The Impact of Rainfall on Urban Human Mobility from Taxi GPS Data (original) (raw)
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International Journal on Advanced Science, Engineering and Information Technology, 2018
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Computing Research Repository, 2011
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PROJECTIONS - CAADRIA, 2021
It is widely accepted that cities cultivate innovation and are the engines of productivity. The identification of strengths and weaknesses will enchant social mobility providing equal opportunities for all. The study at hand investigates the relationship between social mobility and transportation planning in 1,860 central urban areas across the globe. Datamining processes combining open-sourced, automated, and crowdsourced information from four major pillars of social mobility (demographics, human activity, transport infrastructure, and environmental quality) are used to describe each location. Next, unsupervised clustering algorithms are used to analyse the extracted information, in order to identify similar characteristics and patterns among urban areas. The process, which comprises an objective framework for the analysis of urban environments, resulted in four major types of central areas, that represent similar patterns of human activity and transport infrastructure.
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Understanding human mobility dynamics is of fundamental significance for many applications, and a wide range of data-driven mobility studies have been conducted using different datasets. Mobility traces which provide digital records of individual mobility allow analysis of individual mobility patterns, trends, and anomalies. Bicycle Sharing Systems (BSS) with origin-destination (OD) sensing systems that record departure and arrival times of each trip are among the most promising urban transport systems which do have such digital data available. BSS allow users to choose their own origin, route, and destination as well as travel time based on their needs. This flexibility leads to uncertainty on the operator side in terms of system use, and this thesis explores both uncertainty and regularity in demand to gain new insights for improving BSS deployments, services, and operations. Using BSS data from two cities (London and Washington DC), this thesis focuses on three main topics: station neighbourhood analysis, individual next place prediction, and prediction of system demand from system-level to individual station-level. Stations neighbourhood analysis aims to reveal the quality of connections among nearby stations by examining users' behaviour in choosing other stations when their commonly visited station is disturbed because it is of out of service (shutdown) or in an imbalanced state (full or empty). Two methods are proposed to conduct this analysis which are spatial-mobility-motifs and station temporary shutdown. Two metrics are also proposed to measure the quality of connections which are impact distance and usage transformation. Results show that 300 metres of travel distance is the impact distance of a station shutdown as measured by at least 20% usage change for nearby stations. 300 metres is also the most common distance that appears during motif analysis when users choose nearby stations within a neighbourhood. Results from these both analyses could be used to help BSS operators identify potentially ineffective stations and isolated stations. 300 metres can also be used as a standard distance between stations when deploying a new system or redesigning the existing network topology. User clustering aims to group users with similar mobility behaviour. Information theory is then used to measure the next-location predictability of each cluster. The goal is to identify highly predictable users so that useful services might be offered based on their predicted next place. Two temporal clustering metrics are proposed which are total trips (1 feature) and hourly trips across the day (24 features). These metrics adequately reflect the frequency and the regularity of user mobility. Three clusters are identified with distinct spatiotemporal characteristics which are named casual users, regular users, and commuters. Entropy analysis demonstrates that
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Nowadays we witness a rapid increase of people mobility as the world population has become more interconnected and is relying on faster transportation methods, simplified connections and shorter commuting times. Unveiling and understanding human mobility patterns have become a crucial issue to support decisions and prediction activities when managing the complexity of the today’s social organization. The strict connections between human mobility patterns, the planning, deployment and management of a variety of public and commercial services have fueled the rise of a vast research activity. Throughout this work, we are more interested and mainly focusing on urban mobility because here most of the human interactions take place and mobility has the greatest impact on management and optimization of public and commercial services. In this thesis, we provided a general framework for dealing with the modeling importance of locations from a per-user perspective and identified a few novel pr...