Reducing non-recurrent urban traffic congestion using vehicle re-routing

Recently, with the trend of world-wide urbanization, some of the accompanying problems are getting serious, including road traffic congestion. To deal with this problem, city planners now resort to the application of the latest information and communications technologies. One example is the adaptive traffic signal control system (e.g. SCATS, SCOOT). To increase the throughput of each main intersection, it dynamically adjusts the traffic light phases according to real-time traffic conditions collected by widely deployed induction loops and sensors. Another typical application is the on-board vehicle navigation system. It can provide drivers with a personalized route according to their preferences (e.g. shortest/fastest/easiest), utilizing comprehensive geo-map data and floating car data. Dynamic traffic assignment is also one of the key proposed methodologies, as it not only benefits the individual driver, but can also provide a route assignment solution for all vehicles with guaranteed minimum average travel time. However, the non-recurrent road traffic congestion problem is still not addressed properly. Unlike the recurrent traffic congestion, which is predictable by capturing the daily traffic pattern, unexpected road traffic congestion caused by unexpected en-route events (e.g. road maintenance, an unplanned parade, car crashes, etc.), often propagates to larger areas in very short time. Consequently, the congestion level of areas around the event location will be significantly degraded. Unfortunately, the three aforementioned methods cannot reduce this unexpected congestion in real time. The contribution of this thesis firstly lies in emphasizing the importance of the dynamic time constraint for vehicle rerouting. Secondly, a framework for evaluating the performance of vehicle route planning algorithms is proposed along with a case study on the simulated scenario of Cologne city. Thirdly, based on the multi-agent architecture of SCATS, the next road rerouting (NRR) system is introduced. Each agent in NRR can use the locally available information to provide the most promising next road guidance in the face of the unexpected urban traffic congestion. In the last contribution of this thesis, further performance improvement of NRR is achieved by the provision of high-resolution, high update frequency traffic information using vehicular ad hoc networks. Moreover, NRR includes an adaptation mechanism to dynamically determine the algorithmic (i.e. factors in the heuristic routing cost function) and operational (i.e. group of agents which must be enabled) parameters. The simulation results show that in the realistic urban scenario, compared to the existing solutions, NRR can significantly reduce the average travel time and improve the travel time reliability. The results also indicate that for both rerouted and non-rerouted vehicles, NRR does not bring any obvious unfairness issue where some vehicles overwhelmingly sacrifice their own travel time to obtain global benefits for other vehicles

A Multi-Agent Based Vehicles Re-routing System for Unexpected Traffic Congestion Avoidance

As urbanization has been spreading across the world for decades, the traffic congestion problem becomes increasingly serious in most of the major cities. Among the root causes of urban traffic congestion, en route events are the main source of the sudden increase of the road traffic load, especially during peak hours. The current solutions, such as on-board navigation systems for individual vehicles, can only provide optimal routes using current traffic data without considering any traffic changes in the future. Those solutions are thus unable to provide a better alternative route quickly enough if an unexpected congestion occurs. Moreover, using the same alternative routes may lead to new bottlenecks that cannot be avoided. Thus a global traffic load balance cannot be achieved. To deal with these problems, we propose a Multi Agent System (MAS) that can achieve a trade-off between the individual and global benefits by giving the vehicles optimal turn suggestions to bypass a blocked road ahead. The simulation results show that our strategy achieves a substantial gain in average trip time reduction under realistic scenarios. Moreover, the negative impact of selfish re-routing is investigated to show the importance of altruistic re-routing applied in our strategy

Multi-level agent-based modeling - A literature survey

During last decade, multi-level agent-based modeling has received significant and dramatically increasing interest. In this article we present a comprehensive and structured review of literature on the subject. We present the main theoretical contributions and application domains of this concept, with an emphasis on social, flow, biological and biomedical models.Comment: v2. Ref 102 added. v3-4 Many refs and text added v5-6 bibliographic statistics updated. v7 Change of the name of the paper to reflect what it became, many refs and text added, bibliographic statistics update

Vehicle re-routing strategies for congestion avoidance

Traffic congestion causes driver frustration and costs billions of dollars annually in lost time and fuel consumption. This dissertation introduces a cost-effective and easily deployable vehicular re-routing system that reduces the effects of traffic congestion. The system collects real-time traffic data from vehicles and road-side sensors, and computes proactive, individually tailored re-routing guidance, which is pushed to vehicles when signs of congestion are observed on their routes. Subsequently, this dissertation proposes and evaluates two classes of re-routing strategies designed to be incorporated into this system, namely, Single Shortest Path strategies and Multiple Shortest Paths Strategies. These strategies are firstly implemented in a centralized system, where a server receives traffic updates from cars, computes alternative routes, and pushes them as guidance to drivers. The extensive experimental results show that the proposed strategies are capable of reducing the travel time comparable to a state-of-the-art Dynamic Traffic Assignment (DTA) algorithm, while avoiding the issues that make DTA impractical, such as lack of scalability and robustness, and high computation time. Furthermore, the variety of proposed strategies allows the system to be tuned to different levels of trade-off between re-routing effectiveness and computational efficiency. Also, the proposed traffic guidance system is robust even if many drivers ignore the guidance, or if the system adoption rate is relatively low. The centralized system suffers from two intrinsic problems: the central server has to perform intensive computation and communication with the vehicles in real-time, which can make such solutions infeasible for large regions with many vehicles; and driver privacy is not protected since the drivers have to share their location as well as the origins and destinations of their trips with the server, which may prevent the adoption of such solutions. To address these problems, a hybrid vehicular re-routing system is presented in this dissertation. The system off-loads a large part of the re-routing computation at the vehicles, and thus, the re-routing process becomes practical in real-time. To make collaborative re-routing decisions, the vehicles exchange messages over vehicular ad hoc networks. The system is hybrid because it still uses a server to determine an accurate global view of the traffic. In addition, the user privacy is balanced with the re-routing effectiveness. The simulation results demonstrate that, compared with a centralized system, the proposed hybrid system increases the user privacy substantially, while the re-routing effectiveness is minimally impacted

Realtime Vehicle Route Optimisation via DQN for Sustainable and Resilient Urban Transportation Network

Traffic congestion has become one of the most serious contemporary city issues for urban transportation network as it leads to unnecessary high energy consumption, air pollution and extra travelling time. During this decade, many optimization algorithms have been designed to achieve the optimal usage of existing roadway capacity in cities to leverage the problem. However, it is still a challenging task for the vehicles to interact with the complex city environment in a real time manner. In this thesis, we propose a deep reinforcement learning (DRL) method to build a real-time intelligent vehicle navigation system for sustainable and resilient urban transportation network. We designed two rewards methods travel time based and vehicle emissions impact (VEI) based which aim to reduce the travel time for emergency vehicle (resilience), and reduce vehicle emissions for general vehicle (sustainability). In the experiment, several realistic traffic scenarios are simulated by SUMO to test the proposed navigation method. The experimental results have demonstrated the efficient convergence of the vehicle navigation agents and their effectiveness to make optimal decisions under the volatile traffic conditions. Travel time based reward schema perform better in reducing travel time however VEI based show better result in reducing vehicle emissions. Furthermore, the results also show that the proposed method has huge potential to provide a better navigation solution comparing with the benchmark routing optimisation algorithms

Performance of management solutions and cooperation approaches for vehicular delay-tolerant networks

A wide range of daily-life applications supported by vehicular networks attracted the interest, not only from the research community, but also from governments and the automotive industry. For example, they can be used to enable services that assist drivers on the roads (e.g., road safety, traffic monitoring), to spread commercial and entertainment contents (e.g., publicity), or to enable communications on remote or rural regions where it is not possible to have a common network infrastructure. Nonetheless, the unique properties of vehicular networks raise several challenges that greatly impact the deployment of these networks. Most of the challenges faced by vehicular networks arise from the highly dynamic network topology, which leads to short and sporadic contact opportunities, disruption, variable node density, and intermittent connectivity. This situation makes data dissemination an interesting research topic within the vehicular networking area, which is addressed by this study. The work described along this thesis is motivated by the need to propose new solutions to deal with data dissemination problems in vehicular networking focusing on vehicular delay-tolerant networks (VDTNs). To guarantee the success of data dissemination in vehicular networks scenarios it is important to ensure that network nodes cooperate with each other. However, it is not possible to ensure a fully cooperative scenario. This situation makes vehicular networks suitable to the presence of selfish and misbehavior nodes, which may result in a significant decrease of the overall network performance. Thus, cooperative nodes may suffer from the overwhelming load of services from other nodes, which comprises their performance. Trying to solve some of these problems, this thesis presents several proposals and studies on the impact of cooperation, monitoring, and management strategies on the network performance of the VDTN architecture. The main goal of these proposals is to enhance the network performance. In particular, cooperation and management approaches are exploited to improve and optimize the use of network resources. It is demonstrated the performance gains attainable in a VDTN through both types of approaches, not only in terms of bundle delivery probability, but also in terms of wasted resources. The results and achievements observed on this research work are intended to contribute to the advance of the state-of-the-art on methods and strategies for overcome the challenges that arise from the unique characteristics and conceptual design of vehicular networks.O vasto número de aplicações e cenários suportados pelas redes veiculares faz com que estas atraiam o interesse não só da comunidade científica, mas também dos governos e da indústria automóvel. A título de exemplo, estas podem ser usadas para a implementação de serviços e aplicações que podem ajudar os condutores dos veículos a tomar decisões nas estradas, para a disseminação de conteúdos publicitários, ou ainda, para permitir que existam comunicações em zonas rurais ou remotas onde não é possível ter uma infraestrutura de rede convencional. Contudo, as propriedades únicas das redes veiculares fazem com que seja necessário ultrapassar um conjunto de desafios que têm grande impacto na sua aplicabilidade. A maioria dos desafios que as redes veiculares enfrentam advêm da grande mobilidade dos veículos e da topologia de rede que está em constante mutação. Esta situação faz com que este tipo de rede seja suscetível de disrupção, que as oportunidades de contacto sejam escassas e de curta duração, e que a ligação seja intermitente. Fruto destas adversidades, a disseminação dos dados torna-se um tópico de investigação bastante promissor na área das redes veiculares e por esta mesma razão é abordada neste trabalho de investigação. O trabalho descrito nesta tese é motivado pela necessidade de propor novas abordagens para lidar com os problemas inerentes à disseminação dos dados em ambientes veiculares. Para garantir o sucesso da disseminação dos dados em ambientes veiculares é importante que este tipo de redes garanta a cooperação entre os nós da rede. Contudo, neste tipo de ambientes não é possível garantir um cenário totalmente cooperativo. Este cenário faz com que as redes veiculares sejam suscetíveis à presença de nós não cooperativos que comprometem seriamente o desempenho global da rede. Por outro lado, os nós cooperativos podem ver o seu desempenho comprometido por causa da sobrecarga de serviços que poderão suportar. Para tentar resolver alguns destes problemas, esta tese apresenta várias propostas e estudos sobre o impacto de estratégias de cooperação, monitorização e gestão de rede no desempenho das redes veiculares com ligações intermitentes (Vehicular Delay-Tolerant Networks - VDTNs). O objetivo das propostas apresentadas nesta tese é melhorar o desempenho global da rede. Em particular, as estratégias de cooperação e gestão de rede são exploradas para melhorar e optimizar o uso dos recursos da rede. Ficou demonstrado que o uso deste tipo de estratégias e metodologias contribui para um aumento significativo do desempenho da rede, não só em termos de agregados de pacotes (“bundles”) entregues, mas também na diminuição do volume de recursos desperdiçados. Os resultados observados neste trabalho procuram contribuir para o avanço do estado da arte em métodos e estratégias que visam ultrapassar alguns dos desafios que advêm das propriedades e desenho conceptual das redes veiculares

An architectural framework for self-configuration and self-improvement at runtime

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Strategic Trip Planning: Striking a Balance Between Competition and Cooperation

In intelligent transportation systems, cooperative mobility planning is considered to be one of the challenging problems. Mobility planning as it stands today is an in- dividual decision-making effort that takes place in an environment governed by the collective actions of various competing travellers. Despite the extensive research on mobility planning, a situation in which multiple behavioural-driven travellers partic- ipate in a cooperative endeavour to help each other optimize their objectives has not been investigated. Furthermore, due to the inherent multi-participant nature of the mobility problem, the existing solutions fail to produce ground truth optimal mobil- ity plans in the practical sense - despite their claimed and well proven theoretical optimality. This thesis proposes a multi-module team mobility planning framework to address the team trip planning problem with a particular emphasis on modelling the inter- action between behaviour-driven rational travellers. The framework accommodates the travellers’ individual behaviours, preferences, and goals in cooperative and com- petitive scenarios. The individual behaviours of the travellers and their interaction processes are viewed as a team trip planning game. For this game, a theoretical anal- ysis is presented, which includes an analysis of the existence and the balancedness of the final solution. The proposed framework is composed of three principal modules: cooperative trip planning, team formation, and traveller-centric trip planning (TCTP). The cooper- ative trip planning module deploys a bargaining model to manage conflicts between the travellers that could occur in their endeavour to discover a general, satisfactory solution. The number of players and their interaction process is controlled by the team formation module. The TCTP module adopts an alternative perspective to the individualized trip-planning problem in the sense that it is being behavioural driven problem. This allows for multitudes of traveler centric objectives and constraints, as well as aspects of the environment as they pertain to the traveller’s preferences, to be incorporated in the process. Within the scope of the team mobility planning frame- work, the TCTP is utilized to supply the travellers with personalized strategies that are incorporated in the cooperative game. The concentration problem is used in this thesis to demonstrate the effectiveness of the TCTP module as a behavioural-driven trip planner. Finally, to validate the theoretical set-up of the team trip planning game, we introduce the territory sharing problem for social taxis. We use the team mobility framework as a basis to solve the problem. Furthermore, we present an argument for the convergence and the efficiency of a coarse correlated equilibrium. In addition to the validation of a variety of theoretical concepts, the territory sharing problem is used to demonstrate the applicability of the proposed framework in dealing with cooperative mobility planning problems