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.Index Terms-Average speed, data aggregation, forecast, neural networks, vehicular ad hoc networks (VANETs).

Applications of Soft Computing in Intelligent Transportation Systems

“…In a more recent paper [83], Yang et al combined ANN and VANETs to develop a short-term average-speed forecast and adjustment approach to improve gas consumption, decrease CO 2 emissions and reduce travel time. In the proposed method, a Traffic Information Center (TIC) collected average speed from vehicles and road side sensors through VANETs.…”

Vehicle Speed Estimation and Forecasting Methods Based on Cellular Floating Vehicle Data

“…3 in Taiwan during October in 2010, was collected and expressed as the characteristics of road conditions (i.e., traffic flows) and vehicle movement behaviors (i.e., vehicle speeds) for traffic simulation. Furthermore, call holding time is exponentially distributed with the mean 1{µ, and the call inter-arrival time is exponentially distributed with the mean 1{λ in accordance with the mobile communication records from Chunghwa Telecom for the generation of MS communication traces [13]. Then, vehicle movement and MS communication traces can be generated by a traffic simulation program, VISSIM.…”

“…the generation of MS communication traces [13]. Then, vehicle movement and MS communication traces can be generated by a traffic simulation program, VISSIM.…”

Potential of Intelligent Transportation Systems in Mitigating Adverse Weather Impacts on Road Mobility: A Review

“…3) Decision Implementation Techniques: Finally, decisions/ strategies that will be implemented for road weather maintenance and disseminated to different user groups via different mediums, such as in-vehicle display [127], dynamic signing [130], and different media, including TV, radio, and social media [131], need to be identified and evaluated. Emerging new data sources such as social media have been utilized for postdisaster response planning [132], [133], which could play a big role in terms of data collection and information dissemination in road weather management.…”