Intelligent Parking Management System by Multi-Agent Approach:The Case of Urban Area of Tunis

By coordination and cooperation between multi-agents, this paper proposes the network of intelligent agents which can reduce the search time needed to finding a parking place. Based on multi-agent model, the fined solution is designed to help drivers in finding a parking space at anytime and anywhere. Three services are offered: the search for a vacant place, directions to a parking space and booking a place for parking. The results of this study generated by the platform MATSim transport simulation, show that our approach optimizes the operation of vehicles in a parking need with the aim of reducing congestion, and improve traffic flow in urban area. A comparison between the first method where the vehicles are random and the second method where vehicles are steered to vacant parking spaces shows that the minimization of time looking for a parking space could improve circulation by reducing the number of cars in the morning of 2% and 0.7% of the evening. In addition, the traffic per hour per day was reduced by approximately 4.17%.

Urban Big Data and the Development of City Intelligence

This study provides a definition for urban big data while exploring its features and applications of Chi- na＇s city intelligence. The differences between city intelligence in China and the ＂smart city＂ concept in other countries are compared to highlight and contrast the unique definition and model for China＇s city intelligence in this paper. Furthermore, this paper examines the role of urban big data in city intel- ligence by showing that it not only serves as the cornerstone of this trend as it also plays a core role in the diffusion of city intelligence technology and serves as an inexhaustible resource for the sustained development of city intelligence. This study also points out the challenges of shaping and developing of China＇s urban big data. Considering the supporting and core role that urban big data plays in city intel- ligence, the study then expounds on the key points of urban big data, including infrastructure support, urban governance, public services, and economic and industrial development. Finally, this study points out that the utility of city intelligence as an ideal policy tool for advancing the goals of China＇s urban de- velopment. In conclusion, it is imperative that China make full use of its unique advantages-including using the nation＇s current state of development and resources, geographical advantages, and good hu- man relations-in subjective and objective conditions to promote the development of city intelligence through the proper application of urban big data.

A Framework for Intelligent Decision Support System for Traffic Congestion Management System

Traffic congestion problem is one of the major problems that face many transportation decision makers for urban areas. The problem has many impacts on social, economical and development aspects of urban areas. Hence the solution to this problem is not straight forward. It requires a lot of effort, expertise, time and cost that sometime are not available. Most of the existing transportation planning software, specially the most advanced ones, requires personnel with lots practical transportation planning experience and with high level of education and training. In this paper we propose a comprehensive framework for an Intelligent Decision Support System (IDSS) for Traffic Congestion Management System that utilizes a state of the art transportation network equilibrium modeling and providing an easy to use GIS-based interaction environment. The developed IDSS reduces the dependability on the expertise and level of education of the transportation planners, transportation engineers, or any transportation decision makers.

Design and Implementation of GPS Vehicle Navigation and Guidance System

The urban traffic information manageanent has become an important way of solving traffic jam of cities. With the wider use of the third generation of mobile conmmunication （3G） networks, information management based on 3G will be a central issue of application. The paper designs a fi-amework of Global Posititoning System （GPS） vehicle navigating and guiding system using 3G mobile network and Glbal Information System （GIS） electronic map according to moving objects. It discusses moving object＇s time-space attrihltes which will be described by a five-field and a directed graph. It analyzes the GPS mobile apparatus＇ s software functions and hardware structures. Anti it improves the function of GPS mobile apparatus which provide the guiding function utilizing the shared information of traffic. The navigation based on the shortest path algortithm is been advanced to one based on the real-time traffic flow of moving objects, which help people travelling on roads more convenietly.

The role of intelligent technology in the development of urban air mobility systems:A technical perspective

Urban Air Mobility(UAM)is an emerging transportation system that aims at revolutionizing urban mobility through the deployment of small electric vertical takeoff and landing(eVTOL)aircraft.The development of UAM is largely driven by advances in Intelligent Technology(IT).This review article provides an overview of the UAM system and discusses the application of IT in UAM.Major challenges facing UAM are also identified,and an outlook on the future of this promising transportation system is presented.Our main conclusions suggest that IT is a fundamental driver of UAM,enabling a range of applications such as air traffic management and autonomous drone control.However,the UAM system is facing a number of challenges,including eVTOL technology,system integration issues,and noise pollution.Despite these challenges,the future of UAM appears promising;as a disruptive transportation mode,UAM is expected to play an important role in addressing the growing demand of urban transportation in the coming decades.

Research on an Urban Traffic Control System Based on DGPS

The basic principles of GPS (Global Positioning System) and DGPS (Differential GPS) are described. The principle and structure of vehicle navigation systems, and its application to the urban traffic flow guidance are analyzed. Then, an area coordinated adaptive control system based on DGPS and a traffic flow guidance information system based on DGPS are put forward, and their working principles and functions are researched. This is to provides a new way for the development of urban road traffic control systems.

Increasing Efficiency of an Intelligent Semaphore by Implementing an ID3 Approach in Decision-Making

The main objective of a semaphore is to provide a correct and fluent vehicular mobility. Many countries around the world are using such devices in urban areas. However, the traditional semaphore operative ways are outdated. We report in a previous contribution the development of a methodology capable of improving the vehicular mobility by proposing a new green light interval based on road conditions with a CBR approach. However, this proposal should include whether it is needed to modify such light duration. To do this, the paper proposes the adaptation of an inductive decision tree helping to decide when the semaphore should try to fix the green light interval according to specific road requirements. Some experiments are conducted in a simulated environment to evaluate the pertinence of implementing a decision-making before the CBR methodology. For example, using the ID3 approach the decisions of the system improve almost 8% in a set of 10,000 experiments. Finally, some conclusions are drawn to emphasize the benefits of including this technique in a methodology to implement intelligent semaphores.

Modeling of Artificial Intelligence Based Traffic Flow Prediction with Weather Conditions

Short-term traffic flow prediction (TFP) is an important area inintelligent transportation system (ITS), which is used to reduce traffic congestion. But the avail of traffic flow data with temporal features and periodicfeatures are susceptible to weather conditions, making TFP a challengingissue. TFP process are significantly influenced by several factors like accidentand weather. Particularly, the inclement weather conditions may have anextreme impact on travel time and traffic flow. Since most of the existing TFPtechniques do not consider the impact of weather conditions on the TF, it isneeded to develop effective TFP with the consideration of extreme weatherconditions. In this view, this paper designs an artificial intelligence based TFPwith weather conditions (AITFP-WC) for smart cities. The goal of the AITFPWC model is to enhance the performance of the TFP model with the inclusionof weather related conditions. The proposed AITFP-WC technique includesElman neural network (ENN) model to predict the flow of traffic in smartcities. Besides, tunicate swarm algorithm with feed forward neural networks(TSA-FFNN) model is employed for the weather and periodicity analysis. Atlast, a fusion of TFP and WPA processes takes place using the FFNN modelto determine the final prediction output. In order to assess the enhancedpredictive outcome of the AITFP-WC model, an extensive simulation analysisis carried out. The experimental values highlighted the enhanced performanceof the AITFP-WC technique over the recent state of art methods.

A Dynamic Road Incident Information Delivery Strategy to Reduce Urban Traffic Congestion

Advanced information and communication technolo-gies can be used to facilitate traffic incident management.If an incident is detected and blocks a road link,in order to reduce the incident-induced traffic congestion,a dynamic strategy to deliver incident information to selected drivers and help them make detours in urban areas is proposed by this work.Time-dependent shortest path algorithms are used to generate a subnetwork where vehicles should receive such information.A simulation approach based on an extended cell transmission model is used to describe traffic flow in urban networks where path information and traffic flow at downstream road links are well modeled.Simulation results reveal the influences of some major parameters of an incident-induced congestion dissipation process such as the ratio of route-changing vehicles to the total vehicles,operation time interval of the proposed strategy,traffic density in the traffic network,and the scope of the area where traffic incident information is delivered.The results can be used to improve the state of the art in preventing urban road traffic congestion caused by incidents.

Fuzzy Rules to Improve Traffic Light Decisions in Urban Roads

Many researchers around the world are looking for developing techniques or technologies that cover traditional and recent constraints in urban traffic con-trol. Normally, such traffic devices are facing with a large scale of input data when they must to response in a reliable, suitable and fast way. Because of such statement, the paper is devoted to introduce a proposal for enhancing the traffic light decisions. The principal goal is that a semaphore can provide a correct and fluent vehicular mobility. However, the traditional semaphore operative ways are outdated. We present in a previous contribution the development of a methodology capable of improving the vehicular mobility by proposing a new green light interval based on road conditions with a CBR approach. However, this proposal should include whether it is needed to modify such light duration. To do this, the paper proposes the adaptation of a fuzzy inference system helping to decide when the semaphore should try to fix the green light interval according to specific road requirements. Some experiments are conducted in a simulated environment to evaluate the pertinence of implementing a decision-making before the CBR methodology. For example, using a fuzzy inference approach the decisions of the system improve almost 18% in a set of 10,000 experiments. Finally, some conclusions are drawn to emphasize the benefits of including this technique in a methodology to implement intelligent semaphores.

Improving Vehicular Mobility in Urban Traffic Using Ubiquitous Computing

The paper introduces a novel paradigm to use ubiquitous computing in urban traffic control as a methodology to include the benefits of considering physical elements implicated in the environment. This promising idea arises from previous results in the literature, where ubiquitous computing achieves significant and promising results in diverse scenarios. Some works in the state-of- the-art clearly state that traditional traffic light signals are not capable of offering higher service levels when they should control the vehicular mobility because of different constraints. In this sense, the paper proposes an adaptation of the main ideas of ubiquitous computing as a metaphor to facilitate the interaction between users and traffic infrastructures in order to improve the users’ experience on the road.

城镇密集区公路桥群绿色建造创新探索

为提升城镇密集区公路绿色建造水平,结合南(沙)—中(山)高速的项目特点及工程需求,将绿色发展理念贯彻到公路设计、施工、运营、养护和管理的全寿命周期,重点研发了公路桥群工业化智能建造、公路资源综合利用、滨海湿地区公路环境保护与粤港澳大湾区交旅融合设计等四类十项创新技术。其中,预制梁环形生产线、非标准八车道等为项目特色首创技术,初步构建了典型城市群绿色桥群公路技术体系,并对未来重点发展方向进行了展望,以期丰富和完善粤港澳大湾区绿色公路技术体系,为其他高度城镇化区域可持续发展提供参考。

区块链在城市道路智能交通中的应用综述

从安全、共享、运营三个方面介绍了区块链在城市道路智能交通中的应用。在安全方面,区块链的去中心化和不可篡改的特性能够保护数据信息安全和用户隐私安全;在共享方面,区块链的激励机制促进了车辆计算资源和交通信息的共享;在运营方面,区块链的不可篡改和可追溯性保证了运营中对交通违章准确有效的溯源,利用共识机制让交通信号灯配时更加高效。此外,分析了区块链在城市道路智能交通应用中存在的不足,并展望了区块链在城市道路智能交通领域的未来发展方向。

冷链物流配送网络选址-路径-库存的优化

为了控制冷链配送成本,优化3级冷链物流网络节点空间布局,提出了一种基于需求存在模糊随机性的选址-路径-库存多目标优化模型。以京津冀城市群为例,提供了优化方案。针对由物流基地、配送中心、末端网点构成的冷链物流3级配送网络,考虑到冷链产品存在订货提前期及需求具有模糊随机性,引入模糊随机变量,建立了CLRIP集成优化模型。首先,通过穷举法计算出各备选配送中心点的最佳订货周期和最优库存管理方式,将客户需求点与备选配送中心点进行随机组合,确定需要开放的备选配送中心点的位置与数量。然后,基于客户模糊随机需求,综合考虑土地、库存和配送等成本,进行选址-路径动态规划;利用改进禁忌搜索算法修正模型,直至达到最终要求,从而确定需要开放的备选配送中心点及其最优巡回路线。最后,对京津冀城市群多组实例进行分析。结果表明:使用模糊随机变量的试验数据中,最优解目标函数值和偏差稳定度均优于使用固定需求量的对照组数据,验证了CLRIP模型和改进算法的有效性。研究结果可为京津冀城市群冷链物流配送网络构建提供最优选址位置、配送路径和库存管理,为相关冷链物流配送网络优化提供实用可靠的参考方案。

基于异构数据特征的城市轨道交通OD客流短时预测方法

城市轨道交通起讫点(origin-destination,OD)客流短时预测在智能交通系统中意义重大,它为交通管控策略实施以及出行者出行选择提供了重要的决策依据。卷积神经网络被广泛用于交通数据空间相关性提取,但其平移不变性与局部敏感性导致该方法更重视局部特征而忽视全局特征。本研究构建了基于注意力机制的异构数据特征提取机模型(heterogeneous data feature extraction machine,HDFEM)以实现OD矩阵空间相关性的全局感知。该模型从时空特征和用地属性特征出发,构造异构数据OD时空张量与地理信息张量,依托模型张量编码层对异构数据张量进行分割与编码,通过注意力机制连接各张量块特征,提取OD矩阵中各个部分间的空间相关性。该方法不仅实现了异构数据与OD客流数据的融合,还兼顾了卷积神经网络模型未能处理的OD矩阵远距离特征,进而帮助模型更全面地学习OD客流的空间特征。对于OD时序特性,该模型使用了长短时记忆网络来处理。在杭州地铁自动售检票系统(auto fare collection,AFC)数据集上的实验结果表明:HDFEM模型相对于基于卷积神经网络的预测模型,其均方误差、平均绝对误差与标准均方根误差分别下降了4.1%,2.5%,2%,验证了全局OD特征感知对于城市轨道交通OD客流预测的重要性。

智能化视角下城市公园无障碍设计研究——以武汉市中山公园为例

为改善老年人的无障碍环境建设、分析其无障碍设计需求,从智能化的角度出发,研究新时期城市公园无障碍设计的发展需求与智能化融合实践应用,提出改善城市公园无障碍设计的智能化措施,进而构建城市公园无障碍设计智能化体系。文章以武汉市中山公园为例,结合行为注记法、实地调查和问卷调研法,发现其存在诸多无障碍环境建设问题,从智能化角度出发,为其提供改善无障碍环境的思路与策略。

城市智能公交体系优化

随着我国经济的高速发展和城市化进程的加快,城镇居民出行数量出现急剧上升的趋势。同时,机动车数量增长也较快。因此解决当前道路交通体系供需矛盾,仅仅采取控制总需求、增加市场需求量、扩大道路容量等手段,已不再是解决问题的根本方向。为提高城市中公交车系统的运用效率与可持续发展问题,该研究成果以新型的人工智能技术为依托,探索出一种改善公交车辆调配、道路设计与乘客信息服务的城市中智慧化公交车管理系统,成功地提高城市公交车系统的运行效率与信息服务水平。事实证明,这一技术显著改善城市公交出行的便利性和舒适度,也间接降低城市拥堵问题,并获得一定的实用价值,在城市发展中还具有着一定的推动作用。这也是解决目前我国城市交通供给日益紧张矛盾的治本之策。

智能网联环境下单交叉口车辆轨迹优化

为了提高信号灯前车辆的通行效率,改善交通流整体运行水平,本文从减少车辆延误和降低燃油消耗两个角度入手,在智能网联环境下,提出了一种车辆编组识别算法和针对编组头车的多目标线性轨迹优化模型(MOLP-pl)。首先对智能驾驶员跟驰模型(IDM)进行改进,调整车辆状态,减少车辆随机到达状态下车辆速度和车头时距分布的差异,同时为后续MOLP-pl轨迹优化模型的运行提供先决条件。在此基础上,以车辆编组为优化单元,通过车辆编组识别算法识别编组头车和跟随车辆,将编组头车的行驶轨迹作为优化对象并建立相应的数学模型。为了提高车辆轨迹优化模型的求解效率和精度,对其进行线性化重构,采用线性求解器计算编组头车加速度,构建编组头车最佳时空轨迹,然后,利用IDM跟驰模型计算跟随车辆的行驶速度,从而使编组车辆最大效率的通过交叉口。最后,利用SUMO构建的仿真实验表明:本研究提出的车辆轨迹优化算法可显著提高信号灯前车辆的通行效率,在三种不同的交通饱和度条件下,相对于无速度引导场景,车辆延误分别降低了8.56%、12.42%、64.79%,燃油消耗分别降低了17.21%、18.34%、12.64%;相对于逻辑控制场景,延误分别降低了-1.31%、2.63%、60.83%,燃油消耗分别降低了2.47%、7.91%、2.28%。

城市综合交通枢纽智慧化设计研究——以深圳市西丽综合交通枢纽为例

目前,新一代站城融合的城市综合交通枢纽正朝着智慧化方向发展。然而,针对智慧交通枢纽的顶层设计尚未明确,各地智慧交通枢纽、站城融合的实现路径及建设方案也各不相同,尚处于探索阶段。为解决上述问题,文章结合站城一体化设计理念,依托深圳市西丽综合交通枢纽,在阐述枢纽运营管理模式及组织架构的基础上,深入分析枢纽运营管理目前存在的诸如信息化水平低、旅客出行体验差、商业开发乏力等痛点,明确枢纽在生产运营、旅客服务、商业开发方面的智慧化需求,并据此提出智慧枢纽平台设计方案,包括平台定位、平台总体架构,以及IaaS、DaaS、PaaS、SaaS各层和云平台、云管平台、网络安全防护系统等的具体设计,以期为类似城市综合交通枢纽的智慧化建设提供参考和借鉴。

基于卷积神经网络算法的城市轨道交通施工人员不安全行为智能识别技术

[目的]施工人员的不安全行为是城市轨道交通施工事故发生的根本因素,传统的管理模式在约束人的不安全行为方面存在不足,因此需借助高精度定位技术和智能识别技术,从主观上消除事故隐患。[方法]介绍了城市轨道交通施工人员不安全行为的产生机理。结合UWB(超宽带无线通信)高精度定位技术、摄像机自标定技术及基于卷积神经网络算法的智能识别技术,搭建了具有定位、感知、识别、预警及通信功能的一体化智能管理平台。以安全帽识别为例,构建了安全帽识别拓扑流程图,对基于卷积神经网络算法的施工人员不安全行为识别的算法进行了测试。[结果及结论]测试结果表明,该算法可实现对施工现场未佩戴安全帽人员的识别,验证了该算法的准确性。该技术实现了对城市轨道交通施工人员不安全行为的智能识别预警。