Traffic Safety of Disabled Pedestrians at Signalized Crosswalks

Mobility of disabled people has as a basic guarantee the traffic safety. Walking for disabled may have improved considerably compared to the past, but there are stillissues such as the safe crossing at signalized crosswalks. Also, the availability of studies on safe crossing for disabled pedestrians is limited. While it has been understood how...

A traffic responsive control framework for signalized junctions based on hybrid traffic flow representation

The paper proposes a traffic responsive control framework based on a Model Predictive Control (MPC) approach. The framework focuses on a centralized method, which can simultaneously compute the network decision variables (i.e., the green timings at each junction and the offset of the traffic light plans of the network). Furthermore, the...

Induced seismicity red-light thresholds for enhanced geothermal prospects in the Netherlands

Prospects for geothermal energy in the Netherlands have renewed concerns around induced earthquakes. Risks from induced earthquakes are managed by traffic light protocols (TLPs), where the red-light is chosen as the stop-point before exceeding a tolerance to risk. Here, we simulate post-shut-in earthquake scenarios based on realistic...

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...

Optimal routing of individual vehicles in stochastic urban networks: A method to find optimal routes for generic cost functions, including reliability and travel time

Current day routing systems use predicted travel times to calculate optimal routes. Some routing algorithms even use predicted traffic lights states to optimize these routes and to improve the expected travel time. But all these methods optimize the expected travel time and do not take reliability into account. Reliability is a major service...

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...

Validating SuperHuman Automated Driving Performance

Closed-loop validation of autonomous vehicles is an open problem, significantly influencing development and adoption of this technology. The main contribution of this paper is a novel approach to reproducible, scenario-based validation that decouples the problem into several sub-problems, while avoiding to brake the crucial couplings. First,...

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...

Including traffic light recognition in general object detection with YOLOv2

With an in vehicle camera many different things can be done that are essential for ADAS or autonomous driving mode in a vehicle. First, it can be used for detection of general objects, for example cars, cyclists or pedestrians. Secondly, the camera can be used for traffic light recognition, which is localization of traffic light position and...

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...

Search-based optimal motion planning for automated driving

This paper presents a framework for fast and robust motion planning designed to facilitate automated driving. The framework allows for real-time computation even for horizons of several hundred meters and thus enabling automated driving in urban conditions. This is achieved through several features. Firstly, a convenient geometrical...

Car-Following Model using Machine Learning Techniques: Approach at Urban Signalized Intersections with Traffic Radar Detection

This master thesis aims to gain new empirical insights into longitudinal driving behavior by means of the enumeration of a new hybrid car-following (CF) model which combines parametric and non parametric formulation. On one hand, the model, which predicts the drivers acceleration given a set of variables, benefits from innovative machine...