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Campus Bus Network Design and Evaluation Based on the Route Property

Tsinghua Science and Technology 2017年第5期22卷 539-550页

作者： jishiyu ding Shuo Feng Li Li Yi Zhang the Department of Automation Tsinghua University

A campus bus network design and evaluation, taking Tsinghua University as an example, is investigated in this paper. To minimize the total cost for both passengers and operator, the campus bus system planning in a sequential approach is discussed, including the route network design, headway （i.e., the inverse of service frequency） optimization, and system evaluation. The improved genetic algorithm is proposed to optimize the route network based on the route property, and the impacts of the fluctuation of passenger demand and average traveling time are analyzed. The identity proportion in the headway optimization is then introduced with full consideration of its impacts. Based on the actual variety of passenger demand, a non-fixed schedule demonstrates its efficiency. VISSIM is finally adopted to simulate the campus bus system and a comprehensive evaluation system for the campus bus is developed. Compared with the current bus network and the one without considering the route property, the evaluation of the proposed approach shows an improvement of 18.7% and 10.1%, respectively. Moreover, the sequential approach shows an efficiency significance for the development of public transit systems passengers and operator. mprovement over the alternative method. It is of great n large industrial parks to decrease the total cost for both

关键词： genetic algorithm transit network design route property identity proportion transit system evaluation

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Safety benefit of cooperative control for heterogeneous traffic on-ramp merging

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Transportation Safety and Environment 2022年第4期4卷 88-95页

作者： Xiao Jing Xin Pei Song Yan Chunyang Han Selpi Eleonora Andreotti jishiyu ding Department of Automation Beijing National Research Center for Information Science and TechnologyTsinghua UniversityBeijing 100084China Division of Vehicle Safety Department of Mechanics and Maritime SciencesChalmers University of TechnologySE-41296 GothenburgSweden

The safety of heterogeneous traffic is a vital topic in the oncoming era of autonomous vehicles(AVs).The cooperative vehicle infrastructure system(CVIS)is considered to improve heterogeneous traffic safety by connecting and controlling AVs cooperatively,and the connected AVs are so-called connected and automated vehicles(CAVs).However,the safety impact of cooperative control strategy on the heterogeneous traffic with CAVs and human-driving vehicles(HVs)has not been well *** this paper,based on the traffic simulator SUMO,we designed a typical highway scenario of on-ramp merging and adopted a cooperative control method for *** then compared the safety performance for two different heterogeneous traffic systems,*** and HV,CAV and HV,respectively,to illustrate the safety benefits of the cooperative control *** found that the safety performance of the CAV and HV traffic system does not always outperform that of AV and *** random departSpeed and higher arrival rate,the proposed cooperative control method would decrease the conflicts significantly whereas the penetration rate is over 80%.We further investigated the conflicts in terms of the leading and following vehicle types,and found that the risk of a AV/CAV followed by a HV is twice that of a HV followed by another *** also considered the safety effect of communication failure,and found that there is no significant impact until the packet loss probability is greater than 30%,while communication delay’s impact on safety can be ignored according to our experiments.

关键词： Traffic safety heterogeneous traffic flow cooperative control on-ramp merging communication failure traffic simulation

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基于强化学习的通信受限环境多无人机协同策略

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无人系统技术 2022年第5期5卷 12-20页

作者：程进胡寒栋江业帆张一博丁季时雨航天科工集团智能科技研究院有限公司北京100144 航天防务智能系统与技术科研重点实验室北京100144 中国航天科工集团第二研究院北京100854

随着人工智能技术的发展,空域无人作战正由“单平台遥控”向“多平台协同”转变。多无人机协同作战任务具有非完全信息、通信受限、高实时、强动态等特点,给协同决策生成带来巨大挑战。针对通信受限环境中的多无人机协同决策问题,提出... 详细信息

随着人工智能技术的发展,空域无人作战正由“单平台遥控”向“多平台协同”转变。多无人机协同作战任务具有非完全信息、通信受限、高实时、强动态等特点,给协同决策生成带来巨大挑战。针对通信受限环境中的多无人机协同决策问题,提出一种基于动态层级网络通信架构的通信强化学习协同策略,该策略能够显著减少无人机集群间的通信次数,同时准确传递其决策需要的信息,从而得到较优协同策略。针对多无人机协同围捕的典型任务场景,基于OpenAI平台对所提出的算法进行了仿真验证。结果表明,与传统强化学习算法相比,提出的通信强化学习策略可以显著减少无人机间的通信次数,同时在一定程度上避免潜在的信息欺骗问题。完成任务需要的平均通信次数相比于传统两两通信结构减少约77%,为实现通信受限环境中的多无人机协同任务提供技术支撑。

关键词：强化学习通信受限无人系统集群多智能体协同人工智能

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