Functionally critical locations in an urban transportation network: Identification and space–time analysis using taxi trajectories

Zhou, Yang; Fang, Zhixiang; Thill, Jean‐Claude; Li, Qingquan; Li, Yuguang

doi:10.1016/j.compenvurbsys.2015.03.001

Cited by 56 publications

(33 citation statements)

References 27 publications

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“…Third, researchers have used taxicab trajectories to examine land-use types, reflect the spatial structure of urban areas, and examine the interactions between residents and functional zones. Such approaches have been used in, for example, accessibility analysis of urban road networks [27,28], mining alternative space-time path dynamics of travel [29], mining hotspots and points of interest in urban areas [30][31][32], determining the spatiotemporal attractiveness of specific areas [33,34], detection and analysis of functional regions [35][36][37], classification of land-use types [38,39], analysis of the structure of urban regions [40][41][42][43], observing strong links between public transportation terminals [44], evaluating the effectiveness of urban planning after it has been carried out [45], identifying the spatiotemporal patterns of functionally critical locations in urban transportation networks [46], and locating optimal taxi stands on city maps using pick-up and drop-off locations in Singapore [47].…”

Section: Taxi Trajectory Miningmentioning

confidence: 99%

Understanding the Dynamics of the Pick-Up and Drop-Off Locations of Taxicabs in the Context of a Subsidy War among E-Hailing Apps

Fang

Luo

et al. 2018

Sustainability

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The locations where taxicabs pick up and drop off passengers are crucial to understanding the dynamics of taxi trip demand. Investigating their spatial distribution and derived dynamic features is still a key task in the fields of urban geography and transportation. Such investigations are urgently needed, considering the competition created by new communication technology services, specifically e-hailing apps such as Uber, Didi and Kuaidi. For example, a subsidy war between two e-hailing apps occurred in China in 2014. However, how the pickup and drop-off locations of taxicabs change during subsidy wars is still an open question. This paper introduces a methodological framework that can be used to derive the pickup and drop-off dynamics of taxicabs. It also proposes three indexes that can be used to assess the dynamics of the pickup and drop-off locations at the city and sub-district scales, namely the numbers of daily pick ups and drop offs per taxi, average transfer distance per unit area of weighted mean centers of pickup and drop-off locations, and degree of dispersion in the spatial distribution of pickup and drop-off locations. This paper employs data from taxicabs in the city of Shenzhen to uncover the dynamics of their pickup and drop-off locations. The results show that the methodological framework and the indexes introduced are powerful tools for uncovering the dynamics of the pickup and drop-off locations of taxicabs in urban environments.

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Section: Taxi Trajectory Miningmentioning

confidence: 99%

Understanding the Dynamics of the Pick-Up and Drop-Off Locations of Taxicabs in the Context of a Subsidy War among E-Hailing Apps

Fang

Luo

et al. 2018

Sustainability

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“…Recently, owing to the development of technology, the affluent trajectory data of citizens are available by processing massive amounts of emerging geo-tagged data, such as mobile phone record data, taxi trajectory data and social media check-in data. These data have been intensively applied to understand human movements and urban built environments, which contributes to research on human mobility modelling (Brockmann et al 2006, González et al 2008, transportation policymaking (Zheng et al 2011, Wang et al 2012, Zhou et al 2015, regional structure studying , Thiemann et al 2010, Liu et al 2014, urban systems analysis (Zhong et al 2014, Shi et al 2015, Long andThill in press) and many other fields.…”

Section: Introductionmentioning

confidence: 99%

Incorporating spatial interaction patterns in classifying and understanding urban land use

Liu

Kang

Gong

et al. 2015

International Journal of Geographical Information Science

135

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Land use classification has benefited from the emerging big data, such as mobile phone records and taxi trajectories. Temporal activity variations derived from these data have been used to interpret and understand the land use of parcels from the perspective of social functions, complementing the outcome of traditional remote sensing methods. However, spatial interaction patterns between parcels, which could depict land uses from a perspective of connections, have rarely been examined and analysed. To leverage spatial interaction information contained in the above-mentioned massive data sets, we propose a novel unsupervised land use classification method with a new type of place signature. Based on the observation that spatial interaction patterns between places of two specific land uses are similar, the new place signature improves land use classification by trading off between aggregated temporal activity variations and detailed spatial interactions among places. The method is validated with a case study using taxi trip data from Shanghai. ARTICLE HISTORY

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“…Liu et al [6] used the term social sensing to show that big geospatial data provides an alternative approach to uncover land uses and explore how cities function at a fine spatial and temporal resolution. During the past decade, there has been a significant amount of research work focusing on this topic using different types of data, such as taxi GPS data [7,8], mobile phone data [9], social media check-in data [10], etc. These studies enhanced our understanding about how the urban space functions from the perspective of human mobility pattern.…”

Section: Introductionmentioning

confidence: 99%

Inferring Social Functions Available in the Metro Station Area from Passengers’ Staying Activities in Smart Card Data

Zhou

Fang

Zhan

et al. 2017

IJGI

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The function of a metro station area is vital for city planners to consider when establishing a context-aware Transit-Oriented Development policy around the station area. However, the functions of metro station areas are hard to infer using the static land use distribution and other traditional survey datasets. In this paper, we propose a method to infer the functions occurring around the metro station catchment areas according to the patterns of staying activities derived from smart card data. We first define the staying activities by the spatial and temporal constraints of the two consecutive alighting and boarding records from the individual travel profile. Then we cluster and label the whole staying activities by considering the features of duration, frequency, and start time. By analyzing the percentage of different types of aggregated activities happening around each metro station, we cluster and explore the functions of the metro station area. Taking Wuhan as a case study, we analyze the results of Wuhan metro systems and discuss the similarities and differences between the functions and the land use distribution around the station area. The results show that although there exist some agreements, there is also a gap between the human activities and the land uses around the station area. These findings could give us deeper insight into how people act around the stations by metro systems, which will ultimately benefit the urban planning and policy development.

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Functionally critical locations in an urban transportation network: Identification and space–time analysis using taxi trajectories

Cited by 56 publications

References 27 publications

Understanding the Dynamics of the Pick-Up and Drop-Off Locations of Taxicabs in the Context of a Subsidy War among E-Hailing Apps

Understanding the Dynamics of the Pick-Up and Drop-Off Locations of Taxicabs in the Context of a Subsidy War among E-Hailing Apps

Incorporating spatial interaction patterns in classifying and understanding urban land use

Inferring Social Functions Available in the Metro Station Area from Passengers’ Staying Activities in Smart Card Data

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