Analysis and Forecasting of Urban Traffic Condition Based on Categorical Data
Urban 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,na(i)ve 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.
urban traffic prediction traffic condition intelligent transportation systems route planning
Yuan-yuan Chen Yisheng Lv
State Key Laboratory of Management and Control for Complex Systems Institute of Automation,Chinese Academy of Sciences Beijing 100190,China
国内会议
福州
英文
1-6
2015-12-11（万方平台首次上网日期，不代表论文的发表时间）