Signal control for overflow prevention at intersections using partial connected vehicle data

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Various signal control methods assumed that the bottleneck capacity and traffic volume are known. However, both may be unknown in nonrecurrent congestion cases, such as traffic accidents. In this study, a signal control optimisation method is developed for overflow prevention considering unknown traffic volume and bottleneck dropped capacity. The proposed method predicts remaining space at the junction exits and arrival–departure curves using partial connected vehicle data. Subsequently, the signal control is reallocated using the model predictive control technique. The results of case study show that the model is valid to prevent overflow when the connected vehicle penetration rate exceeds 10%. The average delay of the proposed method is reduced by 48.56% and 24.49% compared with those of the adaptive signal control method considering the queue length of only the approach lanes and that considering the queue lengths of both the approach and exit lanes but without prediction, respectively.