Emerging Technologies for Urban Traffic Management

Ibáñez, Juan Antonio Guerrero; Flores-Cortés, Carlos; Damián-Reyes, Pedro; Andrade-Arechiga, M.; Pulido, Jrg

doi:10.5772/37760

Cited by 6 publications

(5 citation statements)

References 37 publications

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“…Communication technologies have aided the process of establishing a vehicular network to reduce the traffic congestion time is suggested by M. Lvet et al 12) , Menon et al 13) . Chen, D et al 14) , Guerrero-ibáñez et al 15) aimed toward advanced technologies, especially in the area of mobile computing, and wireless ad-hoc networks enabling the vehicles to communicate with other vehicles and the surrounding infrastructure (V2V, V2I).The automation of traffic signals is done using inductive loop detection as presented by Mandhare et al 16) . For traffic detection, Wireless Sensor Networks are more useful.…”

Section: Literature Reviewmentioning

confidence: 99%

A Real Time Dynamic Approach for Management of Vehicle Generated Traffic

Chaudhary¹,

Saraswat²,

Yadav³

et al. 2023

Evergreen

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India, with a population of 1.3 billion and almost 300 million vehicles, is one of the biggest contributors to traffic jams, vehicle-specific pollution, and chronic lung diseases. To manage the footfall of these gigantic vehicles, continuous effort and research is taking place in the direction of both active and passive traffic management. This research paper aims towards showcasing a dynamic, fully autonomous deep learning model that uses real-time feeds from existing traffic junctions/ intersection cameras, process them and provide an intensity score based on the density of traffic in each adjoining lane.The proposed CNN model which is based on YOLO framework uses 10 seconds wait analysis time. The proposed system manages traffic, based on the intensity scores which assign traffic a Go time to each lane using an optimal traffic time in the range of 10-50 seconds.The model also scans for emergency vehicles in each lane, to provide a priority pass to such vehicles. Evaluation of model performance Mean Average Precision (mAP) is used.

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Section: Literature Reviewmentioning

confidence: 99%

A Real Time Dynamic Approach for Management of Vehicle Generated Traffic

Chaudhary¹,

Saraswat²,

Yadav³

et al. 2023

Evergreen

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“…Our model Hist is evaluated with other models based on several popular metrics. Mean relative error (MRE) is the average ratio of the absolute error between a prediction and the real value in Equation (1). Mean absolute error (MAE) is the average difference between between a prediction and the real value in Equation (2).…”

Section: Experiments Setup and Datasetsmentioning

confidence: 99%

“…Transportation managers can control the traffic flow to reduce the congestion when a traffic jam happens. Vehicles could avoid passing some busy roads or rush hours in advance [1]. As a result, it is significant for drivers and transportation managers to forecast the traffic condition in real-time.…”

Section: Introductionmentioning

confidence: 99%

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RealTE: Real-time Trajectory Estimation Over Large-Scale Road Networks

Zhou

Yang

2022

JWE

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Real-time traffic estimation focuses on predicting the travel time of one travel path, which is capable of helping drivers selecting an appropriate or favor path. Statistical analysis or neural network approaches have been explored to predict the travel time on a massive volume of traffic data. These methods need to be updated when the traffic varies frequently, which incurs tremendous overhead. We build a system RealTER⁢e⁢a⁢l⁢T⁢E, implemented on a popular and open source streaming system StormS⁢t⁢o⁢r⁢m to quickly deal with high speed trajectory data. In RealTER⁢e⁢a⁢l⁢T⁢E, we propose a locality-sensitive partition and deployment algorithm for a large road network. A histogram estimation approach is adopted to predict the traffic. This approach is general and able to be incremental updated in parallel. Extensive experiments are conducted on six real road networks and the results illustrate RealTE achieves higher throughput and lower prediction error than existing methods. The runtime of a traffic estimation is less than 11 seconds over a large road network and it takes only 619619 microseconds for model updates.

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“…It also contributes to road congestion which worsens the issue of air pollution. According to [13], road congestion increases emissions and thus, there is a need to find a solution that can reduce the travel time in order to minimize such emissions. Many solutions have been proposed to mitigate the issue of road congestion and emissions.…”

Section: Green Communications: Related Workmentioning

confidence: 99%

Vehicle Adhoc Sensor Network Framework to Provide Green Communication for Urban Operation Rescue

Noor¹

2013

LNIT

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The use of wireless technology based on vehicle ad hoc network and wireless sensor network may give opportunity to the operational rescue team for emergency cases. Information transmitted using the vehicular and sensor technologies may improve the rescue communication. In this paper, a green communication framework is proposed to support the operation rescue together with green communication requirements. The proposed model is validated using two parameters which are average packet delivery ratio and level of congestion messages. The results are promising since we are using the 802.11Ext (extension for vehicle ad hoc protocol) to allow vehicle-to-vehicle communications.

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Emerging Technologies for Urban Traffic Management

Cited by 6 publications

References 37 publications

A Real Time Dynamic Approach for Management of Vehicle Generated Traffic

A Real Time Dynamic Approach for Management of Vehicle Generated Traffic

RealTE: Real-time Trajectory Estimation Over Large-Scale Road Networks

Vehicle Adhoc Sensor Network Framework to Provide Green Communication for Urban Operation Rescue

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