Riding to Wall Street: Determinants of Commute Time Using Citibike

Ford, Weixing; Lien, Jaimie W.; Мазалов, Владимир; Zheng, Jie

doi:10.2139/ssrn.3418442

Cited by 2 publications

(3 citation statements)

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“…In the literature, a common method for predicting commute time is to study the major factors that affect commute time in a specific commute scenario and predict the average commute time of the corresponding group by modelling these factors. For example, Ford et al (2019) [3] predicted commute time by studying the major factors affecting the commute time of people who use shared bicycles in Wall Street in New York City. This method dicovers the major factors that affect the commute time of the group; thus, efficient traffic management measures are formulated on the basis of the group's perspective.…”

Section: Related Workmentioning

confidence: 99%

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Individual Commute Time Recognition Based on the Hierarchical Semantic Model

et al. 2020

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Individual commute time recognition is essential for traffic demand management. However, this problem has yet to be studied. In this study, we propose a hierarchical semantic model (HSM) to recognize individual commute time. To the best of our knowledge, this work is the first to integrates large scale travellers commute time prediction at an individual level. HSM consists of a low and a high semantic layer. The low semantic layer models spatial, temporal and environmental information, whereas the high semantic layer recognises commute time using the hidden Markov model on the basis of the low semantic layer outputs. Experimental results demonstrate the effectiveness of our proposed model for individual commute time recognition. INDEX TERMS commute time recognition, hierarchical semantic model, traffic demand management

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Section: Related Workmentioning

confidence: 99%

“…, where M is a transformation matrix used in the quaternion method, and it can be represented as Formula (2). We use matrix M to transform the coordinate system and convert the accelerometer data of mobile phones into carrier coordinates.…”

Section: ) Individual Basic Action Recognitionmentioning

confidence: 99%

Individual Commute Time Recognition Based on the Hierarchical Semantic Model

et al. 2020

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“…In the case where t 1 = t 2 , as a tie breaking rule, we simply assume that route 1 will be chosen by all oblivious commuters. 5 Therefore, the following conditions are satisfied:…”

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Pricing equilibrium of transportation systems with behavioral commuters

Lien¹,

Мазалов²,

Zheng³

2020

Journal of Dynamics &Amp; Games

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We study Wardrop equilibrium in a transportation system with profit-maximizing firms and heterogeneous commuters. Standard commuters minimize the sum of monetary costs and equilibrium travel time in their route choice, while "oblivious" commuters choose the route with minimal idle time. Three possible scenarios can arise in equilibrium: A pooling scenario where all commuters make the same transport choice; A separating scenario where different types of commuters make different transport choices; A partial pooling scenario where some standard commuters make the same transport choice as the oblivious commuters. We characterize the equilibrium existence condition, derive equilibrium flows, prices and firms' profits in each scenario, and conduct comparative analyses on parameters representing route conditions and heterogeneity of commuters, respectively. The framework nests the standard model in which all commuters are standard as a special case, and also allows for the case in which all commuters are oblivious as the other extreme. Our study shows how the presence of behavioral commuters under different route conditions affects equilibrium behavior of commuters and firms, as well a the equilibrium outcome of the transportation system.

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Riding to Wall Street: Determinants of Commute Time Using Citibike

Cited by 2 publications

References 0 publications

Individual Commute Time Recognition Based on the Hierarchical Semantic Model

Individual Commute Time Recognition Based on the Hierarchical Semantic Model

Pricing equilibrium of transportation systems with behavioral commuters

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