Taxi Driver’s Operation Behavior and Passengers’ Demand Analysis Based on GPS Data

Hu, Xiaowei; An, Shi; Jian, Wang

doi:10.1155/2018/6197549

Cited by 24 publications

(19 citation statements)

References 32 publications

(41 reference statements)

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“…Therefore, it is first necessary to associate the pick-up/drop-off locations with the buildings. Existing studies have found that the maximum walking distance of taxi passengers is approximately 300 meters; hence, we used this as the distance from the passenger's pick-up/drop-off location to the destination building and set the building buffer radius parameter as 300 meters [28][29][30], which is used to calculate the total number of pick-ups/drop-offs for the buildings within the range.…”

Section: Construction Of the Spatial-temporal Interaction Matrix Of Tmentioning

confidence: 99%

Identifying Building Functions from the Spatiotemporal Population Density and the Interactions of People among Buildings

Shi

Zhang

et al. 2019

IJGI

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Buildings are fundamental components of cities. Understanding the function of buildings is therefore of great importance for urban development and management. Some studies have identified building functions using spatiotemporal data, which assumes that buildings with the same function have similar temporal activity patterns. However, these methods present difficulties in coping with the situation when buildings with the same function have heterogeneous activity patterns. To solve this problem, this research proposes a new method to identify building functions from the perspective of the spatial distribution and spatial interactions of human activities. First, taxi data were used to acquire the spatiotemporal interaction characteristics among buildings with different functions. Then, the spatiotemporal population density distribution was adopted to depict the building vitality. Finally, an iterative clustering method was introduced to identify the building functions. The proposed scheme was applied in the Haizhu district of Guangzhou and compared with the traditional method. The results prove that the spatial interaction characteristics are more helpful than the temporal variation characteristics and therefore can be used to improve the accuracy of building function identification. A higher accuracy for identifying building functions can be realized by combining the spatiotemporal interactions and building vitality characteristics. The overall accuracy reaches 0.8566, with a Kappa coefficient of 0.8174, which are both better than the results of using a single characteristic only.

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Section: Construction Of the Spatial-temporal Interaction Matrix Of Tmentioning

confidence: 99%

Identifying Building Functions from the Spatiotemporal Population Density and the Interactions of People among Buildings

Shi

Zhang

et al. 2019

IJGI

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“…After the community discovery algorithm based on multi-objective optimization solves the division model of the public bicycle scheduling area, the experimental results shown in Figure 5.6 are obtained. By comparison between Figure 5.4, the result shows that the sites along the Williamsburg Bridge and the riverside along Manhattan is divided into the same dispatch area, which is more n line with the rules of public bicycle rental and resolving the anomaly [27]. The difference between the number of sites and regional sites is too large [28].…”

Section: Resultsmentioning

confidence: 86%

Research on urban public bicycle dispatching optimization method

Lin

Yang

Wang

et al. 2019

Preprint

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Unreasonable public bicycle dispatching area division seriously affects the operational efficiency of the public bicycle system. To solve this problem, this paper innovatively proposes an improved community discovery algorithm based on multi-objective optimization (CDoMO). The data set is preprocessed into a lease/return relationship, thereby it calculated a similarity matrix, and the community discovery algorithm Fast Unfolding is executed on the matrix to obtain a scheduling scheme. For the results obtained by the algorithm, the workload indicators (scheduled distance, number of sites, and number of scheduling bicycles) should be adjusted to maximize the overall benefits, and the entire process is continuously optimized by a multi-objective optimization algorithm NSGA2. The experimental results show that compared with the clustering algorithm and the community discovery algorithm, the method can shorten the estimated scheduling distance by 20%-50%, and can effectively balance the scheduling workload of each area. The method can provide theoretical support for the public bicycle dispatching department, and improve the efficiency of public bicycle dispatching system.

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“…It is well known that GPS does not efficiently function in tunnels and densely built up locations [38]. Likewise, some of the RFID tags may not be detected in very dense crowds, and the local sensor network may not be deployed in places with a lack of space.…”

Section: Radio Frequency Identification (Rfid) and Wireless Sensormentioning

confidence: 99%

Managing Crowds with Wireless and Mobile Technologies

Yamin

Basahel

Sen

2018

Wireless Communications and Mobile Computing

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Thousands of people have lost their lives in stampedes and other crowd related disasters in recent years. Most of these fatalities seem to have been caused by poor control and management of crowds, which is discussed in this article. An efficient and effective crowd management system must also have a plan to deal with the ongoing threat of terrorism and outbreak of various kinds of communicable diseases. In this article, we present a framework of a Crowd Control and Health Management System specially designed to prevent and manage stampedes and other disasters. The system has two subsystems; one for dealing with the management of stampedes and other disasters and the other with healthcare management. As part of the proposed system, an algorithm for an early detection of stampedes, with proof and simulation of implementation, is provided. As part of the healthcare management subsystem, we integrate several mobile applications and develop four of them dealing with relief issues, blood donations, complaints and alerts, and utilizing mobile phones as a sensor device. Our system makes use of various kinds of wireless, mobile, and other technologies and tools including Fog Computing, Smart Phones, Smart Digital Street, IP-Cameras, Radio Frequency Identification (RFID), Voice Alarm, Light Alarm, and Global Positioning System (GPS). We compare merits and effectiveness of RFID and Wireless Sensor Networks (WSNs), as well as those of Cloud and Fog with a view of using them as part of the proposed framework. We also discuss applications of our systems in real-life cases of Hajj, an annual pilgrimage of millions of people to Mecca, and Kumbh Mela, a periodic gathering of tens of millions of people in India, both of which have accounted for the majority of fatalities in stampedes and other disasters.

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Taxi Driver’s Operation Behavior and Passengers’ Demand Analysis Based on GPS Data

Cited by 24 publications

References 32 publications

Identifying Building Functions from the Spatiotemporal Population Density and the Interactions of People among Buildings

Identifying Building Functions from the Spatiotemporal Population Density and the Interactions of People among Buildings

Research on urban public bicycle dispatching optimization method

Managing Crowds with Wireless and Mobile Technologies

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