Real-time traffic flow-based traffic signal scheduling: a queuing theory approach

Antoine, Gatera; Mikeka, Chomora; Bajpai, Gaurav; Valko, Andras

doi:10.1504/writr.2021.119522

Cited by 5 publications

(2 citation statements)

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“…The entire average delay time at these junctions would be reduced only when coordinated control of traffic signals is applied and that has to depend on the traffic situation captured by the RSU imposed on the junctions. As the traffic flow rate depends on the period of the day and the season, respecting real-time traffic flow-based traffic signal scheduling could increase the effectiveness of the battery state of charge [ 34 ]. Figure 1 and Figure 2 show the aerial layouts of our areas of study.…”

Section: Materials and Methodsmentioning

confidence: 99%

Battery-Powered RSU Running Time Monitoring and Prediction Using ML Model Based on Received Signal Strength and Data Transmission Frequency in V2I Applications

Katambire

Musabe²,

Uwitonze

et al. 2023

Sensors

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The application of the Internet of Things (IoT), vehicles to infrastructure (V2I) communication and intelligent roadside units (RSU) are promising paradigms to improve road traffic safety. However, for the RSUs to communicate with the vehicles and transmit the data to the remote location, RSUs require enough power and good network quality. Recent advances in technology have improved lithium-ion battery capabilities. However, other complementary methodologies including battery management systems (BMS) have to be developed to provide an early warning sign of the battery’s state of health. In this paper, we have evaluated the impact of the received signal strength indication (RSSI) and the current consumption at different transmission frequencies on a static battery-based RSU that depends on the global system for mobile communications (GSM)/general packet radio services (GPRS). Machine learning (ML) models, for instance, Random Forest (RF) and Support Vector Machine (SVM), were employed and tested on the collected data and later compared using the coefficient of determination (R2). The models were used to predict the battery current consumption based on the RSSI of the location where the RSUs were imposed and the frequency at which the RSU transmits the data to the remote database. The RF was preferable to SVM for predicting current consumption with an R2 of 98% and 94%, respectively. It is essential to accurately forecast the battery health of RSUs to assess their dependability and running time. The primary duty of the BMS is to estimate the status of the battery and its dynamic operating limits. However, achieving an accurate and robust battery state of charge remains a significant challenge. Referring to that can help road managers make alternative decisions, such as replacing the battery before the RSU power source gets drained. The proposed method can be deployed in other remote WSN and IoT-based applications.

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Section: Materials and Methodsmentioning

confidence: 99%

Battery-Powered RSU Running Time Monitoring and Prediction Using ML Model Based on Received Signal Strength and Data Transmission Frequency in V2I Applications

Katambire

Musabe²,

Uwitonze

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…The design of the system is based on specific requirements, such as the need to operate on various types of roads and with a low recording frequency. In [41], the authors proposed a real-time traffic scheduling that builds on the traffic flow information and actuates the traffic signal based on the real-time flow data. Therefore, building a system that builds on the historical traffic and trained model along with the traffic flow prediction model of each embedded monitoring device in the cloud platform and at the junction level could allow real-time signal length estimation based on existing traffic knowledge and real-time data.…”

Section: Design Of Adaptive Traffic Flow Prediction Embedded Systemmentioning

confidence: 99%

Forecasting the Traffic Flow by Using ARIMA and LSTM Models: Case of Muhima Junction

Katambire,

Musabe,

Uwitonze

et al. 2023

Forecasting

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Traffic operation efficiency is greatly impacted by the increase in travel demand and the increase in vehicle ownership. The continued increase in traffic demand has rendered the importance of controlling traffic, especially at intersections. In general, the inefficiency of traffic scheduling leads to traffic congestion, resulting in a rise in fuel consumption, exhaust emissions, and poor quality of service. Various methods for time series forecasting have been proposed for adaptive and remote traffic control. The prediction of traffic has attracted profound attention for improving the reliability and efficiency of traffic flow scheduling while reducing congestion. Therefore, in this work, we studied the problem of the current traffic situation at Muhima Junction one of the busiest junctions in Kigali city. Future traffic rates were forecasted by employing long short-term memory (LSTM) and autoregressive integrated moving average (ARIMA) models, respectively. Both the models’ performance criteria for adequacy were the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE). The results revealed that LSTM is the best-fitting model for monthly traffic flow prediction. Within this analysis, we proposed an adaptive traffic flow prediction that builds on the features of vehicle-to-infrastructure communication and the Internet of Things (IoT) to control traffic while enhancing the quality of service at the junctions. The real-time actuation of traffic-responsive signal control can be assured when real-time traffic-based signal actuation is reliable.

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Towards the adoption of V2I to Enhance ITS in fast Developing Cities: Case of Kigali City

Katambire,

Musabe,

Uwitonze

et al. 2023

2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)

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Real-time traffic flow-based traffic signal scheduling: a queuing theory approach

Cited by 5 publications

References 0 publications

Battery-Powered RSU Running Time Monitoring and Prediction Using ML Model Based on Received Signal Strength and Data Transmission Frequency in V2I Applications

Battery-Powered RSU Running Time Monitoring and Prediction Using ML Model Based on Received Signal Strength and Data Transmission Frequency in V2I Applications

Forecasting the Traffic Flow by Using ARIMA and LSTM Models: Case of Muhima Junction

Towards the adoption of V2I to Enhance ITS in fast Developing Cities: Case of Kigali City

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