Prediction of short-term average vehicular velocity considering weather factors in urban VANET environments

Recently, accurate prediction of short-term traffic flow is crucial to proactive traffic management systems in ITS; however, the drivers need the average vehicular velocity more than traffic flow while driving. The drivers could change the path immediately according to the average vehicular if the average velocity of the next road segment is predicable. In this paper a neural network is used for prediction of average velocity, besides vehicles can collect the average velocity of current road segment to adjust the predicted average velocity of the next road segment. The collected average velocity is acquired from neighbor vehicles through VANET. There is no research considering the impact of weather factors on the average vehicular velocity previously. An example of weather condition affects the velocity, it is always low vehicular velocity on rainy day or in fog. In this paper, the proposed prediction considers the weather factors that include temperature, humidity and rainfall. This research is focus on urban VANET environments of Taipei in Taiwan, and the results show that the prediction of average velocity considering weather factors is more accurate than that without considering weather factors.

[1]  Ling Zhang,et al.  Short-term Traffic Flow Prediction Based on Incremental Support Vector Regression , 2007, Third International Conference on Natural Computation (ICNC 2007).

[2]  Xiaodong Zang.  The short-term traffic volume forecasting for urban interchange based on RBF Artificial Neural Networks , 2009, 2009 International Conference on Mechatronics and Automation.

[3]  Bing Xu,et al.  Research on Traffic Monitoring Network and Its Traffic Flow Forecast and Congestion Control Model Based on Wireless Sensor Networks , 2009, 2009 International Conference on Measuring Technology and Mechatronics Automation.

[4]  Changhong Liu,et al.  Short-Term Traffic Flow Prediction Methods and the Correlation Analysis of Vehicle Speed and Traffic Flow , 2008, 2008 International Conference on Computational Intelligence and Security.

[5]  Man-Chun Tan,et al.  An Aggregation Approach to Short-Term Traffic Flow Prediction , 2009, IEEE Transactions on Intelligent Transportation Systems.

[6]  Juan Zhicai,et al.  Research on Grid-based Short-term Traffic Flow Forecast Technology , 2009, 2009 International Conference on Computer Engineering and Technology.

[7]  Lee D. Han,et al.  Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions , 2009, Expert Syst. Appl..

[8]  Wei-Chiang Hong,et al.  Application of hybrid genetic algorithm and simulated annealing in a SVR traffic flow forecasting model , 2009, 2009 IEEE Congress on Evolutionary Computation.