Personalized trajectory matching in spatial networks

With the increasing availability of moving-object tracking data, trajectory search and matching is increasingly important. We propose and investigate a novel problem called personalized trajectory matching (PTM). In contrast to conventional trajectory similarity search by spatial distance only, PTM takes into account the significance of each sample point in a query trajectory. A PTM query takes a trajectory with user-specified weights for each sample point in the trajectory as its argument. It returns the trajectory in an argument data set with the highest similarity to the query trajectory. We believe that this type of query may bring significant benefits to users in many popular applications such as route planning, carpooling, friend recommendation, traffic analysis, urban computing, and location-based services in general. PTM query processing faces two challenges: how to prune the search space during the query processing and how to schedule multiple so-called expansion centers effectively. To address these challenges, a novel two-phase search algorithm is proposed that carefully selects a set of expansion centers from the query trajectory and exploits upper and lower bounds to prune the search space in the spatial and temporal domains. An efficiency study reveals that the algorithm explores the minimum search space in both domains. Second, a heuristic search strategy based on priority ranking is developed to schedule the multiple expansion centers, which can further prune the search space and enhance the query efficiency. The performance of the PTM query is studied in extensive experiments based on real and synthetic trajectory data sets.     

A Hybrid Genetic and Ant Colony Algorithm for Finding the Shortest Path in Dynamic Traffic Networks

Solving the dynamic shortest path problem has become important in the development of intelligent transportation systems due to the increasing use of this technology in supplying accurate traffic information. This paper focuses on the problem of finding the dynamic shortest path from a single source to a destination in a given traffic network. The goal of our studies is to develop an algorithm to optimize the journey time for the traveler when traffic conditions are in a state of dynamic change. In this paper, the models of the dynamic traffic network and the dynamic shortest path were investigated. A novel dynamic shortest path algorithm based on hybridizing genetic and ant colony algorithms was developed, and some improvements in the algorithm were made according to the nature of the dynamic traffic network. The performance of the hybrid algorithm was demonstrated through an experiment on a real traffic network. The experimental results proved that the algorithm proposed in this paper could effectively find the optimum path in a dynamic traffic network. This algorithm may be useful for vehicle navigation in intelligent transportation systems.     

基于最短路径的道路网络k近邻查询处理

针对基于空间道路网络的k近部查询处理,提出了分布式移动对象更新策略以有效减少服务器计算代价,利用基于内存的空间道路网络部接矩阵、最短路径矩阵结构和移动对象哈希表索引分别对道路网络无向图与移动对象进行存储管理。提出了基于最短路径度量的网络扩展搜索(SPNE)算法,以通过裁剪网络搜索空间来减少k近部查询搜索代价。实验表明,SPNE算法的性能优于传统的NE和MKNN等k近邻查询处理算法。     

Parallel Algorithms for Dynamic Shortest Path Problems

The development of intelligent transportation systems (ITS) and the resulting need for the solution of a variety of dynamic traffic network models and management problems require faster‐than‐real‐time computation of shortest path problems in dynamic networks. Recently, a sequential algorithm was developed to compute shortest paths in discrete time dynamic networks from all nodes and all departure times to one destination node. The algorithm is known as algorithm DOT and has an optimal worst‐case running‐time complexity. This implies that no algorithm with a better worst‐case computational complexity can be discovered. Consequently, in order to derive algorithms to solve all‐to‐one shortest path problems in dynamic networks, one would need to explore avenues other than the design of sequential solution algorithms only. The use of commercially‐available high‐performance computing platforms to develop parallel implementations of sequential algorithms is an example of such avenue. This paper reports on the design, implementation, and computational testing of parallel dynamic shortest path algorithms. We develop two shared‐memory and two message‐passing dynamic shortest path algorithm implementations, which are derived from algorithm DOT using the following parallelization strategies: decomposition by destination and decomposition by transportation network topology. The algorithms are coded using two types of parallel computing environments: a message‐passing environment based on the parallel virtual machine (PVM) library and a multi‐threading environment based on the SUN Microsystems Multi‐Threads (MT) library. We also develop a time‐based parallel version of algorithm DOT for the case of minimum time paths in FIFO networks, and a theoretical parallelization of algorithm DOT on an ‘ideal’ theoretical parallel machine. Performances of the implementations are analyzed and evaluated using large transportation networks, and two types of parallel computing platforms: a distributed network of Unix workstations and a SUN shared‐memory machine containing eight processors. Satisfactory speed‐ups in the running time of sequential algorithms are achieved, in particular for shared‐memory machines. Numerical results indicate that shared‐memory computers constitute the most appropriate type of parallel computing platforms for the computation of dynamic shortest paths for real‐time ITS applications.     

基于道路网络的对象聚类

大多数的空间聚类算法主要针对欧几何空间中的数据对象.然而在大多真实的应用中,空间对象的访问主要受限于空间网络(如道路网络),因此,对道路网络中的对象进行聚类分析更具有现实意义.道路网络中对象之间的距离度量需要通过基于网络的最短路径距离来重新定义,其计算代价高,这使得已有的基于欧几何距离的聚类算法不能直接运用到这种环境中.因此,通过开发道路网络的特征提出了两种新的聚类算法.算法使用网络中的边和结点信息来缩减搜索空间,避免了一些不必要的距离计算.实验结果表明,算法对于真实道路网络中的对象聚类是高效的.     

Aggregate location recommendation in dynamic transportation networks

Travel planning and location recommendation are increasingly important in recent years. In this light, we propose and study a novel aggregate location recommendation query (ALRQ) of discovering aggregate locations for multiple travelers and planning the corresponding travel routes in dynamic transportation networks. Assuming the scenario that multiple travelers target the same destination, given a set of travelers’ locations Q, a set of potential aggregate location O, and a departure time t, the ALRQ finds an aggregate location o ∈ O that has the minimum global travel time \({\sum }_{q \in Q} T(q,o,t)\), where T(q,o,t) is the travel time between o and q with departure time t. The ALRQ problem is challenging due to three reasons: (1) how to model the dynamic transportation networks practically, and (2) how to compute ALRQ efficiently. We take two types of dynamic transportation networks into account, and we define a pair of upper and lower bounds to prune the search space effectively. Moreover, a heuristic scheduling strategy is adopted to schedule multiple query sources. Finally, we conducted extensive experiments on real and synthetic spatial data to verify the performance of the developed algorithms.     

随机时间依赖网络的K期望最短路径

首先给出了随机时间依赖网络模型，K期望是短路径问题的形式化描述，并针对公交网络推导出到达弧头结点的时刻所服从的概率密度函数，路径期望耗费的计算方法，然后，基于随机一致性假设和胡机优势的概念给出了K期望最短路径问题的理论基础和算法并证明了算法的正确性，最后，给出了公交网络的应用实例和实验结果。     

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.     

A comparison of algorithms for origin–destination matrix generation on real road networks and an approximation approach

This paper considers the generation of the origin–destination (OD) matrix, basic data in any vehicle routing or traveling salesman problem. An OD matrix must be generated by calculating the shortest paths between some nodes. Candidate methods for this are repetitive use of one-to-all shortest path algorithms such as Dijkstra’s algorithm, use of all-to-all shortest path algorithms such as the Floyd–Warshall algorithm, and use of specifically designed some-to-some shortest path algorithms. This paper compares the performance of several shortest path algorithms in computing OD matrices on real road networks. Dijkstra’s algorithm with approximate bucket data structure performed the best for most of the networks tested. This paper also proposes a grouping-based algorithm for OD matrix generation. Although it is an approximation approach, it has several good characteristics: it can find the exact shortest distances for most OD pairs; it guarantees that all the calculated shortest path distance values have corresponding paths; the deviation of any distance from the corresponding true shortest distance is small; and its computation time is short.     

随机时间依赖网络的K期望寿命最短路径算法研究

在交通网络和数据网络中,网络特征（如孤的权值、结点耗费等）既具有随机性又具有时间依赖性,这样的网络称之为随机时间依赖网络,简记为STD网络。在实践中,STD网络模型比传统网络模型具有更广泛的应用。由于随机性和时间依赖性引入到网络模型中,使得最短路径问题变得复杂化和多样化,传统的最短路径算法已不再适应这样复杂的网络环境,这就迫使我们寻求新的解决方法。本文解决的问题是,STD网络中,在任意时刻从单源点出发到达单目的地的预先K期望最短路径问题。我们将可靠性理论应用于该问题的求解中,推导出新优势判别法,使得传统判别法中的参数由二维降到一维,减小了路径间不可比较的可能性,既节省了存储空间又加快了搜索速度。然后。设计并实现了求解该问题的K_RELSP算法,并通过试验验证了该算法具有很高的运行效率。     

一种基于离散变权网络的动态最短路径快速算法

在离散变权动态网络中,求解最短路径的最优化算法的计算复杂性通常远大于O(n2),不适用于实时的动态交通信息导航系统。提出的动态最短路径快速算法,是在所有的当前点与下一个待选点之间以及待选点与目标点之间的动态弧的权值之和中选择一个最小值,然后把该待选点作为当前点继续选择下一个待选点,如此反复,直到达到目标点为止。该算法所得到的路径是一个次优解,但其执行时间却比寻找最优解算法要小得多,并且所得到的解要优于选择最短距离路径的动态解。实验结果证明这是一种适用于动态交通导航的有效算法。     

动态环境中基于增强式学习的路径规划方法

本文结合机器人路径规划问题介绍了增强式学习方法 ,实现了动态环境中基于增强式学习的自适应路径规划 .增强式学习通过采用随机性的控制策略 ,实现策略的优化搜索和在线学习 .并采用具有模式增强输入的BP网络进行决策参数估计 ,加快学习的收敛 .仿真试验证明该方法能有效实现动态环境中机器人的避碰和导航     

Ergodic secrecy rate of two-user MISO interference channels with statistical CSI

This paper considers a two-user multiple-input single-output （MISO） interference channel with confidential messages （IFC-CM）, where the beamforming vectors at the two transmitters are jointly optimized using the closed-form Pareto-optimal parameterization. We prove that artificial noise cannot improve the secrecy rate performance, and coordinated beamforming is secrecy-rate optimal which is achieved by agreeing on the parameters between the two transmitters. We analyze the feasible set of the beamforming parameters that guarantees positiveness of the secrecy rates when the transmitters know only statistical CSI, and then analysis in the ergodic secrecy rate region is discussed. More importantly, we derive the closed form of the ergodic secrecy rates and illustrate the Pareto-optimal structure of the beamforming vectors.     

基于蛋白质相互作用网络的聚类算法研究

蛋白质相互作用网络是计算机科学技术的一个新研究领域。蛋白质相互作用网络中结点之间的距离度量需要通过基于网络的最短路径距离来重新定义,其计算代价高,这使得已有的基于欧几何距离的聚类算法不能直接运用到这种环境中。因此,通过蛋白质相互作用网络的特征提出了一种新的聚类算法。算法使用网络中的边和结点信息来缩减搜索空间,避免了一些不必要的距离计算。实验结果表明,算法对于真实的蛋白质相互作用网络中的结点聚类是高效的。     

Energy-aware routing for delay-sensitive underwater wireless sensor networks

The energy reduction is a challenging problem in the applications of underwater wireless sensor networks （UWSNs）. The embedded battery is difficult to be replaced and it has an upper bound on its lifetime. Multihop relay is a popular method to reduce energy consumption in data transmission. The energy minimum path from source to destination in the sensor networks can be obtained through the shortest path algorithm. However, because of the node mobility, the global path planning approach is not suitable for the routing in UWSNs. It calls for an energy-efficient routing protocol for the high dynamic UWSNs. In this paper, we propose the modified energy weight routing （MEWR） protocol to deal with the energy-efficient routing of delay- sensitive UWSNs. MEWR is a low flooding routing protocol. It can tolerate the node mobility in UWSNs and achieve a low end-to-end packet delay. MEWR can provide lower energy consumption than the existing low delay routing protocols through the dynamic sending power adjustment. The simulation results demonstrate the effectiveness of MEWR.     

网络的粒度存储及在路径搜索中的应用

网络路径搜索是图论中的经典问题,对于大规模网络的最短路径搜索问题是人工智能领域研究热点问题。应用粒计算方法求解问题的思路实现网络的粒度存储,讨论不同基本类型的网络粒化,提出分层递阶商空间链实现网络的粒度存储。就大规模网络,提出社团作为基本粒的网络快速分割方法,实现网络的粒度存储。并将网络的粒度存储的分层递阶商空间链信息作为路径搜索前的预处理工作,提出一种启发式路径搜索方法。通过实验与启发式算法进行对比,验证了该算法的有效性。     

Finding Reliable Shortest Paths in Road Networks Under Uncertainty

The aim of this study is to investigate the solution algorithm for solving the problem of determining reliable shortest paths in road networks with stochastic travel times. The availability of reliable shortest paths enables travelers, in the face of travel time uncertainty, to plan their trips with a pre-specified on-time arrival probability. In this study, the reliable shortest path between origin and destination nodes is determined using a multiple-criteria shortest path approach when link travel times follow normal distributions. The dominance conditions involved in such problems are established, thereby reducing the number of generated non-dominated paths during the search processes. Two solution algorithms, multi-criteria label-setting and A* algorithms, are proposed and their complexities analyzed. Computational results using large scale networks are presented. Numerical examples using data from a real-world advanced traveller information system is also given to illustrate the applicability of the solution algorithms in practice.     

A morphological adaptation approach to path planning inspired by slime mould

Path planning is a classic problem in computer science and robotics which has recently been implemented in unconventional computing substrates such as chemical reaction–diffusion computers. These novel computing schemes utilise the parallel spatial propagation of information and often use a two-stage method involving diffusive propagation to discover all paths and a second stage to highlight or visualise the path between two particular points in the arena. The true slime mould Physarum polycephalum is known to construct efficient transport networks between nutrients in its environment. These networks are continuously remodelled as the organism adapts its body plan to changing spatial stimuli. It can be guided towards attractant stimuli (nutrients, warm regions) and it avoids locations containing hazardous stimuli (light irradiation, repellents, or regions occupied by predatory threats). Using a particle model of slime mould we demonstrate scoping experiments which explore how path planning may be performed by morphological adaptation. We initially demonstrate simple path planning by a shrinking blob of virtual plasmodium between two attractant sources within a polygonal arena. We examine the case where multiple paths are required and the subsequent selection of a single path from multiple options. Collision-free paths are implemented via repulsion from the borders of the arena. Finally, obstacle avoidance is implemented by repulsion from obstacles as they are uncovered by the shrinking blob. These examples show proof-of-concept results of path planning by morphological adaptation which complement existing research on path planning in novel computing substrates.     

Shortest paths on dynamic graphs

Among the variants of the well-known shortest path problem, those that refer to dynamically changing graphs are theoretically interesting, as well as computationally challenging. Application-wise, there is an industrial need for computing point-to-point shortest paths on large-scale road networks whose arcs are weighted with a travelling time that depends on traffic conditions. We survey recent techniques for dynamic graph weights as well as dynamic graph topology.     

过必经节点集的动态剪枝搜索算法

针对过必经节点集的最短路径问题,提出一种基于动态减枝策略的深度优先搜索算法（Depth First Search based on Dynamic Pruning,DP-DFS）,该算法构建一个二维矩阵,每搜索一个节点,比较当前路径的权值和与矩阵中已保存的权值,如果当前路径的权值小于矩阵中保存的权值,则更新矩阵中权值为当前较小的路径权值,否则进行剪枝。该算法比较适合较大规模的图搜索,实验表明,必经节点个数在50以内时,利用该算法可以在30?s内找到一条近似最优的最短路径。