Load Effect of Automated Truck Platooning on Highway Bridges and Loading Strategy

Ling, Tianyang; Deng, Lu; He, Wang; Wu, Haibing; Deng, Jianzhong

doi:10.3390/s22207704

Cited by 4 publications

(1 citation statement)

References 24 publications

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“…As the main contributors to the green gas effect, the sector of transportation is tasked with adopting sustainable approaches for enhanced operations, thereby reducing exhausts which contribute to pollution in general [51]. The development of truck platooning contributes a lot in this regard and champions the aim, thereby optimizing fuel consumption, and reducing air pollution and the decongestion of traffic [52]. However, the platooning system is disadvantaged by the issue of inter-vehicle distancing, which is influenced by the aerodynamic drag coefficient of the vehicles [53].…”

Section: Platooningmentioning

confidence: 99%

Sustainable Traffic Management for Smart Cities Using Internet-of-Things-Oriented Intelligent Transportation Systems (ITS): Challenges and Recommendations

et al. 2023

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The emergence of smart cities has addressed many critical challenges associated with conventional urbanization worldwide. However, sustainable traffic management in smart cities has received less attention from researchers due to its complex and heterogeneous nature, which directly affects smart cities’ transportation systems. The study aimed at addressing traffic-related issues in smart cities by focusing on establishing a sustainable framework based on the Internet of Things (IoT) and Intelligent Transportation System (ITS) applications. To sustain the management of traffic in smart cities, which is composed of a hybridized stream of human-driven vehicles (HDV) and connected automated vehicles (CAV), a dual approach was employed by considering traffic as either modeling- and analysis-based, or/and the decision-making issues of previous research works. Moreover, the two techniques utilized real-time traffic data, and collected vehicle and road users’ information using AI sensors and ITS-based devices. These data can be processed and transmitted using machine learning algorithms and cloud computing for traffic management, traffic decision-making policies, and documentation for future use. The proposed framework suggests that deploying such systems in smart cities’ transportation could play a significant role in predicting traffic outcomes, traffic forecasting, traffic decongestion, minimizing road users’ lost hours, suggesting alternative routes, and simplifying urban transportation activities for urban dwellers. Also, the proposed integrated framework adopted can address issues related to pollution in smart cities by promoting public transportation and advocating low-carbon emission zones. By implementing these solutions, smart cities can achieve sustainable traffic management and reduce their carbon footprint, making them livable and environmentally friendly.

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Section: Platooningmentioning

confidence: 99%