Attract Rank District Attraction Ranking Analysis Based on Taxi Big Data
Abstract : The city’s district attraction ranking plays an essential role in the city’s government because it can be used to reveal the city’s district attraction and thus help government make decisions for urban planning in terms of the smart city .
The traditional methods for urban planning mainly rely on the district’s GDP, employment rate, population density, information from questionnaire surveys and so on.
However, as a comparison , such information is becoming relatively less informative as the explosion of an increasing amount of urban data.
With the development of urban computing, it is possible to make good use of urban data for urban planning.
To this end, based on taxi big data obtained from Guangzhou, China, this paper proposes a district attraction ranking approach called Attract Rank, which for the first time uses taxi big data for district ranking.
An application system is developed for demonstration purposes.
? With the rapidly growing population in urban areas, these days the urban road networks are expanding at a faster rate.
? The frequent movement of people on them leads to traffic congestions. These congestions originate from some crowded road segments, and diffuse towards other parts of the urban road networks creating further congestions.
? This behavior of road networks motivates the need to understand the influence of individual road segments on others in terms of congestion. In this work, we propose Road Rank, an algorithm to compute the influence scores of each road segment in an urban road network, and rank them based on their overall influence.
? Its goal is to use data from various sources to solve some of the problems encountered in today’s urbanization process , such as air pollution
? In the era of big data, data-driven application has become a feature in many areas, such as wastewater treatment process.
? he development of network theory and the emergence of various data such as social data, migration data, taxi operating data and smartcard data, statistical analysis of network and flows between locations can be conducted.
? Based on the aforementioned data , a research direction called smart city has emerged
? In this paper proposes a district attraction ranking approach called Attract Rank, which for the first time uses taxi big data for district ranking .
? Taxis are viewed as sensors of the city traffic situation. There fore we treat them as reasonable sampling of real traffic flow and use them to study the vehicles’ travel behaviour.
? We propose a ranking based visual analysis method to study taxi travel behaviour on a route .
? We develop an interactive system to support ranking based explorations of taxi travel behaviour, and we evaluate it with use cases and a user study.
? We proposed an attractive area clustering algorithm based on grid density. Based on taxi spatio-temporal trajectory data , Mouet al.
? we propose a district attraction ranking method called Attract Rank, which is the first attempt to use taxi big data for district ranking as well as to overcome some shortcomings of the existing related methods.
? To this end, based on taxi big data obtained from Guangzhou, China, this paper proposes a district attraction ranking approach called Attract Rank, which for the first time uses taxi big data for district ranking. An application system is developed for demonstration purposes
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