Optimization of Joint Decision of Transport Mode and Path in Multi-Mode Freight Transportation Network

Lu, Yang; Wang, Shuaiqi

doi:10.3390/s22134887

Cited by 4 publications

(5 citation statements)

References 26 publications

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“…With the development of car networking and internet technology, domestic navigation companies have attempted to connect with signal light data [32,33]. Through dynamic speed guidance, it ensures that vehicles stop at each light controlled intersection on the path with the least number of times, reduces vehicle delays and energy losses caused by signal light control, improves the efficiency and reliability of vehicle navigation, and can reduce tail gas emissions caused by multiple stops [34,35].…”

Section: Literature Reviewmentioning

confidence: 99%

Sustainable Road Planning for Trucks in Urbanized Areas of Chinese Cities Using Deep Learning Approaches

Wang

Zhao

et al. 2023

Sustainability

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Nowadays, urban areas are experiencing heavy traffic, and governments are implementing various policies to manage it. For example, in China, trucks are prohibited from entering urban areas during the daytime to reduce traffic congestion. However, we have found that this policy is not cost-efficient for logistics, which includes gas fees, air pollution fees, and wear and tear expenses, as it cannot adjust to real-time traffic conditions. To minimize logistics costs in real-time, we propose DeepPlan, a deep-learning-based model that optimizes urban planning. Our model calculates the optimal route for each truck based on real-time traffic data in urban areas. We learned the optimal route from the trace data of taxi drivers who are experienced in minimizing logistics costs. Our experimental results show that DeepPlan outperforms existing urban plans by 25% and works well in various circumstances, including different weather and unexpected events.

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Section: Literature Reviewmentioning

confidence: 99%

Sustainable Road Planning for Trucks in Urbanized Areas of Chinese Cities Using Deep Learning Approaches

Wang

Zhao

et al. 2023

Sustainability

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“…Ref. [10] used Dijkstra and multi-objective optimization algorithms that included the time, cost, and risk to solve the joint decision-making problem of the traffic mode and path in complex networks. Dijkstra and A* can be used in grid maps to plan the optimal path.…”

Section: Traditional Algorithmsmentioning

confidence: 99%

“…In this work, the grid map's shapes included rectangles and squares. Its length and width were selected from [10,20,40,60,80] so that 25 sizes of maps were generated. Obstacles, costs, the starting point, and the endpoint in the maps were randomly generated.…”

Section: Experimental Designmentioning

confidence: 99%

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End-to-End One-Shot Path-Planning Algorithm for an Autonomous Vehicle Based on a Convolutional Neural Network Considering Traversability Cost

Bian

Xing

Zolotas

2022

Sensors

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Path planning plays an important role in navigation and motion planning for robotics and automated driving applications. Most existing methods use iterative frameworks to calculate and plan the optimal path from the starting point to the endpoint. Iterative planning algorithms can be slow on large maps or long paths. This work introduces an end-to-end path-planning algorithm based on a fully convolutional neural network (FCNN) for grid maps with the concept of the traversability cost, and this trains a general path-planning model for 10 × 10 to 80 × 80 square and rectangular maps. The algorithm outputs the lowest-cost path while considering the cost and the shortest path without considering the cost. The FCNN model analyzes the grid map information and outputs two probability maps, which show the probability of each point in the lowest-cost path and the shortest path. Based on the probability maps, the actual optimal path is reconstructed by using the highest probability method. The proposed method has superior speed advantages over traditional algorithms. On test maps of different sizes and shapes, for the lowest-cost path and the shortest path, the average optimal rates were 72.7% and 78.2%, the average success rates were 95.1% and 92.5%, and the average length rates were 1.04 and 1.03, respectively.

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“…When these algorithms are used for large-scale maps, the running time is very long, it is not easy to obtain the global optimal solution and the line selection is not ideal. The Dijkstra algorithm [14,15] does not require iteration and takes a short time. As long as there is an optimal route, it can be found, and it is suitable for line-selection problems involving factors such as route length, number of corners and construction costs in different environments.…”

Section: Introductionmentioning

confidence: 99%

Transmission Line-Planning Method Based on Adaptive Resolution Grid and Improved Dijkstra Algorithm

Nan,

Liu,

et al. 2023

Sensors

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An improved Dijkstra algorithm based on adaptive resolution grid (ARG) is proposed to assist manual transmission line planning, shorten the construction period and achieve lower cost and higher efficiency of line selection. Firstly, the semantic segmentation network is used to change the remote sensing image into a ground object-identification image and the grayscale image of the ground object-identification image is rasterized. The ARG map model is introduced to greatly reduce the number of redundant grids, which can effectively reduce the time required to traverse the grids. Then, the Dijkstra algorithm is combined with the ARG and the neighborhood structure of the grid is a multi-center neighborhood. An improved method of bidirectional search mechanism based on ARG and inflection point-correction is adopted to greatly increase the running speed. The inflection point-correction reduces the number of inflection points and reduces the cost. Finally, according to the results of the search, the lowest-cost transmission line is determined. The experimental results show that this method aids manual planning by providing a route for reference, improving planning efficiency while shortening the duration, and reducing the time spent on algorithm debugging. Compared with the comparison algorithm, this method is faster in running speed and better in cost saving and has a broader application prospect.

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Optimization of Joint Decision of Transport Mode and Path in Multi-Mode Freight Transportation Network

Cited by 4 publications

References 26 publications

Sustainable Road Planning for Trucks in Urbanized Areas of Chinese Cities Using Deep Learning Approaches

Sustainable Road Planning for Trucks in Urbanized Areas of Chinese Cities Using Deep Learning Approaches

End-to-End One-Shot Path-Planning Algorithm for an Autonomous Vehicle Based on a Convolutional Neural Network Considering Traversability Cost

Transmission Line-Planning Method Based on Adaptive Resolution Grid and Improved Dijkstra Algorithm

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