ITS (Intelligent transportation system) is a modern way of urban transportation management. By improving road throughput, managing traffic, and enhancing human mobility, it can improve the service performance of urban transport networks in a holistic way. Ultimately, through better traffic management, congestion can be slowed and traffic safety improved. The large-scale deployment of ITS is very meaningful, which can greatly affect all aspects of urban life. The traditional means of traffic management cannot deal with real time traffic management very well. The advanced ITS, in keeping with the times, is the integration of as much IoT as possible (Internet of Things) equipment, for the human to provide new service experience. Recently, the spectrum band beyond 60GHz has attracted attentions with the growth of traffic demand. Previous studies assumed that these bands are not suitable for vehicle communications due to the short range and high rate of blockage. However, it also means there is no existing service or regulation designed for these bands, which makes this area free to apply. One purpose of this study is to construct an applicable map of 0.1-1 THz supported autonomous vehicle system through the study of channel capacity, autonomous relaying and self-driving. For sending a large volume of data, the vehicles keep a gigabit level link through line of sight channels, which share information about traffic condition and captured videos for further processing. The full use of this research can significantly improve the performance of autonomous driving. A brief overview of possible waveforms followed by specific channel characteristics in 0.1-1 THz band is discussed and an autonomous relay system for the gigabit road communication is presented. At last, we describe how the highrate short-range communication helps complement extra data to support the advanced new self-driving technologies. To improve the user experience, taxi sharing service aims to share the taxi resource efficiently between passengers with specific relevant plans. However, due to the lack of social interactions, it is hard to improve user experience without considering passengers’ inner connections. To overcome this problem, we propose a cloud-based taxi-sharing system, which provides real-time taxi sharing services both considering the practical travel needs and QoS in the journey. The purpose of this service is to minimize the travel cost through sharing calculated schemes reasonably and quickly and improve the sharing experience as much as possible. We model the citywide level map into blocks and indexed, in order to search for the potential paths of ride-sharing dynamically and intelligently. Meaning while, we explore the paths among communities to find suitable companions as well. Finally, synthesizing the suggestions of edge exploration on road network and social network, we design a heuristic algorithm called SONETS to achieve the designing goal and make a real deployment. As well as the establishment of the mobile application and online service, we also conduct a large-scale simulation to evaluate the performance with others. The result shows that the proposed solution could make the whole taxi-sharing system less calculation complexity, higher sharing QoS, and most of all, keeping the minimum cost of travel cost within 1.16% increment than distance optimal algorithms. To keep the sustaibility of urban ITS, we also investigate the maintenance of city-wide sharing pedelecs, which apply battery-powered motor to assist pedaling and significantly extend the riding coverage. The large-scale deployment of pedelecs, however, requires a careful design of maintenance system to replace the batteries of pedelecs regularly, which can be very costly. This study develops an optimal path planning scheme for replenishment trucks based on big data analytic and prediction. To the best of our knowledge, we are the first to use the systematic methodology of Analysis/Modeling/Test to advance the bike prediction process using the captured datasets, and conduct the hybrid prediction process learns internal connections between different dataset dimension as well as optimize the overall maintenance efficiency.
ITS（Intelligent Transportation System）は都市交通管理の現代的な方法である。道路のスループットを改善し、交通を管理し、人間のモビリティを強化することにより、都市交通ネットワークのサービスパフォーマンスを全体的に向上させることができる。最終的には、トラフィック管理の改善により、輻輳が緩和され、交通安全性が向上する。大規模なITSの展開は非常に意味があり、都市生活のあらゆる側面に大きな影響を与える可能性がある。高度なITSは、時代に即して、可能な限り多くのIoT（Internet of Things）機器の統合であり、人間が新しいサービス経験を提供するためのものである。近年、交通需要の増加に伴い注目を集めている 60GHzを超える周波数帯域が注目されている。これらのバンド用に設計された既存のサービスや規制が存在しないで、この領域を自由に適用することができる。本研究の目的の 1つは、チャネル容量、自律リレーおよび自己運転の研究を通して、0.1-1 THzに対応した自律車両システムの適用可能なしくみを構築することである。大量のデータを送信するために、0.1-1 THz帯域における特定のチャネル特性に続く可能な波形の簡単な概観が論議され、ギガビット道路通信のための自律リレーシステムが提案される。しかしながら、社会的相互作用の欠如のために、乗客の内部接続を考慮せずにユーザ体験を改善することは困難である。この問題を克服するため、実際の旅行ニーズと旅行のQoSを考慮したリアルタイムのタクシー共有サービスを提供するクラウドベースのタクシー共有システムを提案する。 このサービスの目的は、道路網とソーシャルネットワーク上のエッジ探索の提案を合成し、SONETSというヒューリスティックなアルゴリズムを設計し、設計目標を達成し、実際の展開を行う。提案されたソリューションは、共有QoSを高め、とりわけ移動コストの最小コストを遠距離最適アルゴリズムより1.16％増以内に抑えることができた。都市ITSの持続可能性を維持するために、E-Bikeを助けるためにバッテリー駆動のモーターを適用し、乗車範囲を大幅に拡張する都市規模の共有車のメンテナンスについて研究を続ける。しかしながら、大規模なpedelecsのバッテリーを定期的に交換するメンテナンスシステムを慎重に設計する必要がある。本研究では、大規模なデータ分析と予測に基づく補充トラックの最適な経路計画手法を開発する。我々は捕捉されたデータセットを使用して自転車予測プロセスを進めるために、分析/モデリング/テストの体系的な方法論を使用し、ハイブリッド予測プロセスを実行して、異なるデータセット次元間の最適な結合で、全体的なメンテナンス効率を上昇する。
Transmission Performance, User Experience, System Sustainability in Urban Intelligent Transportation System
A438
(6.83MB)
