A Robust Integrated Multi-Strategy Bus Control System via Deep Reinforcement Learning

An efficient urban bus control system has the potential to significantly reduce travel delays and streamline the allocation of transportation resources, thereby offering enhanced and user-friendly transit services to passengers. However, bus operation efficiency can be impacted by bus bunching. This problem is notably exacerbated when the bus system operates along a signalized corridor with unpredictable travel demand. To mitigate this challenge, we introduce a multi-strategy fusion approach for the longitudinal control of connected and automated buses. The approach is driven by a physics-informed deep reinforcement learning (DRL) algorithm and takes into account a variety of traffic conditions along urban signalized corridors. Taking advantage of connected and autonomous vehicle (CAV) technology, the proposed approach can leverage real-time information regarding bus operating conditions and road traffic environment. By integrating the aforementioned information into the DRL-based bus control framework, our designed physics-informed DRL state fusion approach and reward function efficiently embed prior physics and leverage the merits of equilibrium and consensus concepts from control theory. This integration enables the framework to learn and adapt multiple control strategies to effectively manage complex traffic conditions and fluctuating passenger demands. Three control variables, i.e., dwell time at stops, speed between stations, and signal priority, are formulated to minimize travel duration and ensure bus stability with the aim of avoiding bus bunching. We present simulation results to validate the effectiveness of the proposed approach, underlining its superior performance when subjected to sensitivity analysis, specifically considering factors such as traffic volume, desired speed, and traffic signal conditions

Microsimulation models incorporating both demand and supply dynamics

There has been rapid growth in interest in real-time transport strategies over the last decade, ranging from automated highway systems and responsive traffic signal control to incident management and driver information systems. The complexity of these strategies, in terms of the spatial and temporal interactions within the transport system, has led to a parallel growth in the application of traffic microsimulation models for the evaluation and design of such measures, as a remedy to the limitations faced by conventional static, macroscopic approaches. However, while this naturally addresses the immediate impacts of the measure, a difficulty that remains is the question of how the secondary impacts, specifically the effect on route and departure time choice of subsequent trips, may be handled in a consistent manner within a microsimulation framework. The paper describes a modelling approach to road network traffic, in which the emphasis is on the integrated microsimulation of individual trip-makers’ decisions and individual vehicle movements across the network. To achieve this it represents directly individual drivers’ choices and experiences as they evolve from day-to-day, combined with a detailed within-day traffic simulation model of the space–time trajectories of individual vehicles according to car-following and lane-changing rules and intersection regulations. It therefore models both day-to-day and within-day variability in both demand and supply conditions, and so, we believe, is particularly suited for the realistic modelling of real-time strategies such as those listed above. The full model specification is given, along with details of its algorithmic implementation. A number of representative numerical applications are presented, including: sensitivity studies of the impact of day-to-day variability; an application to the evaluation of alternative signal control policies; and the evaluation of the introduction of bus-only lanes in a sub-network of Leeds. Our experience demonstrates that this modelling framework is computationally feasible as a method for providing a fully internally consistent, microscopic, dynamic assignment, incorporating both within- and between-day demand and supply dynamic

Cellular Automata Models of Road Traffic

In this paper, we give an elaborate and understandable review of traffic cellular automata (TCA) models, which are a class of computationally efficient microscopic traffic flow models. TCA models arise from the physics discipline of statistical mechanics, having the goal of reproducing the correct macroscopic behaviour based on a minimal description of microscopic interactions. After giving an overview of cellular automata (CA) models, their background and physical setup, we introduce the mathematical notations, show how to perform measurements on a TCA model's lattice of cells, as well as how to convert these quantities into real-world units and vice versa. The majority of this paper then relays an extensive account of the behavioural aspects of several TCA models encountered in literature. Already, several reviews of TCA models exist, but none of them consider all the models exclusively from the behavioural point of view. In this respect, our overview fills this void, as it focusses on the behaviour of the TCA models, by means of time-space and phase-space diagrams, and histograms showing the distributions of vehicles' speeds, space, and time gaps. In the report, we subsequently give a concise overview of TCA models that are employed in a multi-lane setting, and some of the TCA models used to describe city traffic as a two-dimensional grid of cells, or as a road network with explicitly modelled intersections. The final part of the paper illustrates some of the more common analytical approximations to single-cell TCA models.Comment: Accepted for publication in "Physics Reports". A version of this paper with high-quality images can be found at: http://phdsven.dyns.cx (go to "Papers written"

Knowledge Discovery and Data Mining for Shared Mobility and Connected and Automated Vehicle Applications

The rapid development of shared mobility and connected and automated vehicles (CAVs) has not only brought new intelligent transportation system (ITS) challenges with the new types of mobility, but also brought a huge opportunity to accelerate the connectivity and informatization of transportation systems, particularly when we consider all the new forms of data that is becoming available. The primary challenge is how to take advantage of the enormous amount of data to discover knowledge, build effective models, and develop impactful applications. With the theoretical and experimental progress being made over the last two decades, data mining and machine learning technologies have become key approaches for parsing data, understanding information, and making informed decisions, especially as the rise of deep learning algorithms bringing new levels of performance to the analysis of large datasets. The combination of data mining and ITS can greatly benefit research and advances in shared mobility and CAVs.This dissertation focuses on knowledge discovery and data mining for shared mobility and CAV applications. When considering big data associated with shared mobility operations and CAV research, data mining techniques can be customized with transportation knowledge to initially parse the data. Then machine learning methods can be used to model the parsed data to elicit hidden knowledge. Finally, the discovered knowledge and extracted information can help in the development of effective shared mobility and CAV applications to achieve the goals of a safer, faster, and more eco-friendly transportation systems.In this dissertation, there are four main sections that are addressed. First, new methodologies are introduced for extracting lane-level road features from rough crowdsourced GPS trajectories via data mining, which is subsequently used as the fundamental information for CAV applications. The proposed method results in decimeter level accuracy, which satisfies the positioning needs for many macroscopic and microscopic shared mobility and CAV applications. Second, macroscopic ride-hailing service big data has been analyzed for demand prediction, vehicle operation, and system efficiency monitoring. The proposed deep learning algorithms increase the ride-hailing demand prediction accuracy to 80% and can help the fleet dispatching system reduce 30% of vacant travel distance. Third, microscopic automated vehicle perception data has been analyzed for a real-time computer vision system that can be used for lane change behavior detection. The proposed deep learning design combines the residual neural network image input with time serious control data and reaches 95% of lane change behavior prediction accuracy. Last but not least, new ride sharing and CAV applications have been simulated in a behavior modeling framework to analyze the impact of mobility and energy consumption, which addresses key barriers by quantifying the transportation system-wide mobility, energy and behavior impacts from new mobility technologies using real-world data

Cooperative adaptive cruise control : a learning approach

Tableau d’honneur de la Faculté des études supérieures et postdoctorales, 2008-2009L'augmentation dans les dernières décennies du nombre de véhicules présents sur les routes ne s'est pas passée sans son lot d'impacts négatifs sur la société. Même s'ils ont joué un rôle important dans le développement économique des régions urbaines à travers le monde, les véhicules sont aussi responsables d'impacts négatifs sur les entreprises, car l'inefficacité du ot de traffic cause chaque jour d'importantes pertes en productivité. De plus, la sécurité des passagers est toujours problématique car les accidents de voiture sont encore aujourd'hui parmi les premières causes de blessures et de morts accidentelles dans les pays industrialisés. Ces dernières années, les aspects environnementaux ont aussi pris de plus en plus de place dans l'esprit des consommateurs, qui demandent désormais des véhicules efficaces au niveau énergétique et minimisant leurs impacts sur l'environnement. évidemment, les gouvernements de pays industrialisés ainsi que les manufacturiers de véhicules sont conscients de ces problèmes et tentent de développer des technologies capables de les résoudre. Parmi les travaux de recherche en ce sens, le domaine des Systèmes de Transport Intelligents (STI) a récemment reçu beaucoup d'attention. Ces systèmes proposent d'intégrer des systèmes électroniques avancés dans le développement de solutions intelligentes conçues pour résoudre les problèmes liés au transport automobile cités plus haut. Ce mémoire se penche donc sur un sous-domaine des STI qui étudie la résolution de ces problèmes gr^ace au développement de véhicules intelligents. Plus particulièrement, ce mémoire propose d'utiliser une approche relativement nouvelle de conception de tels systèmes, basée sur l'apprentissage machine. Ce mémoire va donc montrer comment les techniques d'apprentissage par renforcement peuvent être utilisées afin d'obtenir des contrôleurs capables d'effectuer le suivi automatisés de véhicules. Même si ces efforts de développement en sont encore à une étape préliminaire, ce mémoire illustre bien le potentiel de telles approches pour le développement futur de véhicules plus \intelligents".The impressive growth, in the past decades, of the number of vehicles on the road has not come without its share of negative impacts on society. Even though vehicles play an active role in the economical development of urban regions around the world, they unfortunately also have negative effects on businesses as the poor efficiency of the traffic ow results in important losses in productivity each day. Moreover, numerous concerns have been raised in relation to the safety of passengers, as automotive transportation is still among the first causes of accidental casualties in developed countries. In recent years, environmental issues have also been taking more and more place in the mind of customers, that now demand energy-efficient vehicles that limit the impacts on the environment. Of course, both the governments of industrialized countries and the vehicle manufacturers have been aware of these problems, and have been trying to develop technologies in order to solve these issues. Among these research efforts, the field of Intelligent Transportation Systems (ITS) has been gathering much interest as of late, as it is considered an efficient approach to tackle these problems. ITS propose to integrate advanced electronic systems in the development of intelligent solutions designed to address the current issues of automotive transportation. This thesis focuses on a sub-field ITS since it studies the resolution of these problems through the development of Intelligent Vehicle (IV) systems. In particular, this thesis proposes a relatively novel approach for the design of such systems, based on modern machine learning. More specifically, it shows how reinforcement learning techniques can be used in order to obtain an autonomous vehicle controller for longitudinal vehiclefollowing behavior. Even if these efforts are still at a preliminary stage, this thesis illustrates the potential of using these approaches for future development of \intelligent" vehicles