Assessing Micro-Mobility Services in Pandemics for Studying the 10-Minutes Cities Concept in India Using Geospatial Data Analysis: an Application

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Abstract

Active micro-mobility decreases traffic, bolsters personal health, and helps communities thrive by protecting the environment Moreover, sustainable micro-mobility demand is expected to get boosted in the present and post-COVID society. In this work we highlight the micro-mobility modes of walkability and bicycling to city administrators controlling urban city-space, by adapting the mobility parameters and their use cases through a map-based interface. Software tools and web-based applications are introduced for easy policy decisions by city managers. Present study scope is circumscribed by exploration of different parameters in traditional and state of art data science models, for resource planning like cycle usage prediction and planning. These parameters show hazard safe-distance pedestrian flow, optimal resource planning, amenity reach (10 min cycling and walking distance) and mobility using walking and cycling modes. Parameters of the traditional Social Force Model for Pedestrian Dynamics are also inspected, according to COVID social norms, to capture safe pedestrian flow density. Finally, the analysis of two case studies, of Bhubaneshwar city and New Delhi, in India, are discussed for policy suggestions to enhance mobility via sustainable micro-mobility modes. The developed system assists managers in decisions based on urban data intelligence, and at user end eases commute related mental tension, anxiety and dependencies. The developed application is running live on our server maintained at Edinburgh University.

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APA

Mishra, S., Bhattacharya, D., Chaturvedi, A., & Singh, N. (2022). Assessing Micro-Mobility Services in Pandemics for Studying the 10-Minutes Cities Concept in India Using Geospatial Data Analysis: an Application. In Proceedings of the 15th ACM SIGSPATIAL International Workshop on Computational Transportation Science, IWCTS 2022 (pp. 50–59). Association for Computing Machinery, Inc. https://doi.org/10.1145/3557991.3567788

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