Analysis of cycling accessibility using detour ratios – A large-scale study based on crowdsourced GPS data

Chou, Kuan-Yeh; Paulsen, Mads; Nielsen, Otto Anker; Jensen, Anders Fjendbo

doi:10.1016/j.scs.2023.104500

Cited by 13 publications

(3 citation statements)

References 52 publications

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“…Recent studies provide methods to forecast cycling/pedestrian flow from mobile devices, STRAVA and other web platforms [49][50][51][52], but these methods are limited to work only on existing infrastructure.…”

Section: Micro-mobility Modeling and Simulationmentioning

confidence: 99%

Enhancing Sustainable Mobility: Evaluating New Bicycle and Pedestrian Links to Car-Oriented Industrial Parks with ARAS-G MCDM Approach

Zagorskas,

Turskis

2024

Sustainability

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The aim of this research is to address the challenge of transforming car-oriented industrial parks into pedestrian- and bicycle-friendly environments. Through the implementation of a multi-criteria decision-making (MCDM) approach, the study aims to evaluate alternative pathway connections and assess their potential impact on bicycle and pedestrian traffic volumes. By enhancing the connectivity of the cycling pathway network, the research seeks to demonstrate the potential for substantial increases in cycling and walking within industrial zones. This research leverages a multi-criteria decision-making framework, specifically the ARAS-G method, and integrates geographic information system analysis alongside Python scripting to project future bicycle usage and assess alternative pathway connections. The study underscores the potential for substantial increases in cycling and walking by augmenting the connectivity of the cycling pathway network. The findings hold practical significance for urban planners and industrial zone developers, advocating a holistic approach to sustainable transportation. The research contributes a comprehensive set of criteria encompassing connectivity, safety, accessibility, efficiency, integration within the urban fabric, and cost-effectiveness to evaluate sustainability and prioritize actions and measures for reestablishing industrial zones as bicycle-friendly spaces.

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Section: Micro-mobility Modeling and Simulationmentioning

confidence: 99%

Enhancing Sustainable Mobility: Evaluating New Bicycle and Pedestrian Links to Car-Oriented Industrial Parks with ARAS-G MCDM Approach

Zagorskas,

Turskis

2024

Sustainability

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“…Hou et al quantified the use of public spaces through people's location information [57]. In addition, pedestrian trajectories explain the link between population activity and factors such as climate and accessibility [58,59].…”

Section: Related Research On Pedestrian Activity Trajectoriesmentioning

confidence: 99%

Research on the Spatial and Temporal Dynamics of Crowd Activities in Commercial Streets and Their Relationship with Formats—A Case Study of Lao Men Dong Commercial Street in Nanjing

Hu,

Ren,

Tan

et al. 2023

Sustainability

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Crowd activity is an important indicator of commercial streets’ attractiveness and developmental potential. The development of positioning technologies such as GPS and mobile signal tracking has provided a large amount of trajectory data for studying crowd activities on commercial streets. These data can not only be used for the statistics, extraction, and visualization of crowd information, but they also facilitate the exploration of deeper insights into dynamic behaviors, choices, trajectories, and other details of crowd activities. Based on this, this article proposes a new framework for analyzing crowd activities to explore the spatial activity patterns of crowds and understand the dynamic spatial needs of people by analyzing their correlations with local formats. Specifically, we analyze the spatial activity characteristics of a crowd in the Lao Men Dong Commercial Street area by identifying the stay points and trajectory clusters of the crowd, and we establish a regression analysis model by selecting commercial street format variables to evaluate their impact on crowd activities. Through case analysis of the Lao Men Dong Commercial Street, this study confirms that our method is feasible and suitable for spatial research at different scales, thereby providing relevant ideas for format location selection, spatial layout, and other planning types, and for promoting the sustainable development of urban spaces.

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“…Fortunately, the widespread use of smartphones enables high-resolution spatial and temporal data collection from a large number of active travelers with a relatively low burden [ 1 ]. These rich crowd-sourced data are valuable in various types of walking and cycling analyses such as estimating volumes [ 2 , 3 ], studying air pollution exposure [ 4 ], identifying network improvements [ 5 , 6 ], and studying routes, accessibility, and traffic flow [ 7 – 9 ].…”

Section: Introductionmentioning

confidence: 99%

By cyclists, for cyclists: Road grade and elevation estimation from crowd-sourced fitness application data

Berjisian,

Bigazzi,

Barkh

2023

PLoS ONE

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Road grade or slope is a key factor for walking and cycling behavior and outcomes (influencing route, speed, energy, etc.). For this reason, the scarcity of precise road grade data presents a challenge for travel information and analysis. This paper examines the accuracy of using crowd-sourced GPS data from a fitness application to estimate roadway grade profiles, which can then be used to develop network-wide road grade datasets. We externally validate an elevation estimation method described by McKenzie and Janowicz using field surveying data, and then propose and evaluate modifications for estimation of road grade (which is more directly relevant than elevation for walking and cycling analysis). We find that a modest amount of crowd-sourced GPS data can be used to generate relatively accurate road grade estimates: better than commonly-used low-resolution elevation models, but not as accurate as high-resolution data derived from LiDAR (Light Detection and Ranging). We also find that the grade estimates are more reliable than the elevation estimates, relative to alternative data sources. The most accurate method to aggregate crowd-sourced GPS data builds a composite roadway grade profile using partition-around-medoid clustering of individual grade sequences, first smoothed with a Savitzky-Golay filter and cleaned with Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Implementing this method with an average of 150 GPS traces per location yields a root mean square error (RMSE) of 1% road grade. The findings in this paper can be used to incorporate precise road grade information into street network datasets over a wide spatial scale, which is necessary for walking and cycling analysis that fully considers the physiological aspects of active transportation.

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Analysis of cycling accessibility using detour ratios – A large-scale study based on crowdsourced GPS data

Cited by 13 publications

References 52 publications

Enhancing Sustainable Mobility: Evaluating New Bicycle and Pedestrian Links to Car-Oriented Industrial Parks with ARAS-G MCDM Approach

Enhancing Sustainable Mobility: Evaluating New Bicycle and Pedestrian Links to Car-Oriented Industrial Parks with ARAS-G MCDM Approach

Research on the Spatial and Temporal Dynamics of Crowd Activities in Commercial Streets and Their Relationship with Formats—A Case Study of Lao Men Dong Commercial Street in Nanjing

By cyclists, for cyclists: Road grade and elevation estimation from crowd-sourced fitness application data

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