Traffic management system for smart road networks reserved for self-driving cars

A model of a smart road network consisting of unsignalised intersections and smart roads connecting them is considered in this work with the aim of presenting a traffic management system for self-driving cars (or, more generally, autonomous vehicles) which travel the network. The proposed system repeatedly solves a set of mathematical programming problems (each of them relative to a single intersection or to a single road stretch of the network) within a decentralised control scheme in which each local intersection controller and each local road controller communicates with the fully autonomous vehicles in order to receive travel data from vehicles and to provide speed profiles to them once determined the optimal solution of the problem. In order to reduce the computational effort required to provide the optimal solution, a discrete-time approach is adopted so that, in each time interval, a limited number of vehicles are taken into consideration; in this way, solutions can be determined in a very short time thus making the proposed model compatible with a practical application to real traffic systems. The proposed model is general enough, and can be adapted to different scenarios of smart road networks reserved for self-driving cars

Safe and Efficient Intelligent Intersection Control of Autonomous Vehicles

In this dissertation, we address a problem of safe and efficient intersection crossing traffic management of autonomous and connected ground traffic. Toward this objective, we propose several algorithms to handle different traffic environments. First, an algorithm that is called the Discrete-time occupancies trajectory (DTOT) based Intersection traffic Coordination Algorithm (DICA) is proposed. All vehicles in the system are Connected and Autonomous Vehicles (CAVs) and capable of wireless Vehicle-to-Intersection communication. The main advantage of DICA is that it enables us to utilize the intersection space more efficiently resulting in less delay for vehicles to cross the intersection. In the proposed framework, an intersection coordinates the motions of CAVs based on their proposed DTOTs to let them cross the intersection efficiently while avoiding collisions. In case when there is a potential collision between vehicles\u27 DTOTs, the intersection modifies conflicting DTOTs to avoid the collision and requests CAVs to approach and cross the intersection according to the modified DTOTs. We also prove that the basic DICA is deadlock free and starvation free. We show that the basic DICA has a computational complexity of O(n2 L3m) where n is the number of vehicles granted to cross an intersection and Lm is the maximum length of intersection crossing routes. To improve the overall computational efficiency of the algorithm, the basic DICA is enhanced by several computational techniques. The enhanced algorithm has a reduced computational complexity of O(n2 Lm log2 Lm). The problem of evacuating emergency vehicles as quickly as possible through autonomous and connected intersection traffic is also addressed in this dissertation. The proposed Reactive DICA aims to determine an efficient vehicle-passing sequence which allows the emergency vehicle to cross an intersection as soon as possible while the travel times of other normal vehicles are minimally affected. When there are no emergency vehicles within the intersection area, the vehicles are controlled by DICA. When there are emergency vehicles entering communication range, we prioritize emergency vehicles through the optimal ordering of vehicles. Since the number of possible vehicle-passing sequences increases rapidly with the number of vehicles, finding an efficient sequence of vehicles in a short time is the main challenge of the study. A genetic algorithm is proposed to solve the optimization problem which finds the optimal vehicle sequence in real time that gives the emergency vehicles the highest priority. We then address an optimization problem of autonomous intersection control which provides the optimal trajectory for every entering vehicle. Based on the algorithm DICA, we improve the conservative way of trajectory generation which is the key part of DICA to be an optimization approach using mixed integer programming. The new algorithm is named Mixed integer programming based Intersection Coordination Algorithm (MICA) with the objective of maximizing the final position of a new head vehicle over a fixed time interval. Constraints from space conflicting vehicles are modeled using binary variables to represent the vehicle\u27s future crossing behavior. The influence of immediate front vehicles of the vehicle of interest is also modeled as constraints in the problem formulation to obtain a feasible optimal trajectory while potential collisions are safely avoided. Finally, based on MICA, we propose a novel vehicle-intersection interaction mechanism MICACO which is designed to handle imperfect communication, i.e., message delay and loss. To ensure the successful delivery of messages, we add two more message types and corresponding simple rules. State machines of intersection and vehicles are designed properly to ensure the safety of every vehicle. We verify the efficiency of the proposed algorithms through simulations using SUMO. The simulation results show that DICA performs better than another existing intersection management scheme: Concurrent Algorithm in [1]. The overall throughput, as well as the computational efficiency of the computationally enhanced DICA, are also compared with those of an optimized traffic light control. The efficiency of the proposed Reactive DICA is validated through comparisons with DICA and a reactive traffic light algorithm. The results show that Reactive DICA is able to decrease the travel times of emergency vehicles significantly in light and medium traffic volumes without causing any noticeable performance degradation of normal vehicles. The simulation results show that MICA is able to reduce congestions of an intersection significantly compared with DICA. We also show MICACO\u27s performance through comparisons with MICA and an optimized traffic light

Traffic Management System for the combined optimal routing, scheduling and motion planning of self-driving vehicles inside reserved smart road networks

The topic discussed in this thesis belongs to the field of automation of transport systems, which has grown in importance in the last decade, both in the innovation field (where different automation technologies have been gradually introduced in different sectors of road transport, in the promising view of making it more efficient, safer, and greener) and in the research field (where different research activities and publications have addressed the problem under different points of view). More in detail, this work addresses the problem of autonomous vehicles coordina tion inside reserved road networks by proposing a novel Traffic Management System (TMS) for the combined routing, scheduling and motion planning of the vehicles. To this aim, the network is assumed to have a modular structure, which results from a certain number of roads and intersections assembled together. The way in which roads and intersections are put together defines the network layout. Within such a system architecture, the main tasks addressed by the TMS are: (1) at the higher level, the optimal routing of the vehicles in the network, exploiting the available information coming from the vehicles and the various elements of the network; (2) at a lower level, the modeling and optimization of the vehicle trajectories and speeds for each road and for each intersection in the network; (3) the coordination between the vehicles and the elements of the network, to ensure a combined approach that considers, in a recursive way, the scheduling and motion planning of the vehicles in the various elements when solving the routing problem. In particular, the routing and the scheduling and motion planning problems are formulated as MILP optimization problems, aiming to maximize the performance of the entire network (routing model) and the performance of its single elements - roads and intersections (scheduling and motion planning model) while guaranteeing the requested level of safety and comfort for the passengers. Besides, one of the main features of the proposed approach consists of the integration of the scheduling decisions and the motion planning computation by means of constraints regarding the speed limit, the acceleration, and the so-called safety dynamic constraints on incompatible positions of conflicting vehicles. In particular, thanks to these last constraints, it is possible to consider the real space occupancy of the vehicles avoiding collisions. After the theoretical discussion of the proposed TMS and of its components and models, the thesis presents and discusses the results of different numerical experiments, aimed at testing the TMS in some specific scenarios. In particular, the routing model and the scheduling and motion planning model are tested on different scenarios, which demonstrate the effectiveness and the validity of such approach in performing the addressed tasks, also compared with more traditional methods. Finally, the computational effort needed for the problem solution, which is a key element to take into account, is discussed both for the road element and the intersection element

A comprehensive survey on cooperative intersection management for heterogeneous connected vehicles

Nowadays, with the advancement of technology, world is trending toward high mobility and dynamics. In this context, intersection management (IM) as one of the most crucial elements of the transportation sector demands high attention. Today, road entities including infrastructures, vulnerable road users (VRUs) such as motorcycles, moped, scooters, pedestrians, bicycles, and other types of vehicles such as trucks, buses, cars, emergency vehicles, and railway vehicles like trains or trams are able to communicate cooperatively using vehicle-to-everything (V2X) communications and provide traffic safety, efficiency, infotainment and ecological improvements. In this paper, we take into account different types of intersections in terms of signalized, semi-autonomous (hybrid) and autonomous intersections and conduct a comprehensive survey on various intersection management methods for heterogeneous connected vehicles (CVs). We consider heterogeneous classes of vehicles such as road and rail vehicles as well as VRUs including bicycles, scooters and motorcycles. All kinds of intersection goals, modeling, coordination architectures, scheduling policies are thoroughly discussed. Signalized and semi-autonomous intersections are assessed with respect to these parameters. We especially focus on autonomous intersection management (AIM) and categorize this section based on four major goals involving safety, efficiency, infotainment and environment. Each intersection goal provides an in-depth investigation on the corresponding literature from the aforementioned perspectives. Moreover, robustness and resiliency of IM are explored from diverse points of view encompassing sensors, information management and sharing, planning universal scheme, heterogeneous collaboration, vehicle classification, quality measurement, external factors, intersection types, localization faults, communication anomalies and channel optimization, synchronization, vehicle dynamics and model mismatch, model uncertainties, recovery, security and privacy

Developing and evaluating a coordinated person-based signal control paradigm in a corridor network

Connected Vehicles (CVs) provide both vehicle trajectory data and occupancy information to the junction controller, which make person-based signal controls to be possible by realizing the importance of reducing person delay. This study presents a coordinated person-based signal control algorithm (C-PBC), which has extended a previously developed approach from isolated junctions to multiple junctions. C-PBC incorporates vehicle information that is outside the CV communication range from the adjacent junction. It also updates data inputs for signal optimization algorithms based on formulated different arrival vehicle trajectory situations and coordinated data supplement algorithms. The developed algorithm has been evaluated using simulation with benchmarking signal control methods under a variety of scenarios involving CV penetration rates and predictive horizons. The results indicate that C-PBC is able to significantly improve person delay reduction when compared with fixed time control and vehicle-based control using CV data in 100% CV penetration rate under saturated flow conditions

Eco-Driving Optimization Based on Variable Grid Dynamic Programming and Vehicle Connectivity in a Real-World Scenario

In a context in which the connectivity level of last-generation vehicles is constantly onthe rise, the combined use of Vehicle-To-Everything (V2X) connectivity and autonomous drivingcan provide remarkable benefits through the synergistic optimization of the route and the speedtrajectory. In this framework, this paper focuses on vehicle ecodriving optimization in a connectedenvironment: the virtual test rig of a premium segment passenger car was used for generatingthe simulation scenarios and to assess the benefits, in terms of energy and time savings, that theintroduction of V2X communication, integrated with cloud computing, can have in a real-worldscenario. The Reference Scenario is a predefined Real Driving Emissions (RDE) compliant route,while the simulation scenarios were generated by assuming two different penetration levels of V2Xtechnologies. The associated energy minimization problem was formulated and solved by means of aVariable Grid Dynamic Programming (VGDP), that modifying the variable state search grid on thebasis of the V2X information allows to drastically reduce the DP computation burden by more than95%. The simulations show that introducing a smart infrastructure along with optimizing the vehiclespeed in a real-world urban route can potentially reduce the required energy by 54% while shorteningthe travel time by 38%. Finally, a sensitivity analysis was performed on the biobjective optimizationcost function to find a set of Pareto optimal solutions, between energy and travel time minimization

Development and evaluation of cooperative intersection management algorithm under connected vehicles environment

Recent technological advancements in the automotive and transportation industry established a firm foundation for development and implementation of various automated and connected vehicle (C/AV) solutions around the globe. Wireless communication technologies such as the dedicated short-range communication (DSRC) protocol are enabling instantaneous information exchange between vehicles and infrastructure. Such information exchange produces tremendous benefits with the possibility to automate conventional traffic streams and enhance existing signal control strategies. While many promising studies in the area of signal control under connected vehicle (CV) environment have been introduced, they mainly offer solutions designed to operate a single isolated intersection or they require high technology penetration rates to operate in a safe and efficient manner. Applications designed to operate on a signalized corridor with imperfect market penetration rates of connected vehicle technology represent a bridge between conventional traffic control paradigm and fully automated corridors of the future. Assuming utilization of the connected vehicle environment and vehicle to infrastructure (V2I) technology, all vehicular and signal-related parameters are known and can be shared with the control agent to control automated vehicles while improving the mobility of the signalized corridor. This dissertation research introduces an intersection management strategy for a corridor with automated vehicles utilizing vehicular trajectory-driven optimization method. The Trajectory-driven Optimization for Automated Driving (TOAD) provides an optimal trajectory for automated vehicles while maintaining safe and uninterrupted movement of general traffic, consisting of regular unequipped vehicles. Signal status parameters such as cycle length and splits are continuously captured. At the same time, vehicles share their position information with the control agent. Both inputs are then used by the control algorithm to provide optimal trajectories for automated vehicles, resulting in the reduction of vehicle delay along the signalized corridor with fixed-time signal control. To determine the most efficient trajectory for automated vehicles, an evolutionary-based optimization is utilized. Influence of the prevailing traffic conditions is incorporated into a control algorithm using conventional data collection methods such as loop detectors, Bluetooth or Wi-Fi sensors to collect vehicle counts, travel time on corridor segments, and spot speed. Moreover, a short-term, artificial intelligence prediction model is developed to achieve reasonable deployment of data collection devices and provide accurate vehicle delay predictions producing realistic and highly-efficient longitudinal vehicle trajectories. The concept evaluation through microsimulation reveals significant mobility improvements compared to contemporary corridor management approach. The results for selected test-bed locations on signalized arterials in New Jersey reveals up to 19.5 % reduction in overall corridor travel time depending on different market penetration and lane configuration scenario. It is also discovered that operational scenarios with a possibility of utilizing reserved lanes for movement of automated vehicles further increases the effectiveness of the proposed algorithm. In addition, the proposed control algorithm is feasible under imperfect C/AV market penetrations showing mobility improvements even with low market penetration rates

Proceedings of the 4th Symposium on Management of Future Motorway and Urban Traffic Systems 2022

The 4th Symposium on Management of Future Motorway and Urban Traffic Systems (MFTS) was held in Dresden, Germany, from November 30th to December 2nd, 2022. Organized by the Chair of Traffic Process Automation (VPA) at the “Friedrich List” Faculty of Transport and Traffic Sciences of the TU Dresden, the proceedings of this conference are published as volume 9 in the Chair’s publication series “Verkehrstelematik” and contain a large part of the presented conference extended abstracts. The focus of the MFTS conference 2022 was cooperative management of multimodal transport and reflected the vision of the professorship to be an internationally recognized group in ITS research and education with the goal of optimizing the operation of multimodal transport systems. In 14 MFTS sessions, current topics in demand and traffic management, traffic control in conventional, connected and automated transport, connected and autonomous vehicles, traffic flow modeling and simulation, new and shared mobility systems, digitization, and user behavior and safety were discussed. In addition, special sessions were organized, for example on “Human aspects in traffic modeling and simulation” and “Lesson learned from Covid19 pandemic”, whose descriptions and analyses are also included in these proceedings.:1 Connected and Automated Vehicles 1.1 Traffic-based Control of Truck Platoons on Freeways 1.2 A Lateral Positioning Strategy for Connected and Automated Vehicles in Lane-free Traffic 1.3 Simulation Methods for Mixed Legacy-Autonomous Mainline Train Operations 1.4 Can Dedicated Lanes for Automated Vehicles on Urban Roads Improve Traffic Efficiency? 1.5 GLOSA System with Uncertain Green and Red Signal Phases 2 New Mobility Systems 2.1 A New Model for Electric Vehicle Mobility and Energy Consumption in Urban Traffic Networks 2.2 Shared Autonomous Vehicles Implementation for a Disrupted Public Transport Network 3 Traffic Flow and Simulation 3.1 Multi-vehicle Stochastic Fundamental Diagram Consistent with Transportations Systems Theory 3.2 A RoundD-like Roundabout Scenario in CARLA Simulator 3.3 Multimodal Performance Evaluation of Urban Traffic Control: A Microscopic Simulation Study 3.4 A MILP Framework to Solve the Sustainable System Optimum with Link MFD Functions 3.5 On How Traffic Signals Impact the Fundamental Diagrams of Urban Roads 4 Traffic Control in Conventional Traffic 4.1 Data-driven Methods for Identifying Travel Conditions Based on Traffic and Weather Characteristics 4.2 AI-based Multi-class Traffic Model Oriented to Freeway Traffic Control 4.3 Exploiting Deep Learning and Traffic Models for Freeway Traffic Estimation 4.4 Automatic Design of Optimal Actuated Traffic Signal Control with Transit Signal Priority 4.5 A Deep Reinforcement Learning Approach for Dynamic Traffic Light Control with Transit Signal Priority 4.6 Towards Efficient Incident Detection in Real-time Traffic Management 4.7 Dynamic Cycle Time in Traffic Signal of Cyclic Max-Pressure Control 5 Traffic Control with Autonomous Vehicles 5.1 Distributed Ordering and Optimization for Intersection Management with Connected and Automated Vehicles 5.2 Prioritization of an Automated Shuttle for V2X Public Transport at a Signalized Intersection – a Real-life Demonstration 6 User Behaviour and Safety 6.1 Local Traffic Safety Analyzer (LTSA) - Improved Road Safety and Optimized Signal Control for Future Urban Intersections 7 Demand and Traffic Management 7.1 A Stochastic Programming Method for OD Estimation Using LBSN Check-in Data 7.2 Delineation of Traffic Analysis Zone for Public Transportation OD Matrix Estimation Based on Socio-spatial Practices 8 Workshops 8.1 How to Integrate Human Aspects Into Engineering Science of Transport and Traffic? - a Workshop Report about Discussions on Social Contextualization of Mobility 8.2 Learning from Covid: How Can we Predict Mobility Behaviour in the Face of Disruptive Events? – How to Investigate the Mobility of the FutureDas 4. Symposium zum Management zukünftiger Autobahn- und Stadtverkehrssysteme (MFTS) fand vom 30. November bis 2. Dezember 2022 in Dresden statt und wurde vom Lehrstuhl für Verkehrsprozessautomatisierung (VPA) an der Fakultät Verkehrswissenschaften„Friedrich List“ der TU Dresden organisiert. Der Tagungsband erscheint als Band 9 in der Schriftenreihe „Verkehrstelematik“ des Lehrstuhls und enthält einen Großteil der vorgestellten Extended-Abstracts des Symposiums. Der Schwerpunkt des MFTS-Symposiums 2022 lag auf dem kooperativen Management multimodalen Verkehrs und spiegelte die Vision der Professur wider, eine international anerkannte Gruppe in der ITS-Forschung und -Ausbildung mit dem Ziel der Optimierung des Betriebs multimodaler Transportsysteme zu sein. In 14 MFTS-Sitzungen wurden aktuelle Themen aus den Bereichen Nachfrage- und Verkehrsmanagement, Verkehrssteuerung im konventionellen, vernetzten und automatisierten Verkehr, vernetzte und autonome Fahrzeuge, Verkehrsflussmodellierung und -simulation, neue und geteilte Mobilitätssysteme, Digitalisierung sowie Nutzerverhalten und Sicherheit diskutiert. Darüber hinaus wurden Sondersitzungen organisiert, beispielsweise zu „Menschlichen Aspekten bei der Verkehrsmodellierung und -simulation“ und „Lektionen aus der Covid-19-Pandemie“, deren Beschreibungen und Analysen ebenfalls in diesen Tagungsband einfließen.:1 Connected and Automated Vehicles 1.1 Traffic-based Control of Truck Platoons on Freeways 1.2 A Lateral Positioning Strategy for Connected and Automated Vehicles in Lane-free Traffic 1.3 Simulation Methods for Mixed Legacy-Autonomous Mainline Train Operations 1.4 Can Dedicated Lanes for Automated Vehicles on Urban Roads Improve Traffic Efficiency? 1.5 GLOSA System with Uncertain Green and Red Signal Phases 2 New Mobility Systems 2.1 A New Model for Electric Vehicle Mobility and Energy Consumption in Urban Traffic Networks 2.2 Shared Autonomous Vehicles Implementation for a Disrupted Public Transport Network 3 Traffic Flow and Simulation 3.1 Multi-vehicle Stochastic Fundamental Diagram Consistent with Transportations Systems Theory 3.2 A RoundD-like Roundabout Scenario in CARLA Simulator 3.3 Multimodal Performance Evaluation of Urban Traffic Control: A Microscopic Simulation Study 3.4 A MILP Framework to Solve the Sustainable System Optimum with Link MFD Functions 3.5 On How Traffic Signals Impact the Fundamental Diagrams of Urban Roads 4 Traffic Control in Conventional Traffic 4.1 Data-driven Methods for Identifying Travel Conditions Based on Traffic and Weather Characteristics 4.2 AI-based Multi-class Traffic Model Oriented to Freeway Traffic Control 4.3 Exploiting Deep Learning and Traffic Models for Freeway Traffic Estimation 4.4 Automatic Design of Optimal Actuated Traffic Signal Control with Transit Signal Priority 4.5 A Deep Reinforcement Learning Approach for Dynamic Traffic Light Control with Transit Signal Priority 4.6 Towards Efficient Incident Detection in Real-time Traffic Management 4.7 Dynamic Cycle Time in Traffic Signal of Cyclic Max-Pressure Control 5 Traffic Control with Autonomous Vehicles 5.1 Distributed Ordering and Optimization for Intersection Management with Connected and Automated Vehicles 5.2 Prioritization of an Automated Shuttle for V2X Public Transport at a Signalized Intersection – a Real-life Demonstration 6 User Behaviour and Safety 6.1 Local Traffic Safety Analyzer (LTSA) - Improved Road Safety and Optimized Signal Control for Future Urban Intersections 7 Demand and Traffic Management 7.1 A Stochastic Programming Method for OD Estimation Using LBSN Check-in Data 7.2 Delineation of Traffic Analysis Zone for Public Transportation OD Matrix Estimation Based on Socio-spatial Practices 8 Workshops 8.1 How to Integrate Human Aspects Into Engineering Science of Transport and Traffic? - a Workshop Report about Discussions on Social Contextualization of Mobility 8.2 Learning from Covid: How Can we Predict Mobility Behaviour in the Face of Disruptive Events? – How to Investigate the Mobility of the Futur