Risk forewarning model of freight period based on scenario merging

Pang, Beibei; Gou, Juanqiong; Mu, Wenxin; Qiao, Huan

doi:10.1109/liss.2016.7854527

Cited by 1 publication

(2 citation statements)

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“…In each run, vehicles are encountered with incidents intermittently. e effects of these problems can be minimized by 8 Scientific Programming Scientific Programming making decisions about whether to take rest and using the alternative time to travel. Vehicles with guided systems get familiar with the time-dependent travel condition, and better decisions are made to avoid extreme delays during peak hours.…”

Section: Resultsmentioning

confidence: 99%

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Context-Aware Services Using MANETs for Long-Distance Vehicular Systems: A Cognitive Agent-Based Model

Raees

Khan

Abbasi

et al. 2021

Scientific Programming

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Long-distance transportation systems play an important role in economic growth. Yet, these systems are incurred with multifaceted delays and cost problems. The major incites affecting transportation systems are congestion, breakdowns, emergencies, and inclement weather. Scarcity of information about the environment also exacerbates travel problems. It is essential to employ monitoring and guidance that aid in making timely decisions through premediated information. This work aims to provide a flexible model for the long-distance transport system. The model is based on problems faced in long-distance transportation. Moreover, we examine the possible use of emerging Information and Communication Technologies (ICTs) for better transportation. The system dynamics study the problem at hand through cognitive agent-based modelling (ABM) concepts. The integrated model lays the rules to abate traffic delays. In this model, the distance travelled by vehicles is divided into sections using checkpoints. Every section is composed of different agents such as medical units, police stations, workshops, and petrol pumps. The vehicle shifts connection over the mobile ad hoc network (MANET) when enters or leaves a section. We used NetLogo for simulation of the model. A monitoring and guidance system is tested, and obtained results are analyzed by addressing problems causing delays. The guidance system helps vehicles to take optimal decisions for the time, congestion, and rests. The model can be used to improve decision-making for vehicles through premediated decisions. The proposed model can help to improve the efficiency of the transportation systems by reducing travel time.

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Section: Resultsmentioning

confidence: 99%

“…eft and breakage risks decrease the performance of a transportation system through inhibited areas. It is essential to ameliorate transportation systems by detection and aversion of such risks [8]. Road conditions and rest time also evince the transport system's efficacy.…”

Section: Introductionmentioning

confidence: 99%