Connected and Automated Vehicle Enabled Traffic Intersection Control with Reinforcement Learning

Recent advancements in vehicle automation have led to a proliferation of studies in traffic control strategies for the next generation of land vehicles. Current traffic signal based intersection control methods have significant limitations on dealing with rapidly evolving mobility, connectivity and social challenges. Figures for Europe over the period 2007-16 show that 20% of road accidents that have fatalities occur at intersections. Connected and Automated Mobility (CAM) presents a new paradigm for the integration of radically different traffic control methods into cities and towns for increased travel time efficiency and safety. Vehicle-to-Everything (V2X) connectivity between Intelligent Transportation System (ITS) users will make a significant contribution to transforming the current signalised traffic control systems into a more cooperative and reactive control system. This research work proposes a disruptive unsignalised traffic control method using a Reinforcement Learning (RL) algorithm to determine vehicle priorities at intersections and to schedule their crossing with the objectives of reducing congestion and increasing safety. Unlike heuristic rule-based methods, RL agents can learn the complex non-linear relationship between the elements that play a key role in traffic flow, from which an optimal control policy can be obtained. This work also focuses on the data requirements that inform Vehicle-to-Infrastructure (V2I) communication needs of such a system. The proposed traffic control method has been validated on a state-of-the-art simulation tool and a comparison of results with a traditional signalised control method indicated an up to 84% and 41% improvement in terms of reducing vehicle delay times and reducing fuel consumption respectively. In addition to computer simulations, practical experiments have also been conducted on a scaled road network with a single intersection and multiple scaled Connected and Automated Vehicles (CAV) to further validate the proposed control system in a representative but cost-effective setup. A strong correlation has been found between the computer simulation and practical experiment results. The outcome of this research work provides important insights into enabling cooperation between vehicles and traffic infrastructure via V2I communications, and integration of RL algorithms into a safety-critical control system

Fuzzy logic traffic signal controller enhancement based on aggressive driver behavior classification

The rise in population worldwide and especially in Egypt, together with the increase in the number of vehicles present serious complications regarding traffic congestion and road safety. The elementary solution towards improving congestion is to expand road capacities by building new lanes. This, however, requires time and effort and therefore new methodologies are being implemented. Intelligent transportation systems (ITS) try to approach traffic congestion through the application of computational and engineering techniques. Traffic signal control is a branch of intelligent transportation systems which focuses on improving traffic signal conditions. A traffic signal controllers’ main objective is to improve this assignment in a way which reduces delays. This research proposes a new approach to enhancing traffic signal control and reducing delays of a single intersection, through the integration of an aggressive driving behavior classifier. Previous approaches dealt with traffic control and driver behavior separately, and therefore their successful integration is a new challenging area in the field. Multiple experiment sets were conducted to provide an indication to the effectiveness of our approach. Firstly, an aggressive driver behavior classifier using feed-forward neural network was successfully built utilizing Virginia Tech 100-car naturalistic driving study data. Its performance was compared against long short-term memory recurrent neural networks and support vector machines, and it resulted in better performance as shown by the area under the curve. To the best of our knowledge, this classifier is the first of its kind to be built on this 100-car study data. Secondly, a representation of aggressive driving behavior was constructed in the simulated environment, based on real life data and statistics. Finally, Mamdani’s fuzzy logic controller was modified to accommodate for the integration of the aggressive behavior classifier. The integration results were encouraging and yielded significant improvements at higher traffic flow volumes when compared against the built Mamdani’s controller. The results are promising and provide an initial step towards the integration of driver behavior classification and traffic signal control

Development of an Integrated Incident and Transit Priority Management Control System

The aim of this thesis is to develop a distributed adaptive control system which can work standalone for a single intersection to handle various boundary conditions of recurrent, non-recurrent congestion, transit signal priority and downstream blockage to improve the overall network in terms of productivity and efficiency. The control system uses link detectors’ data to determine the boundary conditions of all incoming and exit links. Four processes or modules are deployed. The traffic regime state module estimates the congestion status of the link. The incident status module determines the likelihood of an incident on the link. The transit priority module estimates if the link is flagged for transit priority based on the transit vehicle location and type. Finally, the downstream blockage module scans all downstream links and determines their recurrent blockage conditions. Three different urban incident detection models (General Regression Model, Neuro-Fuzzy Model and Binary Logit Model) were developed in order to be adopted for the incident status module. Among these, the Binary Logit Model was selected and integrated with the signal control logic. The developed Binary Logit Model is relatively stable and performs effectively under various traffic conditions, as compared to other algorithms reported in the literature. The developed signal control logic has been interfaced with CORSIM micro-simulation for rigorous evaluations with different types of signal phase settings. The proposed system operates in a manner similar to a typical pre-timed signal (with split or protected phase settings) or a fully actuated signal (with splitphase arrangement, protected phase, or dual ring phase settings). The control decisions of this developed control logic produced significant enhancement to productivity (in terms of Person Trips and Vehicle Trips) compared with the existing signal control systems in medium to heavily congested traffic demand conditions for different types of networks. Also, more efficient outcomes (in terms of Average Trip Time/Person and delay in seconds/vehicle) is achieved for relatively low to heavy traffic demand conditions with this control logic (using Split Pre-timed). The newly developed signal control logic yields greater productivity than the existing signal control systems in a typical congested urban network or closely spaced intersections, where traffic demand could be similarly high on both sides at peak periods. It is promising to see how well this signal control logic performs in a network with a high number of junctions. Such performance was rarely reported in the existing literature. The best performing phase settings of the newly developed signal control were thoroughly investigated. The signal control logic has also been extended with the logic of pre-timed styled signal phase settings for the possibility of enhancing productivity in heavily congested scenarios under a closely spaced urban network. The performance of the developed pre-timed signal control signal is quite impressive. The activation of the incident status module under the signal control logic yields an acceptable performance in most of the experimental cases, yet the control logic itself works better without the incident status module with the Split Pre-timed and Dual Actuated phase settings. The Protected Pre-timed phase setting exhibits benefits by activating the incident status module in some medium congested demand

A Hybrid Traffic Responsive Intersection Control Algorithm Using Global Positioning System and Inductive Loop Data

This paper compares the performance of a traffic responsive intersection controller which combines vehicle Global Positioning System (GPS) data and inductive loop information, to fixed-time, inductive loop, and GPS based controllers. The INRIX Global Traffic Scorecard reports that vehicles spent up to 42% of their travel time in congested traffic in 2016. Inefficient signal timing choices by isolated intersection controllers contribute to traffic delays, causing severe negative impacts on the economy and environment. Signal timings can be improved using vehicles’ GPS information combined with vehicle flow information from inductive loops to overcome the control action deficit at isolated intersections. This proposed new signal control algorithm is beneficial for traffic engineers and governmental agencies, as optimised traffic flow can reduce fuel consumption and emissions. The proposed traffic responsive Hybrid Vehicle Actuation (HVA) algorithm uses position and heading data from vehicle status broadcasts, and inferred velocity information to determine vehicle queue lengths and detect vehicles passing through the intersection to actuate intersection signal timings. When vehicle broadcast data are unavailable, HVA uses inductive loop data. Microscopic simulations comparing HVA to fixed-time control, inductive Loop Based Vehicle Actuation (Loop-VA) and GPS Based Vehicle Actuation (GPS-VA) on four urban road networks were carried out to see how the proposed HVA algorithm performs compared to existing control strategies. The results show that HVA is an effective alternative to traditional intersection control strategies, offering delay reductions of up to 32% over Loop-VA, for networks with 0−100% connected vehicle presence

СИСТЕМНЫЙ АНАЛИЗ ОСНОВНЫХ ТЕНДЕНЦИЙ В РАЗВИТИИ АДАПТИВНЫХ МЕТОДОВ УПРАВЛЕНИЯ ТРАНСПОРТНЫМИ ПОТОКАМИ

Adaptive algorithms, which current traffic systems are based on, exist for many decades. Information technologies have developed significantly over this period and it makes more relevant their application in the field of transport. This paper analyses modern trends in the development of adaptive traffic flow control methods. Reviewed the most perspective directions in the field of intelligent transport systems, such as high-speed wireless communication between vehicles and road infrastructure based on such technologies as DSRC and WAVE, traffic jams prediction having such features as traffic flow information, congestion, velocity of vehicles using machine learning, fuzzy logic rules and genetic algorithms, application of driver assistance systems to increase vehicle’s autonomy. Advantages of such technologies in safety, efficiency and usability of transport are shown. Described multi-agent approach, which uses V2I-communication between vehicles and intersection controller to improve efficiency of control due to more complete traffic flow information and possibility to give orders to separate vehicles. Presented number of algorithms which use such approach to create new generation of adaptive transport systems.Алгоритмы адаптивного управления, применяющиеся на сегодняшний день, существуют уже несколько десятилетий. За это время интенсивное развитие получили информационные технологии и сегодня все более актуальным является их применение в сфере транспорта. В статье анализируются современные тенденции развития адаптивных методов управления транспортными потоками. Выполнен обзор наиболее перспективных направлений в сфере интеллектуальных транспортных систем, таких как высокоскоростное беспроводное взаимодействие автомобилей друг с другом и с дорожной инфраструктурой, основанное на использовании технологий DSRC и WAVE, прогнозирование заторов по признакам, включающим информацию о дорожном потоке, степени занятости дороги, скоростях транспортных средств, с помощью методов машинного обучения, правил нечеткой логики и генетических алгоритмов, внедрение систем содействия водителю для повышения автономности транспортных средств. Приведены преимущества, предоставляемые этими технологиями, для повышения безопасности, эффективности и удобства использования транспорта. Описан мультиагентный подход, использующий V2I-взаимодействие между автомобилями и контроллером перекрестка для повышения эффективности управления за счет более полной информации о транспортном потоке и возможности отдавать команды отдельным автомобилям через сообщения. Представлен ряд алгоритмов, использующих этот подход для создания нового поколения адаптивных транспортных систем

СИСТЕМНЫЙ АНАЛИЗ ОСНОВНЫХ ТЕНДЕНЦИЙ В РАЗВИТИИ АДАПТИВНЫХ МЕТОДОВ УПРАВЛЕНИЯ ТРАНСПОРТНЫМИ ПОТОКАМИ

Adaptive algorithms, which current traffic systems are based on, exist for many decades. Information technologies have developed significantly over this period and it makes more relevant their application in the field of transport. This paper analyses modern trends in the development of adaptive traffic flow control methods. Reviewed the most perspective directions in the field of intelligent transport systems, such as high-speed wireless communication between vehicles and road infrastructure based on such technologies as DSRC and WAVE, traffic jams prediction having such features as traffic flow information, congestion, velocity of vehicles using machine learning, fuzzy logic rules and genetic algorithms, application of driver assistance systems to increase vehicle’s autonomy. Advantages of such technologies in safety, efficiency and usability of transport are shown. Described multi-agent approach, which uses V2I-communication between vehicles and intersection controller to improve efficiency of control due to more complete traffic flow information and possibility to give orders to separate vehicles. Presented number of algorithms which use such approach to create new generation of adaptive transport systems.Алгоритмы адаптивного управления, применяющиеся на сегодняшний день, существуют уже несколько десятилетий. За это время интенсивное развитие получили информационные технологии и сегодня все более актуальным является их применение в сфере транспорта. В статье анализируются современные тенденции развития адаптивных методов управления транспортными потоками. Выполнен обзор наиболее перспективных направлений в сфере интеллектуальных транспортных систем, таких как высокоскоростное беспроводное взаимодействие автомобилей друг с другом и с дорожной инфраструктурой, основанное на использовании технологий DSRC и WAVE, прогнозирование заторов по признакам, включающим информацию о дорожном потоке, степени занятости дороги, скоростях транспортных средств, с помощью методов машинного обучения, правил нечеткой логики и генетических алгоритмов, внедрение систем содействия водителю для повышения автономности транспортных средств. Приведены преимущества, предоставляемые этими технологиями, для повышения безопасности, эффективности и удобства использования транспорта. Описан мультиагентный подход, использующий V2I-взаимодействие между автомобилями и контроллером перекрестка для повышения эффективности управления за счет более полной информации о транспортном потоке и возможности отдавать команды отдельным автомобилям через сообщения. Представлен ряд алгоритмов, использующих этот подход для создания нового поколения адаптивных транспортных систем

COLOMBO Deliverable 1.1: Scenario Specifications and Required Modifications to Simulation Tools

While targeting on supporting descriptions of scenarios and extensions to the simulation suite, the document additionally delivers a complete overview of the evaluation procedures to use in COLOMBO. Starting with an overview of the evaluation process, based on work done in the FESTA project, the document includes definitions of the performance indicators to use. These were originally produced by the iTETRIS project (by consortium partners of COLOMBO, mainly) and was extended within COLOMBO by performance indicators that describe the behaviour of inter-vehicle communication. To put the work on a scientific ground, a performed comparison of 40 scientific simulation studies is given, that shows that no standard scenarios and metrics exist. Additionally the document lists feature extensions which shall be implemented into the simulation tools within the COLOMBO project. Applicable software and data yielding to the scenarios were provided to the COLOMBO partners. As targeted, the document lists the scenarios made available within COLOMBO, distinguishing synthetic and real-world scenarios. Overall, seven scenarios based on real-world data were made available. Additionally, a tool that allows generating a large variety of synthetic scenarios is presented. The document ends with an extension (against the one given in D5.1) of requirements put on the simulations suite