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Title: 利用車載智慧物聯網路由技術以調控紅綠燈改善城市交通
A Vehicular Smart IoT Routing Technology for Urban Traffic Light Control
Authors: 林煙童
Lin, Yen-Tung
Contributors: 蔡子傑
Tsai, Tzu-Chieh
Lin, Yen-Tung
Keywords: 物聯網
Internet of Things
Smart Traffic Light
Edge node
Wireless ad hoc Network
Delay Tolerant Network
Date: 2021
Issue Date: 2021-05-03 10:27:38 (UTC+8)
Abstract: 塞車被視為城市交通所需面臨的嚴峻議題,嚴重的交通壅塞會造成通勤的時間增加、燃料耗損、空氣汙染、駕駛易感到憤怒和煩躁等。當前多數的紅綠燈控制系統利用道路的車流量調控,鮮少根據車輛自身資訊,以兼顧到車輛停等時間。要蒐集這些數據不是需部署大量感測裝置在路段上就是讓車主自行提供車輛資訊,前者對一座城市而言是筆龐大的成本負擔,後者則須駕駛消耗4G網路頻寬上傳即時的車況數據,在4G網路不普及的區域,用路人未必會願意協助數據的提供。
本論文探討當一些車輛願意自主提供自身資訊時,在僅利用這些車輛資訊改善城市交通壅塞的惡況,提升交通流暢度。雖然車輛資訊有時效性的問題,但在本研究提出的Hybrid routing概念中,在不利用4G網路上傳數據的前提下,結合無線隨意網路和耐延遲網路的特性,設計一個車載智慧物聯網路由技術將這些車輛資訊繞送到周遭的任一邊緣節點以供路口紅綠燈動態的調控,讓這些數據在本研究中只要“及時”而非“即時”仍可達到相同的效用,以改善城市交通壅塞的現象。
Traffic congestion is regarded as a severe issue in urban traffic, which causes increased commuting time, fuel consumption, air pollution, and driving anger and irritability, etc. At present, traffic light control systems are mostly based on traffic flow and rarely utilize the vehicle's own information to give consideration to vehicle’s waiting time. To collect vehicle information, it is either to deploy a large amount of sensing devices on the roads or drivers need to upload real-time vehicle information by themselves. The former is a huge cost burden for a city, the latter consumes extra 4G bandwidth which drivers may be reluctant to do so.
In this thesis, we study how to use only vehicle information to improve urban traffic congestion when some drivers are willing to provide their vehicle data. Since the vehicle information is time-sensitive, we propose a hybrid routing to deliver these data “in-time” rather than “real-time” to any one of the surrounding edge nodes, even without 4G networks involved. Based on characteristics of vehicular ad hoc networks and delay tolerant networks (DTNs), we develop a vehicular smart IoT routing technology for anycast to edge server for dynamic traffic light control and improving traffic congestion.
To validate our model, we use the SUMO traffic simulator for the experimental simulation. The results show that under about more than 80% congested traffic lanes, our method achieves less the average waiting time compared to the traffic flow count method even only 30% of vehicles are willing to provide and forward data.
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[20]. SUMO User Documentation

[21]. 智慧紅綠燈新聞:

[22]. 交通壅塞相關新聞:
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