Average-Speed Forecast and Adjustment via VANETs

Abstract

A wet road is slippery so vehicles often slow down their speed to increase the safety margin, thus usually reducing the average speed. This reduction in average speed may produce a chain reaction that shifts, extends, or amplifies a slowdown on downstream road segments. Conventional average-speed forecasting approaches are unable to respond to sudden chain reactions because these approaches do not consider the effect of weather factors and upstream road segments. Since accurate forecast of average speed can improve gas consumption, carbon dioxide emissions, and travel time, this paper proposes a short-term average-speed forecast and adjustment (ASFA) approach based on a study of prediction bias correlation among adjacent road segments and on weather factors, such as temperature, humidity, and rainfall. First, this approach applies an artificial neural network to predict the average speed of a road segment. Then, vehicles can monitor current traffic to calculate average speed via vehicular ad hoc networks (VANETs). Finally, the vehicles adjust the predicted average speed according to the observed average speed. Real traffic measurements and weather data are used for the evaluation of the proposed scheme and Civic Boulevard in Taipei City is selected as the prediction target. The results show that the proposed ASFA improves accuracy by 57.4% when compared with a hybrid approach on an urban street during rush hour. This paper estimates and simulates a case study by aggregating traffic data in 186 ms on Shi-Min Boulevard via the VANETs during rush hour.