Mining spatiotemporal patterns of urban dwellers from taxi trajectory data

Mao, Feng; Ji, Minhe; Liu, Ting

doi:10.1007/s11707-015-0525-4

Cited by 54 publications

(32 citation statements)

References 12 publications

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“…Shanthi and Ramani [37] used trajectory data to study traffic congestion monitoring and traffic anomalies. Mao, et al [38] probed the spatiotemporal causation of traffic anomalies in traffic flow; Chawla, et al [39] deduced the root cause of traffic anomalies; Pan, et al [40] conducted congestion perception in traffic anomalies based on crowd movement and social networks Aspects of research. At present, the speed of uploading and processing trajectory data is in demand to meet the real-time requirements.…”

Section: Existing Knowledge Of Trajectory Miningmentioning

confidence: 99%

The impact of Planned Special Events (PSEs) on urban traffic congestion

Fernando¹

2018

ICST Transactions on Scalable Information Systems

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The transport infrastructure of many cities has not been able to keep up with the pace of growth in the motorization rate or to counteract the intensification of urban traffic. Rising in traffic congestion in cities not only impacts the productivity costing billions but also responsible for more than 40% of all CO2 emissions which results in global warming. While expansion and construction of new roads may be considered in some cases, in most, better management of existing infrastructure to lower traffic congestion is the only option.The current state of the art commercial solutions can predict the recurring traffic situations, as this behavior can be easily learned from historical data. The challenge is to predict the non-recurrent congestion caused by events such as accidents, adverse weather, construction zones and Planned Special Event (PSE). Past research has shown, PSEs such as concerts or sports games, festivals and conventions has a huge impact on everyday urban transportation. Therefore, the aim of the proposed research is to investigate the impact PSEs on urban traffic congestion.The proposed research is applying spatial-temporal big data mining methods to predict the impact of PSEs on the urban concession. Specifically, the proposed research will consider the characteristics of PSEs such as location, type, duration, audience and time and day of the event in the proposed analysis approach which enables to predict of urban congestion for future PSEs.

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Section: Existing Knowledge Of Trajectory Miningmentioning

confidence: 99%

The impact of Planned Special Events (PSEs) on urban traffic congestion

Fernando¹

2018

ICST Transactions on Scalable Information Systems

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“…Mao et al presented another novel approach for the spatial clustering of OD pairs based on traffic grid partitioning to discover spatiotemporal patterns in urban commuting and the job-housing balance. This method can create clusters using traffic grids of different sizes to adapt to different density surfaces and identify threshold values from the statistics of OD clusters to extract urban job-housing structures [11].…”

Section: Point Clustering Methods For Od Datamentioning

confidence: 99%

A Simple Line Clustering Method for Spatial Analysis with Origin-Destination Data and Its Application to Bike-Sharing Movement Data

Zhang

Chen

et al. 2018

IJGI

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Clustering methods are popular tools for pattern recognition in spatial databases. Existing clustering methods have mainly focused on the matching and clustering of complex trajectories. Few studies have paid attention to clustering origin-destination (OD) trips and discovering strong spatial linkages via OD lines, which is useful in many areas such as transportation, urban planning, and migration studies. In this paper, we present a new Simple Line Clustering Method (SLCM) that was designed to discover the strongest spatial linkage by searching for neighboring lines for every OD trip within a certain radius. This method adopts entropy theory and the probability distribution function for parameter selection to ensure significant clustering results. We demonstrate this method using bike-sharing location data in a metropolitan city. Results show that (1) the SLCM was significantly effective in discovering clusters at different scales, (2) results with the SLCM analysis confirmed known structures and discovered unknown structures, and (3) this approach can also be applied to other OD data to facilitate pattern extraction and structure understanding.

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“…As an essential part of urban public transportation, taxis provide point-to-point services to urban residents. In recent years, with the rapid development of sensor technology, most taxis in China have been equipped with GPS track recording devices to track the route and space-time information of taxis (Mao et al, 2016;Yang et al, 2018). Taxi GPS trajectories data records abundant and detailed spatial-temporal traveling information of passengers, which reflects the traveling behaviors of urban residents to a certain degree (Wang et al, 2015;Zhang et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Local Maximum Density Approach for Small-Scale Clustering of Urban Taxi Stops

Wang

Chen

Wang

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Taxi trajectory data contains the detailed spatial and temporal traveling information of urban residents. By using a clustering algorithm, the hotspots’ distributions of pick-up and drop-off points can be extracted to explore the patterns of taxi traveling behaviors and its relationship with urban environment. Comparing with traditional methods that determine hotspots at a relatively large scale, we propose an approach to detect small-scale hotspots, so called docking points, to represent the local clusters in both sparse and dense stops areas. In this method, we divide the research area into grids and extract the docking points by finding local maximums of a certain range. The extracted docking points are classified into five levels for the subsequent analysis. Finally, to uncover detail characteristics of taxi mobility patterns, we analyze the distributions of docking points from three aspects &ndash; the overall, by day of the week, and by time of the day.</p>

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Mining spatiotemporal patterns of urban dwellers from taxi trajectory data

Cited by 54 publications

References 12 publications

The impact of Planned Special Events (PSEs) on urban traffic congestion

The impact of Planned Special Events (PSEs) on urban traffic congestion

A Simple Line Clustering Method for Spatial Analysis with Origin-Destination Data and Its Application to Bike-Sharing Movement Data

Local Maximum Density Approach for Small-Scale Clustering of Urban Taxi Stops

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