  Single occupancy vehicles are the most attractive transportation alternative
for many commuters, leading to increased traffic congestion and air pollution.
Advancements in information technologies create opportunities for smart
solutions that incentivize ridesharing and mode shift to higher occupancy
vehicles (HOVs) to achieve the car lighter vision of cities. In this study, we
present HumanLight, a novel decentralized adaptive traffic signal control
algorithm designed to optimize people throughput at intersections. Our proposed
controller is founded on reinforcement learning with the reward function
embedding the transportation-inspired concept of pressure at the person-level.
By rewarding HOV commuters with travel time savings for their efforts to merge
into a single ride, HumanLight achieves equitable allocation of green times.
Apart from adopting FRAP, a state-of-the-art (SOTA) base model, HumanLight
introduces the concept of active vehicles, loosely defined as vehicles in
proximity to the intersection within the action interval window. The proposed
algorithm showcases significant headroom and scalability in different network
configurations considering multimodal vehicle splits at various scenarios of
HOV adoption. Improvements in person delays and queues range from 15% to over
55% compared to vehicle-level SOTA controllers. We quantify the impact of
incorporating active vehicles in the formulation of our RL model for different
network structures. HumanLight also enables regulation of the aggressiveness of
the HOV prioritization. The impact of parameter setting on the generated phase
profile is investigated as a key component of acyclic signal controllers
affecting pedestrian waiting times. HumanLight's scalable, decentralized design
can reshape the resolution of traffic management to be more human-centric and
empower policies that incentivize ridesharing and public transit systems.
