Characterizing Passenger Flow for a Transportation Hub Based on Mobile Phone Data

Zhong, Gang; Wan, Xiongbo; Zhang, Jian; Ran, Bin

doi:10.1109/tits.2016.2607760

Cited by 26 publications

(24 citation statements)

References 16 publications

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“…Common visualization formats are summarized as follows. A scatter plot on a map is used to visualize trajectories and important locations, such as the trip example in [11] and base station map in [12]. A bar chart or histogram is often used to study mobility attribute values that are accumulated in consecutive time intervals, such as the histogram of flow densities in [1].…”

Section: B Common Data Analysis Methodsmentioning

confidence: 99%

“…M.6 -Clustering: In mobility research, clustering is mainly used to: 1) find people's staying spots and PoIs [4], 2) cluster people into different groups by their mobility patterns [13], 3) cluster cell stations (or BDRs and WFPRRs sensing stations) into different groups by their utility patterns [12]. Popular algorithms used are Density-based spatial clustering of applications with noise (DBSCAN) and k-means clustering.…”

Section: B Common Data Analysis Methodsmentioning

confidence: 99%

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Understanding Urban Human Mobility through Crowdsensed Data

et al. 2018

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Understanding how people move in the urban area is important for solving urbanization issues, such as traffic management, urban planning, epidemic control, and communication network improvement. Leveraging recent availability of large amounts of diverse crowdsensed data, many studies have made contributions to this field in various aspects. They need proper review and summary. In this paper, therefore, we first review these recent studies with a proper taxonomy with corresponding examples. Then, based on the experience learnt from the studies, we provide a comprehensive tutorial for future research, which introduces and discusses popular crowdsensed data types, different human mobility subjects, and common data preprocessing and analysis methods. Special emphasis is made on the matching between data types and mobility subjects. Finally, we present two research projects as case studies to demonstrate the entire process of understanding urban human mobility through crowdsensed data in city-wide scale and building-wide scale respectively. Beyond demonstration purpose, the two case studies also make contributions to their category of certain crowdsensed data type and mobility subject.

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Section: B Common Data Analysis Methodsmentioning

confidence: 99%

Section: B Common Data Analysis Methodsmentioning

confidence: 99%

Understanding Urban Human Mobility through Crowdsensed Data

et al. 2018

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“…have been applied to characterise human activity. Among these data sources, the application of mobile phone data has shown more potential for the extensive popularity of mobile phones, even in the developing countries [2].…”

Section: Previous Workmentioning

confidence: 99%

Support vector machine and back propagation neutral network approaches for trip mode prediction using mobile phone data

Zhou

Yang

et al. 2018

IET Intelligent Transport Systems

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“…A GPS-based WLT can provide more accurate location information especially when AGPS chips are assisted with cell phone signals. (Zahradnik, F., Assisted GPS, A-GPS, AGPS, https://www.lifewire.com/assisted-gps-1683306) Zhong et al [14] estimated passenger traffic flows of a transportation hub using mobile phone data using AGPS-enabled phones.…”

Section: Traffic Monitoringmentioning

confidence: 99%

Real-Time Transportation Mode Identification Using Artificial Neural Networks Enhanced with Mode Availability Layers: A Case Study in Dubai

Byon

Cho

et al. 2017

Applied Sciences

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Traditionally, departments of transportation (DOTs) have dispatched probe vehicles with dedicated vehicles and drivers for monitoring traffic conditions. Emerging assisted GPS (AGPS) and accelerometer-equipped smartphones offer new sources of raw data that arise from voluntarily-traveling smartphone users provided that their modes of transportation can correctly be identified. By introducing additional raster map layers that indicate the availability of each mode, it is possible to enhance the accuracy of mode detection results. Even in its simplest form, an artificial neural network (ANN) excels at pattern recognition with a relatively short processing timeframe once it is properly trained, which is suitable for real-time mode identification purposes. Dubai is one of the major cities in the Middle East and offers unique environments, such as a high density of extremely high-rise buildings that may introduce multi-path errors with GPS signals. This paper develops real-time mode identification ANNs enhanced with proposed mode availability geographic information system (GIS) layers, firstly for a universal mode detection and, secondly for an auto mode detection for the particular intelligent transportation system (ITS) application of traffic monitoring, and compares the results with existing approaches. It is found that ANN-based real-time mode identification, enhanced by mode availability GIS layers, significantly outperforms the existing methods.

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Characterizing Passenger Flow for a Transportation Hub Based on Mobile Phone Data

Cited by 26 publications

References 16 publications

Understanding Urban Human Mobility through Crowdsensed Data

Understanding Urban Human Mobility through Crowdsensed Data

Support vector machine and back propagation neutral network approaches for trip mode prediction using mobile phone data

Real-Time Transportation Mode Identification Using Artificial Neural Networks Enhanced with Mode Availability Layers: A Case Study in Dubai

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