Logistics Sprawl and Artificial Intelligence Revolving Urban Freight Transport DOI
Manal El Yadari, Fouad Jawab,

Imane Moufad

и другие.

Advances in logistics, operations, and management science book series, Год журнала: 2024, Номер unknown, С. 191 - 244

Опубликована: Дек. 20, 2024

Artificial intelligence has made great strides in various fields, especially improving logistics operations and freight transportation. This chapter aims to highlight the importance of applying AI manage sprawl phenomenon. The research focused on analyzing impact use performance urban transport under sprawling conditions. To achieve this, authors carried out a literature review explore different categories including machine learning, deep natural language processing, visual data reinforcement learning (RL), specialized algorithms, optimise activities within context.

Язык: Английский

Explainable artificial intelligence in transport Logistics: Risk analysis for road accidents DOI
Ismail Abdulrashid, Reza Zanjirani Farahani,

Shamkhal Mammadov

и другие.

Transportation Research Part E Logistics and Transportation Review, Год журнала: 2024, Номер 186, С. 103563 - 103563

Опубликована: Апрель 30, 2024

Язык: Английский

Процитировано

13

Inferring freeway traffic volume with spatial interaction enhanced betweenness centrality DOI Creative Commons
Beibei Zhang, Shifen Cheng, Peixiao Wang

и другие.

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2024, Номер 129, С. 103818 - 103818

Опубликована: Апрель 6, 2024

Freeway traffic volume is strongly correlated with the intensity of regional socioeconomic spatial interactions and road network structure. Although existing studies have proposed indicators betweenness centrality (BC) integrated into interactions, socio-economic drivers freeway formation been neglected. More importantly, not established a non-linear response relationship among BC, city volume, which severely limits comprehensive quantification role flow drivers. Therefore, this study proposes inference method that integrates interaction to enhance BC. First, factors origin destination cities are incorporated BC indicator create an enhanced (ODBC), quantifies strength between cities. Second, machine learning approach used develop ODBC accurately infer volume. Finally, utilizing SHapley additive explanation approach, vectors intercity quantified. Experiments conducted on data from toll stations demonstrate surpasses baseline based weighted by considering only potential or attractiveness, improvement in R2 14%, 4.2%, 4%, maximum reduction RMSE 40%, 24.5%, 26%. The yields higher accuracy for unknown segments easily interpretable.

Язык: Английский

Процитировано

6

Spatiotemporal dynamics and determining factors of intercity mobility: A comparison between holidays and non-holidays in China DOI
Weijie Yu,

De Wen Zhao,

Xuedong Hua

и другие.

Cities, Год журнала: 2024, Номер 153, С. 105306 - 105306

Опубликована: Июль 27, 2024

Язык: Английский

Процитировано

4

Quantifying city freight mobility segregation associated with truck multi-tours behavior DOI
Yitao Yang, Yan Chen,

Ying-Yue Lv

и другие.

Sustainable Cities and Society, Год журнала: 2024, Номер 113, С. 105699 - 105699

Опубликована: Авг. 3, 2024

Язык: Английский

Процитировано

4

On the calibration and improvement of human mobility models in intercity transportation system DOI
Weijie Yu, Haosong Wen, Wei Wang

и другие.

Physica A Statistical Mechanics and its Applications, Год журнала: 2024, Номер unknown, С. 130116 - 130116

Опубликована: Сен. 1, 2024

Язык: Английский

Процитировано

3

Prediction greenhouse gas emissions from road freight flow in South Korea for sustainable transportation planning DOI Creative Commons
Hoseok Nam, Jihye Byun, Hyungseok Nam

и другие.

Heliyon, Год журнала: 2025, Номер 11(2), С. e41937 - e41937

Опубликована: Янв. 1, 2025

Road freight modeling was conducted to project flow and greenhouse gas (GHG) emissions in 16 administrative regions of South Korea through 2050. Origin-destination matrices were constructed using a gravity model for each region. The covered seven product categories both inter-regional intra-regional transportation validated 2017 data. total future is projected increase from 1399 million tons 2019 1701 by 2035. However, after peaking 2035, it expected decline 1618 2050, indicating that population will impact demand, causing reduction despite continued economic growth. GHG are slightly decrease 19.0 kgCO2eq. 2025 18.6 followed steeper 15.5 This attributed long-term reductions emission factors. Changes between 2050 be more pronounced within five the capital extended areas, which account approximately 50.3 % due concentration. As result, these contribute 26.5 potential. minimum growth rates required maintain same volume as 2035 estimated at 5 2040, 13 2045, 26

Язык: Английский

Процитировано

0

Assessing the resilience of urban truck transport networks under the COVID-19 pandemic: A case study of China DOI
Yitao Yang, Erjian Liu, Yan Chen

и другие.

Transportation Research Part E Logistics and Transportation Review, Год журнала: 2025, Номер 197, С. 104087 - 104087

Опубликована: Март 20, 2025

Язык: Английский

Процитировано

0

Identifying the critical features influencing warehouse rental prices and their nonlinear associations: A spatial machine learning approach DOI
Nannan He, Sijing Liu, Xinyu Cao

и другие.

Transportation Research Part E Logistics and Transportation Review, Год журнала: 2025, Номер 197, С. 104092 - 104092

Опубликована: Март 28, 2025

Язык: Английский

Процитировано

0

Deep Learning Empowered Intermodal Path Optimization in Logistics: Deep Shortest Approach DOI Open Access
Safi̇ye Turgay,

Mert Kadem Omeroglu,

S.S. Erdogan

и другие.

WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS, Год журнала: 2025, Номер 22, С. 832 - 844

Опубликована: Май 2, 2025

This is particularly important in logistics, where path planning critical for adequate transport and distribution processes. That why classical approaches like Dijkstra’s algorithm have been essential, though they are too weak to handle the complications typical of actual logistics networks. To this end, paper proposes a new framework called DeepShortest, which improves optimization process using deep learning methods. DeepShortest uses neural network training flexibility complexity various logistical contexts. Thus, successfully implements within base deliver high result finding shortest most effective paths transporting goods through global chains. In paper, DEEP Define strategy describes how methodologies cast into component approach. addition, real-world case studies substantiate effectiveness advantage compared with previous methods, generally providing stepped-up route performance resource management. an innovative approach solving problems creative solution issues today’s supply chain. With their capacity work areas conditions change often suggest optimal delivery vehicles, presents itself as invaluable that could drastically transform worldwide.

Язык: Английский

Процитировано

0

A spatial–temporal graph-based AI model for truck loan default prediction using large-scale GPS trajectory data DOI
Liao Chen,

Shoufeng Ma,

Changlin Li

и другие.

Transportation Research Part E Logistics and Transportation Review, Год журнала: 2024, Номер 183, С. 103445 - 103445

Опубликована: Фев. 6, 2024

Язык: Английский

Процитировано

2