2nd Symposium on Management of Future motorway and urban Traffic Systems (MFTS 2018): Booklet of abstracts: Ispra, 11-12 June 2018

The Symposium focuses on future traffic management systems, covering the subjects of traffic control, estimation, and modelling of motorway and urban networks, with particular emphasis on the presence of advanced vehicle communication and automation technologies. As connectivity and automation are being progressively introduced in our transport and mobility systems, there is indeed a growing need to understand the implications and opportunities for an enhanced traffic management as well as to identify innovative ways and tools to optimise traffic efficiency. In particular the debate on centralised versus decentralised traffic management in the presence of connected and automated vehicles has started attracting the attention of the research community. In this context, the Symposium provides a remarkable opportunity to share novel ideas and discuss future research directions.JRC.C.4-Sustainable Transpor

UNDERSTANDING THE IMPACT OF INCIDENTS AND INCIDENT MANAGEMENT PROGRAMS ON FREEWAY MOBILITY AND SAFETY

Despite significant technological achievements over past decades, and institutional support for Intelligent Transportation System (ITS), it is not possible to prevent all traffic incidents. Numerous incidents occur every day along U.S. freeways and traffic incident management (TIM) programs have been proposed and implemented to mitigate their impact. This dissertation proposes various tools to aid in the evaluation of proposed TIM programs, contributing, thus, to the general study area of freeway incident management. In addition, moving violations specific to concurrent flow lane operations are conceived as a type of transient incident. Their impact on mobility and safety is considered. Techniques to address four key areas are proposed. First, a methodology that considers the dynamics of incident impact given a primary incident's properties and prevailing traffic conditions for identifying secondary incidents from a database is proposed. This method is computationally efficient and overcomes deficiencies of other existing techniques, with utility in any context in which the study of secondary incidents is warranted. A three-stage time-saving process is developed for conducting TIM program benefit evaluations. The process aids in sampling a relatively small set of good quality incident scenarios that can represent historical incident data and overcomes the computational burden encountered when evaluating TIM program's benefit by simulation. Modeling techniques are proposed for simulating violations associated with the operation of concurrent flow lanes. Results from a case study show significant impact to mobility that grows nonlinearly with increasing violation rate. Such illegal traffic maneuvers contribute to increased speed variation and congestion, ultimately affecting safety. Finally, diversion strategies that exploit existing capacity of managed lanes for the purpose of reducing the impact of an incident in the general purpose lanes are evaluated. Simulation modeling methodologies were developed for modeling freeway incidents and studied diversion strategy implementations. Experimental findings indicate benefits of diversion that are contrary to qualitatively developed recommendations in the literature

Risk analysis of autonomous vehicle and its safety impact on mixed traffic stream

In 2016, more than 35,000 people died in traffic crashes, and human error was the reason for 94% of these deaths. Researchers and automobile companies are testing autonomous vehicles in mixed traffic streams to eliminate human error by removing the human driver behind the steering wheel. However, recent autonomous vehicle crashes while testing indicate the necessity for a more thorough risk analysis. The objectives of this study were (1) to perform a risk analysis of autonomous vehicles and (2) to evaluate the safety impact of these vehicles in a mixed traffic stream. The overall research was divided into two phases: (1) risk analysis and (2) simulation of autonomous vehicles. Risk analysis of autonomous vehicles was conducted using the fault tree method. Based on failure probabilities of system components, two fault tree models were developed and combined to predict overall system reliability. It was found that an autonomous vehicle system could fail 158 times per one-million miles of travel due to either malfunction in vehicular components or disruption from infrastructure components. The second phase of this research was the simulation of an autonomous vehicle, where change in crash frequency after autonomous vehicle deployment in a mixed traffic stream was assessed. It was found that average travel time could be reduced by about 50%, and 74% of conflicts, i.e., traffic crashes, could be avoided by replacing 90% of the human drivers with autonomous vehicles

Existing and Required Modeling Capabilities for Evaluating ATM Systems and Concepts

ATM systems throughout the world are entering a period of major transition and change. The combination of important technological developments and of the globalization of the air transportation industry has necessitated a reexamination of some of the fundamental premises of existing Air Traffic Management (ATM) concepts. New ATM concepts have to be examined, concepts that may place more emphasis on: strategic traffic management; planning and control; partial decentralization of decision-making; and added reliance on the aircraft to carry out strategic ATM plans, with ground controllers confined primarily to a monitoring and supervisory role. 'Free Flight' is a case in point. In order to study, evaluate and validate such new concepts, the ATM community will have to rely heavily on models and computer-based tools/utilities, covering a wide range of issues and metrics related to safety, capacity and efficiency. The state of the art in such modeling support is adequate in some respects, but clearly deficient in others. It is the objective of this study to assist in: (1) assessing the strengths and weaknesses of existing fast-time models and tools for the study of ATM systems and concepts and (2) identifying and prioritizing the requirements for the development of additional modeling capabilities in the near future. A three-stage process has been followed to this purpose: 1. Through the analysis of two case studies involving future ATM system scenarios, as well as through expert assessment, modeling capabilities and supporting tools needed for testing and validating future ATM systems and concepts were identified and described. 2. Existing fast-time ATM models and support tools were reviewed and assessed with regard to the degree to which they offer the capabilities identified under Step 1. 3 . The findings of 1 and 2 were combined to draw conclusions about (1) the best capabilities currently existing, (2) the types of concept testing and validation that can be carried out reliably with such existing capabilities and (3) the currently unavailable modeling capabilities that should receive high priority for near-term research and development. It should be emphasized that the study is concerned only with the class of 'fast time' analytical and simulation models. 'Real time' models, that typically involve humans-in-the-loop, comprise another extensive class which is not addressed in this report. However, the relationship between some of the fast-time models reviewed and a few well-known real-time models is identified in several parts of this report and the potential benefits from the combined use of these two classes of models-a very important subject-are discussed in chapters 4 and 7

Design and Analysis of Mobility Permit-based Traffic Management Schemes

High demand for mobility has undeniably been causing numerous negative impacts on the economy, the society and the environment. As a potential solution to address this challenge, a rapid transition is taking place in the transportation sector with emerging concepts of mobility marketplace. The basic premise is to treat the transportation system and its use as a collection of commodities or services that can be bought from the transportation market. This concept is increasingly becoming a reality with the technological developments in automotive industry such as connected and autonomous vehicles (CAVs). However, there are many policy, design and operation related issues that must be addressed before these traffic management schemes become reality. This thesis research aims at addressing some of these challenges and issues with a specific focus on the two most promising market-driven instruments, namely, mobility permits (MP)- and mobility credits (MC)-based traffic management schemes, which have been proposed to manage travel demand and mitigate traffic congestion by controlling roadway-use right. This research has made several distinctive contributions into the literature. We first conduct a critical review of the state-of-the-art methodological advances on MP- and MC-based travel demand management schemes. We synthesize the relevant body of literature with an in-depth discussion on related studies to provide an improved understanding of the fundamental constructs of these problems, including problem variants, methodologies, and modeling attributes. We also discuss the research gaps and challenges and suggest some possible perspectives and directions for future research. Based on the gaps identified in the literature review, an integrated framework is proposed for implementing various roadway-use right-based traffic management programs such as MP and MC-based schemes. This framework entails a unique construct for integrating the needs of multiple stakeholders (e.g., road users and authorities), diverse network conditions, and traffic control methods. It allows easy incorporation of different components required for implementing a coordinative mobility scheme, taking into account the influence of the participating players and the underlying issues. The framework can be served as a road-map to future studies on different roadway-use right-based solutions for traffic congestion management. With our proposed framework, we then focus on addressing various specific challenges arising in designing and implementing MP-based and MC-based schemes, such as, representation of realistic user characteristics (e.g., utility function, user priorities and cooperation), availability of information on users and traffic conditions, uncertainty in system conditions and user behaviors, and circulation of mobility rights in market place. For the MP-based scheme, we focus specifically on designing a mobility scheme for single-bottleneck roadways. Roads with bridges, tunnels and business districts with limited parking spaces are the most obvious examples of a simple roadway with a single-bottleneck in a transportation network. We deal with observing operational objectives, specifically, balancing efficiency, equity (users priorities), and revenue outcome of distributing mobility permits under the “fairness” constraint. We explore the theoretical properties of the proposed scheme and show that the proposed scheme can achieve an optimal traffic pattern. Particularly, we show that the proposed scheme is a Pareto-improving and strategy-proof scheme capable of achieving efficient and effective market prices suitable for travelers. Our computational results indicate the effectiveness of the proposed scheme as an alternative solution for MP-based traffic management on single-bottleneck roadways. We then investigate the case of traffic congestion management in a general road network through a MC-based scheme. Specifically, we propose a MC-based traffic management scheme in a road network consisting of a mixed-fleet traffic with connected and autonomous vehicles (CAVs) and conventional vehicles (non-CAVs). The basic premise of the proposed scheme is to regulate or influence travel demand and congestion with regards to the supply (capacity) of road networks, implementing a market-driven traffic management paradigm. A set of revenue-neutral, Pareto-improving MC-based charge and reward policies applicable to stochastic traffic environments are developed, considering different characteristics of users such as cooperative versus selfish routing behaviors, human-associated factors (e.g., level of uncertainty) and interactions due to a shared infrastructure setting. Path-free mathematical programming models are formulated, obviating computationally intractable path enumeration process pertinent to the existing studies. This makes the proposed scheme suitable for examining the theoretical characteristics of large-scale realistic transport networks. We examine several theoretical properties related to the proposed MC-based scheme, including the existence and uniqueness of the equilibrium price, and existence of Pareto-improving credit charges and rewards rates that can promote travel decision behaviors of individual travelers towards a network-wide optimal state. Our comprehensive computational results indicate that the proposed MC-based scheme can be an effective tool for managing travel demand and routing decisions in mixed-vehicle traffic settings

Road transport and emissions modelling in England and Wales: A machine learning modelling approach using spatial data

An expanding street network coupled with an increasing number of vehicles testifies to the significance and reliance on road transportation of modern economies. Unfortunately, the use of road transport comes with drawbacks such as its contribution to greenhouse gases (GHG) and air pollutant emissions, therefore becoming an obstacle to countries’ objectives to improve air quality and a barrier to the ambitious targets to reduce Greenhouse Gas emissions. Unsurprisingly, traffic forecasting, its environmental impacts and potential future configurations of road transport are some of the topics which have received a great deal of attention in the literature. However, traffic forecasting and the assessment of its determinants have been commonly restricted to specific, normally urban, areas while road transport emission studies do not take into account a large part of the road network, as they usually focus on major roads. This research aimed to contribute to the field of road transportation, by firstly developing a model to accurately estimate traffic across England and Wales at a granular (i.e., street segment) level, secondly by identifying the role of factors associated with road transportation and finally, by estimating CO2 and air pollutant emissions, known to be responsible for climate change as well as negative impacts on human health and ecosystems. The thesis identifies potential emissions abatement from the adoption of novel road vehicles technologies and policy measures. This is achieved by analysing transport scenarios to assess future impacts on air quality and CO2 emissions. The thesis concludes with a comparison of my estimates for road emissions with those from DfT modelling to assess the methodological robustness of machine learning algorithms applied in this research. The traffic modelling outputs reveal traffic patterns across urban and rural areas, while traffic estimation is achieved with high accuracy for all road classes. In addition, specific socioeconomic and roadway characteristics associated with traffic across all vehicle types and road classes are identified. Finally, CO2 and air pollution hot spots as well as the impact of open spaces on pollutants emissions and air quality are explored. Potential emission reduction with the employment of new vehicle technologies and policy implementation is also assessed, so as the results can support urban planning and inform policies related to transport congestion and environmental impacts mitigation. Considering the disaggregated approach, the methodology can be used to facilitate policy making for both local and national aggregated levels

e-Sanctuary: open multi-physics framework for modelling wildfire urban evacuation

The number of evacuees worldwide during wildfire keep rising, year after year. Fire evacuations at the wildland-urban interfaces (WUI) pose a serious challenge to fire and emergency services and are a global issue affecting thousands of communities around the world. But to date, there is a lack of comprehensive tools able to inform, train or aid the evacuation response and the decision making in case of wildfire. The present work describes a novel framework for modelling wildfire urban evacuations. The framework is based on multi-physics simulations that can quantify the evacuation performance. The work argues that an integrated approached requires considering and integrating all three important components of WUI evacuation, namely: fire spread, pedestrian movement, and traffic movement. The report includes a systematic review of each model component, and the key features needed for the integration into a comprehensive toolkit

Verification and validation of TRAF-NETSIM model through actual field observations in Amman-Jordan

As urban traffic continues to increase in metropolitan cities around the globe, transportation engineers are constantly attempting to improve the utilization of existent transportation systems. Their objective is to achieve maximum efficiency of these systems, in terms of movement of persons, services, and goods in a safe and convenient manner. Although congestion problems have existed in some major cities for some time, it has become a significant issue until recent years. The substantial increase in automobile ownership to meet our changing lifestyles in the last few decades, coupled with a decline of new highway construction, has stretched many roadway networks beyond their design capacity. To evaluate different traffic management strategies and their effect on the behavior of an urban street system is a very complex process due to the interrelationships between its components. Therefore, engineers have to rely on mathematical, computer-based simulation models to accurately predict the behavior of the system over a period of time. One of the most effective tools of traffic management is the application of computer simulation models to represent the traffic system, in order to determine the effects of traffic management strategies on the system\u27s operational performance. This performance can be stated in terms of Measures of Effectiveness (MOE) on specific traffic parameters such as average vehicle speed, average travel time, vehicle stops, maximum queue length and fuel consumption. These MOE\u27s can provide the traffic engineer with valuable insight into the responsiveness of the traffic stream to different operational strategies. Among the many computer simulation programs, the TRAF-NETSIM, an Integrated Traffic Network Simulation model, is probably one of the most widely used and accepted traffic simulation models in The United States of America. TRAF-NETSIM is a very complex microscopic simulation model, which simulates the individual car movements stochastically. This research used TRAF-NETSIM Version 5.0 to determine the applicability and adaptability of this model to assess the traffic performance in Amman - Jordan, which was accomplished by the following steps: A typical street network in Amman - Jordan, was selected and all the required input information to run NETSIM was collected from the field (The Test Network).Two Measures of Effectiveness, Travel Time and Route Delay Time, were measured concurrently during the collection of traffic related input data. The Test Network was used to collect the following traffic parameters needed to calibrate the NETSIM model.Mean Start-Up Lost TimeMean Queue Discharge HeadwayDistribution of Start-Up Lost TimeDistribution of Queue Discharge HeadwayCalibration on TRAF-NETSIM, in which the simulated results were compared with the observed field values using both the default and calibrated parameters. A second street was selected in the same city to test the performance of the calibrated model (The Validation Network). It was found that the TRAF-NETSIM model using the default traffic parameters did not adequately predict the traffic performance in the test network. However, after changing the embedded default parameters in TRAF-NETSIM with the measured values in the field, the simulated travel times and delay times, for weekdays other than Fridays, were similar to those observed for both street networks. Conversely, for Friday the model did not predict the measured travel time and delay time within a given accuracy for both the test and validation networks. It should be brought to the reader\u27s attention that Friday is a holiday in Jordan and that the traffic on Friday is synonymous to Sunday traffic found in the United States of America

Dynamic Vehicular Routing in Urban Environments

Traffic congestion is a persistent issue that most of the people living in a city have to face every day. Traffic density is constantly increasing and, in many metropolitan areas, the road network has reached its limits and cannot easily be extended to meet the growing traffic demand. Intelligent Transportation System (ITS) is a world wide trend in traffic monitoring that uses technology and infrastructure improvements in advanced communication and sensors to tackle transportation issues such as mobility efficiency, safety, and traffic congestion. The purpose of ITS is to take advantage of all available technologies to improve every aspect of mobility and traffic. Our focus in this thesis is to use these advancements in technology and infrastructure to mitigate traffic congestion. We discuss the state of the art in traffic flow optimization methods, their limitations, and the benefits of a new point of view. The traffic monitoring mechanism that we propose uses vehicular telecommunication to gather the traffic information that is fundamental to the creation of a consistent overview of the traffic situation, to provision real-time information to drivers, and to optimizing their routes. In order to study the impact of dynamic rerouting on the traffic congestion experienced in the urban environment, we need a reliable representation of the traffic situation. In this thesis, traffic flow theory, together with mobility models and propagation models, are the basis to providing a simulation environment capable of providing a realistic and interactive urban mobility, which is used to test and validate our solution for mitigating traffic congestion. The topology of the urban environment plays a fundamental role in traffic optimization, not only in terms of mobility patterns, but also in the connectivity and infrastructure available. Given the complexity of the problem, we start by defining the main parameters we want to optimize, and the user interaction required, in order to achieve the goal. We aim to optimize the travel time from origin to destination with a selfish approach, focusing on each driver. We then evaluated constraints and added values of the proposed optimization, providing a preliminary study on its impact on a simple scenario. Our evaluation is made in a best-case scenario using complete information, then in a more realistic scenario with partial information on the global traffic situation, where connectivity and coverage play a major role. The lack of a general-purpose, freely-available, realistic and dependable scenario for Vehicular Ad Hoc Networks (VANETs) creates many problems in the research community in providing and comparing realistic results. To address these issues, we implemented a synthetic traffic scenario, based on a real city, to evaluate dynamic routing in a realistic urban environment. The Luxembourg SUMO Traffic (LuST) Scenario is based on the mobility derived from the City of Luxembourg. The scenario is built for the Simulator of Urban MObiltiy (SUMO) and it is compatible with Vehicles in Network Simulation (VEINS) and Objective Modular Network Testbed in C++ (OMNet++), allowing it to be used in VANET simulations. In this thesis we present a selfish traffic optimization approach based on dynamic rerouting, able to mitigate the impact of traffic congestion in urban environments on a global scale. The general-purpose traffic scenario built to validate our results is already being used by the research community, and is freely-available under the MIT licence, and is hosted on GitHub

Strategic Planning for Connected and Automated Vehicles In Massachusetts

ISA#92312Connected and Automated Vehicle (CAV) technologies are evolving at a fast pace, and new developments are occurring on a daily basis. These technologies have potential to significantly change transportation and travel, to make them safer, more accessible, and more efficient than they are today. There are many issues that will have to be investigated, such as the pace and extent to which CAV technologies become pervasive, the socioeconomic impacts of CAVs, their implication on privacy and security; to what extent will these technologies replace human drivers, and the legal and regulatory responsibilities for the safe operation of CAVs. State departments of transportation (DOTs) will need to plan to prepare for and accommodate CAV technologies. The purpose of this study is to provide baseline information pertaining to strategic planning for CAV technologies in Massachusetts. This information may be used to assist the Massachusetts Department of Transportation (MassDOT) to develop a strategic plan to accommodate the deployment of such technologies and for related infrastructure investment decisions