实时交通信息作用下公交多公司博弈研究

研究了提供公交车实时到站信息服务,对乘客出行行为的影响以及公交公司在基于自身利润最大化目标下,决策是否提供信息进行分析.模型中假设,理性的出行者随机到达车站或者利用公交信息提前规划自己的行程.计划出行的人数依赖于公交车的发车频率以及提供信息的质量的高低.基于固定需求的乘客出行分析,研究了公交公司在选择是否提供信息和设定运营频率以最大化利润.得到不同运营策略的公交公司的发车频率及最优利润.发现乘客利用信息成本越低,公交公司在提供信息方面越积极,公交公司可能在降低社会总成本的情况下落入“囚徒困境”.如果乘客利用信息的成本较高,而信息提供成本较低,则可能出现一家公交公司提供信息而另一家通过增加服务频率来差异竞争的均衡结果.     

信息条件下的城市运输规划方法

交通网络中信息的引入,改变了以往人们的出行行为。常规的出行需求预测模型在信息条件下,需要进行合理改进。本文提出了将传统的出行产生模型与动态交通模拟模型集成进行信息条件下的城市运输规划研究的一种新的方法框架     

Passenger and pilot risk minimization in offshore helicopter transportation

In the offshore petroleum industry, employees are transported to and from the offshore installations by helicopter, which represents a major risk. This paper analyzes how to improve transportation safety by solving the helicopter routing problem with a risk objective expressed in terms of expected number of fatalities. A mathematical model is proposed and a tabu search heuristic is applied to this problem. Three routing policies are considered: a direct routing policy, a Hamiltonian routing policy, and a general routing policy. Extensive computational experiments are conducted on instances derived from real data in order to assess and compare these policies under a travel time, a passenger risk and a combined passenger and pilot risk objective. Several management insights can be derived from this study. In particular, our results show that passenger transportation risk can be reduced by increasing travel time at the expense of pilot risk. This can be achieved through a reduction of the average number of passengers onboard by applying either a Hamiltonian or a general routing policy. Our methodology can also be used to derive an equitable distribution of risk between passengers and pilots, considering that pilots fly much more frequently than passengers.     

基于Dijkstra-PD-ACO算法的大城市公交线路优化与评价方法研究

城市公交线路的布设受到诸多影响因素的作用,单纯的追求线路单一指标的"最优"在实际公交线路优化时往往难以取得满意的效果。首先针对传统公交线路优化算法在求解线路优化问题中存在的不足,基于蚁群优化算法的寻优特性,结合Dijkstra算法在局部路径寻优中的优点,提出了Dijkstra蚁群混合优化算法。其次对于线路优化所得到的可行备选方案集,基于分层聚类主成分分析评价法进行优化效能评价。最后以合肥市一环内公交线路优化为实例进行验证。结果表明, 本文提出的方法在兼顾客流密度最大、出行路径最短的同时给出了有效公交线路备选方案,优化结果也符合合肥市的实际情况。同时,该方法对我国大中城市公交线网优化问题的研究具有一定的借鉴意义。     

一种求解时变条件下有宵禁限制最短路的算法

在组合优化过程中,往往需要获得从起点到终点之间的最短路.由于道路、天气、交通条件等因素的影响,使得网络具有很强的时变特性.同时,对于网络中的节点往往有宵禁的限制.对时变条件下有宵禁限制并有到达时间限制的最短路进行了研究,建立了软、硬宵禁限制下的数学模型,给出并证明了时变条件下获得有宵禁限制最短路的最优条件,并设计了求解的多项式算法,通过此算法可以获得时变条件下有宵禁限制的最短路.同时,算法和模型还考虑了不同的起点出发时间,使路径决策者可以根据自身的情况,选择合适的出发时间和路径.最后给出了一个应用算例,分析了宵禁对于获得的最短路的影响.     

碳交易政策下多式联运路径选择问题的鲁棒优化研究

针对碳交易政策下的多式联运路径选择问题,考虑运输时间和单位运费率不确定且其概率分布未知的情况,引入鲁棒优化建模方法对其进行研究。首先利用box不确定集合刻画分布未知的运输时间和运费率,然后在碳交易政策下确定模型的基础上,构建鲁棒性可调节的多式联运路径选择模型,并通过对偶转化得到相对易求解的鲁棒等价模型。实例分析表明,鲁棒模型能较好地处理参数概率分布未知的多式联运路径选择问题,方便决策者根据偏好调整不确定预算水平进行决策。运输时间和单位运费率的不确定性都会影响多式联运路径决策,但是作用机理有所不同。将上述碳交易政策下的模型拓展到其他低碳政策,结果表明多种低碳政策的组合能更好实现多式联运减排。     

实时交通信息诱导下的出行模式及效益评价

以往针对交通出行信息系统的研究主要集中在静态交通网络上,而很少研究实时出行信息诱导下驾驶员的动态行为反应.该文研究交通信息诱导下驾驶员的动态行为反应和交通网络上的非均衡交通演化,并在此基础上评价了出行信息系统的效益.首先建立了3种情况下的交通出行行为:正常交通情况下驾驶员的路径和出发时间选择行为;交通事故发生后驾驶员在...     

基于时间价值的交通出行方式选择行为研究

通勤出行是城市居民最基本和最重要的出行目的,通勤出行时间价值是评价通勤出行者对交通方式选择的重要参数之一,以累积前景理论为基础,将通勤出行时间价值引入广义出行成本函数中,以权重函数和改进的广义出行成本函数作为交通方式选择模型的依据,并在模型中自定义广义出行成本参考点,选择累积前景值最大的前景作为出行者最优决策。分别基于“期望效用最大化理论”和“累积前景理论”对通勤出行者在三种不同出行场景下进行仿真模拟,研究最优交通方式选择行为。研究结果表明:期望效用理论框架下,通勤出行者的交通方式选择行为不受出行场景的影响,累积前景理论更适用于出行方式选择行为的研究。构建以通勤出行时间价值为核心变量的交通方式选择模型,可帮助通勤出行者选择合理的交通方式,并为政府及相关部门制定交通管理规划和实施缓解交通拥堵政策提供依据。     

Breaking Carbon Lock-In: Path Dependencies in Large-Scale Transportation Infrastructure Projects

The central focus of this paper is to highlight the ways in which path dependencies and increasing returns (network effects) serve to reinforce carbon lock-in in large-scale road transportation infrastructure projects. Breaking carbon lock-in requires drastic changes in the way we plan future transportation infrastructure projects, and documentary evidence presented here from the metropolitan regions of Copenhagen, Denmark and Portland, USA, indicate that there may be a discontinuity in the system of automobility (Urry, 2004), thereby increasing the likelihood that such drastic measures may in fact be successfully realized.     

基于改进蚁群算法的低碳车辆路径问题研究

全球气候恶化危及人类生存环境,物流运输过程中产生的大量温室气体则是祸源之一。本文考虑带有碳排放约束的车辆路径问题（VRP）,以车辆行驶里程最短和碳排放量最小为目标,构建了多目标的VRP非线性规划模型。提出了一种改进的蚁群系统算法对该模型进行求解,算法在更新路径上的蚂蚁信息素时引入了混沌扰动机制,此举能降低算法运行时陷入局部最优解的概率并有效提高算法的适应性。同时,对启发因子、状态转移概率、信息素更新等环节进行了优化设计,提高了最优路径的搜索效率。最后,数值仿真实验证明了该算法的求解表现优于同类研究常用的遗传算法和禁忌搜索算法,具有较强的全局寻优能力。在灵敏性和有效性的保证下,本研究所设计的改进蚁群算法能够较好地处理低碳车辆路径问题（LCVRP）。     

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.     

最短时限运输问题及解法

提出了存在于实际中的最短时限运输问题,研究了其解的最优性充分条件,并给出了求解这一问题的具体步骤,最后用实例说明了解法的可操作性,该解法是解决这一类问题的一个好算法。     

Locating Facilities in the Presence of Disruptions and Incomplete Information*

In this article, we analyze a location model where facilities may be subject to disruptions. Customers do not have advance information about whether a given facility is operational or not, and thus may have to visit several facilities before finding an operational one. The objective is to locate a set of facilities to minimize the total expected cost of customer travel. We decompose the total cost into travel, reliability, and information components. This decomposition allows us to put a value on the advance information about the states of facilities and compare it to the reliability and travel cost components, which allows a decision maker to evaluate which part of the system would benefit the most from improvements. The structure of optimal solutions is analyzed, with two interesting effects identified: facility centralization and co‐location; both effects appear to be stronger than in the complete information case, where the status of each facility is known in advance.     

A PRACTICAL APPROACH TO THE LARGE-SCALE FOREST SCHEDULING PROBLEM*

A large-scale forest cutting schedule problem involving 1166 forest units to be cut over a 24–year planning period is discussed. The problem is formulated as a generalized version of the basic transportation problem. The conversion procedure for such a problem to the standard transportation format is outlined. The proposed approach is then compared with a linear programming decomposition approach on the basis of operating results obtained and computer time required by each approach. It is shown that this new approach will solve a real world problem about 40 times faster than the usual linear programming decomposition approach.     

随机旅行时间局内车辆路径问题的模型及其算法

人们在生活中会经常遇到随机旅行时间的局内车辆路径问题,如现实物流配送中的交通堵塞现象.文章在Laporte等的研究基础上,提出了一个考虑堵塞点动态产生、一个个遇到,堵塞时间为随机变量的模型,并构造了求解该模型的算法.     

GRASP with a New Local Search Scheme for Vehicle Routing Problems with Time Windows

The NP-Hard Vehicle Routing Problem with Time Windows (VRPTW) is one of the major transportation problems. In this paper, a Greedy Randomized Adaptive Search Procedure (GRASP) for VRPTW is discussed for minimizing the fleet size and the travel distance. There are two phases within each GRASP iteration: construction phase and local search phase. In the construction phase, the initial solution is computed by applying an adaptive randomized greedy function. In the local search phase, the search procedure is applied to the constructed initial solution obtained by the construction phase for an improvement. In this paper, we propose an improved solution technique by using new local search ideas and new lower bounding procedures. In addition, we report computational results and address some practical issues in this area.     

城市交通出行方式选择的演化博弈分析

交通出行方式的选择是交通管理者、交通用户等交通参与者之间的一个博弈过程,在用户之间的长期动态博弈过程中,交通出行方式的结构会在一定条件下达到演化稳定状态.本文假设用户是有限理性的,对交通出行方式的博弈和演化过程进行了分析,建立了单总体出行方式演化博弈模型,运用演化博弈理论导出出行方式选择的演化稳定策略,从而为交通政策的制定、交通管理策略效果的评价等提供决策支持.     

Medical Surveillance, Frequent Sets, and Closure Operations

Surveillance of hospital-acquired infections, especially those caused by antibiotic resistant bacteria, is an important component of hospital infection control. A computer program for this purpose experienced a combinatorial computational explosion in time and space when processing data describing certain multi-drug resistant organisms. The blowup occurred while the program was generating frequent sets, a common phase in data mining algorithms. We present a modified algorithm for computing frequent sets that more efficiently handles the computational burden. The algorithm's proof of correctness involves the concepts of closure, independent sets, and circuits in a space more general than a matroid. Of central concern in the theory are inferences about a closure operation that can be obtained from limited information about the circuits.     

On step fixed-charge transportation problem

The fixed-charge problem is a nonlinear programming problem of practical interest in business and industry. One of its variations is the fixed-charge transportation problem (FCTP) where fixed cost is incurred for every route that is used in the solution, along with the variable cost that is proportional to the amount shipped. That cost structure causes the value of the objective function Z to also behave like a step function. Each time we open or close a route the objective function jumps a step. The step fixed-charge transportation problem (SFCTP) is a variation of the FCTP where the fixed cost is in the form of a step function dependent on the load in a given route. While the value of the objective function Z in the FCTP is a step function, the introduction of the step fixed cost in the SFCTP results in the objective function Z being itself a step function with many more steps. Fixed-charge problems are usually solved using sophisticated analytical or computer software. This paper discusses the theory of SFCTP and presents a computationally simple heuristic algorithm for solving small SFCTPs.     

Location and reliability problems on a line: Impact of objectives and correlated failures on optimal location patterns

In this paper we study a class of locations models where facilities are not perfectly reliable and failures may be correlated. We analyze problems with Median and Center objectives under complete and incomplete customer information regarding the state of facilities. The goal is to understand how failure probabilities, correlations, availability of information, and problem objective affect the optimal location patterns. In particular, we want to find analytical confirmations for location patterns observed in numerical experiments with network location models. To derive closed-form analytical results the analysis is restricted to a simple (yet classic) setting: a 2-facility problem on a unit segment, with customer demand distributed uniformly over the segment (results can be extended to other demand distributions as well). We derive explicit expressions for facility trajectories as functions of model parameters, obtaining a number of managerial insights. In addition we provide the decomposition of the optimal cost into the closed form components corresponding to the cost of travel, the cost of facility unreliability and the cost of incomplete information. Most of the theoretical insights are confirmed via numerical experiments for models with larger (3–5) number of facilities.