ASSESSMENT OF SURROGATE SAFETY BENEFITS OF AN ADAPTIVE TRAFFIC CONTROL SYSTEM

ABSTRACT When Adaptive Traffic Control Systems are evaluated their safety benefits are rarely investigated. This 'lack of interest' in safety performance may be attributed to two major factors: 1) field assessments of safety improvements are impractical (i.e. time consuming and costly), and 2) methods for obtaining safety metrics from microscopic simulation models are uncommon. While collecting and analyzing field safety metrics (e.g. crash rates) remains operationally and analytically challenging, new tools are becoming available to provide surrogate safety measures based on microscopic simulation program outputs. The framework of this study uses a microsimulation model connected to Sydney Coordinated Adaptive Traffic System (SCATS) to generate vehicular trajectories which are fed into a Surrogate Safety Assessment Model. Realworld data from two Utah state routes are collected to reflect crashes before and after the adaptive system was deployed in 2006. The crash data are analyzed and compared to relevant surrogate safety measures to detect potential correlations between the two data sets. Results show that SCATS generates fewer rear-end and total conflicts than traditional traffic control while traditional control generates fewer crossing and lane-changing conflicts. Field crashes have increased in the last few years likely as a consequence of more numerous road construction activities. Correlation indicators between field crashes and simulated conflicts are not very strong but follow trends reported by other studies

INTELLIGENT INTERSECTION MANAGEMENT THROUGH GRADIENT-BASED MULTI-AGENT COORDINATION OF TRAFFIC LIGHTS AND VEHICLES

This dissertation examines the problem of coordinating two different types of actors in a vehicular traffic network system, namely: the traffic lights and the connected and automated vehicles traversing the traffic network. The work is motivated by an extensive previous literature showing that traffic network synchronization has substantial potential throughput and fuel economy benefits. The literature presents many algorithms for synchronizing the traversal of intersections by connected and automated vehicles (CAVs), as well as the synchronization of traffic lights within a given network. However, the integrated solution of these two synchronization problems remains relatively unexplored. The main challenge of any algorithm proposed in this area consists of managing the trade-off between computational efficiency, communication requirements, and performance. This dissertation seeks to contribute to the list of proposed coordination strategies for CAVs and smart traffic lights by formulating a decentralized framework based on combining ideas from gradient-based multi-agent control, trajectory planning and control barrier functions. The overall proposed control framework consists of describing vehicles and traffic lights by an extra state that directly or indirectly represent its timing (i.e arrival time for the vehicles, and switching time for the traffic lights). This timing variable evolves according to a networked multi-agent system, where the planned timing of neighboring agents governs the evolution of the planned timing of the ego agent. The planned timing state is then translated into a control action for the agents (i.e. acceleration for the vehicles, switching actuation for the traffic lights), through trajectory planning and safety regulation. The proposed coordination framework (i) can coordinate both vehicles and traffic lights, (ii) scales efficiently to large numbers of vehicles and intersections, (iii) is computationally efficient, (iv) can work under different levels of connectivity assumptions and in the presence of human drivers, and (v) can allow for different types of coordination strategies encoded in the underlying ETFs

Deep Reinforcement Learning Models for Real-Time Traffic Signal Optimization with Big Traffic Data

One of the most significant changes that the globe has faced in recent years is the changes brought about by the COVID19 pandemic. While this research was started before the pandemic began, the pandemic has exposed the value that data and information can have in modern society. During the pandemic traffic volumes changed substantially, leaving the inefficiencies of existing methods exposed. This research has focussed on exploring two key ideas that will become increasingly relevant as societies adapt to these changes: Big Data and Artificial Intelligence. For many municipalities, traffic signals are still re-timed using traditional approaches and there is still significant reliance on static timing plans designed with data collected from static field studies. This research explored the possibility of using travel-time data obtained from Bluetooth and WiFi sniffing. Bluetooth and WiFi sniffing is an emerging Big Data approach that takes advantage of the ability to track and monitor unique devices as they move from location to location. An approach to re-time signals using an adaptive system was developed, analysed, and tested under varying conditions. The results of this work showed that this data could be used to improve delays by as much as 10\% when compared to traditional approaches. More importantly, this approach demonstrated that it is possible to re-time signals using a readily available and dynamic data source without the need for field volume studies. In addition to Big Data technologies, Artificial Intelligence (AI) is increasingly playing an important role in modern technologies. AI is already being used to make complex decisions, categorise images, and can best humans in complex strategy games. While AI shows promise, applications to Traffic Engineering have been limtied. This research has advanced the state-of-the art by conducting a systematic sensitivity study on an AI technique, Deep Reinforcement Learning. This thesis investigated and identified optimal settings for key parameters such as the discount factor, learning rate, and reward functions. This thesis also developed and tested a complete framework that could potentially be applied to evaluate AI techniques in field settings. This includes applications of AI techniques such as transfer learning to reduce training times. Finally, this thesis also examined framings for multi-intersection control, including comparisons to existing state-of-the art approaches such as SCOOT

New Signal Priority Strategies to Improve Public Transit Operations

Rapid urbanization is causing severe congestion on road transport networks around the world. Improving service and attracting more travellers could be part of the solution. In urban areas, improving public transportation efficiency and reliability can reduce traffic congestion and improve transportation system performance. By facilitating public buses' movement through traffic signal-controlled intersections, a Transit Signal Priority (TSP) strategy can contribute to the reduction of queuing time at intersections. In the last decade, studies have focused on TSP systems to help public transportation organizations attract more travellers. However, the traditional TSP also has a significant downside; it is detrimental to non-prioritized movements and other transport modes. This research proposes new TSP strategies that account for the number of passengers on board as well as the real-time adherence of buses to their present schedules. Two methods have been proposed. First, buses are prioritized based on their load and their adherence to their schedules, while in the second method, the person delay at an intersection is optimized. The optimization approach in the first method uses a specific priority for public transit, while additional parameters are considered in the second method, like residual queue and arrival rate at the intersection. One of this research's main contributions is providing insight into the benefits of these new TSP methods along a corridor and on an isolated signalized intersection. The proposed methods need real-time information on transit operations, traffic signals status and vehicular flows. The lack of readily available infrastructure to provide all these data is compensated by using a traffic simulator, VISSIM, for an isolated intersection and an arterial corridor. The study area simulation results indicated that the new TSP methods performed better than the conventional TSP. For the investigated study area, it was shown that the second method performed better in an isolated signalized intersection, while the first method reduced traffic and environmental indices when used for an arterial corridor. Future research can investigate the effects of the proposed methodology on the urban network by using macrosimulation to see the effects of the proposed TSP on the network. Also, considering conflicting TSP requests in these methodologies could be another area for further research

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

Topics in perturbation analysis for stochastic hybrid systems

Control and optimization of Stochastic Hybrid Systems (SHS) constitute increasingly active fields of research. However, the size and complexity of SHS frequently render the use of exhaustive verification techniques prohibitive. In this context, Perturbation Analysis techniques, and in particular Infinitesimal Perturbation Analysis (IPA), have proven to be particularly useful for this class of systems. This work focuses on applying IPA to two different problems: Traffic Light Control (TLC) and control of cancer progression, both of which are viewed as dynamic optimization problems in an SHS environment. The first part of this thesis addresses the TLC problem for a single intersection modeled as a SHS. A quasi-dynamic control policy is proposed based on partial state information defined by detecting whether vehicle backlogs are above or below certain controllable threshold values. At first, the threshold parameters are controlled while assuming fixed cycle lengths and online gradient estimates of a cost metric with respect to these controllable parameters are derived using IPA techniques. These estimators are subsequently used to iteratively adjust the threshold values so as to improve overall system performance. This quasi-dynamic analysis of the TLC\ problem is subsequently extended to parameterize the control policy by green and red cycle lengths as well as queue content thresholds. IPA estimators necessary to simultaneously control the light cycles and thresholds are rederived and thereafter incorporated into a standard gradient based scheme in order to further ameliorate system performance. In the second part of this thesis, the problem of controlling cancer progression is formulated within a Stochastic Hybrid Automaton (SHA) framework. Leveraging the fact that cell-biologic changes necessary for cancer development may be schematized as a series of discrete steps, an integrative closed-loop framework is proposed for describing the progressive development of cancer and determining optimal personalized therapies. First, the problem of cancer heterogeneity is addressed through a novel Mixed Integer Linear Programming (MILP) formulation that integrates somatic mutation and gene expression data to infer the temporal sequence of events from cross-sectional data. This formulation is tested using both simulated data and real breast cancer data with matched somatic mutation and gene expression measurements from The Cancer Genome Atlas (TCGA). Second, the use of basic IPA techniques for optimal personalized cancer therapy design is introduced and a methodology applicable to stochastic models of cancer progression is developed. A case study of optimal therapy design for advanced prostate cancer is performed. Given the importance of accurate modeling in conjunction with optimal therapy design, an ensuing analysis is performed in which sensitivity estimates with respect to several model parameters are evaluated and critical parameters are identified. Finally, the tradeoff between system optimality and robustness (or, equivalently, fragility) is explored so as to generate valuable insights on modeling and control of cancer progression

Work Practice Simulation of Complex Human-Automation Systems in Safety Critical Situations: The Brahms Generalized berlingen Model

The transition from the current air traffic system to the next generation air traffic system will require the introduction of new automated systems, including transferring some functions from air traffic controllers to on-board automation. This report describes a new design verification and validation (V&V) methodology for assessing aviation safety. The approach involves a detailed computer simulation of work practices that includes people interacting with flight-critical systems. The research is part of an effort to develop new modeling and verification methodologies that can assess the safety of flight-critical systems, system configurations, and operational concepts. The 2002 Ueberlingen mid-air collision was chosen for analysis and modeling because one of the main causes of the accident was one crew's response to a conflict between the instructions of the air traffic controller and the instructions of TCAS, an automated Traffic Alert and Collision Avoidance System on-board warning system. It thus furnishes an example of the problem of authority versus autonomy. It provides a starting point for exploring authority/autonomy conflict in the larger system of organization, tools, and practices in which the participants' moment-by-moment actions take place. We have developed a general air traffic system model (not a specific simulation of berlingen events), called the Brahms Generalized Ueberlingen Model (Brahms-GUeM). Brahms is a multi-agent simulation system that models people, tools, facilities/vehicles, and geography to simulate the current air transportation system as a collection of distributed, interactive subsystems (e.g., airports, air-traffic control towers and personnel, aircraft, automated flight systems and air-traffic tools, instruments, crew). Brahms-GUeM can be configured in different ways, called scenarios, such that anomalous events that contributed to the berlingen accident can be modeled as functioning according to requirements or in an anomalous condition, as occurred during the accident. Brahms-GUeM thus implicitly defines a class of scenarios, which include as an instance what occurred at berlingen. Brahms-GUeM is a modeling framework enabling "what if" analysis of alternative work system configurations and thus facilitating design of alternative operations concepts. It enables subsequent adaption (reusing simulation components) for modeling and simulating NextGen scenarios. This project demonstrates that BRAHMS provides the capacity to model the complexity of air transportation systems, going beyond idealized and simple flights to include for example the interaction of pilots and ATCOs. The research shows clearly that verification and validation must include the entire work system, on the one hand to check that mechanisms exist to handle failures of communication and alerting subsystems and/or failures of people to notice, comprehend, or communicate problematic (unsafe) situations; but also to understand how people must use their own judgment in relating fallible systems like TCAS to other sources of information and thus to evaluate how the unreliability of automation affects system safety. The simulation shows in particular that distributed agents (people and automated systems) acting without knowledge of each others' actions can create a complex, dynamic system whose interactive behavior is unexpected and is changing too quickly to comprehend and control

New applications of data science for intelligent transportation systems

Streets and motorways are the basic blocks in the core of our transportation networks. In recent years, increases in available sensory and computing power have allowed us to start massive gatherings of data related to their use and performance, and to obtain insightful information via data science. This, in turn, has increased our ability to create systems that estimate the state of the transportation networks and provide us with control capabilities over it, giving rise to the concept of Intelligent Transportation Systems. These systems aim to provide deeper levels of observability to our transportation networks so that their capacity can be increased without the need of further heavy investment to develop traffic infrastructure, especially in terms of laying new roads and streets. In this thesis we aim to contribute to this process in both urban and interurban settings. Here we propose two different algorithms to estimate and forecast expected travel time in motorways over the long term, ranging from hours to a week. The first of them is centred around the identification of the different traffic regimes and leveraging their specific characteristics to improve estimation and forecasting. The second of them looks further into the differentiation between recurrent and non recurrent congestion from the point of view of statistical analysis in the frequency space, using the natural frequencies of the traffic system to tell them apart and exert prediction. We also delve into how Intelligent Transportation Systems can affect our cities, looking at how reinforcement learning can create independent agents capable of controlling traffic lights at intersections. We do this by first looking at the most effective agent architectures in different junctions of increasing complexity. Then we dive into the difference in performance for agents in charge of vehicular intersections, provided by an array of reward functions that use different measures obtained from the traffic flow. Finally, we expand these systems to also take pedestrians into account, investigating the rewards that produce the lowest waiting times when serving different modes of transportation with opposing needs