TRIP DISTRIBUTION MODELLING USING FUZZY LOGIC AND A GENETIC ALGORITHM

This article examines possibilities for the application of soft computing techniques for the prediction of travel demand. The model, based on fuzzy logic and a genetic algorithm, successfully solves the trip distribution problem. The possibilities of using the proposed model in solving trip generation, modal split and route choice problems have also been indicated. The model has been tested on a real numerical example. Exceptionally good correspondences between estimated and real values of passenger flows have been obtained.     

Camera calibration and vehicle tracking: Highway traffic video analytics

We describe a real-time highway surveillance system (RHSS), which operates autonomously to collect statistics (speed and volume) and generates incident alerts (e.g., stopped vehicles). The system is designed to optimize long-term real-time performance accuracy. It also provides convenient integration to an existing surveillance infrastructure with different levels of service. Innovations include a novel 3-D Hungarian algorithm which is utilized for object tracking and a practical, hands-off mechanism for camera calibration. Speed is estimated based on trajectories after mapping/alignment with respect to dominant paths learned based on an evolutionary dynamics model. The system, RHSS, is intensively evaluated under different scenarios such as rain, low-contrast and high-contrast lightings. Performance is presented in comparison to a current commercial product. The contribution is innovation of new technologies that enable hands-off calibration (i.e., automatic detection of vanishing points) and improved accuracy (i.e., illumination balancing, tracking via a new 3-D Hungarian algorithm, and re-initialization of background detection on-the-fly). Results indicate the capability and applicability of the proposed system in real-time and real-world settings.     

A study on smart parking guidance algorithm

Parking problem becomes one of major issues in the city transportation management since the spatial resource of a city is limited and the parking cost is expensive. Lots of cars on the road should spend unnecessary time and consume energy during searching for parking due to limited parking space. To cope with these limitations and give more intelligent solutions to drivers in the selection of parking facility, this study proposes a smart parking guidance algorithm. The proposed algorithm supports drivers to find the most appropriate parking facility considering real-time status of parking facilities in a city. To suggest the most suitable parking facility, several factors such as driving distance to the guided parking facility, walking distance from the guided parking facility to destination, expected parking cost, and traffic congestion due to parking guidance, are considered in the proposed algorithm. To evaluate the effectiveness of the proposed algorithm, simulation tests have been carried out. The proposed algorithm helps to maximize the utilization of space resources of a city, and reduce unnecessary energy consumption and CO₂ emission of wandering cars since it is designed to control the utilization of parking facility efficiently and reduce traffic congestion due to parking space search.     

公路网络路线布局优化的双层规划模型及算法研究

首先分析了省、市级公路网布局的特点和要求,然后通过将连续变量离散化、引入双层规划模型,建立了适合省、市级公路网络的路线布局双层规划模型。最后研究了模型的求解算法,将模拟退火算法应用于公路网络路线布局优化问题中,应用V isual Basic 6.0语言开发了相应的算法程序。     

基于索网找形的吊弦长度计算方法探讨

在总结现有吊弦计算方法的基础上提出一种新的计算方法,阐述了新方法的基本理论基础和计算流程.最后对2种方法优缺点进行了总结.本文提出的新方法对传统的计算方法的不足进行了弥补,有理论指导意义.     

兼顾公平与效率的入口匝道协调控制

为解决高速公路匝道控制片面追求控制效率而忽略交通公平性的问题,提出一种兼顾公平与效益的入口匝道协调控制策略.定义排队延误基尼系数,有效刻画匝道控制的公平性.基于宏观交通流模型,构建双目标优化模型,优化目标包含反映控制效率的总旅行时间和反映公平性的基尼系数.改进差分演化算法的约束惩罚和罚函数构造方法,使差分演化算法能适于约束多目标优化问题的求解.应用研究表明,改进差分演化方法能获取Pareto最优解,决策者可根据个人喜好选择满意解.     

Methods to reduce dimensionality and identify candidate solutions in multi-objective signal timing problems

Adjusting traffic signal timings is a practical way for agencies to manage urban traffic without the need for significant infrastructure investments. Signal timings are generally selected to minimize the total control delay vehicles experience at an intersection, particularly when the intersection is isolated or undersaturated. However, in practice, there are many other potential objectives that might be considered in signal timing design, including: total passenger delay, pedestrian delays, delay inequity among competing movements, total number of stopping maneuvers, among others. These objectives do not tend to share the same relationships with signal timing plans and some of these objectives may be in direct conflict. The research proposes the use of a new multi-objective optimization (MOO) visualization technique—the mosaic plot—to easily quantify and identify significant tradeoffs between competing objectives using the set of Pareto optimal solutions that are normally provided by MOO algorithms. Using this tool, methods are also proposed to identify and remove potentially redundant or unnecessary objectives that do not have any significant tradeoffs with others in an effort to reduce problem dimensionality. Since MOO procedures will still be needed if more than one objective remains and MOO algorithms generally provide a set of candidate solutions instead of a single final solution, two methods are proposed to rank the set of Pareto optimal solutions based on how well they balance between the competing objectives to provide a final recommendation. These methods rely on converting the objectives to dimensionless values based on the optimal value for each specific objectives, which allows for direct comparison between and weighting of each. The proposed methods are demonstrated using a simple numerical example of an undersaturated intersection where all objectives can be analytically obtained. However, they can be readily applied to other signal timing problems where objectives can be obtained using simulation outputs to help identify the signal timing plan that provides the most reasonable tradeoff between competing objectives.     

基于最短路径搜索的动态随机交通分配

提出了一个基于Logit方法的动态交通随机分配模型,该模型通过最短路算法和随机分配来解决动态均衡交通分配中的出行选择问题,避免了以往动态交通均衡分配中解凸规划或非线性规划问题,同时不需要路径枚举,从而使本模型更适合于大型路网的动态交通分配．随后的算例证实了该模型的可行性。     

基于遗传算法的动态交通分配研究

主要研究动态系统最优（DSO）模型。以总的花费时间最小为目标函数,对模型的节点集、路段集分别进行研究。通过模拟路段的流入量、流出量和路段上的交通量来建模。用传统的迭代算法解决动态交通分配问题非常复杂,而对传统迭代算法进行改进或用遗传算法等新的智能算法求解则会很简洁和方便。     

基于最优加权Steiner树的枢纽型物流中心选址问题

为了满足近年来物流运输业快速发展的需要,促进物流中转运输网络的合理化建设,研究了枢纽型物流中心的功能和选址原则,详细分析了影响枢纽型物流中心选址的各种因素,提出了基于结点带权的欧氏Steiner最优树的枢纽型物流中心选址方案.针对该方案设计了相应的智能优化算法,并进行了具体的程序实现.借助该方案不仅可以使总的运输成本最小,而且能够在无需事先确定备选点的数量和位置的情况下实现同时确定枢纽型物流中心的数量及位置的目标.最后以长三角地区枢纽型物流中心的建设问题为背景,对各种数据进行了仔细的分析比较,从中确定若干区域作为物流服务需求点集,并将各种因素的综合效用作为物流需求点的权值,对上述算法进行了有效性验证.