Autonomic Navigation System Based on Predicted Traffic and VANETs
作者： Jyun-Yan Yang , Li-Der Chou , Li-Ming Tseng , Yi-Ming Chen
DOI: 10.1007/S11277-016-3555-7
关键词:
摘要: To avoid an expected traffic jam, drivers make detours based on limited information; however, the majority following alike routes may result in unexpected congestion. Conventional navigation approaches are unable to respond congestion because these do not consider taken by other vehicles. Navigation systems that utilize global information can improve gas consumption, CO2 emissions and travel time. Therefore, this paper, authors propose autonomic system (ANS) operating over vehicular ad-hoc networks (VANETs). The proposed ANS adopts a hierarchical algorithm plan vehicle routes. imitates human nervous when managing system, which vehicles monitor via VANETs. Moreover, paper proposes time-dependent routing uses novel prediction method of This EstiNet as simulator tool dominates hundreds or thousands VANET-based two maps, Manhattan area, Taipei city. results show improves average speed 60.02 % compared with shortest path first (SPF) 15.49 distributed simulation area. also 30.5 SPF 15.8 Furthermore, emulate real environments, there is scenario only portion complies ANS.
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