A Case for Time-Dependent Shortest Path Computation in Spatial Networks (original) (raw)

Online Computation of Fastest Path in Time-Dependent Spatial Networks

The problem of point-to-point fastest path computation in static spatial networks is extensively studied with many precomputation techniques proposed to speed-up the computation. Most of the existing approaches make the simplifying assumption that travel-times of the network edges are constant. However, with real-world spatial networks the edge travel-times are time-dependent, where the arrival-time to an edge determines the actual travel-time on the edge. In this paper, we study the online computation of fastest path in time-dependent spatial networks and present a technique which speeds-up the path computation. We show that our fastest path computation based on a bidirectional time-dependent A* search significantly improves the computation time and storage complexity. With extensive experiments using real data-sets (including a variety of large spatial networks with real traffic data) we demonstrate the efficacy of our proposed techniques for online fastest path computation.

Fast shortest path computation in time-dependent traffic networks

2007

In agent based traffic simulations which use systematic relaxation to reach a steady state of the scenario, the performance of the routing algorithm used for finding a path from a start node to an end node in the network is crucial for the overall performance. For example, a systematic re- laxation process for a large scale scenario with about 7.5 million inhabitants (roughly the popu- lation of Switzerland) performing approximately three trips per day on average requires about 2.25 million route calculations, assuming that 10% of the trips are adapted per iteration. Expect- ing about 100 iterations to reach a stable state, 225 million routes have to be delivered in total. This paper focuses on routing algorithms and acceleration methods for point-to-point shortest path computations in directed graphs that are time-dependent, i.e. link weights vary during time. The work is done using MATSim-T (Multi-Agent Traffic Simulation Toolkit) which used for large-scale agent-based traffic sim...

Spatio-temporal Network Databases and Routing Algorithms: A Summary of Results

Advances in Spatial and Temporal Databases

Spatio-temporal networks are spatial networks whose topology and parameters change with time. These networks are important due to many critical applications such as emergency traffic planning and route finding services and there is an immediate need for models that support the design of efficient algorithms for computing the frequent queries on such networks. This problem is challenging due to potentially conflicting requirements of model simplicity and support for efficient algorithms. Time expanded networks which have been used to model dynamic networks employ replication of the network across time instants, resulting in high storage overhead and algorithms that are computationally expensive. In contrast, proposed time-aggregated graphs do not replicate nodes and edges across time; rather they allow the properties of edges and nodes to be modeled as a time series. Since the model does not replicate the entire graph for every instant of time, it uses less memory and the algorithms for common operations (e.g. connectivity, shortest path) are computationally more efficient than those for time expanded networks. One important query on spatio-temporal networks is the computation of shortest paths. Shortest paths can be computed either for a given start time or to find the start time and the path that leads to least travel time journeys (best start time journeys). Developing efficient algorithms for computing shortest paths in a time varying spatial network is challenging because these journeys do not always display greedy property or optimal substructure, making techniques like dynamic programming inapplicable. In this paper, we propose algorithms for shortest path computations in both contexts. We present the analytical cost models for the algorithms and provide an experimental comparison of performance with existing algorithms.

Dynamic Shortest Path Monitoring in Spatial Networks

Journal of Computer Science and Technology, 2016

With the increasing availability of real-time traffic information, dynamic spatial networks are pervasive nowadays and path planning in dynamic spatial networks becomes an important issue. In this light, we propose and investigate a novel problem of dynamically monitoring shortest paths in spatial networks (DSPM query). When a traveler aims to a destination, his/her shortest path to the destination may change due to two reasons: 1) the travel costs of some edges have been updated and 2) the traveler deviates from the pre-planned path. Our target is to accelerate the shortest path computing in dynamic spatial networks, and we believe that this study may be useful in many mobile applications, such as route planning and recommendation, car navigation and tracking, and location-based services in general. This problem is challenging due to two reasons: 1) how to maintain and reuse the existing computation results to accelerate the following computations, and 2) how to prune the search space effectively. To overcome these challenges, filter-and-refinement paradigm is adopted. We maintain an expansion tree and define a pair of upper and lower bounds to prune the search space. A series of optimization techniques are developed to accelerate the shortest path computing. The performance of the developed methods is studied in extensive experiments based on real spatial data.

Fast Shortest Path Routing in Transportation Networks with Time-Dependent Road Speeds

2015

The current paper deals with the subject of shortest path routing in transportation networks (in terms of travelling time), where the speed in several of the network's roads is a function of the time interval. The main contribution of the paper is a procedure that is faster compared to the conventional approaches, that derives the road's traversal time according to the time instant of departure, for the case where the road's speed has a constant value inside each time interval (in general, different value for each time interval). Furthermore, the case where the road's speed is a linear function of time inside each time interval (in general, different linear function for each time interval) is investigated. A procedure that derives the road's traversal time according to the time instant of departure is proposed for this case as well. The proposed procedures are combined with Dijkstra's algorithm and the resulting algorithms, that are practically applicable and...

Arc Routing with Time-Dependent Travel Times and Paths

Transportation Science, 2021

Vehicle routing algorithms usually reformulate the road network into a complete graph in which each arc represents the shortest path between two locations. Studies on time-dependent routing followed this model and therefore defined the speed functions on the complete graph. We argue that this model is often inadequate, in particular for arc routing problems involving services on edges of a road network. To fill this gap, we formally define the time-dependent capacitated arc routing problem (TDCARP), with travel and service speed functions given directly at the network level. Under these assumptions, the quickest path between locations can change over time, leading to a complex problem that challenges the capabilities of current solution methods. We introduce effective algorithms for preprocessing quickest paths in a closed form, efficient data structures for travel time queries during routing optimization, and heuristic and exact solution approaches for the TDCARP. Our heuristic use...

Temporal shortest paths: Parallel computing implementations

Parallel Computing, 2001

We explore two types of parallel computing implementations for three algorithms for computing temporal shortest paths on transportation networks. One implementation is done on a distributed network of SUN SPARC workstations using PVM and the other on a shared memory computing platform, a SUN SPARC server equipped with eight processors, using threads. Computational results obtained by using three networks originating from practice are presented. The shared memory computing platform is preferred for this application.

On the Acceleration of Shortest Path Calculations in Transportation Networks

15th Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM 2007), 2007

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Fully Dynamic Speed-Up Techniques for Multi-criteria Shortest Path Searches in Time-Dependent Networks

Lecture Notes in Computer Science, 2010

We introduce two new speed-up techniques for time-dependent point-to-point shortest path problems with fully-dynamic updates in a multi-criteria setting. Our first technique, called SUBITO, is based on a specific substructure property of time-dependent paths which can be lower bounded by their minimal possible travel time. It requires no preprocessing, and the bounds can be computed on-the-fly for each query. We also introduce k-flags, an extension of arc flags, which assigns to each arc one of k levels for each region of a vertex partition. Intuitively, the higher the level of an arc for a certain destination, the larger the detour with respect to travel time. k-flags allow us to handle dynamic changes without additional time-consuming preprocessing.

EFFECTIVELY MINIMIZE THE OVERALL TRIP DISTANCE USING CONTINUOUS DETOUR QUERY IN SPATIAL NETWORK

The top-k shortest path discovery is a key process on graphs to determine k-shortest paths between a two nodes with the minimal length. This work precisely holds three processes for ranking the shortest path problem without loop by the way of using top-k shortest path join (TKSPJ) in spatial network. First, Construct transformed graph with side cost by using of input original graph. Second, structural encoding label is used for loop detection and third to find top k shortest path without loop. The main advantage of this work is to reduce the cost and prune the search space. The pre computed shortest paths translating the original graph based on the threshold value has also been introduced, to reduce the search space in a spatial network.