M. Laarabi, A. Boulmakoul, A. Mabrouk, R. Sacile, E. Garbolino
{"title":"Real-timefastest path algorithm using bidirectional point-to-point search on a Fuzzy Time-Dependent transportation network","authors":"M. Laarabi, A. Boulmakoul, A. Mabrouk, R. Sacile, E. Garbolino","doi":"10.1109/ICADLT.2014.6864086","DOIUrl":null,"url":null,"abstract":"Nowadays management of information systems within the transport industry for effective and efficient decision making requires the use of latest technological development such real-time monitoring and traffic simulation. This will lead to the development of methods and algorithms, for instance, of fleet management, routing within a specified time windows and risk assessment. In this paper we will focus on proposing a method for finding itineraries that has the fastest travel-time on a time-dependent transportation network. It is modelled as a weighted graph, whose weight are time duration that depends on the time at which the road segment is traversed. This problem can be solved in polynomial time with a Single-Source algorithm, by the definition of some restrictions on the edge weights. However, its application on a graph with several millions nodes and edges is highly memory and time consuming. Alternatively, a bidirectional Point-to-Point path search, using A-star, offers far better performance. The novelty of the proposed approach is based on the modelling of an appropriate degree of dynamics of a real-world network by considering the fuzzy nature of the travel-time using Zadeh's fuzzy concept. In addition, we speed-up search by integrating a pre-computation phase, which consists in network partitioning using network Voronoi diagrams with implicit calculation of the lower-bound travel-time label for each node-to-border, border-to-border and border-to-node. Those labels should never overestimate the travel-time at any moment, to ensure the reliability of the suggested heuristic cost function.","PeriodicalId":166090,"journal":{"name":"2014 International Conference on Advanced Logistics and Transport (ICALT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 International Conference on Advanced Logistics and Transport (ICALT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICADLT.2014.6864086","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
Nowadays management of information systems within the transport industry for effective and efficient decision making requires the use of latest technological development such real-time monitoring and traffic simulation. This will lead to the development of methods and algorithms, for instance, of fleet management, routing within a specified time windows and risk assessment. In this paper we will focus on proposing a method for finding itineraries that has the fastest travel-time on a time-dependent transportation network. It is modelled as a weighted graph, whose weight are time duration that depends on the time at which the road segment is traversed. This problem can be solved in polynomial time with a Single-Source algorithm, by the definition of some restrictions on the edge weights. However, its application on a graph with several millions nodes and edges is highly memory and time consuming. Alternatively, a bidirectional Point-to-Point path search, using A-star, offers far better performance. The novelty of the proposed approach is based on the modelling of an appropriate degree of dynamics of a real-world network by considering the fuzzy nature of the travel-time using Zadeh's fuzzy concept. In addition, we speed-up search by integrating a pre-computation phase, which consists in network partitioning using network Voronoi diagrams with implicit calculation of the lower-bound travel-time label for each node-to-border, border-to-border and border-to-node. Those labels should never overestimate the travel-time at any moment, to ensure the reliability of the suggested heuristic cost function.