Analysis and forecasting of urban traffic condition based on categorical data
Chen, Yuanyuan; Lv, Yisheng
2016
Conference Name2016 IEEE International Conference on Service Operations and Logistics, and Informatics
Conference Date10-12 July 2016
Conference PlaceBeijing, China
AbstractUrban traffic prediction is a critical component in intelligent transportation systems for both citizens and traffic management agencies. It is beneficial to know current and future traffic conditions prior a trip or a route for travelers. And it is also very helpful for proactive traffic management for transportation administrative sectors. In this paper, we apply classification techniques to forecast traffic conditions based on categorical data collected from open web maps. To this end, we first collect traffic condition data from AMAP which is a web map, navigation and location based services provider in China. Then we primarily analyze AMAP data with trend analysis and power spectrum analysis. Finally, we employ random walk, naïve Bayes, decision tree and support vector machine methods to forecast traffic conditions in the future based on historical and current conditions. Experimental results demonstrate that it is feasible to make forecast on traffic conditions with reasonable accuracy.
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20172
Collection复杂系统管理与控制国家重点实验室_先进控制与自动化
AffiliationState Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Y. y. Chen and Y. Lv, "Analysis and forecasting of urban traffic condition based on categorical data," 2016 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI), Beijing, 2016, pp. 113-118.
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