A traffic responsive control framework for signalized junctions based on hybrid traffic flow representation

Abstract

The paper proposes a traffic responsive control framework based on a Model Predictive Control (MPC) approach. The framework focuses on a centralized method, which can simultaneously compute the network decision variables (i.e., the green timings at each junction and the offset of the traffic light plans of the network). Furthermore, the framework is based on a hybrid traffic flow model operating as a prediction model and plant model in the control procedure. The hybrid traffic flow model combines two sub-models: an aggregate model (i.e., the Cell Transmission Model; CTM) and a disaggregate model (i.e., the Cellular Automata model; CA), using a transition cell to connect them. The whole framework is tested on a signalized arterial, performing several analyses to calibrate the MPC strategy and evaluate the traffic control approach using fixed and adaptive control strategies. All analyses are made in terms of total time spent, network total delay, queue lengths and degree of saturation.

Keywords:

• Interacting junctions
• model predictive control
• network
• traffic flow model
• traffic lights

Acknowledgements

The authors also wish to thank the anonymous reviewers for their helpful comments.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 That is, the traffic state that the driver will experience (Bifulco et al., 2009; Cascetta, 2006; de Luca & Di Pace, 2015).

2 For sake of brevity only the results for J1 and J2 are shown.

3 The fixed time strategy is based on a synchronisation method (Cantarella et al., 2015) aiming to minimise network total delay.

4 To carry out the application, a simplified procedure was considered. Indeed, optimisation is not the focus of the paper, and the application aims to highlight model suitability in the presence of connected and automated vehicles and human-driven vehicles. Therefore, it was not necessary, regarding the context, to investigate other more sophisticated approaches.

Additional information

Funding

This research was partially supported by the University of Salerno, under the PhD programme on transportation (PhD School in Environmental Engineering), local grant no. ORSA180377-2018, local grant no. ORSA191831-2019.