Research Output

@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}

}

@article{Yan2020,

title = {Investigating private parking space owners’ propensity to engage in shared parking schemes under conditions of uncertainty using a hybrid random-parameter logit-cumulative prospect theoretic model},

author = {Q. Yan and T. Feng and H. Timmermans},

doi = {10.1016/j.trc.2020.102776},

issn = {0968090X},

year  = {2020},

date = {2020-01-01},

journal = {Transportation Research Part C: Emerging Technologies},

volume = {120},

abstract = {Shared parking allows the effective use of undersupplied parking spaces and contributes to the alleviation of urban parking problems, traffic congestion, environmental pollution, and other negative externalities of traffic. However, little is known about the acceptance of shared parking by consumers of a different socio-demographic profile. To understand the feasibility and potential success of shared parking, this paper develops a stated choice experiment with three choice options: fixed mode shared parking, flexible mode shared parking and not interested, to investigate parking space owners’ propensity to engage in shared parking under varying conditions. Because the demand for shared parking is uncertain, the revenues owners may generate are uncertain. As one of the most popular theories of decision making under uncertainty, the cumulative prospect theory is incorporated into a multinomial logit model to capture the decision problem in which some variables are uncertain and others are not. The revenue that owners expect shared parking can bring is used as the reference point to differentiate between gains and losses. Gains refer to outcomes that exceed the reference point, while losses refer to outcomes that fall short. To examine unobserved heterogeneity, a random parameter version of the model is specified to estimate the distribution of decision weights across the sample. Results show that socio-demographic characteristics, context variables, revenues and psychological concerns are all important factors in explaining parking space owners’ propensity to engage in platform-based shared parking schemes. Incorporating unobserved heterogeneous improves the overall goodness-of-fit of the model. Understanding parking space owners’ propensity to share their parking spaces in relation to their psychological concerns and uncertain conditions is critical to improve shared parking policies. The results of this paper may help designers and planners in the delivery of shared parking services and promote the success and future growth of the shared parking industry.},

keywords = {Multinomial logit model, Prospect theory, Random parameter model, Shared parking},

pubstate = {published},

tppubtype = {article}

}