Urban Transport and Traffic Systems: An Approach to the Shortest Path Problem and Network Flow Through Colored Graphs

2019 ◽  
pp. 23-43 ◽  
Author(s):  
Juliana Verga Shirabayashi ◽  
Akebo Yamakami ◽  
Ricardo Coelho Silva ◽  
Wesley Vagner Inês Shirabayashi
Keyword(s):  
Shortest Path ◽  
Network Flow ◽  
Shortest Path Problem ◽  
Urban Transport ◽  
Colored Graphs ◽  
Flow Through ◽  
Traffic Systems

scholarly journals Solution to Shortest Path Problem Using a Connective Probe Machine

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Jiuyun Sun ◽  
Huanhe Dong ◽  
Yuan Kong ◽  
Yong Fang
Keyword(s):  
Shortest Path ◽  
Traffic Engineering ◽  
Shortest Path Problem ◽  
Urban Transport ◽  
True Solution ◽  
The Road ◽  
Computing Platform ◽  
Computing Model ◽  
Engineering Study ◽  
Data Library

With the continuous urban scale expansion, traffic networks have become extremely complex. Finding an optimal route in the shortest time has become a difficult and important issue in traffic engineering study. In this study, a novel computing model, namely, probe machine, is used to solve this problem. Similar to previous studies, urban transport networks can be abstracted into maps, in which points representing places of origin, destinations, and other buildings constitute the data library and edges representing the road make up the probe library. The true solution can be obtained after one probe operation on the computing platform. And by comparing the solving process with Dijkstra’s and Floyd’s algorithms, the computing efficiency of the probe machine is clearly superior, although all three methods can solve the shortest path problem and obtain the same solution.

Application of Genetic Algorithm in modeling of Shortest Path problem

2020 ◽  
Author(s):  
Zhihui Yang ◽  
Huiwen Xia ◽  
Fuwen Su ◽  
Jiayu Zhao ◽  
Fan Feng
Keyword(s):  
Genetic Algorithm ◽  
Shortest Path ◽  
Shortest Path Problem

Distributionally robust maximum probability shortest path problem

2021 ◽  
Author(s):  
Rashed Khanjani-Shiraz ◽  
Ali Babapour-Azar ◽  
Zohreh Hosseini-Noudeh ◽  
Panos M. Pardalos
Keyword(s):  
Shortest Path ◽  
Shortest Path Problem ◽  
Maximum Probability ◽  
Distributionally Robust

scholarly journals A Comparison of Genetic Representations and Initialisation Methods for the Multi-objective Shortest Path Problem on Multigraphs

2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Lilla Beke ◽  
Michal Weiszer ◽  
Jun Chen
Keyword(s):  
Shortest Path ◽  
Time Budget ◽  
Exact Algorithm ◽  
Simple Graph ◽  
Shortest Path Problem ◽  
Future Application ◽  
Multi Objective ◽  
Trade Offs ◽  
Conflicting Objectives ◽  
Problem Instances

AbstractThis paper compares different solution approaches for the multi-objective shortest path problem (MSPP) on multigraphs. Multigraphs as a modelling tool are able to capture different available trade-offs between objectives for a given section of a route. For this reason, they are increasingly popular in modelling transportation problems with multiple conflicting objectives (e.g., travel time and fuel consumption), such as time-dependent vehicle routing, multi-modal transportation planning, energy-efficient driving, and airport operations. The multigraph MSPP is more complex than the NP-hard simple graph MSPP. Therefore, approximate solution methods are often needed to find a good approximation of the true Pareto front in a given time budget. Evolutionary algorithms have been successfully applied for the simple graph MSPP. However, there has been limited investigation of their applications to the multigraph MSPP. Here, we extend the most popular genetic representations to the multigraph case and compare the achieved solution qualities. Two heuristic initialisation methods are also considered to improve the convergence properties of the algorithms. The comparison is based on a diverse set of problem instances, including both bi-objective and triple objective problems. We found that the metaheuristic approach with heuristic initialisation provides good solutions in shorter running times compared to an exact algorithm. The representations were all found to be competitive. The results are encouraging for future application to the time-constrained multigraph MSPP.

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