Structure-free model-based predictive signal control: A sensitivity analysis on a corridor with spillback

• Published in: Transportation research. Part C, Emerging technologies Emerging technologies, 2023-08, Vol.153, p.104174, Article 104174
• Main Authors: Poelman, M.C., Hegyi, A., Verbraeck, A., van Lint, J.W.C.
• Format: Article
• Language: English
• Subjects: Model predictive control; Prediction error; Sensitivity analysis; Traffic signal control
• ISSN: 0968-090X; 1879-2359
• DOI: 10.1016/j.trc.2023.104174

•An extensive sensitivity analysis shows the influence of prediction errors on the system performance of structure-free model-based predictive signal control in an urban corridor with spillback.•The sensitivity analysis provides new insights in the impact of model aggregation and model biases that are valuable for the development of future model-based predictive control systems.•Even under errors, longer prediction horizons lead to better performance, up to a certain optimal prediction horizon length.•More accurate and vehicle-based predictions will lead to a performance gain in future control applications.•Identifying the bias direction with the least performance loss indicates guidelines towards more robust control applications. Model-based predictive signal control is a popular method to pro-actively control traffic and to reduce the effects of congestion in urban networks. In combination with structure-free controllers, which adapt signal settings in arbitrary order and combination (no imposed cycles), these predictive control methods have a high potential to increase system performance by adapting to individual vehicle patterns, which are increasingly available due to new technology. However, most of these control methods assume perfect predictions, while in practice there are prediction errors due to various reasons. In this paper, the sensitivity of the system performance to these prediction errors is analyzed, for an urban corridor with spillback. In a microscopic simulator, first the ideal world is created for the structure-free model-based predictive signal controller, in which perfect predictions are made and the controller can reach its optimal performance. Then prediction errors are introduced in this perfect world, distinguished in aggregation errors that arise using a macroscopic prediction model and biases that represent structural errors in the prediction model or in its demand and state input. The effects of these prediction errors on the system performance are analyzed, as a function of the prediction horizon and update frequency of the control system. The results show that, even under errors, longer prediction horizons lead to better performance, up to a certain optimal prediction horizon length. A high update frequency dampens the influence of prediction errors, enabling the structure-free controller to correct mistakes faster. However, there remains a significant performance loss due to aggregation errors and biases in the prediction