基于TLBO优化算法的电动汽车充电站选址

随着电动汽车使用规模的不断增加,建设电动汽车充电站已成为当务之急。文中将每个区域中心作为电动汽车充电负荷中心,以分区内电动汽车充电桩数量作为充电站选址的权重系数,建立了充电站位置和定容的优化模型。采用TLBO优化算法计算出电动汽车充电站的数量、建设地点、每个充电站的覆盖范围以及充电站的充电桩数量。仿真结果表明,TLBO优化算法在电动汽车充电站的选址和定容方面,计算准确、收敛速度快,并且具有良好的全局优化性能。     

大数据背景下智慧城管数据采集策略分析

随着我国城市的快速发展,城市管理问题也日益凸显,如夜市摊贩问题、交通拥堵问题、道路破损问题、施工工地问题、交通路灯故障问题、车辆随意停放问题、城市清洁问题等,这些问题会影响到城市的正常运转,给市民生活造成不良体验,从而受到社会的广泛关注。基于此,文中首先简要介绍了智慧城管的相关概念,然后详细讨论了传统数据采集和大数据下的智慧城管相关策略,阐述了智慧城管的相关应用。     

Extended MULE Concept for Traffic Congestion Monitoring

Wireless Sensor Networks are being recently studied to monitor real-time traffic conditions on roads and highways. Idea of using vehicles to convey information from sensors placed alongside roads to the dedicated base stations has also been under scrutiny for some time. In this paper, we argue that a sensor placed on a vehicle instead of a fixed location can effectively sense traffic congestion on the road and report it to the already available WLAN Access Points (APs) instead of the dedicated base stations. This way, instead of deploying series of base stations to collect traffic information, congestion information can be sent over the ISM links between the vehicular sensor nodes and the WLAN APs. This paper investigates, as we call it, the Extended MULE concept by using actual experimental data obtained from the test drives across the city. Our results show that adopting this idea is effective in reporting traffic congestion on the roads.     

电动汽车充电模式及方式浅析

电动汽车是未来新能源汽车的发展方向,能实现交通领域能源利用的多元化和清洁化。充电设施的建设是电动汽车产业发展的重要支撑和前提。文中介绍了三种充电模式及各类电动汽车适用的充电方式,以深圳市为例分析了充电设施的现状及问题,并对其规划布局和规范接口提出了原则性建议。     

基于深度学习的无人机遥感影像车辆检测

伴随着城市的发展,车辆数量在不断地增加。这一现象不仅增加了城市拥挤状态,而且还促使交通事故频发。要提高城市治理能力,就必须提高对城市车辆的监测能力。使用无人机对上海、赤峰地区的四个场景进行了低空摄影,获取了航空遥感影像数据,然后结合深度学习的Unet卷积神经网络技术对无人机影像中的车辆进行了单目标提取。结果表明,深度学习对无人机影像中车辆的识别能力远高于传统机器学习中的随机森林方法,达到了99%的超高精确度,且每个场景内汽车数的估算结果与真实数量极其接近。根据研究结果可知,将无人机和深度学习技术相结合的车辆检测方法具备实时性和现实可行性,可为城市的车辆实时监测和交通管理提供可靠的技术手段。     

出租车智能计价器优化设计与研究

随着城市发展进程的加快,出租车在城市交通中扮演着非常重要的角色,而目前出租车计价器存在不少缺陷,为此需对计价系统进行优化设计。以MCS51单片机为控制核心,通过记录安装在车轮上集成霍尔传感器发出的脉冲个数,实现正常的里程计费,设计的出租车智能计价器性能更优。文中提出了出租车智能计价系统总体设计方案,重点介绍了硬件系统和软件系统设计过程,最后还对系统进行了仿真实验。结果表明:系统功能良好,能实现智能计价功能。     

电动汽车充电站功率控制策略的研究

电动汽车及充电站近年来得到了快速的发展。充电站在为电动汽车充电时将消耗巨大的电能,如果任由众多的充电站无序无限制地进行充电工作,将会对电网形成巨大的冲击,从而影响电网的稳定。从长远来看,将充电站纳入电网调度的体系,充电站根据电网调度值对站内充电机的充电功率实施控制与分配,从而降低大规模充电对电网的冲击,具有重要的意义。     

Autonomic Navigation System Based on Predicted Traffic and VANETs

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

Sustainable Framework for Smart Transportation System: A Case Study of Karachi

In this paper, a framework of smart transportation system is proposed, aiming to address the transportation problem in Karachi city. In modern day world, the mega cities and urban areas are on the edge of transformation into smart cities. With the advancement of engineering and technology, smart cities are designed to integrate and utilize these scientific innovations to provide smart solutions and social innovations for sustainable infrastructure, thus they are able to provide its resident highest quality of life by utilizing its resources effectively. One of the major application of smart cities is the Smart Transportation System, which provides safer, quick, environment friendly service to the residents. Thus, this study highlights the current traffic situation of Karachi and propose a framework to transform it into a smart transportation system. In order to have a smart transportation system, it is necessary to have in-depth knowledge and information about the city dynamics and its traffic related issues. Therefore, this study also highlights current traffic situation of Karachi, its road conditions and capacity, vehicles condition, alternate mean of transport (other than road-based system) and its present condition, and finally proposes a framework to develop a smart transportation system while keeping in mind the aforesaid traffic problems.

     

智能停车引导及NFC支付系统的设计与分析

为了解决当前快速增长的私家车数量和有限的停车位所产生的停车难问题,文中给出了一种智能停车引导及NFC支付系统的解决方案,该方案包含查找停车场、预约停车位、停车场路线导航、NFC识别与支付以及推荐周边商户信息等功能,使用户找车位更具有针对性和高效性,停车费使用NFC手机支付更加简单快捷且降低了停车场的管理成本;同时也使得停车场出入口交通拥堵问题得到有效缓解。分析与对比表明,新方案具有可行性强、易于实现的优点,符合目前智慧城市建设的理念。     

电动汽车充放电优化管理分析

随着电动汽车普及保有量逐年上升,电动汽车无序充放电将会对电力系统产生极大的冲击。文中首先对影响充电负荷的各个因素进行分析,建立数据特征函数,并通过最小二乘法拟合得出电动汽车的函数模型。然后,根据数学模型特征,对多个电动汽车的负荷进行规划,建立多目标优化函数,并使用蒙特卡洛算法构造概率过程从已知概率分布中抽样建立对电动汽车充电负荷曲线。通过仿真验证,证明提出的模型能通过少量样本精确的模拟大规模电动汽车接入状况。文中对电动汽车充放电优化问题进行的改良,能有效减少设备投资、降低无序充电的峰谷差,维护系统稳定运行。     

电动汽车有序充放电控制研究与应用综述

随着政府对电动汽车的重视,电动汽车规模日益增大,电动汽车的充放电有序控制不仅减小对电网的负面影响,还能将其应用于系统的调峰,调频,事故备用等方面。本文介绍了电动汽车充电对电网影响的研究方向,从时间维度和空间维度两方面总结了目前采用的研究方法和存在的问题,并总结了常用的优化方法、目标函数和重要的约束条件。指出完善电价激励机制,实现智能充电导航,开发便捷友好的控制终端是有序充电控制未来的研究方向。     

基于浮动车的高速公路重点地段精细管理

本文主要研究采用现有大型行业车辆监控平台的车辆动态GNS S定位数据,作为浮动车[1]数据资源,对高速公路重点路段车辆通行情况、停靠情况实时分析,生成事件预警[2]。本文重点研究了海量车辆动态GNS S数据的快速实时地理分析方法,同时也对路段拥堵判断及车辆异常停靠行为分析的方法做了探讨。     

新能源电动汽车充电技术研究

在当前的社会背景下,城市的环境问题变得更加严重,能源问题也日益突出。我国现阶段主要发展了以电动车为主的新能源汽车产业,在进行产业发展的过程中,电动车的充电桩等配套设施,在建设的过程中面临更大的挑战,同时给汽车企业带来了一定的机遇。但因为当前的能源比较短缺,在进行新能源使用的过程中,必须对光伏发电站的建设进行全面的分析,才能保证充电站,在建设的过程中更加的顺利。因此汽车企业在发展的过程中,应该研发新型的技术,改善现有的充电技术。本文就新能源电动汽车充电技术进行相关的分析和研究。     

基于OPS智能车载终端的设计

提出一种新型的基于里程定位的智能车载终端系统。通过将里程和站点结合起来,实现自动语音报站的目的,通过GPRS将智能车载终端和地理信息系统结合,能够对定线行驶的车辆进行实时监控,了解车辆的运行状态,车辆可以与中心之间互相发送信息,从而实现智能化管理的目的。此终端还预留出软硬件接口,可以将IC卡收费系统融合起来,将车辆的收费记录通过GPRS传回中心,就可以不再需要人工采集收费数据。给出了整个智能车载终端系统的组成、设计和实现技术。     

Power Optimization Strategy Considering Electric Vehicle in Home Energy Management System

With the development of smart grid, residents have the opportunity to schedule their household appliances (HA) for the purpose of reducing electricity expenses and alleviating the pressure of the smart grid. In this paper, we introduce the structure of home energy management system (EMS) and then propose a power optimization strategy based on household load model and electric vehicle (EV) model for home power usage. In this strategy, the electric vehicles are charged when the price is low, and otherwise, are discharged. By adopting this combined system model under the time-of-use electricity price (TOUP), the proposed scheduling strategy would effectively minimize the electricity cost and reduce the pressure of the smart grid at the same time. Finally, simulation experiments are carried out to show the feasibility of the proposed strategy. The results show that crossover genetic particle swarm optimization algorithm has better convergence properties than traditional particle swarm algorithm and better adaptability than genetic algorithm.     

基于CC2430的城市车辆限行系统研究

随着车辆的增多，城市交通拥堵越来越严重，影响了市民日常出行。实行车辆限行是缓解交通拥堵的有效方法。利用ChipconAS公司的射频芯片CC2430进行车辆无线识别，结合单片机与以太网实现远程通信，设计了一套根据车辆出行天数进行城市车辆限行的系统。为了获得车辆出行天数，对根据出行天数进行车辆限行的方法进行了论述，重点是系统的硬件设计，并对软件设计中的难点给出了说明。     

基于群智感知的智慧城市交通系统研究

针对日益严重的城市交通拥堵、城市建设、生活节奏等问题,提出以物联网为基础,利用群智感知技术实现信息采集,利用广播电视技术、移动电话技术、车联网技术实现信息共享的智慧城市交通系统。不同的组织和人群将会从中得到不同的支持,并对关键问题从数据采集、数据纠正、大数据处理、数据发布等方面做了阐述。     

Optimized traffic flow prediction based on cluster formation and reinforcement learning

In recent days, the traffic flow information is collected using the global positioning system through the Internet, which is yet to become ubiquitous. A novel technique is proposed for the intelligent transportation system, which leads to reduce the traffic congestion that will become an unavoidable phenomenon in the near future. This system uses a magnetic sensor to identify the type of the vehicle and the exact vehicle count in the traffic environment based on variation in the magnetic flux. This information is transmitted to the cloud server with the help of cluster by utilizing the nearby proximity services. An intelligent agent that uses reinforcement learning is implemented in the cloud server to learn the real-time traffic flow from multiple sources for the prediction of a valid and optimized route suggestion for the registered users. This work is implemented, and implementation results show that the proposed work achieves an accuracy of 98.36%. Hence, this intelligence method for VANETs will certainly account for improved traffic prediction to the vehicle transportation. It can reduce the vehicles waiting time in traffic and that would minimize the fuel consumption. It will make an eco-friendly environment of reduced carbon dioxide emissions in urban cities.     

Infrastructure‐based vehicular congestion detection scheme for V2I

With the increasing number of vehicles, traffic jam becomes one of the major problems of the fast‐growing world. Intelligent transportation system (ITS) communicates perilous warnings and information on forthcoming traffic jams to all vehicles within its coverage region. Real‐time traffic information is the prerequisite for ITS applications development. In this paper, on the basis of the vehicle‐to‐infrastructure (V2I) communication, a novel infrastructure‐based vehicular congestion detection (IVCD) scheme is proposed to support vehicular congestion detection and speed estimation. The proposed IVCD derives the safety time (time headway) between vehicles by using iterative content‐oriented communication (COC) contents. Meanwhile, the roadside sensor (RSS) provides an infrastructure framework to integrate macroscopic traffic properties into the estimation of both the traffic congestion and vehicle safety speed. The main responsibilities of RSS in IVCD are to preserve privacy, aggregate data, store information, broadcast routing table, estimate safety speed, detect traffic jam, and generate session ID (S‐ID) for vehicles. Monte Carlo simulations in four typical Chinese highway settings are presented to show the advantage of the proposed IVCD scheme over the existing Greenshield's and Greenberg's macroscopic congestion detection schemes in terms of the realized congestion detection performance. Real road traces generated by Simulation of Urban Mobility (SUMO) over NS‐3.29 are utilized to demonstrate that the proposed IVCD scheme is capable of effectively controlling congestion in both single and multilane roads in terms of density and speed health with previous schemes in this field.