scholarly journals Dynamic Route Optimization for Heterogeneous Agent Envisaging Topographic of Maps

10.29007/ht91 ◽  
2018 ◽  
Author(s):  
Muhammad Shafiq
Keyword(s):  
Road Network ◽  
Smart Cities ◽  
Routing Algorithm ◽  
Time Profile ◽  
Route Optimization ◽  
Dijkstra Algorithm ◽  
Heterogeneous Agent ◽  
Shortest Path Routing ◽  
Topographic Features ◽  
Dynamic Route

Dynamic Route Optimization is a generic problem for the commuter traveling diagonally in the smart cities; complex road network poses challenges for the heterogeneous agents to opt for route from source to destination. In smart grid of road network where intersections, roundabouts, footpaths, pedestrian bridges and tunnels having variant topographic features as a result route optimization create diversity. In various part of the cities where gridlock observed, consequently routing application recommend a route where a grid of intersecting streets completely congested where no vehicular movement is possible. In the paper we explored that how to enhance the utility of an existing application, prevent the gridlock affects and non- deterministic delay by considering topographic features of road networks using optimal shortest path routing algorithm Dijkstra. For this purpose, instituting a profile of Agents and feature recording of road network, e.g. height, width, speed and capacity is a prerequisite. Augmentation of Dijkstra algorithm according to the topographies of a Heterogeneous Agent, road network and agent simulation using SUMO (Simulation of Urban Mobility) in real time environment.Commuter described the intricate parameter of an agent, e.g. height, width and capacity at the time profile creation, recall the parameter while devising route and its calculation of alternative preferences. Functional utility authenticated and compared with the existing application, e.g. Google Maps, OSM (Open Street Maps).

scholarly journals A Lower Bound for the Query Phase of Contraction Hierarchies and Hub Labels and a Provably Optimal Instance-Based Schema

Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 164
Author(s):  
Tobias Rupp ◽  
Stefan Funke
Keyword(s):  
Lower Bound ◽  
Lower Bounds ◽  
Real World ◽  
Road Network ◽  
Upper And Lower Bounds ◽  
Bounded Degree ◽  
Shortest Path Routing ◽  
Graph Classes ◽  
Speed Up ◽  
To Come

We prove a Ω(n) lower bound on the query time for contraction hierarchies (CH) as well as hub labels, two popular speed-up techniques for shortest path routing. Our construction is based on a graph family not too far from subgraphs that occur in real-world road networks, in particular, it is planar and has a bounded degree. Additionally, we borrow ideas from our lower bound proof to come up with instance-based lower bounds for concrete road network instances of moderate size, reaching up to 96% of an upper bound given by a constructed CH. For a variant of our instance-based schema applied to some special graph classes, we can even show matching upper and lower bounds.

A Recursive Shortest Path Routing Algorithm With Application for Wireless Sensor Network Localization

2016 ◽  
Vol 16 (11) ◽  
pp. 4631-4637 ◽  
Author(s):  
Juan Cota-Ruiz ◽  
Pablo Rivas-Perea ◽  
Ernesto Sifuentes ◽  
Rafael Gonzalez-Landaeta
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Shortest Path ◽  
Routing Algorithm ◽  
Wireless Sensor ◽  
Sensor Network Localization ◽  
Shortest Path Routing ◽  
Network Localization

OPTIMUM CONGESTED ROUTING STRATEGY ON TWISTED CUBES

2000 ◽  
Vol 01 (02) ◽  
pp. 115-134 ◽  
Author(s):  
TSENG-KUEI LI ◽  
JIMMY J. M. TAN ◽  
LIH-HSING HSU ◽  
TING-YI SUNG
Keyword(s):  
Shortest Path ◽  
Time Complexity ◽  
Interconnection Network ◽  
Routing Algorithm ◽  
Optimum Time ◽  
Shortest Path Routing ◽  
Routing Strategy ◽  
Bisection Width ◽  
Twisted Cube ◽  
Twisted Cubes

Given a shortest path routing algorithm of an interconnection network, the edge congestion is one of the important factors to evaluate the performance of this algorithm. In this paper, we consider the twisted cube, a variation of the hypercube with some better properties, and review the existing shortest path routing algorithm8. We find that its edge congestion under the routing algorithm is high. Then, we propose a new shortest path routing algorithm and show that our algorithm has optimum time complexity O(n) and optimum edge congestion 2n. Moreover, we calculate the bisection width of the twisted cube of dimension n.

scholarly journals Optimizing Route for Hazardous Materials Logistics Based on Hybrid Ant Colony Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Haixing Wang ◽  
Guiping Xiao ◽  
Zhen Wei
Keyword(s):  
Optimization Model ◽  
Network Flow ◽  
Road Network ◽  
Ant Colony Algorithm ◽  
Hazardous Materials ◽  
Construction Rule ◽  
Ant Colony ◽  
Route Optimization ◽  
Total Cost ◽  
Risk Limits

Optimizing Route for Hazardous Materials Logistics (ORHML) belongs to a class of problems referred to as NP-Hard, and a strict constraint of it makes it harder to solve. In order to dealing with ORHML, an improved hybrid ant colony algorithm (HACA) was devised. To achieve the purpose of balancing risk and cost for route based on the principle of ACA that used to solve TSP, the improved HACA was designed. Considering the capacity of road network and the maximum expected risk limits, a route optimization model to minimize the total cost is established based on network flow theory. Improvement on route construction rule and pheromone updating rule was adopted on the basis of the former algorithm. An example was analyzed to demonstrate the correctness of the application. It is proved that improved HACA is efficient and feasible in solving ORHML.

scholarly journals Hierarchical Sarsa Learning Based Route Guidance Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Feng Wen ◽  
Xingqiao Wang ◽  
Xiaowei Xu
Keyword(s):  
State Space ◽  
Road Network ◽  
Large Scale ◽  
Road Networks ◽  
Modern Society ◽  
Guidance System ◽  
Route Guidance ◽  
Guidance Algorithm ◽  
Reinforcement Learning Models ◽  
Dynamic Route

In modern society, route guidance problems can be found everywhere. Reinforcement learning models can be normally used to solve such kind of problems; particularly, Sarsa Learning is suitable for tackling with dynamic route guidance problem. But how to solve the large state space of digital road network is a challenge for Sarsa Learning, which is very common due to the large scale of modern road network. In this study, the hierarchical Sarsa learning based route guidance algorithm (HSLRG) is proposed to guide vehicles in the large scale road network, in which, by decomposing the route guidance task, the state space of route guidance system can be reduced. In this method, Multilevel Network method is introduced, and Differential Evolution based clustering method is adopted to optimize the multilevel road network structure. The proposed algorithm was simulated with several different scale road networks; the experiment results show that, in the large scale road networks, the proposed method can greatly enhance the efficiency of the dynamic route guidance system.

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