基于出行时间可靠性的交通配流问题

提出一类由需求随机性所导致的基于出行时间可靠性的交通配流问题.由于每一天交通需求的随机变化,出行者的出行时间不是确定的,而是随机变量.假设出行者在过去经验的基础上能够得知出行时间的随机分布,提出一类新准则去刻画出行者在出行时间不确定情况下的路径选择行为.这种准则可以表示为一种以路径流量为变量的变分不等式模型.对于这类新的模型,给出了解的存在性证明,并且引入一个启发式的算法去求解该问题.数值算例展示了模型在应用上的特性和算法的有效性.     

基于道路扩容的道路拥挤定价收入再分配研究

道路拥挤已经成为世界各国面临的一个普遍难题,道路扩容和道路拥挤定价是缓解交通拥挤的有效手段.本文在分析道路拥挤定价收入用于道路扩容后对交通出行者效用的影响的基础上,构建了基于道路扩容的道路拥挤定价收入再分配双层规划模型,并给出求解算法和实证分析.     

考虑延误时间成本的最优路边停车策略研究

本文研究了延误时间成本作用下,出行者面对路边停车空位随机分布的寻位策略选择.出行者将做出提前多久出发和提前多远寻位的组合决策,其成本不但包括确定的路上行驶成本,还包括依赖于实时变化路边停车空位分布的延误时间成本和期望寻车成本.依据出行者到达目的地可能的延误状态,区分了三种不同的寻位策略,其中“不早到”策略被证明不可能成为最优选择,而“早停早到”策略和“早停迟到”策略则在不同条件下可能成为最优选择,尤其是单位迟到时间成本小于单位早到时间成本时,“早停迟到”策略是最优停车策略.算例结果支持了模型分析的结论.     

基于累积前景理论的随机网络用户均衡模型

出行者行为假设对交通网络用户均衡建模具有重要影响,更符合人们的真实决策行为的累积前景理论,为随机交通网络中的路径选择行为研究提供了新的分析框架.基于累积前景理论,将出行者的路径选择决策与网络的随机性相结合,建立了随机网络用户均衡的等价变分不等式模型;提出了求解模型的算法,通过简单的算例对模型和算法的合理性进行验证.通过...     

基于累积前景理论的出发时间选择SDUO模型

以早高峰工作出行为研究对象,基于累积前景理论建立了一个随机动态用户最优(SDUO)交通分配模型,模型可以同时选择出发时间和出行路径,给出了等价的变分不等式,设计了求解算法并通过算例进行了验证。结果显示,路径及OD对之间的动态出发流量、出发时间选择满意函数均与到达工作地点的价值函数形态相似,绝大多数出行者(96.45%)都能在"可以接受的最早到达时刻"与"工作开始时刻"之间到达。模型拓展了传统出发时间选择模型中"时间窗"的概念和出行者完全理性假设的局限,在有限理性框架下考察出行者的决策行为,模型及算法可以为出行行为分析、动态路径诱导,以及拥挤收费和错时上下班等交通管理措施的制订提供理论依据。     

合理制定铁路客票价格的优化模型及算法

在充分考虑出行者和铁路客运部门两方面的利益情况下 ,提出一个双层规划模型以得到在多种交通方式竞争条件下的铁路客票价格制定的最优策略 .既保障了出行者使自己的广义出行费用最小 ,又能使铁路客运部门在运输市场竞争中取得的经济效益最大 .并且给出了求解该模型的 SAB算法。最后用一个简单的算例说明了模型及算法的应用     

基于系统最优的城市公交专用道网络设计模型及算法

设置公交专用道是实现"公交优先"的重要手段,然而,专用道设置将改变不同交通方式的道路通行能力,进而影响交通网络的整体性能。本文试图提出一种基于系统最优思想的公交专用道网络设计方法,既保证出行者的利益,又能满足交通系统总费用最优的目标。首先,本文分析了公交专用道设置对公交车辆和社会车辆这两种交通方式道路通行能力的影响,基于经典的BPR函数,构造了考虑专用道设置的不同方式的路段阻抗函数。其次,分析了出行者在多方式交通网络中的模式选择和路径选择问题,采用用户平衡理论分析了城市多方式交通平衡配流问题,给出了相应的变分不等式模型。更进一步,采用双层规划方法构造了基于系统最优的城市公交专用道网络设计模型,该模型以交通网络总费用最小为优化目标,并考虑了不同交通方式的平衡流量约束,采用分支定界算法对该双层规划模型进行求解。最后,通过一个简单算例对模型及算法的可行性和有效性进行了分析和验证。     

基于ART及多风险规避的交通网络均衡模型

随着对交通系统不确定性认识的深入,以绝对理性为基础的“期望效用理论”在风险环境下的路径选择分析中显示出局限性,而“预期后悔理论”则为之提供了新的分析思路.将预期后悔理论应用到风险环境下的路径选择分析中,将出行者一致风险规避的假设扩展到多风险规避,建立了基于后悔理论及多风险规避出行特征的交通网络随机用户均衡变分不等式模型,并给出了求解算法.通过算例分析发现,后悔心理对出行者的路径选择并不总是显著的.在非风险环境及极端风险环境中,后悔心理对出行者的路径选择影响是微弱的,但是当环境处于极端风险与非风险之间时,后悔心理对出行者路径选择有着较为显著的影响.     

激励性和惩罚性交通拥堵治理政策的比较——基于动态演化博弈模型及仿真分析

随着城市化的快速发展,城市人口越来越多,私家车保有量急剧增长,这使得城市道路拥堵问题变得越来越严重,拥堵问题成为了城市健康可持续发展的重要阻碍。为了缓解交通拥堵,城市管理者基于不同的治理思路提出并实施了多项治理政策。本文从博弈论的角度阐述了交通拥堵的形成,并建立了一个出行者交通方式选择行为的演化博弈模型,揭示了不同治理措施对城市交通拥堵问题施加影响的过程,并对这些影响的结果做出了解释。该演化模型将出行者整体分为两类,一类表现为公交偏好,另一类为私家车偏好,我们分析在不同的交通拥堵治理政策下,出行者选择不同出行模式的成本和收益,得到随政策变化的收益矩阵,并分析两类出行者在不同政策下的演化稳定策略。为了更为直观地对演化过程及结果进行分析,我们对该模型进行了仿真模拟,最终发现惩罚和激励措施都能缓解交通拥堵,但在迅速性和持久性上,惩罚性措施比激励性措施更为有效。     

城镇居民预防性储蓄动机的异质性及强度研究

在具有不确定出行时间的交通网络中,假设出行者的参考点是异质的,服从某一连续分布函数,且风险偏好参数是依赖于参考点的,建立了一个基于累积前景理论的多用户网路均衡模型。研究表明,不同类的用户对风险的反应是不一样的,具有高参考点和低参考点的出行者均倾向于选择风险高的路径以期获得较高的感知价值,而具有中等参考点的出行者则倾向于选择风险较低的路径。此外,对两种交通管理措施进行了敏感性分析,结果表明,道路拓宽对于提高整个社会的感知价值是一个有效的方法,但其边际效益是递减的。然而,就风险管理而言,不同类的用户表现出不同的喜好。一般来说,具有低参考点的出行者能够从这一方法中受益,而具有高参考点的出行者则会失去潜在的风险收益。     

ATIS环境下交通配流的动态演化模型

考虑交通网络中实现用户均衡态的过程,引入决策出行费用的概念,提出一个 ATIS 环境下的交通配流动态演化模型,模型中的 OD 需求是可变内生的.利用稳定性定理分析了该动态系统的稳定性,说明在一定条件下,系统的用户均衡态是稳定的.采用改进的欧拉法完成了模型的数值试验,结果表明该动态系统确实可以达到用户均衡态,同时也发现,某些模型参数的变化可能导致路径流量和 OD 费用演化轨迹的无规则摆动.     

集成实证和定性模拟的移动增值战略匹配优化

提出实证驱动的定性模拟模型,研究价值链中增值战略匹配优化问题。通过问卷调研获取基于时间序列的实证数据,结合动态网络分析法DANP得到变量交互关系。并利用 QSIM 算法驱动其动态演化,确定价值链均衡时的最优决策。研究发现,根据结果能够优化资源配置,明确各维度的发展优先级: 1) 初始状态较高的参与方承担更多环节对价值链最有利;2)服务商因素对总收益的影响更显著,实践中资源配置应向服务商各维度倾斜,以发展其优先级。同数理解析方法相比,实证驱动的定性模拟方法在没有精确数学模型时也可以对管理优化问题进行分析。     

作业成本动因选择、合并与评价的系统方法构建

目前学术界关于作业成本动因选择与合并的研究,缺乏对动因留选与留选个数的探讨,而关于对成本动因选择合并的评价的研究未见。可以通过聚类分析建立模型,得到成本动因的相关类,再结合主成分分析指导留选动因的个数,最终确定留选动因,并首次建立评价动因选择与合并优劣性的主成分分析模型。实证分析表明,此系统方法更好地解决了成本动因选择与合并的问题,相同数据下该方法比矩阵理论与距离聚类分析结果更优,作业成本管理系统的复杂性与信息成本显著降低。本文为作业成本动因的选择与合并以及评价提供了一套系统方法。     

The planning of cycle trips in the province of East Flanders

Traditional route planners assist in finding the shortest or fastest route from one place to another. This paper presents a novel approach to path finding in a directed graph, namely a target distance, motivated by the problem that a recreational cyclist deals with when searching a nice route of a certain length. The problem is defined as a variant of the arc orienteering problem (AOP), a new combinatorial optimisation problem in which the score of a route in a directed graph has to be maximised by visiting arcs, while each arc can be visited at most once and the total cost of the route should not exceed a predefined cost. The contribution of this paper is threefold: (1) a mathematical model of the AOP is provided, (2) a metaheuristic method that solves AOP instances to near optimality in 1 s of execution time, is proposed and evaluated, and (3) two real-life applications of the method are presented. An on-line cycle route planning application offers personalised cycle routes based on user preferences, and an SMS service provides cyclists “in the field” with routes on demand.     

An investigation into a new class of vehicle routing problem with backhauls

A new version of the vehicle routing problem with backhauls is presented. In this new problem backhauls are not restricted to be visited once all linehaul customers have been served, neither are backhaul customers fully mixed with linehaul customers. In this problem the user based on his or her experience, the vehicle capacity, the type of products and the type of vehicle used, can define the position along a route from which the first backhaul customer may be visited. An insertion-type heuristic is put forward for this class of problems. An analysis of the improvement in route cost obtained by allowing a relaxation in the restriction of the mix of linehaul and backhaul customers is reported.     

Districting for parcel delivery services – A two-Stage solution approach and a real-World case study

This paper studies a real-world problem arising in the context of parcel delivery. Given a heterogeneous set of resources, i.e., different drivers and different vehicles, the problem for each day consists of assigning a driver and a vehicle to each customer requiring service. Two conflicting aspects must be taken into account. On the one hand, service consistency is desirable, meaning that a customer should always be served by the same driver. On the other hand, daily demand fluctuations and tight resource constraints prohibit fixed resource assignments. With the aim of finding a reasonable compromise between these aspects, we propose a novel two-stage districting approach, which establishes delivery districts in the first stage and adapts them to the daily demand realizations in the second stage. For the first stage problem we propose three models that differ in the level of detail of their input data, their expected compliance with service consistency and the driver’s contractual working times, and their computational effort. Our two-stage approach merges the two dominant approaches in the literature, which either determine a priori routes and then adapt them on a daily basis, or derive fixed service regions for drivers. We present a case study based on a real-world data set. The results highlight the differences between the three first stage models and show that only few adaptations of the districts are necessary in the second stage to achieve feasible daily delivery tours along with a very good workload balance for drivers. We also analyze the effects of a homogeneous vs a heterogeneous fleet, of full time drivers vs full and part time drivers, and of the location of the depot and the length of the planning horizon.     

A memetic algorithm for the patient transportation problem

This paper addresses a real-life public patient transportation problem derived from the Hong Kong Hospital Authority (HKHA), which provides ambulance transportation services for disabled and elderly patients from one location to another. We model the problem as a multi-trip dial-a-ride problem (MTDARP), which requires designing several routes for each ambulance. A route is a sequence of locations, starting and terminating at the depot (hospital), according to which the ambulance picks up clients at the origins and delivers them to the destinations. A route is feasible only if it satisfies a series of side constraints, such as the pair and precedence constraints, capacity limit, ride time, route duration limit and time windows. Owing to the route duration limit, in particular, every ambulance is scheduled to operate several routes during the working period. To prevent the spread of disease, the interior of the ambulances needs to be disinfected at the depot between two consecutive trips. The primary aim of the problem investigated herein is to service more requests with the given resources, and to minimize the total travel cost for the same number of requests. In this paper, we provide a mathematical formulation for the problem and develop a memetic algorithm with a customized recombination operator. Moreover, the segment-based evaluation method is adapted to examine the moves quickly. The performance of the proposed algorithm is assessed using the real-world data from 2009 and compared with results obtained by solving the mathematical model. In addition, the proposed algorithm is adapted to solve the classic DARP instances, and found to perform well on medium-scale instances.     

A multi-objective geographic information system for route selection of nuclear waste transport

Nuclear power is widely used throughout the world today. Functioning nuclear power plants produce large quantities of radioactive wastes needing to be transported to safe sites for proper management. With public emphasis on environmental protection and concern for safe transport of nuclear wastes, the problem of selecting an appropriate route for transporting nuclear wastes is a vitally important issue. The aforementioned route selection problem involves conflicting objectives among interested parties; therefore, we develop a multi-objective geographic information system (GIS) with ESRI ArcView GIS 3.x interface to practically support the involved parties for such a multi-objective route selection problem in engineering practices.     

Stress and the bus driver in the UK transport industry

This study was carried out to identify potential sources of stress for bus drivers working for the established operator in a major UK city, six months after the deregulation of bus transport. The focus was to assess the mental health and job satisfaction of the drivers. An interview programme was followed by a questionnaire survey of 376 male bus drivers. Major problem areas related to health and home-related concerns, problems intrinsic to the job, lack of involvement and support during the organizational changes occurring and fears regarding physical assault. Existing problems had been intensified as a result of streamlining and increased competition. The bus drivers were found to demonstrate lower levels of job satisfaction and unfavourable scores on mental-health indices when compared to normative samples, which was associated with their work-related stressors. The impact was significantly reduced for those men driving the newer minibuses in contrast to the traditional 72-seater double-decker buses.     

An efficient approach for searching optimal multi-keywords coverage routes

Journal of Combinatorial Optimization - Keyword-aware optimal route queries are a combinatorial optimization problem with three factors, namely keyword coverage, route budget constraint and route...