Optimal path finding based on traffic information extraction from Twitter

Author(s):  
Muhammad Hasby ◽  
Masayu Leylia Khodra
Author(s):  
Jooin Lee ◽  
Hyeongcheol Lee

Intelligent Transportation System (ITS) is actively studied as the sensor and communication technology in the vehicle develops. The Intelligent Transportation System collects, processes, and provides information on the location, speed, and acceleration of the vehicles in the intersection. This paper proposes a fuel optimal route decision algorithm. The algorithm estimates traffic condition using information of vehicles acquired from several ITS intersections and determines the route that minimizes fuel consumption by reflecting the estimated traffic condition. Simplified fuel consumption models and road information (speed limit, average speed, etc.) are used to estimate the amount of fuel consumed when passing through the road. Dynamic Programming (DP) is used to determine the route that fuel consumption can be minimized. This algorithm has been verified in an intersection traffic model that reflects the actual traffic environment (Korea Daegu Technopolis) and the corresponding traffic model is modeled using AIMSUN.


2011 ◽  
Vol 58-60 ◽  
pp. 1959-1965 ◽  
Author(s):  
Zheng Yu Zhu ◽  
Wei Liu ◽  
Lin Liu ◽  
Ming Cui ◽  
Jin Yan Li

The complexity of a real road network structure of a city and the variability of its real traffic information make a city’s intelligent transportation system (ITS) hard to meet the needs of the city’s vehicle navigation. This paper has proposed a simplified real-time road network model which can take into account the influence of intersection delay on the guidance for vehicles but avoid the calculation of intersection delay and troublesome collection of a city’s traffic data. Based on the new model, a navigation system has been presented, which can plan a dynamic optimal path for a vehicle according to the real-time traffic data received periodically from the city’s traffic center. A simulated experiment has been given. Compared with previous real-time road network models, the new model is much simpler and more effective on the calculation of vehicle navigation.


Author(s):  
Pei Cao ◽  
Zhaoyan Fan ◽  
Robert X. Gao ◽  
J. Tang

This research aims at unleashing the potential of additive manufacturing technology in industrial design that can produce structures/devices with irregular component geometries to reduce sizes/weights. We explore, by means of path-finding, the length minimization of freeform hydraulic piping network in compact space under given constraints. Previous studies on path-finding have mainly focused on enhancing computational efficiency due to the need to produce rapid results in such as navigation and video-game applications. In this research, we develop a new Focal Any-Angle A* approach that combines the merits of grid-based method and visibility graph-based method. Specifically, we formulate pruned visibility graphs preserving only the useful portion of the vertices and then find the optimal path based on the candidate vertices using A*. The reduced visibility graphs enable us to outperform approximations and maintain the optimality of exact algorithms in a more efficient manner. The algorithm proposed is compared to the traditional A* on Grids, Theta* and A* on visibility graphs in terms of path length, number of nodes evaluated, as well as computational time. As demonstrated and validated through case studies, the proposed method is capable of finding the shortest path with tractable computational cost, which provides a viable design tool for the additive manufacturing of piping network systems.


2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Liang Shen ◽  
Hu Shao ◽  
Long Zhang ◽  
Jian Zhao

There is a growing interest in finding a global optimal path in transportation networks particularly when the network suffers from unexpected disturbance. This paper studies the problem of finding a global optimal path to guarantee a given probability of arriving on time in a network with uncertainty, in which the travel time is stochastic instead of deterministic. Traditional path finding methods based on least expected travel time cannot capture the network user’s risk-taking behaviors in path finding. To overcome such limitation, the reliable path finding algorithms have been proposed but the convergence of global optimum is seldom addressed in the literature. This paper integrates the K-shortest path algorithm into Backtracking method to propose a new path finding algorithm under uncertainty. The global optimum of the proposed method can be guaranteed. Numerical examples are conducted to demonstrate the correctness and efficiency of the proposed algorithm.


2016 ◽  
Vol 19 (4) ◽  
pp. 2179-2188 ◽  
Author(s):  
Bin Hu ◽  
Huan-yan Qian ◽  
Yi Shen ◽  
Jia-xing Yan

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