A vehicle path planning method based on a dynamic traffic network that considers fuel consumption and emissions

Guo, Dong; Wang, Juan; Zhao, Jin B.; Sun, Fang Hong; Gao, Song; Li, Chun D.; Li, Ming H.; Li, Chao C.

doi:10.1016/j.scitotenv.2019.01.222

Cited by 47 publications

(28 citation statements)

References 11 publications

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“…Big Data analysis based on demographics, facility usage, and social welfare indicators can fulfill the overall needs of society and urban development [12]. People also can reduce fuel consumptions and emissions by using dynamic Big Data traffic analysis [13].…”

Section: Urban Planningmentioning

confidence: 99%

Classification and Big Data Usages for Industrial Applications

Lim¹

2019

IJATCSE

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Recently, Big Data has become a significant concept in various areas, especially with business sectors. Digital devices, social media, and simulation results are producing a vast amount of data which are used by various applications to find insights. However, conceptual work integrating the Big Data applications into categories has been limited. The goal of this paper is to classify Big Data use cases by application's characteristics and to provide a systematic review of Big Data usages in each application classification which is based on examination of the available use cases and survey of related works. Big Data applications are classified into three categories such as industry and business, scientific research, urban planning. The critical aspects of the classified areas are described to determine the importance of Big Data in these areas. Fog computing and cloudlet are presented as the core technology of collecting data. Twitter, LinkedIn, Facebook, and Netflix are discussed as industry and business cases of Big Data.

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Section: Urban Planningmentioning

confidence: 99%

Classification and Big Data Usages for Industrial Applications

Lim¹

2019

IJATCSE

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“…The authors Dong Guo et al [ 3 ] has mentioned about emissions effect while driving and also about fuel consumption by using the shortest path method. The automatic guided vehicle transmission in an environment with the grid method using Dijkstra algorithm was represented by Zheng Zhang et al [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Hardware Schemes for Autonomous Navigation of Cooperative-type Multi-robot in Indoor Environment

Vani

Rao

Chinnaiah³

2021

J. Inst. Eng. India Ser. B

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This study proposes about the autonomous navigation of a multi-robot for transportation in indoor environments. This transportation is integrated with three foldings of VLSI architectures; they are 1) shortest optimal path planning 2) behavioral control between multi-robot with leadership swapping methods as per dynamic conditions and 3) obstacle avoidance by multi-robot. The hardware schemes have been designed for navigation of multi-robot with shortest path planning, based on an extended Dijkstra algorithm along with the Delaunay triangulation method. The behavioral control mechanism between the multi-robot is another challenge at the time of navigation and obstacle avoidance at both static and dynamic conditions in real-time scenario. The leader and follower approaches are deployed for cooperation between multi-robot to accomplish the task. The VLSI architectures are proposed for multi-robot navigation in the warehouse-type indoor environment. It is developed using Verilog HDL, simulated and synthesized with Xilinx Vivado 17.1. The Zynq-7000 SoC ZC702 FPGA is used as the target device.

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“…Dong Guo et al improved the traditional Dijkstra algorithm and combined it with the vehicle fuel consumption and emission measurement model to reduce vehicle fuel consumption and emissions effectively during driving (2019) [6]. This method employed a rectangular area (the smallest bounding rectangle of the ellipse) to limit the search area, thereby improving the efficiency of the Dijkstra's algorithm.…”

Section: Introductionmentioning

confidence: 99%

Surface Optimal Path Planning Using an Extended Dijkstra Algorithm

2020

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Extensive studies have been conducted on the Dijkstra algorithm owing to its bright prospect. However, few of them have studied the surface path planning of mobile robots. Currently, some application fields (e.g., wild ground, planet ground, and game scene) need to solve the optimal surface path. This paper proposes an extended Dijkstra algorithm. We utilize the Delaunay triangulation to model the surface environment. Based on keeping the triangle side length unchanged, the triangle mesh on the surface is equivalently converted into a triangle on the two-dimensional plane. Through this transformation, we set up the two-dimensional developable passable channel of the surface and solve the optimal route on this channel. Traversing all the two-dimensional developable and passable paths of the surface, we can get the shortest route among all the optimal paths. Then the inverse transformation from the two-dimensional plane coordinates to the corresponding surface coordinates obtains the surface optimal path. The simulation results show that, compared with the traditional Dijkstra algorithm, this method improves the accuracy of the surface optimization path in single-robot single-target and multi-robot multi-target path planning tasks.

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A vehicle path planning method based on a dynamic traffic network that considers fuel consumption and emissions

Cited by 47 publications

References 11 publications

Classification and Big Data Usages for Industrial Applications

Classification and Big Data Usages for Industrial Applications

Hardware Schemes for Autonomous Navigation of Cooperative-type Multi-robot in Indoor Environment

Surface Optimal Path Planning Using an Extended Dijkstra Algorithm

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