined mode, individual and household socio-demographics, travel-related attributes and built environment characteristics were extracted from multi-source data. A negative binomial regression model was used to examine the effects of these factors on usage frequency. We found that young commuters are the biggest group using metro-bikeshare system. They use public bikes frequently to transfer to/from metro when the cycling time is less than 10 min and the transfer happens during the morning peak. Built environment characteristics also influence usage frequencies, with high-density bike facilities being related to higher cycling rates in inner areas, and residential /employment locations related to lower rates of cycling in the core areas. This suggests that different measures and policies designed to encourage the integrated use of metro-bikeshare should be put forward for different regions.