Average-speed forecast and adjustment via VANETs

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Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/106376

Title:	Average-speed forecast and adjustment via VANETs
Authors:	周立德;Yang, Jyun-Yan;Chou, Li-Der;Tung, Chi-Feng;Huang, Shu-Min;Wang, Tong-Wen
Contributors:	資訊電機學院資訊工程學系
Keywords:	Applied sciences;Artificial intelligence;Average speed;Business and industry local networks;Computer science;control theory;systems;Connectionism. Neural networks;Correlation;data aggregation;Electric, optical and optoelectronic circuits;Electronics;Exact sciences and technology;forecast;Forecasting;Meteorology;Networks and services in france and abroad;Neural networks;Predictive models;Rain;Relays;Roads;Speed limits;Systems, networks and services of telecommunications;Telecommunications;Telecommunications and information theory;Teleprocessing networks. Isdn;Teletraffic;Traffic accidents & safety;Vehicles;vehicular ad hoc networks (VANETs)
Date:	2013-01-01
Issue Date:	2026-04-23 13:19:07 (UTC+8)
Publisher:	Institute of Electrical and Electronics Engineers Inc.;New York, NY: IEEE
Abstract:	摘要： 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. 其他題名： TVT 出版者： New York, NY: IEEE 出版日期： 2013-11-01 出處： IEEE transactions on vehicular technology, 2013-11, Vol.62 (9), p.4318-4327 資源來源： IEEE All-Society Periodicals Package (ASPP) 1998–Present 版權： 2015 INIST-CNRS 版權： Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Nov 2013 識別號： ISSN: 0018-9545 識別號： EISSN: 1939-9359 識別號： DOI: 10.1109/TVT.2013.2267210 識別號： CODEN: ITVTAB
Appears in Collections:	[Department of Computer Science and information Engineering] journal & Dissertation

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