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An entropy-based measurement for understanding origin-destination trip distributions: a case study of New York City taxis
Authors:
Yuqin Jiang,
Yihong Yuan,
Su Yeon Han
Abstract:
A comprehensive understanding of human mobility patterns in urban areas is essential for urban development and transportation planning. In this study, we create entropy-based measurements to capture the geographical distribution diversity of trip origins and destinations. Specifically, we develop origin-entropy and destination-entropy based on taxi and ride-sharing trip records. The origin-entropy…
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A comprehensive understanding of human mobility patterns in urban areas is essential for urban development and transportation planning. In this study, we create entropy-based measurements to capture the geographical distribution diversity of trip origins and destinations. Specifically, we develop origin-entropy and destination-entropy based on taxi and ride-sharing trip records. The origin-entropy for a given zone accounts for all the trips that originate from this zone and calculates the level of geographical distribution diversity of these trips destinations. Likewise, the destination-entropy for a given zone considers all the trips that end in this zone and calculates the level of geographical distribution diversity of these trips origins. Furthermore, we have created an interactive geovisualization that enables researchers to delve into and juxtapose the spatial and temporal dynamics of origin and destination entropy, in conjunction with trip counts for both origins and destinations. Results indicate that entropy-based measurements effectively capture shifts in the diversity of trips geographical origins and destinations, reflecting changes in travel decisions due to major events like the COVID-19 pandemic. These measurements, alongside trip counts, offer a more comprehensive understanding of urban human flows.
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Submitted 16 April, 2024; v1 submitted 30 January, 2024;
originally announced January 2024.
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Estimating hourly population distribution change at high spatiotemporal resolution in urban areas using geo-tagged tweets, land use data, and dasymetric maps
Authors:
Ming-Hsiang Tsou,
Hao Zhang,
Atsushi Nara,
Su Yeon Han
Abstract:
This paper introduces a spatiotemporal analysis framework for estimating hourly changing population distribution in urban areas using geo-tagged tweets (the messages containing users' physical locations), land use data, and dasymetric maps. We collected geo-tagged social media (tweets) within the County of San Diego during one year (2015) by using Twitter's Streaming Application Programming Interf…
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This paper introduces a spatiotemporal analysis framework for estimating hourly changing population distribution in urban areas using geo-tagged tweets (the messages containing users' physical locations), land use data, and dasymetric maps. We collected geo-tagged social media (tweets) within the County of San Diego during one year (2015) by using Twitter's Streaming Application Programming Interfaces (APIs). A semi-manual Twitter content verification procedure for data cleaning was applied first to separate tweets created by humans and non-human users (bots). The next step is to calculate the number of unique Twitter users every hour with the two different geographical units: (1) census blocks, and (2) 1km by 1km resolution grids of LandScan. The final step is to estimate actual dynamic population by transforming the numbers of unique Twitter users in each census block or grid into estimated population densities with spatial and temporal variation factors. A temporal factor was based on hourly frequency changes of unique Twitter users within San Diego County, CA. A spatial factor was estimated by using the dasymetric method with land use maps and 2010 census data. Several comparison maps were created to visualize the spatiotemporal pattern changes of dynamic population distribution.
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Submitted 15 October, 2018;
originally announced October 2018.
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Biomechanical conditions of walking
Authors:
Y. F. Fan,
L. P. Luo,
Z. Y. Li,
S. Y. Han,
C. S. Lv,
B. Zhang
Abstract:
The development of rehabilitation training program for lower limb injury does not usually include gait pattern design. This paper introduced a gait pattern design by using equations (conditions of walking). Following the requirements of reducing force to the injured side to avoid further injury, we developed a lower limb gait pattern to shorten the stride length so as to reduce walking speed, to d…
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The development of rehabilitation training program for lower limb injury does not usually include gait pattern design. This paper introduced a gait pattern design by using equations (conditions of walking). Following the requirements of reducing force to the injured side to avoid further injury, we developed a lower limb gait pattern to shorten the stride length so as to reduce walking speed, to delay the stance phase of the uninjured side and to reduce step length of the uninjured side. This gait pattern was then verified by the practice of a rehabilitation training of an Achilles tendon rupture patient, whose two-year rehabilitation training (with 24 tests) has proven that this pattern worked as intended. This indicates that rehabilitation training program for lower limb injury can rest on biomechanical conditions of walking based on experimental evidence.
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Submitted 2 September, 2015; v1 submitted 14 August, 2015;
originally announced August 2015.