粒子群优化算法在公交车智能调度中的应用

运营车辆的智能排班是公交车辆智能调度需要解决的问题之一,关系到公交企业的经济效益与社会效益。采用兼顾公交公司与乘客双方利益的公交车辆调度模型,将带收缩因子和线性递减惯性权重的粒子群优化算法（W-K-PSO）应用到公交智能排班中。实例仿真结果表明该算法具有比其它优化算法更好的效率,是解决公交车智能调度问题的一个有效方法。     

基于遗传算法的公交智能排班方法的研究

研究公交车智能排班问题,建立数学模型,兼顾乘客出行效率和公交企业运营效益,给出利用遗传算法解决此问题的方法.重点阐述了智能排班的目标函数、适应度函数和遗传算子的设计.编制程序进行仿真实验,结果证明利用遗传算法解决公交智能排班问题,可以优化车辆调度,能在交通高峰和平峰期不均匀排班,符合公众和企业的利益,并提高了交通运行效率.     

基于遗传算法的公交智能排班系统的设计与实现

运营车辆智能排班问题是公交车辆智能调度需要解决的典型问题之一。它可以描述为:利用某种智能化的算法,在有限的步骤内,找出所有满足约束条件的最优排班方案或者接近最优的方案。文章应用已有的客流信息,并兼顾到乘客和公交公司的双重利益,引入了遗传算法(GA),构造了符合行车规律的编码方式、遗传算子,并给出了公交智能排班系统的设计,完成了程序的编码工作。     

基于细菌觅食优化算法的城市轨道交通调度优化

为了合理高效地制定城市轨道交通调度方案,实现客流与车次的优化配置,提出了一种基于细菌觅食优化算法的城市轨道交通调度优化策略。兼顾乘客与运营企业双方利益,以发车间隔为决策变量,乘客平均候车时间最短和发车次数最少为优化目标,建立调度优化模型,并对细菌觅食优化算法求解该调度模型的过程进行分析。结合某城市轨道交通一号线实际运营数据进行仿真实验,并与其他算法的优化结果进行对比分析,实验表明该算法和模型能有效解决城市轨道交通调度优化问题。     

基于双指标带权线性规划的公交车调度模型研究

公交车辆调度系统的优化可以提高公交车辆的运营效率,缓解城市交通压力,改善交通环境.针对公交车辆调度的现状,首先引入了公交车载客率和乘客不满率两个指标,并为这两个指标建立了带权优化模型;然后求得每个时间段最佳公交车发车数量,获得最优解;最后通过带入最优解,求得封闭线路（有来回）的最少备车数.通过代入数据验证,所得解在允许误差范围内符合实际结果,因此模型准确可靠,且基于本模型算法实现的程序能够应用于公交车调度系统.     

BRT公交车辆智能调度系统的设计及实现

公交调度是整个公交运营组织当中最关键的一个环节，直接影响运营和服务质量。该系统的目的在于研究在快速公交系统（BRT，Bus Rapid Trdnsit）这样一个环境下，如何利用客流数据及相关因素制定静态行车计划，包括发车时刻表制定和计划配班；如何利用该环境下提供的充足、丰富的实时信息，采取适当的动态调度策略，提供有效的动态调度支持方法，最终设计并实现一个公交车辆智能调度系统。     

改进的遗传算法在公交车调度中的应用

应用单亲遗传算法解决公交车辆的调度问题。在兼顾公交公司与乘客双方利益的情况下,用单亲遗传算法对调度模型进行优化。仿真结果表明,该方法可有效提高公交车辆运营效率。     

遗传-牛顿算法在公交智能调度中的应用

针对公交公司需要解决的典型问题公交车辆的优化调度,提出了一种遗传算法与牛顿算法相结合的智能优化算法;并用该混合算法对调度模型进行优化;首先在兼顾公交公司与乘客双方利益的情况下,建立了以发车间隔时间为决策变量的公交车优化调度模型,再利用遗传算法对决策变量进行优化,然后用牛顿法对其优化的结果进行深一步的搜索,使其优化结果精度提高;仿真结果表明,该混合算法比标准的遗传算法更有效地提高公交车辆运营效率并降低其费用成本.     

基于probe car的BRT计划编制优化方案

根据南中轴路BRT智能公交系统的实际应用情况,提出了一种BRT行车计划编制的优化方案：利用装载了智能车载设备的快速公交车（即Probe car）采集车辆信息、客流信息、路况信息等,统计分析出BRT车辆运营规律以及线路客流分布特征,按照车辆运营指标,优化制定行车计划.     

公交智能调度系统的设计与开发

近年,随着我国公共交通的迅猛发展,公交公司对公交车辆的管理难度却越来越高。如何管理庞大数量的车辆以及如何安排错综复杂的线路,就成为了公交公司的当务之急。通过对公交公司运营模式的理解,进行了系统的需求分析,并按照功能将系统划分为"车辆运营计划生成"、"车辆发车调度"、"统计报表查询"、"基础信息管理"、"系统角色维护"五个功能模块。公交智能调度系统采用B/S开发模式,根据对系统整体架构的设计,系统采用目前JAVA WEB项目中普遍使用的企业级框架SSH(Struts+Hibernate+Spring),完成了系统中不同层次间的集成。通过使用公交智能调度系统,公交调度人员只需要录入一次排班时刻表和轮换规则等数据,则可让系统自动生成每天的车辆计划,调度员无需在每月末集中安排下月的车辆计划。同时,本系统会自动记录车辆每天的运营情况并进行数据汇总,省去了调度员计算运营数据的工作,大大降低了其工作强度。本系统的应用也大大降低了公交公司对纸张的需求,形成无纸化办公,这不仅降低了成本,也间接地进行了环境的保护。     

基于公共交通智能卡数据的可视化分析

针对现有公共交通数据的可视分析方法很难在不同空间粒度下对乘客时空分布、客流时空分布、区域间客流时序变化进行多任务分析的问题,设计实现了一个多视图融合的可视化分析系统。该系统结合城市公共交通的智能卡数据、车辆GPS数据、地铁和公交线路信息,利用出行链路模型和基于出行时空特征的回归模型完成了乘客起讫点（origin-destination,OD）推断;然后,设计了层次聚类的地图可视化方法,结合了融合方位信息的玫瑰图和动态对比堆叠折线流图来分析各区域间的客流时序特点、关联关系;最后,利用真实的深圳市公共交通数据的可视分析结果验证了系统的有效性。     

基于改进遗传—模拟退火算法的公交排班优化研究

结合公交车辆调度自身的特点,兼顾公交公司与乘客双方的利益,建立了公交排班优化模型,以发车时刻为基因变量进行编码,对两个相邻的发车间隔之差、最大最小发车时间间隔、乘客的满载率等条件进行约束限制,提出了基于改进的遗传—模拟退火算法;对该模型进行优化求解,克服了传统优化算法的缺陷,提高了优化设计过程的求解效率。通过仿真实验得到了利用改进的遗传—模拟退火算法进行求解的不均匀发车时刻表。结果表明,改进的遗传—模拟退火算法能够在公交智能排班优化问题的巨大搜索空间中可靠地找到近似最优解,大大提高了计算效率。     

遗传算法在公交调度中的应用

针对泰州市公交智能化建设方面存在的技术问题,提出将遗传算法(Genetic Algorithms,GA)应用到公交调度优化中。以公交公司和乘客费用最少、社会效益最大为目标函数建立公交调度优化的数学模型,并应用遗传算法实现对模型的求解,通过对模型设置不同的参数,利用Matlab模拟实验验证模型的合理性、科学性。实验证明,优化的调度模型可降低公交公司的运营成本,提高乘客的满意度,确保社会效益和经济效益得到最大满足。     

Multiresponse optimization of dispatch rules for public bus services

A multiple response optimization approach based on an integrated simulation model and a service reliability measure is proposed for optimizing the dispatch rules of public bus services. The simulation model is based on two novel Markov models for modeling passenger loads and headway fluctuations. In consideration of the importance of passenger loads for bus services of high frequency, a service reliability measure based on the odds of seeing a full bus plying the route is designed. Such a simulation based multiresponse optimization approach can easily consider sampling variations and is scalable to deal with a larger public bus transit system with other objectives, such as minimizing the cost of operations. Practical data collection, analysis and estimation procedures for the load and headway distributions are also presented. A case study based on an actual public bus route in Singapore is used to demonstrate the usefulness of such an integrated simulation framework.     

校园公交定位与调度服务系统的设计与实现

针对高校校园公交运营中存在的乘车难问题,设计并实现了一种“校园公交定位与调度服务系统”,通过手机 实现对乘车人和车辆的实时定位,以此为基础为乘车人提供车辆定位、到达预测、预约叫车、校园导航等服务,从而解决校内乘 车难问题。同时,为调度人员和公交驾驶员提供乘车人位置显示和播报、车辆状态查询、实时消息通信等服务,为车辆调度提 供辅助决策,提高车辆运营效率。经测试,本文系统工作正常,能够达到设计目的。     

Glowworm Swarm Optimization for Dispatching System of Public Transit Vehicles

The intelligent schedule of vehicles operation is one of the problems which need to be solved in the dispatching system of public transit vehicles, it relates to the development of the city and civic daily life. In this paper, a transit vehicle scheduling optimization algorithm which balancing between the benefits of bus companies and passengers is proposed. The glowworm swarm optimization (GSO) with random disturbance factor, namely R-GSO is applied to the schedule of vehicles. Finally, we provide some comparisons of R-GSO with artificial fish-swarm algorithm, particle swarm optimization and GSO, the simulation results show R-GSO algorithm has higher efficiency and is an effective way to optimize the public transit vehicle dispatching.     

A fast hybrid computer vision technique for real-time embedded bus passenger flow calculation through camera

Bus passenger flow calculation system is a critical part of the smart public transportation framework. Bus passenger flow information can help to make data statistics report of the passenger at a bus station which can be used by public transport operator to evaluate the quality of the transportation. Statistics report of crowded passengers in the bus station help managers to understand the bus transit operations, can provide the database for the intelligent transportation scheduling, help to provide more and better services for passengers, overall data statistics of passengers has important practical significance to improve public transport environment. This paper presents a passenger counting algorithm based on hybrid machine learning approach. In the first step, an advanced method is used to extract the Histogram of oriented gradients (HOG) feature of passenger’s heads. Classification of head features is done by using support vector machine (SVM) as a classifier for the liner model. Heads are detected successfully after performing all steps. In next step Kanade-Lucas-Tomasi (KLT) is used to reality head tracking, the multiple target tracking is achieved and the head motion trajectory of passenger target is captured stably. At last, the trajectory is analyzed and the automatic counting of bus passenger flow is realized. In the last step, the proposed algorithm is move to embedded system for practical implementation. In this paper, the algorithm intends to use ADSP-BF609 embedded platform for transplantation. The experimental results demonstrate that the statistical accuracy of the proposed algorithm is enhanced successfully; especially during the daytime with the good illustration, the effective counting of the passenger flow is achieved and the inward and outward passenger counting can be realized. In this paper three feature extraction models are used namely local binary patterns, histograms of oriented gradients and binarized statistical image in order to get accurate features. Furthermore, three common classification techniques including naïve bayes classifier, boosted tress and support vector machines are used for fine classification of extracted vectors obtained from different features extractors model. 94.50% accuracy is achieved when support vector machine (SVM) classifies the features extracted using Histogram of oriented gradients (HOG). SVM surpasses the accuracy obtained by Boosted tree namely 81.30% using Histogram of oriented gradients (HOG) features.