Bicycle parking security and built environments

Chen, Peng; Liu, Qian; Sun, Feiyang

doi:10.1016/j.trd.2018.02.020

Cited by 25 publications

(17 citation statements)

References 21 publications

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“…The ordinary least squares (OLS) regression model was the most common method in previous studies ( 21 ). Moreover, other traditional statistical models like the structural equation model (SEM), zero-inflated negative binomial model, hierarchical Bayesian models, and Poisson regression model were also frequently used in the field of transit ridership analysis ( 2 , 31 , 35 , 36 ). In a word, these global statistical models assume that the relationships between independent variables and dependent variables are consistent over space, but they ignored the spatial characteristics of spatial data, like spatial auto-correlation and spatial non-stationarity.…”

Section: Related Workmentioning

confidence: 99%

“…Counties like the U.S., Russia, and some Asian countries like China and Japan have recognized the importance and potential benefits of ridesharing modes and have formulated corresponding policies. Therefore, for government and transit agencies that follow the guidance of transit-oriented development (TOD) strategies, it is important to explore the relationship between built environment and this emerging transport mode ( 2 ). This understanding will inform and enhance the references used by government and transit agencies for strategic planning.…”

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confidence: 99%

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How Built Environment Impacts Online Car-Hailing Ridership

Wang

Chen

et al. 2020

Transportation Research Record: Journal of the Transportation R

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Extensive research has shown that unilateral optimization of transit systems is not effective enough to significantly increase its transport efficiency. Considering that urban land-use characteristics, including residential, work, consumption, transit, and so forth, are significantly interrelated with travel demands and travel behaviors, this paper provides a way to optimize transit system by raising awareness of the relation between ridership and built environment. This paper adopted point of interest (POI) data to investigate the effect of physical built environment on online car-hailing ridership in Chengdu, China. The study area was tessellated with several Voronoi cells; these cells were further clustered into three ridership patterns based on the time-varying characteristic of ridership. Given that some differences existed in the three ridership patterns, a separate spatial ridership model was developed to understand the factors that influence ridership patterns using geographic weighted regression (GWR) analysis. The data and results verified that the built environment had various influences on online car-hailing alighting ridership in spatial and temporal dimensions, of which the significant POI factors for determining the ridership pattern during different periods were detected. Remarkably, this study took the ridership dataset from the online car-hailing transit system, mainly because the pick-up (PU) and drop-off (DO) locations generated by this service are closest to the origin and destination of the trip, except that it is more popular recently. Therefore, the analysis of the impact of built environment on travel based on the online car-hailing dataset can be captured in greater detail, with a higher degree of accuracy.

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Section: Related Workmentioning

confidence: 99%

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confidence: 99%

How Built Environment Impacts Online Car-Hailing Ridership

Wang

Chen

et al. 2020

Transportation Research Record: Journal of the Transportation R

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“…Moreover, people using DBS don't need to take responsibility for maintenance and repair compared with private bicycle. Also, people riding DBS do not worry about the theft of the bicycle which used to be the serious concern of private bikes [14][15][16]. Consequently, DBS becomes prevalent among people as a new type of bike sharing, providing fast and flexible mobility for commuting trips during peak periods [17].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Effects of the Built Environment on Two Transfer Modes for Metros: Dockless Bike Sharing and Taxis

Chen

2020

Sustainability

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The success of metro systems depends on effective multimodal solutions that bridge the first-and-last-mile gaps. Both dockless bike sharing (DBS) and taxis are important feeder modes for metros, which provide on-demand travel options with high flexibility and accessibility. Based on one-week trip data of DBS and taxis during a concurrent period in Beijing, China, the paper aims to compare the temporal-spatial distribution of two modes as first-and-last-mile connectors and find out the socio-demographic and built-environment factors that impact their usage. K-means clustering is implemented to visualize the spatial distribution of DBS and taxis around metro stations, and the spatial lag model incorporating spatial autocorrelations of variables is developed. The results show that people prefer to use DBS as a substitutable mode for bus services to serve first-mile interchange in the morning. Also, less economically developed areas with a high density of branches and fewer signalized intersections are more favored by DBS users, whereas people in the central areas with high housing price and developed arterial road network tend to take a taxi, especially during evening peak period. The study can offer the policy guidance to improve DBS services, and several recommendations are suggested to ensure the sustainable development of DBS.

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“…The number of informal settlements in the streets is positively correlated with the distribution of property crime Beavon (1984) Transit facilities Bus stations, metro stations, car parks, and gas stations Transit facilities in the streets will increase activities and opportunities for crime, thus attracting more potential offenders and street crimes Brantingham and Brantingham (1993), Chen, Liu, and Sun (2018), Groff and Lockwood (2014), McCord and Ratcliffe (2009), Haberman and Ratcliffe (2015), and Smith, Frazee, and Davison (2000) Schools…”

Section: Residencementioning

confidence: 99%

Review of Empirical Studies on Relationship between Street Environment and Crime

Mao

Yin

Zeng

et al. 2021

Journal of Planning Literature

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Creating a safe street environment is the primary goal for urban planners and urban designers. However, the existing research findings on the relationship between street environment and criminal behavior are various and contradictory, which brings confusion to the practice of urban planning and design. This article reviews literature on crime prevention through street environmental design on three spatial levels—street networks, paths, and nodes. This article also explores the causes of diversity and contradiction in the existing conclusions. Finally, limitations of the existing research, directions of optimization, and further studies in this field are put forward.

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Bicycle parking security and built environments

Cited by 25 publications

References 21 publications

How Built Environment Impacts Online Car-Hailing Ridership

How Built Environment Impacts Online Car-Hailing Ridership

Exploring the Effects of the Built Environment on Two Transfer Modes for Metros: Dockless Bike Sharing and Taxis

Review of Empirical Studies on Relationship between Street Environment and Crime

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