First-and-last mile solution via bicycling to improving transit accessibility and advancing transportation equity

Zuo, Ting; Wei, Heng; Chen, Na; Zhang, Chun

doi:10.1016/j.cities.2020.102614

Cited by 95 publications

(45 citation statements)

References 60 publications

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“…On this basis, the accessibility of each sharing-bike traffic zone was obtained. e final model is in the format of equation ( 9) and could be expressed as equation (13). e in-closeness centrality and betweenness centrality indexes exerted a stronger influence on the accessibility, which demonstrated that the difficulty of reaching other nodes and closeness are the critical factors in accessibility assessment.…”

Section: Resultsmentioning

confidence: 99%

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Assessing Accessibility of Dockless Sharing-Bike Networks by the Social Network Analysis Method

Liu

Chen

Sun

et al. 2021

Journal of Advanced Transportation

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Dockless sharing bikes play an increasingly significant role in transit transfer, especially for the first/last mile. However, it is not always accessible for users to find sharing bicycles. The objective of this paper is to assess the accessibility of dockless sharing bikes from a network perspective, which would provide a decision-making basis not only for potential bike users but also for urban planners, policymakers, and bicycle suppliers to optimize sharing-bike systems. Considering bicycle travel characteristics, a hierarchical clustering algorithm was applied to construct the dockless sharing-bike network. The social network analysis (SNA) method was adopted to assess the accessibility of the bike network. Then, a spatial interaction model was chosen to conduct a correlation analysis to compare the accessibility obtained from the SNA approach. The case study of Shanghai indicates a strong connection between the accessibility and the SNA indicators with the correlation coefficient of 0.779, which demonstrates the feasibility of the proposed method. This paper contributes to a deep understanding of dockless sharing-bike network accessibility since the SNA approach considers both the interaction barriers and the network structure of a bicycle network. The developed methodology requires fewer data and is easy to operate. Thus, it can serve as a tool to facilitate the smart management of sharing bikes for improving a sustainable transportation system.

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Section: Resultsmentioning

confidence: 99%

“…Access is the process of getting users from their origin to the transportation network, and geographic coverage is the transportation network across a spatial medium [12]. Zuo et al defined bicycle-transit accessibility as the number of locations reachable within a time threshold [13].…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing Accessibility of Dockless Sharing-Bike Networks by the Social Network Analysis Method

Liu

Chen

Sun

et al. 2021

Journal of Advanced Transportation

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“…Although there seem to be merits in implementing those methods, due to the high investment cost for PRT [32] and other limitations, there is still a need for a better alternative in solving this issue. Another study proposed the use of bicycling to tackle FMLM issues and provides a method to assist the bike/pedestrian network planning for better transit point-to-point mobility [33]. This method only works when the city infrastructure allows for flexibility in additional network planning for bikes/pedestrians.…”

Section: First-mile and Last-milementioning

confidence: 99%

A Study of Spatiotemporal Distribution of Mobility-On-Demand in Generating Pick-Up/Drop-Offs Location Placement

Gunawan

Susilawati

2021

Smart Cities

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The location placement of pick-up/drop-offs of ride hailing usually only considers spatial distribution within a certain area. Previous studies often mapped out the potential hotspots for pick-up/drop-offs, benefitting the ride-hailing company and not considering the passengers. Therefore, in this study, we incorporated the spatiotemporal distribution of mobility-on-demand on generating pick-up/drop-off location placement using a genetic algorithm considering the walking distance and minimum demand data served within the radius. The data collected are analyzed through the clustering method, and the resulting cluster centers are fed into the location optimization algorithm. The genetic algorithm is used to optimize the location placement with the consideration of the user’s walking distance and demand. The algorithm is able to find an appropriate placement and determine reliable pick-up/drop-off stations within the study area based on observed spatiotemporal demand despite the difference in demand distribution through different time periods.

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“…The estimation of weight was based on the data of travelling by shared bike. This study used the log-likelihood maximization method to estimate the parameter b in Equation (10). Figure 4a plotted the optimization process.…”

Section: Parameter Specificationmentioning

confidence: 99%

“…BAcc is often used in bicycle infrastructure, land use, and transportation system planning [4][5][6][7]. BAcc also provides a measure of feeder mobility because bicycling is a solution for transit's "last mile" problem [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Impact Evaluation of Bike-Sharing on Bicycling Accessibility

et al. 2020

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The presence of bike-sharing has a significant influence on the ease of trips by bike, which is one critical aspect of bicycling accessibility (BAcc). The existing measurements of BAcc rarely consider the factor of ownership of bikes, which means that no distinction is made between private-bikes and shared bikes. To measure BAcc more fully, this paper proposes a method to evaluate the influences of bike-sharing on BAcc and to perform the method on a real-world case study in Beijing. It is found that bike-sharing has a boosting effect on BAcc, and the increased rate of BAcc is significantly affected by bicycling frequency and shared-bike availability. A case study in Beijing utilizing geo-location data collected from two major bike-sharing companies (OFO and Mo-bike) illustrates the significance of the impact of bike-sharing on BAcc and the necessity to include bike-sharing in the measurement of BAcc. Besides, the case study shows BAcc around the transit station is better than that over the whole area. Given that bicycling feeds transit, this research lays the foundation for analyzing the combination of bike-sharing and transit from the perspective of accessibility and can further support transportation planning.

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First-and-last mile solution via bicycling to improving transit accessibility and advancing transportation equity

Cited by 95 publications

References 60 publications

Assessing Accessibility of Dockless Sharing-Bike Networks by the Social Network Analysis Method

Assessing Accessibility of Dockless Sharing-Bike Networks by the Social Network Analysis Method

A Study of Spatiotemporal Distribution of Mobility-On-Demand in Generating Pick-Up/Drop-Offs Location Placement

Impact Evaluation of Bike-Sharing on Bicycling Accessibility

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