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Deep reinforcement learning (RL) has been widely studied in traffic signal control. Despite the promising results that indicate the superiority of deep RL in terms of the quality of solution and optimality over fixed time signal control, the real-world multi-modal traffic flows, especially pedestrians, are not properly considered nor sufficiently investigated. This study presents a novel deep RL-based adaptive traffic signal model to control the vehicles and pedestrian flows by allocating an equitable green time to each, aiming at minimizing “total user delays” as opposed to “total vehicle delays” dominantly being used in the literature. Our proposed intelligent vehicle pedestrian light (IVPL) method can perform in the absence or presence of pedestrians, especially when there is jaywalking at the intersection, interrupting vehicle flows. To this end, an extended reward function is designed to capture delays due to vehicle-to …