A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation

Intelligent transportation (e.g., intelligent traffic light) makes our travel more convenient and efficient. With the development of mobile Internet and position technologies, it is reasonable to collect spatio-temporal data and then leverage these data to achieve the goal of intelligent transportat...

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Vydáno v:Data Science and Engineering Ročník 6; číslo 1; s. 63 - 85
Hlavní autoři: Yuan, Haitao, Li, Guoliang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Singapore Springer Singapore 01.03.2021
Springer
Springer Nature B.V
Témata:
Algorithm Analysis and Problem Complexity
Artificial Intelligence
Cable television broadcasting industry
Chemistry and Earth Sciences
Computer Science
Data compression
Data Mining and Knowledge Discovery
Data storage
Database Management
Geospatial data
Information storage and retrieval
Physics
Preprocessing
Spatiotemporal data
Statistics for Engineering
Systems and Data Security
Traffic information
Traffic signals
Traffic surveys
Transportation applications
Transportation industry
United States
Intelligent transportation
Spatio-temporal data
Traffic prediction
ISSN:2364-1185, 2364-1541
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Shrnutí:Intelligent transportation (e.g., intelligent traffic light) makes our travel more convenient and efficient. With the development of mobile Internet and position technologies, it is reasonable to collect spatio-temporal data and then leverage these data to achieve the goal of intelligent transportation, and here, traffic prediction plays an important role. In this paper, we provide a comprehensive survey on traffic prediction, which is from the spatio-temporal data layer to the intelligent transportation application layer. At first, we split the whole research scope into four parts from bottom to up, where the four parts are, respectively, spatio-temporal data, preprocessing, traffic prediction and traffic application. Later, we review existing work on the four parts. First, we summarize traffic data into five types according to their difference on spatial and temporal dimensions. Second, we focus on four significant data preprocessing techniques: map-matching, data cleaning, data storage and data compression. Third, we focus on three kinds of traffic prediction problems (i.e., classification, generation and estimation/forecasting). In particular, we summarize the challenges and discuss how existing methods address these challenges. Fourth, we list five typical traffic applications. Lastly, we provide emerging research challenges and opportunities. We believe that the survey can help the partitioners to understand existing traffic prediction problems and methods, which can further encourage them to solve their intelligent transportation applications.
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ISSN:2364-1185
2364-1541
DOI:10.1007/s41019-020-00151-z