Equity in traffic light control: Identifying- and measuring equity traffic light control. A case study to improve equity at the intersection A050 in Deventer for intelligent Traffic Light Controller: Flowtack

A new generation of intelligent Traffic Light Controllers uses Model Predictive Control to minimise delay at signalised intersections. The conventional cycle of set sequences of green phases is dropped for optimisation purposes. This research uses the ethical theories utilitarianism, egalitarianism, sufficientarianism & deontology to define...

Evaluation of lane change predictions for schedule-driven intersection control

Optimization of traffic signal control has been widely investigated by means of model-based strategies. In 2012 a new model-based controller was published, named Schedule-driven Intersection Control (SCHIC). This controller uses a job-scheduling algorithm to minimize the cumulative delay for all observed vehicles. The algorithms of SCHIC are at...

Comparing Exploration Approaches in Deep Reinforcement Learning for Traffic Light Control

Identifying the most efficient exploration approach for deep reinforcement learning in traffic light control is not a trivial task, and can be a critical step in the development of reinforcement learning solutions that can effectively reduce traffic congestion. It is common to use baseline dithering methods such as $\epsilon$-greedy. However,...

Enabling GLOSA for on-street operating traffic light controllers

The bottleneck of the maximum road volume in urban areas is the maximum capacity of the traffic flow on the intersection, which is coordinated with Traffic Light Controllers (TLCs). A promising method to decrease the number of stops are Green Light Optimal Speed Advice (GLOSA) systems. These systems will give a speed advice to arriving vehicles...

Traffic optimization using a novel traffic controller featuring distributed agents and forecasting

Using some sort of adaptive traffic light control system is becoming standard policy among metropolitan areas. However, controlling traffic lights efficiently on a city-wide scale is computationally intensive and theoretically complex. This paper aims to show a proof of concept of an efficient and modular traffic. light controller with...

Optimal traffic light control: performance evaluation applying a general evaluation methodology

The ongoing increase in urbanization and traffic congestion creates an urgent need to operate our transportation systems with maximum efficiency. Traffic signal control optimization is considered one of the main ways to solve traffic problems in urban networks. In publications in the field of intelligent transportation systems, a vast amount of...

DIRECTOR: Enabling advanced driver assistance systems with predictive signalized intersection control using LSTM networks: An AI approach to signalized intersection control

Traffic congestion at signalized intersections is a big economical and ecological problem. Handcrafted traffic light controllers (TLCs) are currently used to minimize the impact, but they are expensive to design and maintain and their performance degrades over time. Predictive TLCs and advanced driver assistance systems (ADAS) form a potential...