Using Stochastic Variation of Cyclic Green Distributions to Populate SAE J2735 Message Confidence Values along a Signalized Corridor

Mathew, Jijo K.; Li, Howell; Bullock, Darcy M.

doi:10.1177/0361198120929337

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…This development supports the assumption that there can be an average duration for the red and green phases. In article [12], the authors developed a statistical methodology to populate the SPaT messages as per SAE J2735. A four-mile corridor was studied to incorporate the uncertainties due to pedestrian actuation and different timing plans.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Real-Time Suitable Predictive Control Using SPaT Information from Automated Traffic Lights

Bhat

Chen

2023

Future Transportation

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Traffic intersections throughout the United States combine fixed, semi-actuated, and fully actuated intersections. In the case of the semi-actuated and actuated intersections, uncertainties are considered in phase duration. These uncertainties are due to car waiting queues and pedestrian crossing. Intelligent transportation systems deployed in traffic infrastructure can communicate Signal and Phase Timing messages (SPaT) to vehicles approaching intersections. In the connected and automated vehicle ecosystem, the fuel savings potential has been explored. Prior studies have predominantly focused on fixed time control for the driver. However, in the case of actuated signals, there is a different and significant challenge due to the randomness caused by uncertainties. We have developed a predictive control using the SPaT information communicated from the actuated traffic intersections. The developed MPC-based algorithm was validated using model-based design platforms such as AMBER®, Autonomie®, MATLAB®, and SIMULINK®. It was observed that the proposed algorithm can save energy in a single phase, in multiple phase scenarios, and in compelled stopping at stop signs when employed considering communications.

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Section: Introduction and Literature Reviewmentioning

confidence: 99%

Real-Time Suitable Predictive Control Using SPaT Information from Automated Traffic Lights

Bhat

Chen

2023

Future Transportation

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Predicting the Future Signalization of Traffic-Actuated Signals Using Extreme Gradient Boosting

Heckmann,

Budde,

Schneegans

et al. 2024

Transportation Research Record: Journal of the Transportation R

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Stops, braking, and acceleration maneuvers at traffic signals cause high fuel consumption and emission levels. Green light optimized speed advisory (GLOSA) can help to reduce a proportion of such unnecessary vehicle maneuvers. For this, GLOSA systems require reliable switching time estimations as input. However, estimating the switching times of traffic-actuated signals can be challenging. The signalization of traffic-actuated lights is adjusted to match the current traffic. Based on green-time requests, the status of signals can change with almost no lead time. This paper presents an approach for predicting the signalization of traffic-actuated signals. We exploit the limited nature of the space for switched signal state combinations at intersections, and express combinations of motorized traffic-related signal states as one feature to depict the motorized traffic signalization state (MTSSt) at an intersection. Predicting MTSSt-switches allows later determination of the switching times of individual signals. To conduct the predictions, the machine learning method “extreme gradient boosting” was used. A three-step methodology—data preparation, tuning the prediction procedure, and testing the approach—was applied and evaluated on the historical data of four traffic-actuated signalized intersections. The results showed that within a period of the next 30 s, signal changes were predicted with an overall precision and sensitivity of about 95% and an average mean absolute error of less than 1.1 s. In this period, the sequence of switched signals was predicted accurately to the second, without any deviation, in, on average, over 82% of cases.

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Using Stochastic Variation of Cyclic Green Distributions to Populate SAE J2735 Message Confidence Values along a Signalized Corridor

Cited by 2 publications

References 14 publications

Real-Time Suitable Predictive Control Using SPaT Information from Automated Traffic Lights

Real-Time Suitable Predictive Control Using SPaT Information from Automated Traffic Lights

Predicting the Future Signalization of Traffic-Actuated Signals Using Extreme Gradient Boosting

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