Prediction of short-term average vehicular velocity considering weather factors in urban VANET environments

Yang, Jyun-Yan; Chou, Li-Der; Li, Yuchen; Lin, Yuhong; Huang, Shu-Min; Tseng, Gwojyh; Wang, Tongwen; Lu, Shu-Ping

doi:10.1109/icmlc.2010.5580743

Cited by 7 publications

(3 citation statements)

References 8 publications

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“…They also compared the evaluation results of proposed model with different algorithms like ARIMA and SVR-based models. However, there are some studies about traffic prediction considering weather conditions using different deep learning algorithms [26]- [30].…”

Section: B Road Traffic Predictionmentioning

confidence: 99%

Network Traffic Prediction Model Considering Road Traffic Parameters Using Artificial Intelligence Methods in VANET

Sepasgozar

Pierre

2022

IEEE Access

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Vehicular Ad hoc Networks (VANETs) are established on vehicles that are intelligent and can have Vehicle-to-Vehicle (V2V) and Vehicle-to-Road Side Units (V2R) communications. In this paper, we propose a model for predicting network traffic by considering the parameters that can lead to road traffic happening. The proposed model integrates a Random Forest-Gated Recurrent Unit-Network Traffic Prediction algorithm (RF-GRU-NTP) to predict the network traffic flow based on the traffic in the road and network simultaneously. This model has three phases including network traffic prediction based on V2R communication, road traffic prediction based on V2V communication, and network traffic prediction considering road traffic happening based on V2V and V2R communication. The hybrid proposed model which implements in the third phase, selects the important features from the combined dataset (including V2V and V2R communications), by using the Random Forest (RF) machine learning algorithm, then the deep learning algorithms to predict the network traffic flow apply, where the Gated Recurrent Unit (GRU) algorithm gives the best results. The simulation results show that the proposed RF-GRU-NTP model has better performance in execution time and prediction errors than other algorithms which used for network traffic prediction.

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Section: B Road Traffic Predictionmentioning

confidence: 99%

Network Traffic Prediction Model Considering Road Traffic Parameters Using Artificial Intelligence Methods in VANET

Sepasgozar

Pierre

2022

IEEE Access

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“…Weather prediction applications are based on surveillance, monitoring, and prediction of weather and roadway conditions to implement the appropriate management actions that improve the driving experience and mitigate the impacts of any adverse conditions [ 73 ]. Road weather applications are used to facilitate decisions on maintenance strategies and driver advisories.…”

Section: Taxonomy Of Its Applicationsmentioning

confidence: 99%

Sensor Technologies for Intelligent Transportation Systems

Ibáñez

Zeadally

Contreras-Castillo

2018

Sensors

472

200

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Modern society faces serious problems with transportation systems, including but not limited to traffic congestion, safety, and pollution. Information communication technologies have gained increasing attention and importance in modern transportation systems. Automotive manufacturers are developing in-vehicle sensors and their applications in different areas including safety, traffic management, and infotainment. Government institutions are implementing roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. By seamlessly integrating vehicles and sensing devices, their sensing and communication capabilities can be leveraged to achieve smart and intelligent transportation systems. We discuss how sensor technology can be integrated with the transportation infrastructure to achieve a sustainable Intelligent Transportation System (ITS) and how safety, traffic control and infotainment applications can benefit from multiple sensors deployed in different elements of an ITS. Finally, we discuss some of the challenges that need to be addressed to enable a fully operational and cooperative ITS environment.

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“…Such PHEV power management systems need to utilize not only historical driving cycles, but also real-time driving states and trip information. With the rapid development of vehicular networks and intelligent transportation system (ITS), on-road traffic prediction will be available for PHEVs in the near future [11], [12]. Besides, individual cars can retrieve realtime traffic information via their on-board smartphones from vehicular networks [13], [14], [15].…”

Section: Introductionmentioning

confidence: 99%

On-road PHEV power management with hierarchical strategies in vehicular networks

Jiang

Fei

2014

2014 IEEE Intelligent Vehicles Symposium Proceedings

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In plug-in hybrid electric vehicles (PHEVs), the power management system coordinates powertrain operations to achieve high energy efficiency. Conventional PHEV power management systems work in either an online or offline mode. Most online systems are based on some pre-set power balancing strategies without utilizing the driving cycle or route information. Offline management strategies solved from historical driving cycles are not optimal for real specific driving routes. With the rapid development of vehicular networks and proliferation of smartphones, real-time traffic information can be collected by smartphones from a vehicular network so as to facilitate online PHEV power management. This paper proposes an on-road PHEV power management cyberphysical system (CPS) with 2-level hierarchical optimizations to minimize the fuel consumption of a trip. The high-level online stochastic optimization generates a battery energy budget for each road at runtime according to the traffic prediction and trip information. The low-level powertrain policies are solved offline from historical driving cycles. During driving, the high-level battery energy budgets and low-level policies are combined to get the optimal power decisions according to current driving states. Simulation results show that the proposed method significantly outperforms other three methods in fuel savings.

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Prediction of short-term average vehicular velocity considering weather factors in urban VANET environments

Cited by 7 publications

References 8 publications

Network Traffic Prediction Model Considering Road Traffic Parameters Using Artificial Intelligence Methods in VANET

Network Traffic Prediction Model Considering Road Traffic Parameters Using Artificial Intelligence Methods in VANET

Sensor Technologies for Intelligent Transportation Systems

On-road PHEV power management with hierarchical strategies in vehicular networks

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