基于出行时间预算的有限理性出行者路网均衡模型

为研究有限理性出行者逐日出行中出发时刻及路径调整的出行行为,引入前景理论,分析出行者依据最大准点到达概率来选择出行时间预算,将此出行时间预算作为到达参考点,进而在给定参考点下选择前景值最大的路径出行,并利用前次流量分配结果调整下次出行时间预算,经过多次出行达到路网流量平衡及准点到达概率最大的稳定状态。基于出行时间预算和前景理论建立了双层模型进行路网逐日均衡配流,用遗传算法求解最佳出行时间预算,用相继平均法计算路径平衡流量。最后基于算例验证模型和算法,并设定不同的出行选择机制分析出行时间预算、路径前景值及准点到达概率三者间的博弈关系。     

Optimal Pair of Minimal Paths Under Both Time and Budget Constraints

The quickest path (QP) problem is to find a path which sends a given amount of data from the source to the sink such that the transmission time is minimized. Two attributes are involved, namely, the capacity and the lead time. The capacity of each arc is assumed to be deterministic. However, in many real-life flow networks such as computer systems, telecommunication systems, etc., the capacity of each arc should be stochastic due to failure, maintenance, etc. Such a network is named a stochastic-flow network. Hence, the minimum transmission time is not a fixed number. We modify the QP problem to a stochastic case. The new problem is to evaluate the probability that d units of data can be sent from the source to the sink under both time T and budget B constraints. Such a probability is named the system reliability. In particular, the data can be transmitted through two disjoint minimal paths (MPs) simultaneously. A simple algorithm is proposed to generate all (d, T, B)-QPs, and the system reliability can subsequently be computed. The optimal pair of MPs with highest system reliability could further be obtained.     

Route finding by using knowledge about the road network

Traveling is a part of every person's day-to-day life. With the massive and complicated road network of a modern city or country, finding a good route to travel from one place to another is not a simple task. In network theory, this is the shortest path problem. Shortest-path algorithms are often used to solve this problem. However, these algorithms are wasteful in terms of computation when applied to the route-finding task. They may also produce routes that are not suitable for human users. In practice, knowledge about the road network can often be used to reduce the time and space required in computation, and to produce human-oriented solutions. In this project, we have integrated knowledge-based technique and algorithmic method to solve the problem. This integrated approach substantially reduces the computation time and space required for route finding. Within the approach we present three alternative designs, which may be suitable for different situations     

Popular route planning with travel cost estimation from trajectories

With the increasing number of GPS-equipped vehicles,more and more trajectories are generated continuously,based on which some urban applications become feasible,such as route planning.In general,popular route that has been travelled frequently is a good choice,especially for people who are not familiar with the road networks.Moreover,accurate estimation of the travel cost(such as travel time,travel fee and fuel consumption)will benefit a wellscheduled trip plan.In this paper,we address this issue by finding the popular route with travel cost estimation.To this end,we design a system consists of three main components.First,we propose a novel structure,called popular traverse graph where each node is a popular location and each edge is a popular route between locations,to summarize historical trajectories without road network information.Second,we propose a self-adaptive method to model the travel cost on each popular route at different time interval,so that each time interval has a stable travel cost.Finally,based on the graph,given a query consists of source,destination and leaving time,we devise an efficient route planning algorithmwhich considers optimal route concatenation to search the popular route from source to destination at the leaving time with accurate travel cost estimation.Moreover,we conduct comprehensive experiments and implement our system by a mobile App,the results show that our method is both effective and efficient.     

出租车数据的城市道路网路段通行时间估计方法

城市路段通行时间估计能够更好地运营和管理城市交通。针对包含起点-终点位置,行程时间和距离信息的GPS行程数据,提出了一种城市道路网短时通行时间的估计模型。首先将城市道路网按照交叉路口分解为多个路段,并基于k-最短路径搜索方法分析司机行进路线。然后针对每一个路段,提出了双车道通行时间多项式关联关系模型,既能提升道路网通行时间精细度,又能避免因训练数据不足导致的路网通行时间过拟合问题。最后以最小化行程期望时间和实际行程时间之间的均方误差为优化目标,拟合道路网通行时间。在纽约出租车数据集上的实验结果表明,所提模型及方法相对于传统单车道估计方法能够更准确地估计城市道路网路段的通行时间。     

Two-phase evacuation route planning approach using combined path networks for buildings and roads

This paper addresses the problem of modeling evacuation routes from a building and out of an affected area. The evacuation route involves pathways such as corridors, and stairs in buildings and road networks and sidewalks outside the building. To illustrate such an approach, we consider the problem of finding evacuation paths from an urban building and out of a predetermined neighborhood of the building on foot. A case study for a college campus building and small set of road around it is provided. There are a pre-defined set of exit points out of the target building and out of the road network serving the building. A two-step approach with an uncapacitated network model for route finding and a capacitated scheduling algorithm for evacuation time computation is proposed. A recent efficient heuristic algorithm is selected as a reference for comparative analysis. The process of creating a combined building and road path network data is discussed. The key results are the competitive evacuation time provided by the proposed uncapacitated route planning model, simple pedestrian flow capacity formulas for corridors and roads from readily available geometric data, and the illustration of the creation and use of combined building and road path network.     

An intelligent search path

In graph theory, the shortest path problem is the problem of finding a path between two vertices (or nodes) such that the sum of the weights of its constituent edges is minimized. An example is finding the quickest way to get from one location to another on a road map; In this case, the vertices represent locations and the edges represent segments of road and are weighted by the time needed to travel that segment. In this paper, a simple method to find the shortest path in a fuzzy environment is proposed. Here the edge weights of the network are considered as fuzzy numbers so that the imprecise data values can be represented. © 2010 Wiley Periodicals, Inc.     

关键词最优路径查询的分段拓展算法

关键词最优路径查询（KOR）查找在满足关键词全覆盖和路径长度约束条件下,时间开销最小的路线常用于旅行规划。现有优化算法虽然采用各种剪枝策略缩小搜索规模,但是本质上是广度优先搜索,在查找长路径时,搜索规模依然过大,执行时间长。针对该问题,提出一种关键词最优路径查询的分段拓展算法（SE-KOR）。SE-KOR算法根据关键词倒排索引表构建关键词顶点路径,将路径划分为多段分别拓展,降低搜索规模,从而缩短执行时间。该算法在路径拓展时给出路径走向,而现有剪枝策略不控制路径拓展方向,因此提出局部代价阈值剪枝,控制路径的走向沿关键词顶点路径拓展,并综合运用近似支配、可行解目标值剪枝和全局优先拓展策略加速拓展。实验结果表明,在不损失精度的情况下,该算法执行时间分别在不同关键词个数、代价阈值与查询图规模下至少缩短8.0%、61.0%和57.7%。     

基于出租车轨迹数据的最优路径规划方法

针对传统的路径规划算法并不一定能计算得到现实中最优路径的问题,提出一种融合了出租车驾驶经验并以时间为度量的路径规划算法。该算法的实现是将路径规划这个以计算为中心的技术变为以数据为中心的数据驱动挖掘技术。首先,从大量的出租车轨迹数据中提取真实的载人轨迹数据,并将载人轨迹数据匹配到路网数据中;然后,根据地图匹配结果计算路段的访问频次,选取前Top-k个路段作为热点路段;其次,计算热点路段间行车轨迹的相似度,对轨迹进行聚类分析,在路网的基础上构建该k个路段的热点路段图;最后,使用一种改进的A^*算法实现路径规划。实验结果表明,与传统的最短路径规划算法和基于驾驶经验路网分层的路径规划算法相比,所提出的基于热点路段图的路径规划方法有效地缩短规划路径的长度及路径行驶时间,提高路径规划的用时效率。     

基于道路风险评估的城市路网实时路径选择

为了缓解城市交通拥堵、避免交通事故的发生,城市路网的路径选择一直以来是一个热门的研究课题.随着边缘计算和车辆智能终端技术的发展,城市路网中的行驶车辆从自组织网络朝着车联网(Internet of vehicles,IoV)范式过渡,这使得车辆路径选择问题从基于静态历史交通数据的计算向实时交通信息计算转变.在城市路网路径选择问题上,众多学者的研究主要聚焦如何提高出行效率,减少出行时间等.然而这些研究并没有考虑所选路径是否存在风险等问题.基于以上问题,首次构造了一个基于边缘计算技术的道路风险实时评估模型(real-time road risk assessment model based on edge computing, R³A-EC),并提出基于该模型的城市路网实时路径选择方法(real-time route selection method based on risk assessment, R²S-RA). R3A-EC模型利用边缘计算技术的低延迟,高可靠性等特点对城市道路进行实时风险评估,并利用最小风险贝叶斯决策验证道路是否存在风险问...     

Path selection model and algorithm for emergency logistics management

Path selection is one of the fundamental problems in emergency logistics management. Two mathematical models for path selection in emergency logistics management are presented considering more actual factors in time of disaster. First a single-objective path selection model is presented taking into account that the travel speed on each arc will be affected by disaster extension. The objective of the model is to minimize total travel time along a path. The travel speed on each arc is modeled as a continuous decrease function with respect to time. A modified Dijkstra algorithm is designed to solve the model. Based on the first model, we further consider the chaos, panic and congestions in time of disaster. A multi-objective path selection model is presented to minimize the total travel time along a path and to minimize the path complexity. The complexity of the path is modeled as the total number of arcs included in the path. An ant colony optimization algorithm is proposed to solve the model. Simulation results show the effectiveness and feasibility of the models and algorithms presented in this paper.     

Models and a relaxation algorithm for continuous network design problem with a tradable credit scheme and equity constraints

The sustainable problems of transportation have become noticeable in the majority of cities worldwide. Many researchers are devoted themselves into traffic congestion. Generally, traffic congestion could be alleviated via increasing road capacity (supply) or reducing traffic (demand). In this paper, we model CNDP which has a tradable credit scheme and equity constraints in order to research on the way of releasing congestion by combining increasing supply and reducing demand. Firstly, the bilevel programming problem is proposed to model the CNDP with a tradable credit scheme. The upper level (the government) chooses optimal capacity enhancement for some existing links to minimize the total system costs under a budget constraint. The lower level chooses the optimal route based on considering the generalized travel cost in which both travel time and credit charging for using the link are involved. And then, considering the inequity problem in terms of equilibrium O–D travel cost and link travel time, the model is proposed by incorporating equity constraints into CNDP with a tradable credit scheme. After presenting a relaxation algorithm, the experiments on Sioux Falls network are illustrated. Finally, conclusion and some future research directions are presented.     

A novel solution for maze traversal problems using artificial neural networks

In this paper we have addressed the problem of finding a path through a maze of a given size. The traditional ways of finding a path through a maze employ recursive algorithms in which unwanted or non-paths are eliminated in a recursive manner. Neural networks with their parallel and distributed nature of processing seem to provide a natural solution to this problem. We present a biologically inspired solution using a two level hierarchical neural network for the mapping of the maze as also the generation of the path if it exists. For a maze of size S the amount of time it takes would be a function of S (O(S)) and a shortest path (if more than one path exists) could be found in around S cycles where each cycle involves all the neurons doing their processing in a parallel manner. The solution presented in this paper finds all valid paths and a simple technique for finding the shortest path amongst them is also given. The results are very encouraging and more applications of the network setup used in this report are currently being investigated. These include synthetic modeling of biological neural mechanisms, traversal of decision trees, modeling of associative neural networks (as in relating visual and auditory stimuli of a given phenomenon) and surgical micro-robot trajectory planning and execution.     

突发灾害下可靠路径搜索模型与算法

在分析突发灾害爆发时可靠路径搜索问题特点的基础上,提出了一种在不确定网络中不依赖于弧的旅行时间概率分布的可靠路径搜索方法。该方法通过场景集描述网络旅行时间的不确定性,应用Minimax理论构建求解所有场景下可靠路径的数学模型,并设计了问题求解算法,分析了算法的时间复杂性,最后通过典型算例对算法进行了验证。     

基于个性化线路定制的景区导游APP设计与实现

计算机和互联网技术日新月异,为旅游产业提供了新的发展思路。基于中国互联网信息中心发布的旅游数据进行分析,得出借助计算机和互联网技术实现个性化定制旅游将成为未来旅游市场的主流方式。文章从用户特点出发,以景区内游玩线路的定制为切入点,着重阐述线路定制对个性化旅游的重要性,最后设计并实现了一款基于线路定制的景区导游APP,该APP通过模块化设计思想,将系统分为定位模块、线路定制模块、线路查询模块、推荐模块等,引入最优路径、最短路径算法思路来满足游客的个性化需求。文章的思想对未来旅游产业的转型有着一定探索和指导作用。     

A modified ant colony system for finding the expected shortest path in networks with variable arc lengths and probabilistic nodes

The problem of finding the expected shortest path in stochastic networks, where the presence of each node is probabilistic and the arc lengths are random variables, have numerous applications, especially in communication networks. The problem being NP-hard we use an ant colony system (ACS) to propose a metaheuristic algorithm for finding the expected shortest path. A new local heuristic is formulated for the proposed algorithm to consider the probabilistic nodes. The arc lengths are randomly generated based on the arc length distribution functions. Examples are worked out to illustrate the applicability of the proposed approach.     

Multicriteria Stochastic Shortest Path Problem for Electric Vehicles

This paper formulates the reliable routing of electric vehicles in stochastic networks as a multicriteria shortest path problem with travel time and charging cost components. The reliability term is defined as the probability of finishing the trip without running out of charge. The arc travel times are represented as stochastic variables, and arc energy consumption is modeled as a linear function of arc length and arc travel time. The traveler aims to minimize the generalized cost, formulated as a linear function of travel time and charging cost, subject to a minimum reliability threshold, representing the level of risk a traveler is willing to take in favor of routes with lower cost. We propose a solution algorithm based on generalized dynamic programming and show that the optimal solution may include cycles that visit at least one charging station. The properties of the proposed multicriteria shortest path problem are mathematically proved. The simulation results on randomly-generated networks show that cyclic paths are very rare, and that the generalized cost of travel is a monotone increasing function of minimum reliability threshold.     

Route planning model of multi-agent system for a supply chain management

Sometimes in travel planning, finding the best route to the road transportation network by considering the environmental conditions that are affecting the actual time travel of the travellers are vital especially in handling the logistic operations in supply chain management (SCM). Furthermore, the policy strategy is needed in order to influence the managers or drivers to find the optimum and the most effective route for a trip plan in supporting the logistic operations of SCM. In this paper we analyze the effectiveness of the coordination model of the environmental conditions that are affecting for the travelling time based on multi-agent system for a road transportation network for supply chain management. A number of experimental cases have been used to evaluate the proposed approach transportation network problems in some Malaysian cities. Finally, experimental results affirmed that the proposed approach is practical and efficient.     

A percentile system optimization approach with and without path enumeration

In this paper we deal with the travel time reliability PUE (probabilistic user equilibrium) problem studied by Lo et al. (2006) [12] and Nie (2011) [15] and we propose an alternative model that assumes a location-scale family for the path travel times, whose means and variances are evaluated in terms of link travel times. This avoids the use of the central limit theorem and convolutions providing a flexible and simple alternative. Contrary to the most existing models that require path enumeration or an iterative method to add paths sequentially, we present a percentile system optimization in its two versions: with and without path enumeration. Two examples of applications, one of them real, are used to illustrate the power of the proposed method. The cpu times required to solve the problem seem reasonable. In addition, we answer an open question raised by Nie (2011) [15] about the permutability of percentiles and partial derivatives of route travel times with respect to route flows. A family of counterexamples is given to demonstrate that the two operations: (a) obtain percentiles and (b) partial derivation of route travel times do not commute. Finally, to reproduce the trial-and-error sequence followed by users when selecting paths, we also present an algorithm that simulates this iterative process and shows that the final long-term user behavior coincides with PUE (probabilistic user equilibrium) problem resulting from some existing models.     

School bus routing and scheduling: An expert system approach

This paper proposes an expert system approach to routing and scheduling school buses for a rural school system. The expert system is programmed in TURBO PROLOG for use on an IBM/XT and is applied to rural county school systems in Alabama.

The busing problem considered here consists of two components: routing and scheduling. The routing problem is concerned with the determination of a stop-to-stop route to be traversed to each school by each bus whereas the scheduling problem, the determination of times at all bus stops for each bus. A bus may be used for multiple runs. Each route is designed in such a way that the bus capacity, student riding time, school time window and road condition constraints are satisfied while attempting to minimize the number of buses required in operation, minimize the fleet travel time and balance the bus loads.

Conventionally, a predetermined algorithm is coded into computer programs for generating efficient routes and schedules. The user or route designer neither has any knowledge about the algorithm nor has any input of personal expertise into the solution process. As a result, a veteran designer is skeptical of the computer-generated routes and schedules. Moreover, non-quantifiable factors such as safety, preference and judgment are not taken into consideration in the traditional approach.

To alleviate these deficiencies, an expert system approach, which enables the expert knowledge to be kept separately from its execution, is utilized. This knowledge base contains factual knowledge such as road map, school locations, but capacities, stop locations, number of students at each stop, and drivers' homes. It also contains procedural knowledge such as heuristics for finding a route and scheduling multiple runs for a bus subject to various constraints. The inference engine or control program chooses the appropriate heuristics used in constructing efficient routes and schedules with respect to various objectives or goals. A user interface includes the graphic display of road maps and determined routes.