People have been working long to tackle the traffic congestion problem. Among the different
measures, traffic tolling has been recognized as an effective way to mitigate citywide …

With the increasing availability of traffic data and advance of deep reinforcement learning
techniques, there is an emerging trend of employing reinforcement learning (RL) for traffic …

Over the past decades, Electronic Toll Collection (ETC) systems have been proved the
capability of alleviating traffic congestion in urban areas. Dynamic Electronic Toll Collection …

Road pricing is an urban traffic management mechanism to reduce traffic congestion.
Currently, most of the road pricing systems based on predefined charging tolls fail to …

Existing inefficient traffic signal plans are causing traffic congestions in many urban areas. In
recent years, many deep reinforcement learning (RL) methods have been proposed to …

Many recent works have turned to multi-agent reinforcement learning (MARL) for adaptive
traffic signal control to optimize the travel time of vehicles over large urban networks …

The problem of traffic congestion incurs numerous social and economical repercussions and
has thus become a central issue in every major city in the world. For this work we look at the …

Deep reinforcement learning (RL) has been applied to traffic signal control recently and
demonstrated superior performance to conventional control methods. However, there are …

Reinforcement learning (RL) has recently become a promising approach in various decision-
making tasks. Among them, traffic signal control is the one where RL makes a great …

The recent advancements in Deep Reinforcement Learning (DRL) have significantly
enhanced the performance of adaptive Traffic Signal Control (TSC). However, DRL policies …