An effective heuristic for computing many shortest path alternatives in road networks

Vanhove, Stéphanie; Fack, Veerle

doi:10.1080/13658816.2011.620572

Cited by 22 publications

(32 citation statements)

References 14 publications

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“…Travelers tend to take the least‐cost path, either in time or distance. This can be simulated by numerous algorithms (Li, Chen, Wang, & Lam, ; Liu, Li, Yang, Cai, & Zhang, ; Vanhove & Fack, ). This study adopted the A* (Table ) algorithm to assign trip paths between origins and destinations with the objective of minimizing travel time.…”

Section: Methodsmentioning

confidence: 99%

Modeling the traffic disruption caused by pluvial flash flood on intra‐urban road network

Huang

Wang

et al. 2018

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Pluvial flash flood (PFF) can cause serious traffic disruption in big cities. We conducted interdisciplinary research by integrating flood modeling and traffic analysis to reveal the spatiotemporal pattern of the interplay between these two processes. A simplified simulation tool, which is capable of building a road network model, assigning trip paths with the effect of road closures, and evaluating travel delay and vehicle volume redistribution in a given PFF scenario, was developed to capture the traffic disruption in the face of PFF events. Modeling outputs from a case study in the city center of Shanghai showed that the delay of vehicles diverted to dry links or trapped in flooded links may reach 0.5 to 8 times the travel time in no-flood scenarios. Overall, approximately 1-7% of vehicle volumes on flooded links would be redistributed onto dry links (more likely major arterial roads). However, the vehicle volume variation during each time interval demonstrated evident disparity with the spatiotemporal change of flood inundations. Simulating and mapping the congestion can largely facilitate the identification of vulnerable links. Future research willtest the method in other intra-urban areas and try to bridge the gap between modeling outputs and smart city planning and management.

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Section: Methodsmentioning

confidence: 99%

Modeling the traffic disruption caused by pluvial flash flood on intra‐urban road network

Huang

Wang

et al. 2018

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“…The KSP problem can be further classified into two variants. The first variant is to find the K shortest simple paths, in which repeated nodes are not allowed (Chen et al, 2020; Martins & Pascoal, 2003; Vanhove & Fack, 2012; Yen, 1971). The second variant is to find the K shortest non‐simple paths, in which repeated nodes may exist (Eppstein, 1998; Martins, 1984; Minieka, 1974).…”

Section: Introductionmentioning

confidence: 99%

A fast algorithm for finding K shortest paths using generalized spur path reuse technique

Chen

et al. 2020

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The problem of finding the K shortest paths (KSPs) between a pair of nodes in a road network is an important network optimization problem with broad applications. Yen's algorithm is a classical algorithm for exactly solving the KSP problem. However, it requires numerous shortest path searches, which can be computationally intensive for real large networks. This study proposes a fast algorithm by introducing a generalized spur path reuse technique. Using this technique, shortest paths calculated during the KSP finding process are stored. Accordingly, many shortest path searches can be avoided by reusing these stored paths. The results of computational experiments on several large‐scale road networks show that the introduced generalized spur path reuse technique can avoid more than 98% of shortest path searches in the KSP finding process. The proposed algorithm speeds up Yen's algorithm by up to 98.7 times in experimental networks.

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“…There is a major need to have full access to underlying real road networks to experiment and evaluate various algorithms, which include shortest path queries [14,28,35,36,38,39], k-nearest-neighbor queries [4,11,12,13,17,19,37,40], reverse nearest neighbor query [15,30], range queries [3], skyline query [5,33], among others (e.g., [16,32,39]). Unfortunately, it is always challenging to get such real road network, which imposes a major obstacle in advancing the research in such algorithms.…”

Section: Introductionmentioning

confidence: 99%

Tareeg

Alarabi

Eldawy

Alghamdi

et al. 2014

Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

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Real spatial data, e.g., detailed road networks, rivers, buildings, parks, are not easily available for most of the world. This hinders the practicality of many research ideas that need a real spatial data for testing and experiments. Such data is often available for governmental use, or at major software companies, but it is prohibitively expensive to build or buy for academia or individual researchers. This paper presents TAREEG; a web-service that makes real spatial data, from anywhere in the world, available at the fingertips of every researcher or individual. TAREEG gets all its data by leveraging the richness of OpenStreetMap data set; the most comprehensive available spatial data of the world. Yet, it is still challenging to obtain OpenStreetMap data due to the size limitations, special data format, and the noisy nature of spatial data. TAREEG employs MapReduce-based techniques to make it efficient and easy to extract OpenStreetMap data in a standard form with minimal effort. Experimental results show that TAREEG is highly accurate and efficient.

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An effective heuristic for computing many shortest path alternatives in road networks

Cited by 22 publications

References 14 publications

Modeling the traffic disruption caused by pluvial flash flood on intra‐urban road network

Modeling the traffic disruption caused by pluvial flash flood on intra‐urban road network

A fast algorithm for finding K shortest paths using generalized spur path reuse technique

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