User satisfaction based model for resource allocation in bike-sharing systems

Caggiani, Leonardo; Camporeale, Rosalia; Marinelli, Mario; Ottomanelli, Michele

doi:10.1016/j.tranpol.2018.03.003

Cited by 55 publications

(22 citation statements)

References 25 publications

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“…In past decades, many studies have focused on three main issues related to public bike systems with docking stations: the spatial structure of a city [8][9][10][11], the inflow and outflow of vehicles at each station [12][13][14], and the rebalancing of the vehicles among stations [15][16][17][18]. In a station-based BSS, the supply of the vehicles must be compatible with the scale of the fixed stations.…”

Section: Literature Reviewmentioning

confidence: 99%

Heuristic Bike Optimization Algorithm to Improve Usage Efficiency of the Station-Free Bike Sharing System in Shenzhen, China

Zhu

Zhang

et al. 2019

IJGI

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Station-free bike sharing systems (BSSs) are a new type of public bike system that has been widely deployed in China since 2017. However, rapid growth has vastly outpaced the immediate demand and overwhelmed many cities around the world. This paper proposes a heuristic bike optimization algorithm (HBOA) to determine the optimal supply and distribution of bikes considering the effect of bicycle cycling. In this approach, the different bike trips with separate bikes can be connected in space and time and converted into a continuous trip chain for a single bike. To improve this cycling efficiency, it is important to properly design the bicycle distribution. Taking Shenzhen as an example, we implement the algorithm with OD matrix data from Mobike and Ofo, the two large bike sharing companies which account for 80% of the shared bike market in Shenzhen, over two days. The HBOA results are as follows. 1) Only one-fifth of the bike supply is needed to meet the current usage demand if the bikes are used efficiently, which means a large number of shared bikes in Shenzhen remain in an idle state for long periods. 2) Although the cycling demand is high in many areas, it does not mean that large numbers of bikes are needed because the continuous inflow caused by the cycling effect of bikes will meet most of the demand by itself. 3) The areas with the highest demands for optimal bikes are residential, followed by industrial, public transportation, official and commercial areas, on both working and non-working days. This algorithm can be an objective basis for city related departments to manage station-free BSSs and be applied to design the layout of bikes in small-scale spatial units to help station-free BSSs operate efficiently and minimize the need to relocate the bikes without reducing the level of user satisfaction.

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Section: Literature Reviewmentioning

confidence: 99%

Heuristic Bike Optimization Algorithm to Improve Usage Efficiency of the Station-Free Bike Sharing System in Shenzhen, China

Zhu

Zhang

et al. 2019

IJGI

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“…The solution most frequently proposed by scholars is to develop an algorithm that would calculate the most time-and cost-effective system for redistributing bicycles between stations (Schuijbroek et al 2017) depending on the number of operator's vehicles and the daily number of interventions (Bulhões et al 2018). As shown in numerous examples, the problem may also be solved by analysing collected data regarding the scale of bicycle use in individual stations (Caggiani et al 2018), as well as by decreasing the rental costs for users who return bicycles at less popular stations (Fricker, Gast 2016;Haider et al 2018). A different solution may consist in implementing a system based on booking spots for returning bicycles rented in the system (Kaspi et al 2014(Kaspi et al , 2016; such change would drastically reduce the system's unpredictability stemming from a lack of information about the station which is the user's destination.…”

Section: The Bicycle-sharing System As An Innovation In the Urban Tramentioning

confidence: 99%

Urban Cycling as an Indicator of Socio-Economic Innovation and Sustainable Transport

Kwiatkowski

2018

Quaestiones Geographicae

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Environmental pollution in cities is an increasingly popular issue tackled in research. One of the elements with a significant impact on the state of urban ecosystems is transport with its pressure on individual components of the environment. Sustainable urban transport is gaining prominence as a postulate expressed in cities’ strategic documents, constituting an element alleviating the negative effects of anthropopressure. In the light of the need to introduce innovative solutions for clean forms of transport, numerous papers indicate the bicycle as an answer to some of those problems. The article presents a review of literature referring to the socio-economic aspects of using the bicycle in cities as a means of transport for daily commuting. The analysis is based on publications on cyclist safety in road traffic, the perception of the bicycle as a means of transport in cities, and the introduction of innovative solutions, such as bicycle-sharing systems.

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“…Bike-sharing is a convenient way for residents to travel, but it also suffers from some shortcomings, such as unreasonable bicycle parking and the failure to transfer bikes in time. Thus, a better understanding of the spatiotemporal characteristics of bike-sharing is needed and could provide management and operational support for enterprises and government departments [4][5][6]. Station service areas are especially interesting for their unique characteristics that affect bike-sharing usage.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Characteristics of Bike-Sharing Usage around Rail Transit Stations: Evidence from Beijing, China

Wang

Cheng

et al. 2020

Sustainability

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As an emerging mode of transport, bike-sharing is being quickly accepted by Chinese residents due to its convenience and environmental friendliness. As hotspots for bike-sharing, railway-station service areas attract thousands of bikes during peak hours, which can block roads and pedestrian walkways. Of the many works devoted to the connection between bikes and rail, few have addressed the spatial‒temporal pattern of bike-sharing accumulating around station service areas. In this work, we investigate the distribution patterns of bike-sharing in station service areas, which are influenced not only by railway-station ridership but also by the built environment around the station, illustrating obvious spatial heterogeneity. To this end, we established a geographic weighted regression (GWR) model to capture this feature considering the variables of passenger flow and the built environment. Using the data from bike-sharing in Beijing, China, we applied the GWR model to carry out a spatiotemporal characteristic analysis of the relationship between bike-sharing usage in railway-station service areas and its determinants, including the passenger flow in stations, land use, bus lines, and road-network characteristics. The influence of these factors on bike-sharing usage is quite different in time and space. For instance, bus lines are a competing mode of transport with bike-sharing in suburban areas but not in city centers, whereas industrial and residential areas could also heavily affect the bike-sharing demand as well as railway-station ridership. The results of this work can help facilitate the dynamic allocation of bike-sharing and increase the efficiency of this emerging mode of transport.

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User satisfaction based model for resource allocation in bike-sharing systems

Cited by 55 publications

References 25 publications

Heuristic Bike Optimization Algorithm to Improve Usage Efficiency of the Station-Free Bike Sharing System in Shenzhen, China

Heuristic Bike Optimization Algorithm to Improve Usage Efficiency of the Station-Free Bike Sharing System in Shenzhen, China

Urban Cycling as an Indicator of Socio-Economic Innovation and Sustainable Transport

Spatiotemporal Characteristics of Bike-Sharing Usage around Rail Transit Stations: Evidence from Beijing, China

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