Mining urban recurrent congestion evolution patterns from GPS-equipped vehicle mobility data

An, Shi; Yang, Haiqiang; Wang, Jian; Cui, Na; Cui, Jianxun

doi:10.1016/j.ins.2016.06.033

Cited by 70 publications

(28 citation statements)

References 27 publications

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“…Fortunately, urban crowd flows manifest significant spatial and temporal correlations [36], as they usually have daily and weekly periodic patterns as well as instantaneous responses due to environmental and social conditions [37]. For instance, adjacent crowded spots have strong interactions with each other, and a crowded spot remains crowded in consecutive time periods [38,39]. A great deal of research has formed a macroscopic description of urban crowd flows and their propagation in time and space based on crowd simulation [40] and traffic flow theories [41].…”

Section: Related Workmentioning

confidence: 99%

Modeling Spatio-Temporal Evolution of Urban Crowd Flows

Qin

Kang

et al. 2019

IJGI

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Metropolitan cities are facing many socio-economic problems (e.g., frequent traffic congestion, unexpected emergency events, and even human-made disasters) related to urban crowd flows, which can be described in terms of the gathering process of a flock of moving objects (e.g., vehicles, pedestrians) towards specific destinations during a given time period via different travel routes. Understanding the spatio-temporal characteristics of urban crowd flows is therefore of critical importance to traffic management and public safety, yet it is very challenging as it is affected by many complex factors, including spatial dependencies, temporal dependencies, and environmental conditions. In this research, we propose a novel matrix-computation-based method for modeling the morphological evolutionary patterns of urban crowd flows. The proposed methodology consists of four connected steps: (1) defining urban crowd levels, (2) deriving urban crowd regions, (3) quantifying their morphological changes, and (4) delineating the morphological evolution patterns. The proposed methodology integrates urban crowd visualization, identification, and correlation into a unified and efficient analytical framework. We validated the proposed methodology under both synthetic and real-world data scenarios using taxi mobility data in Wuhan, China as an example. Results confirm that the proposed methodology can enable city planners, municipal managers, and other stakeholders to identify and understand the gathering process of urban crowd flows in an informative and intuitive manner. Limitations and further directions with regard to data representativeness, data sparseness, pattern sensitivity, and spatial constraint are also discussed.

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Section: Related Workmentioning

confidence: 99%

Modeling Spatio-Temporal Evolution of Urban Crowd Flows

Qin

Kang

et al. 2019

IJGI

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“…For example, grid G i, j was congested during 12 time intervals, so has a congestion frequency (c f ) of 12. The spatial adjacent grids that had greater congestion frequency (c f ) value were labeled recurrent congestion areas, which were considered during the clustering process [6]. In Figure 5,…”

Section: Rc Area Identificationmentioning

confidence: 99%

“…One-week data, from 16-22 November 2015, were used to verify this detecting method. The time interval was set to 10 min, which adequately reflects the change in traffic state in each grid, to ensure each grid obtains enough data to apply the methods [6]. Figure 6 illustrates the N~V distribution of type 2 (i.e., G 12, 39 ), type 3 (i.e., G 38, 40 ) and type 4 (i.e., G 9, 46 ) on 19 December 2015.…”

Section: Detecting Grid Congestionmentioning

confidence: 99%

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Revealing Recurrent Urban Congestion Evolution Patterns with Taxi Trajectories

Yang

Wang

2018

IJGI

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Urban congestion can be classified into two types: Recurrent Congestion (RC) and Non-Recurrent Congestion (NRC). RC is more regular than NRC, having fixed and long-standing patterns. Mining urban recurrent congestion evolution patterns can assist with congestion cause analysis and the creation of alleviating strategies. Most existing methods for analyzing urban congestion patterns are based on traditional traffic detector data, which is inflexible and expensive. Additionally, prior research primarily focused on the microscopic model, which simulated congestion propagation based on theoretical models and hypothetical networks. As such, most previous models and methods are difficult to apply to real case scenarios. Therefore, we investigated recurrent congestion patterns by mining historical taxi trajectory data that were collected in Harbin, China. A three-step method is proposed to reveal urban recurrent congestion evolution patterns. Firstly, a grid-based congestion detection method is presented by calculating the change in taxi global positioning system (GPS) trajectory patterns. Secondly, a customized cluster algorithm is applied to measure the recurrent congestion area. Finally, a series of indicators are proposed to reflect RC evolution patterns. A case study was competed in the Harbin urban area to evaluate the main methods. Finally, RC cause analysis and alleviating strategy are discussed. The results study are expected to provide a better understanding of urban RC evolution patterns.

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“…With the rapid development of data-based technology, various intelligent transportation systems are widely applied in public transit system. These systems could collect residents' mobility data every day, including longitude, latitude, boarding time, and dropping off time [14]. In the last decade, various researches based on these data have been carried out, for example, mining urban recurrent congestion evolution patterns from GPS-equipped vehicle mobility data [14], comparing accessibility in urban slums using smart card and bus GPS data [15,16], discovering functional zones using bus smart card data [17], and partitioning bus operating hours into time of day intervals based on bus GPS data [18], which makes the data based transportation research to be a hot spot of transportation field [19].…”

Section: Introductionmentioning

confidence: 99%

Discovering Public Transit Riders’ Travel Pattern from GPS Data: A Case Study in Harbin

Wang

Yang

et al. 2017

Journal of Sensors

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This paper proposes a public transit riders' travel pattern measuring method based on divided cells and public transit vehicle's GPS data. The method consists of two parts: detecting urban origin and destination areas and measuring the public transit riders' travel pattern. Moreover, a series of indicators are proposed to reflect the public transit riders' travel pattern. A case study is carried out to evaluate the methods, which use the GPS data collected from taxis and buses in Harbin, China. The study is expected to provide a better understanding of public transit riders' travel patterns.

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Mining urban recurrent congestion evolution patterns from GPS-equipped vehicle mobility data

Cited by 70 publications

References 27 publications

Modeling Spatio-Temporal Evolution of Urban Crowd Flows

Modeling Spatio-Temporal Evolution of Urban Crowd Flows

Revealing Recurrent Urban Congestion Evolution Patterns with Taxi Trajectories

Discovering Public Transit Riders’ Travel Pattern from GPS Data: A Case Study in Harbin

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