Impacts of large-scale driverless truck adoption on the freight transport system

Engholm, Albin; Kristoffersson, Ida; Pernestål, Anna

doi:10.1016/j.tra.2021.10.014

Cited by 12 publications

(5 citation statements)

References 22 publications

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“…Of late, several monitoring practices have been used to ensure the activities in logistics and transport are done well or not. Artificial Intelligence (AI) and the Internet of Things (IoT) are fascinating technologies for large-scale logistics transport vehicles owing to their potential safety, operational, and economic benefits [1]. Liang J Wireless Com Network (2023) 2023:75 Combining artificial intelligence, electronic sensing, data communication, computer processing, and other high-tech intelligent technologies, intelligent modern transportation is enabled to build an intelligent management system that can comprehensively and accurately monitor traffic conditions [2].…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Intelligent monitoring methodology for large-scale logistics transport vehicles based on parallel Internet of Vehicles

Liang

2023

J Wireless Com Network

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The essence of the Internet of Vehicles is a social and physical information system, including the psychological and organizational factors of human beings. The complexity of the Internet will lead to certain deviations when monitoring vehicles. Therefore, the Parallel Internet of Vehicles is employed to monitor the information on large-scale logistics transport vehicles. This platform is built based on the ACP intelligent approach, which consists of three parts: An artificial system (A), a computational experiment (C), and parallel execution (P). The Adaboost algorithm is used to extract information on large-scale logistics transport vehicles from the ACP parallel Internet of Vehicles, and the Tabu search strategy is applied to optimize the Monte Carlo positioning algorithm. The approximate optimal estimation is obtained by optimizing the filtering to eliminate vehicle positions with fewer possibilities. The weight of important sampling values of the independent vehicle node positions is integrated to complete the posterior probability distribution estimation of the possible positions of vehicles, in order to realize vehicle position monitoring. It is verified that the root-mean square error of the algorithm when positioning a vehicle is less than 0.18, and the monitoring deviation is quite small.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Intelligent monitoring methodology for large-scale logistics transport vehicles based on parallel Internet of Vehicles

Liang

2023

J Wireless Com Network

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“…Autonomous trucks can provide a much-needed solution to an industry that has grappled with high driver turnover for nearly five decades [23,24]. Despite these prospects, the trucking industry faces challenges such as poor roadway conditions, traffic congestion, accidents, weather events, and pressure to decrease pollutive emissions [3,25].…”

Section: Literature Reviewmentioning

confidence: 99%

Data-Driven Deployment of Cargo Drones: A U.S. Case Study Identifying Key Markets and Routes

Bridgelall

2023

Algorithms

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Electric and autonomous aircraft (EAA) are set to disrupt current cargo-shipping models. To maximize the benefits of this technology, investors and logistics managers need information on target commodities, service location establishment, and the distribution of origin–destination pairs within EAA’s range limitations. This research introduces a three-phase data-mining and geographic information system (GIS) algorithm to support data-driven decision-making under uncertainty. Analysts can modify and expand this workflow to scrutinize origin–destination commodity flow datasets representing various locations. The algorithm identifies four commodity categories contributing to more than one-third of the value transported by aircraft across the contiguous United States, yet only 5% of the weight. The workflow highlights 8 out of 129 regional locations that moved more than 20% of the weight of those four commodity categories. A distance band of 400 miles among these eight locations accounts for more than 80% of the transported weight. This study addresses a literature gap, identifying opportunities for supply chain redesign using EAA. The presented methodology can guide planners and investors in identifying prime target markets for emerging EAA technologies using regional datasets.

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“…Several studies have demonstrated that the operation and planning of a new travel mode are significantly related to the individuals’ acceptance of it [ 20 , 21 ]. Indeed, several researchers believe that acceptance of a new travel mode is related to sociodemographic characteristics, such as gender, age, income, and education [ 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Understanding the effect of sociodemographic and psychological latent characteristics on flex-route transit acceptance

Zhang

et al. 2023

PLoS ONE

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Flex-route transit (FRT) has significant advantages in low-demand areas. Existing studies have focused on practical experience, strategic planning, and operational planning. Few studies have addressed the effect of sociodemographic and psychological latent characteristics on the acceptance of FRT. This study aims at exploring the effect of sociodemographic and psychological latent characteristics on FRT acceptance. To finish the goal, a household survey is conducted from April to May 2020 in Nanjing, China. The survey includes sociodemographic characteristics and observed variables of individuals. Firstly, the study extracts six psychological latent characteristics to reflect individuals’ attitudes based on previous and mature researches in the field of technology acceptance model (TAM) and theory of planned behavior (TPB). Then, a multiple indicators and multiple causes (MIMIC) is applied to calculate six psychological latent characteristics. Finally, an integrated model, consisting of the MIMIC and a binary logit model (BLM), is applied to match sociodemographic and psychological latent characteristics. The BLM with sociodemographic characteristics is developed as the reference model to compare the effects of psychological latent characteristics. Results show that psychological latent factors play a significant role in estimating the effect on FRT acceptance. Results of the integrated model show that the parameter of car is -0.325, displaying individuals with private cars are more reluctant to use FRT. Therefore, restricting private cars is an effective measure to facilitate FRT. Improving flexibility (0.241) is a significant measure to facilitate FRT. Findings are expected to facilitate decision-making of transport planners and engineers, and therefore enhance the service of the FRT system.

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Impacts of large-scale driverless truck adoption on the freight transport system

Cited by 12 publications

References 22 publications

RETRACTED ARTICLE: Intelligent monitoring methodology for large-scale logistics transport vehicles based on parallel Internet of Vehicles

RETRACTED ARTICLE: Intelligent monitoring methodology for large-scale logistics transport vehicles based on parallel Internet of Vehicles

Data-Driven Deployment of Cargo Drones: A U.S. Case Study Identifying Key Markets and Routes

Understanding the effect of sociodemographic and psychological latent characteristics on flex-route transit acceptance

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