Spatial and temporal analysis of bike-sharing use in Cologne taking into account a public transit disruption

Schimohr, Katja; Scheiner, Joachim

doi:10.1016/j.jtrangeo.2021.103017

Cited by 37 publications

(18 citation statements)

References 27 publications

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“…Bike sharing is mostly used for trips and commuting within 30 min [ 8 ]. Bike sharing is sometimes a faster alternative for trips within 3 km in prosperous urban areas [ 37 ].…”

Section: Literature Reviewmentioning

confidence: 99%

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The Impact of COVID-19 on Travel Mode Choice Behavior in Terms of Shared Mobility: A Case Study in Beijing, China

Zhang

Shao

Wang

et al. 2022

IJERPH

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Shared mobility is growing rapidly and changing the mobility landscape. The COVID-19 pandemic has complicated travel mode choice behavior in terms of shared mobility, but the evidence on this impact is limited. To fill this gap, this paper first designs a stated preference survey to collect mode choice data before and during the pandemic. Different shared mobility services are considered, including ride hailing, ride sharing, car sharing, and bike sharing. Then, latent class analysis is used to divide the population in terms of their attitudes toward shared mobility. Nested logit models are applied to compare travel mode choice behavior during the two periods. The results suggest that shared mobility has the potential to avoid the high transmission risk of public transport and alleviate the intensity of private car use in the COVID-19 context, but this is limited by anxiety about shared spaces. As the perceived severity of the pandemic increases, preference for ride hailing and ride sharing decreases, and a price discount for ride hailing is more effective than that for ride sharing at maintaining the ridership despite the impact of COVID-19. These findings contribute to understanding the change in travel demand and developing appropriate strategies for shared mobility services to adapt to the pandemic.

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“…Bike sharing is mostly used for trips and commuting within 30 min [ 8 ]. Bike sharing is sometimes a faster alternative for trips within 3 km in prosperous urban areas [ 37 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Many parking stations are located in car sharing regions, and users can pick up and return vehicles to different nearby stations as required. The only non-motorized form of shared mobility is bike sharing, consisting of almost 18 million bicycles in more than 2100 cities worldwide as of early 2020 [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

The Impact of COVID-19 on Travel Mode Choice Behavior in Terms of Shared Mobility: A Case Study in Beijing, China

Zhang

Shao

Wang

et al. 2022

IJERPH

View full text Add to dashboard Cite

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“…Consequently, the difference between the utilization in normal weather and the plum rain weather is the rough impact on the public bicycle system. Such subjective accounts have been studied by scholars [44,45] through descriptive and inferential statistics for understanding the demographic and socio-economic characteristics of the user. As confirmed by the results of this study, an important issue lies in the selection of the number of cyclists, place semantic, and riding distance as the affected dimensions.…”

Section: Perspectives Selection Of Affected Dimensionsmentioning

confidence: 99%

Understanding Plum Rain’s Effects on Urban Public Bicycle Unavailability Considering Both Place Semantics and Riding Distance

Chen

Zhang

Wang

et al. 2021

IJGI

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The effect of the plum rain weather event on cycling trips reflects the climate resilience of the public bicycle system. However, quantitative studies regarding the impact of plum rain on public bicycle users and corresponding spatial heterogeneity have not been paid much attention. This paper explores the spatial pattern of affected levels from the perspective of cyclist number, place semantics and riding distance. Corresponding public bicycle trips in normal weather are predicted by spatial-temporal random forest prediction. GIS neighborhood statistics and clustering algorithms are adapted to analyze and visualize the affected levels using origin-destination data of public bicycle trips and point of interest data of city public facilities. It is proved that there is an obvious spatial difference in affected levels by plum rain from three dimensions. In the dimension of the number of cyclists, the docking stations with different affected levels are distributed across the whole urban area. In the place semantic dimension, the docking stations with high affected levels show a clustered zonal distribution in the city center. In the dimension of cycling distance, the docking stations with high affected levels are mainly distributed in the periphery of the central urban area. The study theoretically expands the impact mechanism of environment and active transport. It is beneficial for the early monitoring, warning and assessment of climate change risks for public bicycle planning and management.

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“…In the second step, we cluster the amenities and categorise eleven geospatial factors according to Wagner et al (2014), Bendler et al (2016), Klemmer et al (2016), Valizade-Funder et al (2018 and Schimohr and Scheiner (2021). The defined categories are shopping, health, food services, leisure time, grocery, services and specialty retail, finance and insurance, education, public sector, religion and others.…”

Section: Clustering and Categorisation (B)mentioning

confidence: 99%

“…The results of the regression analysis generate knowledge, which they transfer on a further city (Berlin) to predict car sharing behaviour and approve their approach. Wang and Chen (2020), Schimohr and Scheiner (2021) and Wang et al (2021) apply the approaches to bike sharing use cases on different continents. They identify a spacetime relationship between bike sharing and geospatial factors with regression analysis and diverse methods of machine learning.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Evaluation of Geospatial Factors in Smart Cities: A Study of Off-Street Parking in Mainz, Germany

Rolwes

Böhm

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Geospatial data often build the basis for planning decisions in smart cities. In the decision-making process, geospatial relationships have not yet been fully considered and represented. The aim of our research is to investigate these geospatial relationships. This paper presents a four-step process to identify geospatial key factors in smart city use cases. We further develop and evaluate an existing metric to measure the impact of geospatial factors in urban area. For this, we use three variables to characterise distance decay, opening hours and an attractiveness weight for customers depends on the use case. The use case of this study is off-street parking over a period of four years in Mainz, Germany. The results show temporal relationships between parking and geospatial factors. Consequently, our research indicates the impact of different factors on car parks. This knowledge enriches decisions for sustainable planning in cities.

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Spatial and temporal analysis of bike-sharing use in Cologne taking into account a public transit disruption

Cited by 37 publications

References 27 publications

The Impact of COVID-19 on Travel Mode Choice Behavior in Terms of Shared Mobility: A Case Study in Beijing, China

The Impact of COVID-19 on Travel Mode Choice Behavior in Terms of Shared Mobility: A Case Study in Beijing, China

Understanding Plum Rain’s Effects on Urban Public Bicycle Unavailability Considering Both Place Semantics and Riding Distance

Analysis and Evaluation of Geospatial Factors in Smart Cities: A Study of Off-Street Parking in Mainz, Germany

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