Spatial heterogeneity in distance decay of using bike sharing: An empirical large-scale analysis in Shanghai
TL;DR: The findings provide insights into the distance decay patterns of using DLBS in different urban contexts and their interactions with the built environment, which can support accurate planning and management of sustainable DLBS as per specific urban characteristics.
Abstract: Distance decay is a vital aspect for modeling spatial interactions of human movements and an indispensable input for land use planning and travel demand prediction models. Although many studies have investigated the usage demand of bike-sharing systems in an area, research investigating the distance decay patterns of using dockless bike-sharing systems (DLBS) from a spatially heterogeneous perspective based on large-scale datasets is lacking. This study firstly utilizes massive transaction record data from DLBS in Shanghai of China and online map navigator Application Programming Interface to empirically estimate the distance decay patterns of using DLBS and reveal the spatial heterogeneity in distance decay of using DLBS across different urban contexts. Afterward, this study examines the mechanism of spatial heterogeneity in distance decay, leveraging multiple data resources including Point of Interest (POI) data, demographic data, and road network data. The associations among the distance decay of using DLBS with built environment factors are investigated by multiple linear regression. Results indicate that factors such as population density, land use entropy, branch road density, and metro station density are significantly related to larger distance decay of using DLBS, while factors such as commercial land use ratio, industrial land use ratio, and motorway density are significantly linked to smaller distance decay in Shanghai. Lastly, we further employ an adaptative geographically weighted regression to investigate the spatial divergences of the influences of built environment factors on distance decay. Results reveal notably distinct and even inverse influences of a built environment factor on the distance decay of using DLBS in different urban contexts. The findings provide insights into the distance decay patterns of using DLBS in different urban contexts and their interactions with the built environment, which can support accurate planning and management of sustainable DLBS as per specific urban characteristics.
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TL;DR: Wang et al. as discussed by the authors conducted a comparative study to reveal the similarity and difference of e-scooter sharing mobility by collecting and analyzing vehicle availability data from 30 European cities during post COVID-19 pandemic.
Abstract: Although e-scooter sharing has become increasingly attractive, little attention has been paid to a comprehensive comparison of e-scooter sharing mobility in multiple cities. To fill this gap, we conduct a comparative study to reveal the similarity and difference of e-scooter sharing mobility by collecting and analyzing vehicle availability data from 30 European cities during post COVID-19 pandemic. The comparisons are implemented from four perspectives, including temporal trip patterns, statistical characteristics (i.e., trip distance and duration), utilization efficiency, and wasted electricity during idle time. Results suggest that the similarity and difference co-exist between e-scooter sharing services in the cities, and utilization efficiency is significantly related with the number of e-scooters per person and per unit area. Surprisingly, on average nearly 33% of electricity are wasted during idle time in these cities. These research findings can be beneficial to further optimizing e-scooter sharing mobility services for transportation planners and micro-mobility operators.
29 citations
TL;DR: Wang et al. as mentioned in this paper investigated the riding behavior in the time and space dimensions based on multisource datasets, and compared the DBS usage based on the traffic grid between the two study areas.
Abstract: To better understand dockless bike-sharing (DBS) usage and advance the knowledge on shared bicycle service, this study empirically investigated the riding behavior in the time and space dimensions based on multisource datasets. Taking Central Business District (CBD) and Beijing West Railway Station (BWRS) as study areas, this study analyzed and compared the DBS usage based on the traffic grid between the two study areas. Furthermore, the random forest (RF) model was applied to investigate the contribution of influencing factors on origin/ destination and origin–destination pair trip volume. Partial Dependence Plots (PDP) analysis was conducted to explore the nonlinear effects of influencing factors. Results show considerable variation across different scenarios. Variables such as government agencies, restaurants, bus stop distance, and metro distance show nonlinear and threshold effects on DBS usage. The findings offer valuable insights for urban infrastructure development and bike rebalancing strategies, and the formulation of green and sustainable transportation policies.
17 citations
TL;DR: Based on the license plate recognition (LPR) data, the authors quantitatively assesses the impact of the COVID-19 pandemic on individual travel behavior and analyzes the adjustment of travelers' behaviors under the influence of the pandemic, and the behavior adjustment is related to the individual's past travel habits.
Abstract: The outbreak and spreading of the COVID-19 pandemic have had a significant impact on transportation system. By analyzing the impact of the pandemic on the transportation system, the impact of the pandemic on the social economy can be reflected to a certain extent, and the effect of anti-pandemic policy implementation can also be evaluated. In addition, the analysis results are expected to provide support for policy optimization. Currently, most of the relevant studies analyze the impact of the pandemic on the overall transportation system from the macro perspective, while few studies quantitatively analyze the impact of the pandemic on individual spatiotemporal travel behavior. Based on the license plate recognition (LPR) data, this paper analyzes the spatiotemporal travel patterns of travelers in each stage of the pandemic progress, quantifies the change of travelers' spatiotemporal behaviors, and analyzes the adjustment of travelers' behaviors under the influence of the pandemic. There are three different behavior adjustment strategies under the influence of the pandemic, and the behavior adjustment is related to the individual's past travel habits. The paper quantitatively assesses the impact of the COVID-19 pandemic on individual travel behavior. And the method proposed in this paper can be used to quantitatively assess the impact of any long-term emergency on individual micro travel behavior.
12 citations
TL;DR: Zhang et al. as mentioned in this paper examined the impacts of social influence metrics and the moderation effects of social circle and similarity on the individual's green travel behavior in rural areas in China.
Abstract: With the rapid growth of rural cars ownership, the environmental pollution caused by rural traffic has become a new problem restricting the sustainable development of social economy. Therefore, encouraging and supporting rural residents’ green travel has become the focus of government and academic circles. Based on the context of China's local rural social culture, by adopting the secondary data from the China Land Economics Survey included 2600 farmers in 52 Administrative Villages from 13 prefecture-level cities in Jiangsu Province, this paper examines the impacts of social influence metrics and the moderation effects of social circle and similarity on the individual’s green travel behavior in rural areas in China. The results show that the significant and different impacts of social influence metrics. Significant and positive impact exist in the social influence from the neighbors who adopt the green travel behavior, while significant and negative impact also exist in the social influence from the neighbors who adopt the non-green travel behavior. Moreover, we also find the significant moderators including the social circle and similarity between the target family and the neighbors who adopt green or non-green travel behavior. This paper has important theoretical and practical significance for enriching and developing the relevant theories of green travel.
8 citations
TL;DR: In this paper , the transfer ratio between buses and metros is identified based on large-scale transaction data from automated fare collection systems and various influencing factors, including weather, socioeconomic, the intensity of business activities, and built environment factors, are obtained from multivariate sources.
3 citations
References
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TL;DR: A Computer Movie Simulating Urban Growth in the Detroit Region as discussed by the authors was made to simulate urban growth in the city of Detroit, Michigan, United States of America, 1970, 1970.
Abstract: (1970). A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography: Vol. 46, PROCEEDINGS International Geographical Union Commission on Quantitative Methods, pp. 234-240.
7,533 citations
"Spatial heterogeneity in distance d..." refers background in this paper
...A crucial task of modeling the patterns of spatial interactions is to determine the distance decay (Zhu et al., 2020; Yang et al., 2019), which reflects the famous first law of geography “everything is related to everything else, but near things are more related than distant things” (Tobler, 1970)....
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TL;DR: In this article, the authors examined how the built environment affects trip rates and mode choice of residents in the San Francisco Bay Area using 1990 travel diary data and land-use records obtained from the U.S. census, regional inventories, and field surveys.
Abstract: The built environment is thought to influence travel demand along three principal dimensions —density, diversity, and design. This paper tests this proposition by examining how the ‘3Ds’ affect trip rates and mode choice of residents in the San Francisco Bay Area. Using 1990 travel diary data and land-use records obtained from the U.S. census, regional inventories, and field surveys, models are estimated that relate features of the built environment to variations in vehicle miles traveled per household and mode choice, mainly for non-work trips. Factor analysis is used to linearly combine variables into the density and design dimensions of the built environment. The research finds that density, land-use diversity, and pedestrian-oriented designs generally reduce trip rates and encourage non-auto travel in statistically significant ways, though their influences appear to be fairly marginal. Elasticities between variables and factors that capture the 3Ds and various measures of travel demand are generally in the 0.06 to 0.18 range, expressed in absolute terms. Compact development was found to exert the strongest influence on personal business trips. Within-neighborhood retail shops, on the other hand, were most strongly associated with mode choice for work trips. And while a factor capturing ‘walking quality’ was only moderately related to mode choice for non-work trips, those living in neighborhoods with grid-iron street designs and restricted commercial parking were nonetheless found to average significantly less vehicle miles of travel and rely less on single-occupant vehicles for non-work trips. Overall, this research shows that the elasticities between each dimension of the built environment and travel demand are modest to moderate, though certainly not inconsequential. Thus it supports the contention of new urbanists and others that creating more compact, diverse, and pedestrian-orientated neighborhoods, in combination, can meaningfully influence how Americans travel.
3,439 citations
TL;DR: A technique is developed, termed geographically weighted regression, which attempts to capture variation by calibrating a multiple regression model which allows different relationships to exist at different points in space by using Monte Carlo methods.
Abstract: Spatial nonstationarity is a condition in which a simple “global” model cannot explain the relationships between some sets of variables. The nature of the model must alter over space to reflect the structure within the data. In this paper, a technique is developed, termed geographically weighted regression, which attempts to capture this variation by calibrating a multiple regression model which allows different relationships to exist at different points in space. This technique is loosely based on kernel regression. The method itself is introduced and related issues such as the choice of a spatial weighting function are discussed. Following this, a series of related statistical tests are considered which can be described generally as tests for spatial nonstationarity. Using Monte Carlo methods, techniques are proposed for investigating the null hypothesis that the data may be described by a global model rather than a non-stationary one and also for testing whether individual regression coefficients are stable over geographic space. These techniques are demonstrated on a data set from the 1991 U.K. census relating car ownership rates to social class and male unemployment. The paper concludes by discussing ways in which the technique can be extended.
2,330 citations
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05 Dec 1988
682 citations
"Spatial heterogeneity in distance d..." refers background in this paper
...Spatial interactions in geography refer to the dynamic movement flows of human beings or goods in the spatial dimension (Fotheringham and O’Kelly, 1989)....
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TL;DR: In this article, the authors examined how road facility designs, like street density, connectivity, and proximity to Ciclovia lanes, are associated with physical activity, while other attributes of the built environment, like density and land-use mixtures, are not.
652 citations