基于改进蚁群算法的动态路径规划算法研究

针对交通导航最优路径问题,文章分析了传统蚁群算法的基本原理及其局限性,并为提高路径搜索效率,解决路径搜索初始阶段缺乏方向性、正反馈机制引发局部最优解的问题,提出了新的改进蚁群算法,通过信息素更新策略改进、启发因子改进和引入探索因子来寻求动态全局最优解。由MATLAB实验仿真可知,改进后的蚁群算法对比基本蚁群算法,在遍历相同道路网络节点时,其循环的平均路径长度和最短路径长度都较短,能够有效实现动态最短路径诱导,具有良好的收敛性和全局寻优性。     

空间索引技术在路径选择中的应用研究

针对现有的最优路径选择模型结果很好但搜索速度相对较慢的问题,为了提升最优路径的搜索速度,并获取满意路径,采取空间索引技术,对最优路径问题进行了模型和算法的改进,以运输时间最短、算法搜索速度最快为目标进行了研究。通过与常见算法比较,空间索引技术在路径选择的应用上取得较满意的效果。     

基于实时交通信息的动态路径规划算法研究

针对车辆智能交通最优路径问题,文章提出了一种基于层次分析法和消弧法结合的最优路径算法,通过建立权重判断矩阵求解特征向量,计算得出影响动态路径值的各因子的权重值;应用消弧法,求得指定的两个节点之间的最短路径。结果表明:动态路径规划算法能为驾驶员提供一条最优路径。     

基于改进蚁群算法的城市交通最短路径算法

蚁群算法作为一种寻优性能良好的智能算法,是解决最短路径问题的一种有效的方式。但是,基本蚁群算法真接运用于交通网络中路径的寻优存在一些不足。文章针对基本蚁群算法的不足对其进行了改进,根据交通网络的特点限定了解的搜索范围和改进了蚁群算法的转移规则,并对信息素的更新规则作了改进。仿真实验结果表明,改进了的蚁群算法在求解最短路径时比基本蚁群算法性能有较大的提高。     

基于列生成的城市轨道交通乘务排班计划优化

乘务排班计划是城市轨道交通运输计划的重要组成部分，高效的排班计划可以有效降低城市轨道交通运营成本。本文基于列生成算法，构建以班次数最小为目标的乘务排班模型。列生成算法的限制主问题采用集合覆盖模型，通过建立网络图模型，将定价子问题转化为带资源约束的最短路问题。提出了考虑复杂约束的改进的邻接矩阵构建方法，采用Floyed算法求得最短路径集合。在求解具有最小检验数的新班次时，提出了基于工作效率和工作时长综合惩罚的优化规则，以提高新班次集合的质量。最后求解0-1整数规划问题，即从可行班次集合中找到满足乘务片段全覆盖约束的最优子集，构成最终的排班方案。案例结果表明本文模型能够显著地减少乘务班次数，同时各项指标均有所改善。     

基于增量搜索的车辆动态路径规划算法研究

D＊Lite算法是一种非常高效的增量搜索算法,适合于动态环境下的路径规划。文章基于D＊Lite算法的原理,对该算法的性能及应用于车辆路径规划的适用性进行改进,提高了算法的稳定性和结果的可靠性,并通过试验和算法评价验证了改进的D＊Lite算法在复杂、动态环境下规划路径的有效性。     

模糊矩阵算法在交通导航系统中的应用

针对车载导航动态路径最短算法问题,文章研究了影响交通运输效率的几个重要因素,利用模糊逻辑运算求解各因素对动态路径权值的影响,提出了基于矩阵算法的最佳导航路径规划算法,并通过南宁市某地区的交通网络图,实验验证了该算法的准确性和有效性。     

动态车辆路径问题的遗传算法研究

文章针对动态车辆路径的特点及模型对其算法进行了研究,并设计了改进的遗传算法对最优路径进行求解,结果显示采用改进的遗传算法提高了全局寻优能力与收敛速度,取得了较好的效果。     

基于多源数据的铁路货运客户服务提升一体化方案

为进一步实现铁路货运部门对客户的精准营销,本文提出基于多源数据的客户服务提升一体化方案。首先建立铁路货运客户服务提升整体指标体系,利用数据挖掘算法对二级指标进行聚类,得到客户细分方案;进一步,以服务提升路径最短为目标,以参照客户对象的搜索角度和搜索半径为约束条件建立客户服务提升数理模型,并设计客户服务提升算法。最后,通过案例分析验证了一体化方案的可行性和有效性。结果表明：可量化的服务提升路径在客户服务提升效果上明显优于定性得出的服务提升策略。     

基于改进Dijkstra算法的路径优化仿真研究

文章针对传统Dijkstra算法在路径优化过程中存在的不足,从路网拓扑关系存储和快速搜索技术两方面对其进行了改进,得到了一种改进的Dijkstra算法,并通过实例仿真分析,验证了该算法的有效性。     

Solving the Median Shortest Path Problem in the Planning and Design of Urban Transportation Networks Using a Vector Labeling Algorithm

This paper proposes an alternative algorithm to solve the median shortest path problem (MSPP) in the planning and design of urban transportation networks. The proposed vector labeling algorithm is based on the labeling of each node in terms of a multiple and conflicting vector of objectives which deletes cyclic, infeasible and extreme-dominated paths in the criteria space imposing cyclic break (CB), path cost constraint (PCC) and access cost parameter (ACP) respectively. The output of the algorithm is a set of Pareto optimal paths (POP) with an objective vector from predetermined origin to destination nodes. Thus, this paper formulates an algorithm to identify a non-inferior solution set of POP based on a non-dominated set of objective vectors that leaves the ultimate decision to decision-makers. A numerical experiment is conducted using an artificial transportation network in order to validate and compare results. Sensitivity analysis has shown that the proposed algorithm is more efficient and advantageous over existing solutions in terms of computing execution time and memory space used.     

An improved Dial's algorithm for logit-based traffic assignment within a directed acyclic network

Abstract

Dial's algorithm is one of the most effective and popular procedures for a logit-type stochastic traffic assignment, as it does not require path enumeration over a network. However, a fundamental problem associated with the algorithm is its simple definition of ‘efficient paths’, which sometimes produces unrealistic flow patterns. In this paper, an improved algorithm based on the route extension coefficient is proposed in order to circumvent this problem, in which ‘efficient paths’ simultaneously consider link travel cost and minimum travel cost. Path enumeration is still not required and a similar computing efficiency with the original algorithm is guaranteed. A limitation of the algorithm is that it can only be applied to a directed acyclic network because a topological sorting algorithm is used to decide the order of the sequential calculation. A numerical example based on the Beijing subway network illustrates the effectiveness of the proposed algorithm. It is found that it is able to exclude most unrealistic paths, but include all reasonable paths when compared with path enumeration and the original Dial's algorithm.     

Optimizing one‐way traffic network reconfiguration and lane‐based non‐diversion routing for evacuation

Unfortunately, situations such as flood, hurricanes, chemical accidents, and other events occur frequently more and more. To improve the efficiency and practicality of evacuation management plan, an integrated optimization model of one‐way traffic network reconfiguration and lane‐based non‐diversion routing with crossing elimination at intersection for evacuation is constructed in this paper. It is an integrated model aiming at minimizing the network clearance time based on Cell Transmission Model. A hybrid algorithm with modified genetic algorithm and tabu search method is devised for approximating optimal problem solutions. To verify the effectiveness of the proposed model and solving method, two cases are illustrated in this paper. Through the first example, it can be seen that the proposed model and algorithm can effectively solve the integrated problems, and compared with the objective value of the original network, the network clearance time of the final solution reduces by 47.4%. The calculation results for the realistic topology and size network of Ningbo in China, which locates on the east coast of the Pacific Ocean, justify the practical value of the model and solution method, and solutions under different settings of reduction amount of merging cell capacity embody obvious differences. Copyright © 2015 John Wiley & Sons, Ltd.     

Constraint reformulation and a Lagrangian relaxation-based solution algorithm for a least expected time path problem

Using a sample-based representation scheme to capture spatial and temporal travel time correlations, this article constructs an integer programming model for finding the a priori least expected time paths. We explicitly consider the non-anticipativity constraint associated with the a priori path in a time-dependent and stochastic network, and propose a number of reformulations to establish linear inequalities that can be easily dualized by a Lagrangian relaxation solution approach. The relaxed model is further decomposed into two sub-problems, which can be solved directly by using a modified label-correcting algorithm and a simple single-value linear programming method. Several solution algorithms, including a sub-gradient method, a branch and bound method, and heuristics with additional constraints on Lagrangian multipliers, are proposed to improve solution quality and find approximate optimal solutions. The numerical experiments investigate the quality and computational efficiency of the proposed solution approach.     

Entropy weighted average method for the determination of a single representative path flow solution for the static user equilibrium traffic assignment problem

The formulation of the static user equilibrium traffic assignment problem (UETAP) under some simplifying assumptions has a unique solution in terms of link flows but not in terms of path flows. Large variations are possible in the path flows obtained using different UETAP solution algorithms. Many transportation planning and management applications entail the need for path flows. This raises the issue of generating a meaningful path flow solution in practice. Past studies have sought to determine a single path flow solution using the maximum entropy concept. This study proposes an alternate approach to determine a single path flow solution that represents the entropy weighted average of the UETAP path flow solution space. It has the minimum expected Euclidean distance from all other path flow solution vectors of the UETAP. The mathematical model of the proposed entropy weighted average method is derived and its solution stability is proved. The model is easy to interpret and generalizes the proportionality condition of Bar-Gera and Boyce (1999). Results of numerical experiments using networks of different sizes suggest that the path flow solutions for the UETAP using the proposed method are about identical to those obtained using the maximum entropy approach. The entropy weighted average method requires low computational effort and is easier to implement, and can therefore serve as a potential alternative to the maximum entropy approach in practice.     

Finding the most reliable path with and without link travel time correlation: A Lagrangian substitution based approach

Path travel time reliability is an essential measure of the quality of service for transportation systems and an important attribute in travelers’ route and departure time scheduling. This paper investigates a fundamental problem of finding the most reliable path under different spatial correlation assumptions, where the path travel time variability is represented by its standard deviation. To handle the non-linear and non-additive cost functions introduced by the quadratic forms of the standard deviation term, a Lagrangian substitution approach is adopted to estimate the lower bound of the most reliable path solution through solving a sequence of standard shortest path problems. A subgradient algorithm is used to iteratively improve the solution quality by reducing the optimality gap. To characterize the link travel time correlation structure associated with the end-to-end trip time reliability measure, this research develops a sampling-based method to dynamically construct a proxy objective function in terms of travel time observations from multiple days. The proposed algorithms are evaluated under a large-scale Bay Area, California network with real-world measurements.     

Optimization of bus stop placement for routes on uneven topography

The improvement and expansion of public transport is an increasingly important solution to the high congestion costs and worsening environmental impacts of the car dominated transport systems seen in many cities today. The intelligent design of stop locations is one way to improve the quality of PT and thereby improve its ridership. Stop placement is a relatively complex task as it involves a trade-off between two competing goals; accessibility and operation; however this trade-off can be made explicit using an appropriate mathematical model. Many such models have been developed in the literature, however none consider the effects of uneven topography. Topography is an important but often neglected factor in the design of public transportation systems, with the potential to have a significant impact on the accessibility, operation and planning of a transit service. In this work a mathematical modelling approach to bus stop placement is developed which includes considerations of uneven topography in three ways; (1) Its effect on walking speed; (2) Its impact on the attractiveness of an access path to a transit service; and (3) Its effect on acceleration rates at stops. Because of the complexity of the model developed, a heuristic evolutionary algorithm’ is employed to approximate an optimal solution to the model. Finally, the model and solution method are applied to a case study in the Auckland CBD area in New Zealand.     

Integrated optimization on train scheduling and preventive maintenance time slots planning

We address the problem of simultaneously scheduling trains and planning preventive maintenance time slots (PMTSs) on a general railway network. Based on network cumulative flow variables, a novel integrated mixed-integer linear programming (MILP) model is proposed to simultaneously optimize train routes, orders and passing times at each station, as well as work-time of preventive maintenance tasks (PMTSs). In order to provide an easy decomposition mechanism, the limited capacity of complex tracks is modelled as side constraints and a PMTS is modelled as a virtual train. A Lagrangian relaxation solution framework is proposed, in which the difficult track capacity constraints are relaxed, to decompose the original complex integrated train scheduling and PMTSs planning problem into a sequence of single train-based sub-problems. For each sub-problem, a standard label correcting algorithm is employed for finding the time-dependent least cost path on a time-space network. The resulting dual solutions can be transformed to feasible solutions through priority rules. Numerical experiments are conducted on a small artificial network and a real-world network adapted from a Chinese railway network, to evaluate the effectiveness and computational efficiency of the integrated optimization model and the proposed Lagrangian relaxation solution framework. The benefits of simultaneously scheduling trains and planning PMTSs are demonstrated, compared with a commonly-used sequential scheduling method.     

智能车避障路径规划方法研究

避障路径规划是智能车技术的关键部分,对智能车研究具有重大意义,正确的选择规划方法至关重要。本文首先初步选定人工势场法规划路径,仿真得知传统人工势场法规划出的路径存在局部最优、碰撞及达不到目标点的问题。因此提出改进人工势场法,通过改变势场函数和设定初始条件来解决此问题。最后,为了规划出能够同时避开静态和动态障碍物且能到达目标点的理想路径,在改进人工势场法中加入速度元素,经过仿真,证明了基于速度的改进人工势场法在智能车避障路径规划中的可行性和优越性。