Cellular automata model for Urban Road traffic flow Considering Internet of Vehicles and emergency vehicles

Zhao, Hongfu; Zhao, Xin; Jian-cheng, LU; Xin, Liuyan

doi:10.1016/j.jocs.2020.101221

Cited by 17 publications

(9 citation statements)

References 13 publications

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“…The adaptability to different vehicles (e.g. emergency vehicles ( Zhao et al., 2020a ) and trucks ( Zhao et al., 2019b )) is also significant. In addition, the adaptability to the change of traffic patterns is also worth noticing.…”

Section: Discussionmentioning

confidence: 99%

“…Reference ( Korček et al., 2011 ) eliminated the unwanted properties of Cellular Automata and applied the model in the speed prediction of large-scale networks. Considering the influence of special vehicles, reference ( Zhao et al., 2020a ) introduced some special rules for Cellular Automata( Zhao et al., 2020a ), such as the low reaction time of connected-automated vehicle and regional avoidance of emergency vehicles.…”

Section: Prediction Methods Of Traffic Speedmentioning

confidence: 99%

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A comprehensive study of speed prediction in transportation system: From vehicle to traffic

et al. 2022

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Section: Discussionmentioning

confidence: 99%

Section: Prediction Methods Of Traffic Speedmentioning

confidence: 99%

A comprehensive study of speed prediction in transportation system: From vehicle to traffic

et al. 2022

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“…It is integrated into vehicles, smartphones, and wearable devices to provide real-time location information. It is also used in agriculture, transportation, logistics, and military fields for precise navigation and tracking [3]. GPS is a satellite-based navigation system that provides location and time information in all weather conditions.…”

Section: Introductionmentioning

confidence: 99%

Emergency Vehicle Driving Assistance System Using Recurrent Neural Network with Navigational Data Processing Method

Anjum

Shahab

2023

Sustainability

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Emergency vehicle transportation is important for responding to and transporting individuals during emergencies. This type of transportation faces several issues, such as road safety, navigation and communication, time-critical operations, resource utilisation, traffic congestion, data processing and analysis, and individual safety. Vehicle navigation and coordination is a critical aspect of emergency response that involves guiding emergency vehicles, such as ambulances, to the location of an emergency or medical centre as quickly and safely as possible. Therefore, it requires additional effort to reduce driving risks. The roadside units support emergency vehicles and infrastructure to decrease collisions and enhance optimal navigation routes. However, during the emergency vehicle’s data communication and navigation process, communication is interrupted due to vehicle outages. Therefore, this study proposes the Navigation Data Processing for Assisted Driving (NDP-AD) method to address the problem. The proposed approach assimilates infrastructure and neighbouring location information during driving. The integrated information is processed for distance and traffic during the previous displacement interval. The NDP-AD method employs a recurrent neural network learning approach to analyse opposing vehicle distance and traffic to provide accurate, independent guidance. This effective learning-based guidance process minimises false navigations and deviation in displacement. System efficiency is evaluated based on processing latency, displacement error, data utilisation, false rate, and accuracy metrics.

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“…On the other hand, it can offer suitable travel routes in advance so that people can avoid congested areas or times, saving travel time. Moreover, with the development demand of intelligent transportation, traffic flow statistics in the IoV environment has become an important issue in computer vision ( Zhao et al, 2020 ). Meanwhile, fast and effective traffic flow statistics can well relieve traffic congestion and reduce the possibility of traffic accidents, thus reducing the pressure of urban traffic.…”

Section: Introductionmentioning

confidence: 99%

A novel traffic optimization method using GRU based deep neural network for the IoV system

Wen

Yang³

2023

PeerJ Computer Science

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At present, China is moving towards the direction of “Industry 4.0”. The development of the automobile industry, especially intelligent automobiles, is in full swing, which brings great convenience to people’s life and travel. However, at the same time, urban traffic pressure is also increasingly prominent, and the situation of traffic congestion and traffic safety is not optimistic. In this context, the Internet of Vehicles (also known as “IoV”) opens up a new way to relieve urban traffic pressure. Therefore, in order to further optimize the road network traffic conditions in the IoV environment, this research focuses on the traffic flow prediction algorithm on the basis of deep learning to enhance traffic efficiency and safety. First, the study investigates the short-time traffic flow prediction by combining the characteristics of the IoV environment. To address the issues that existing algorithms cannot automatically extract data features and the model expression capability is weak, the study chooses to build a deep neural network using GRU model in deep learning for short-time traffic flow prediction, thereby improving the prediction accuracy of algorithm. Secondly, a fine-grained traffic flow statistics approach suitable for the IoV situation is suggested in accordance with the deep learning model that was built. The algorithm sends the vehicle characteristic data obtained through GRU model training into the fine-grained traffic flow statistics algorithm, so as to realize the statistics of traffic information of various types of vehicles. The advantage of this algorithm is that it can well count the traffic flow of multiple lanes, so as to better predict the current traffic status and achieve traffic optimization. Finally, the IoV environment is constructed to confirm the effectiveness of the prediction model. The prediction results prove that the new algorithm has good performance in traffic flow statistics in different scenarios.

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Cellular automata model for Urban Road traffic flow Considering Internet of Vehicles and emergency vehicles

Cited by 17 publications

References 13 publications

A comprehensive study of speed prediction in transportation system: From vehicle to traffic

A comprehensive study of speed prediction in transportation system: From vehicle to traffic

Emergency Vehicle Driving Assistance System Using Recurrent Neural Network with Navigational Data Processing Method

A novel traffic optimization method using GRU based deep neural network for the IoV system

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