Establishment & Assessment of the Macroscopic Fundamental Diagrams for the City of Durban Freeway Network Using Empirical Data

The history of traffic flow studies dates to the years between the 1960s and 1970s. This paper reviews the history of traffic flow studies in the context of Macroscopic Fundamental Diagrams (MFD) to date. The recent findings have shown that understanding the Macroscopic Fundamental Diagrams (MFD) in cities can bring success in managing congestions. This study aimed to establish the Macroscopic Fundamental Diagrams (MFD) for the City of Durban Freeway network. Motivated the study was a failure seen in various transportation systems after the 2010 FIFA world cup in South African cities. This failure was associated with the adoption of the transportation system from first-world countries. The South African cities are not densified when compared to the first world countries' cities, of course, due to spatial urban planning and segregation of the past. The key lesson was that SA transportation systems problems are unique; solutions should be attributed to the existing travel demand conditions. This birthed the idea that the performance of a traffic system should uniquely serve the specific travel demands. The core aim of this study was to establish the MFD for the freeway network in the City of Durban, South Africa. Two major freeway corridors were evaluated, i,e. the National route 2 and 3. The study used loop detector data extracted from a total of 88 loop detector stations. The loop detector stations were dispersed 100-500 m apart over the network. The data was recorded in September 2019. The collected data was analysed in five minutes intervals. When the MFD was established on the network, aggregated detector loops produced a well defined MFD on the 2 nd,3rd ,16th, and the 23rd of September, whereas, on a separate day (30th of September), a scattered MFD forming a hysteresis loop was observed. The formation of the hysteresis was associated with the lack of alternative routes for drivers to avoid congestions in the network. These observations are discussed later in this paper. The study was a success as it did reveal that the MFD was an attribute of the City of Durban freeway network. The established MFD showed that the freeway network operates between the unsaturated and saturated state. This MFD does not reach the saturated flow. The highest recorded density was found at 10.11 veh/km while the highest recorded flow was found at 733.28 veh/hr. The network operates at an average speed of vav= 79.84 km/hr. The estimated average density for the network to reach the gridlocked state was calculated to be 75veh/km

Road network recovery from concurrent capacity-reducing incidents : model development and optimisation

Local and regional economies are highly dependent on the road network. The concurrent closure of multiple sections of the network following a hazardous event is likely to have significant negative consequences for those using the network. In situations such as these, infrastructure managers must decide how best to restore the network to protect users, maximise connectivity and minimise overall disruption. Furthermore, many hazardous events are forecast to become more frequent and extreme in the future as a result of climate change. Extensive research has been undertaken to understand how to improve the resilience of degraded transport networks. Whilst network robustness (that is, the ability of a network to withstand stress) has been considered in numerous studies, the recovery of the network has captured less attention among researchers. Methodologies developed to date are overly simplistic, especially when simulating the dynamics of traffic demand and drivers’ decision-making in multi-day situations where there is considerable interplay between actual and perceived network states and behaviour. This thesis presents a decision-support tool that optimises the recovery of road transport networks after major day-to-day disruptions, maximising network connectivity and minimising total travel costs. This work expands upon previous efforts by introducing a new approach that models the damage-capacity-time relationship and improves the existing reinforcement-learning traffic-assignment models to be applicable to disrupted scenarios. An efficient metaheuristic approach (NSGA-II) is proposed to find optimal solutions for the recovery problem. The model is also applied to a real-world scenario based on the Scottish road network. Results from this case study clearly highlight the potential applicability of this model to evaluate different recovery strategies and optimise the recovery of road networks after multi-day major disruptions.Local and regional economies are highly dependent on the road network. The concurrent closure of multiple sections of the network following a hazardous event is likely to have significant negative consequences for those using the network. In situations such as these, infrastructure managers must decide how best to restore the network to protect users, maximise connectivity and minimise overall disruption. Furthermore, many hazardous events are forecast to become more frequent and extreme in the future as a result of climate change. Extensive research has been undertaken to understand how to improve the resilience of degraded transport networks. Whilst network robustness (that is, the ability of a network to withstand stress) has been considered in numerous studies, the recovery of the network has captured less attention among researchers. Methodologies developed to date are overly simplistic, especially when simulating the dynamics of traffic demand and drivers’ decision-making in multi-day situations where there is considerable interplay between actual and perceived network states and behaviour. This thesis presents a decision-support tool that optimises the recovery of road transport networks after major day-to-day disruptions, maximising network connectivity and minimising total travel costs. This work expands upon previous efforts by introducing a new approach that models the damage-capacity-time relationship and improves the existing reinforcement-learning traffic-assignment models to be applicable to disrupted scenarios. An efficient metaheuristic approach (NSGA-II) is proposed to find optimal solutions for the recovery problem. The model is also applied to a real-world scenario based on the Scottish road network. Results from this case study clearly highlight the potential applicability of this model to evaluate different recovery strategies and optimise the recovery of road networks after multi-day major disruptions

Book of abstracts of the 24th Euro Working Group on Transportation Meeting

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Safety impact of connected and autonomous vehicles on motorways: a traffic microsimulation study

Connected and Autonomous Vehicles (CAVs) promise to improve road safety greatly. Despite the numerous CAV trials around the globe, their benefit has yet to be proven using real-world data. The lack of real-world CAV data has shifted the focus of the research community from traditional safety impact assessment methods to traffic microsimulation in order to evaluate their impacts. However, a plethora of operational, tactical and strategic challenges arising from the implementation of CAV technology remain unaddressed. This thesis presents an innovative and integrated CAV traffic microsimulation framework that aims to cover the aforementioned shortcomings.A new CAV control algorithm is developed in C++ programming language containing a longitudinal and lateral control algorithm that for the first time takes into consideration sensor error and vehicle platoon formulation of various sizes. A route-based decision-making algorithm for CAVs is also developed. The algorithm is applied to a simulated network of the M1 motorway in the United Kingdom which is calibrated and validated using instrumented vehicle data and inductive loop detector data. Multiple CAV market penetration rate, platoon size and sensor error rate scenarios are formulated and evaluated. Safety evaluation is conducted using traffic conflicts as a safety surrogate measure which is a function of time-to-collision and post encroachment time. The results reveal significant safety benefit (i.e. 10-94% reduction of traffic conflicts) as CAV market penetration increases from 0% to 100%; however, it is underlined that special focus should be given in the motorway merging and diverging areas where CAVs seem to face the most challenges. Additionally, it is proven that if the correct CAV platoon size is implemented at the appropriate point in time, greater safety benefits may be achieved. Otherwise, safety might deteriorate. However, sensor error does not affect traffic conflicts for the studied network. These results could provide valuable insights to policy makers regarding the reconfiguration of existing infrastructure to accommodate CAVs, the trustworthiness of existing CAV equipment and the optimal platoon size that should be enforced according to the market penetration rate.Finally, in order to forecast the conflict reduction for any given market penetration rate and understand the underlying factors behind traffic conflicts in a traffic microsimulation environment in-depth, a hierarchical spatial Bayesian negative binomial regression model is developed, based on the simulated CAV data. The results exhibit that besides CAV market penetration rate, speed variance across lanes significantly affects the production of simulated conflicts. As speed variance increases, the safety benefit decreases. These results emphasize the importance of speed homogeneity between lanes in a motorway as well as the increased risk in the motorway merging/diverging areas.</div

Understanding the costs of urban transportation using causal inference methods

With urbanisation on the rise, the need to transport the population within cities in an efficient, safe and sustainable manner has increased tremendously. In serving the growing demand for urban travel, one of the key policy question for decision makers is whether to invest more in road infrastructure or in public transportation. As both of these solutions require substantial spending of public money, understanding their costs continues to be a major area of research. This thesis aims to improve our understanding of the technology underlying costs of operation of public and private modes of urban travel and provide new empirical insights using large-scale datasets and application of causal econometric modelling techniques. The thesis provides empirical and theoretical contributions to three different strands in the transportation literature. Firstly, by assessing the relative costs of a group of twenty-four metro systems across the world over the period 2004 to 2016, this thesis models the cost structure of these metros and quantifies the important external sources of cost-efficiency. The main methodological development is to control for confounding from observed and unobserved characteristics of metro operations by application of dynamic panel data methods. Secondly, the thesis provides a quantification of the travel efficiency arising from increasing the provision of road-based urban travel. A crucial pre-condition of this analysis is a reliable characterisation of the technology describing congestion in a road network. In pursuit of this goal, this study develops novel causal econometric models describing vehicular flow-density relationship, both for a highway section and for an urban network, using large-scale traffic detector data and application of non-parametric instrumental variables estimation. Our model is unique as we control for bias from unobserved confounding, for instance, differences in driving behaviour. As an important intermediate research outcome, this thesis also provides a detailed association of the economic theory underlying the link between the flow-density relationship and the corresponding production function for travel in a highway section and in an urban road network. Finally, the influence of density economies in metros is investigated further using large-scale smart card and train location data from the Mass Transit Railway network in Hong Kong. This thesis delivers novel station-based causal econometric models to understand how passenger congestion delays arise in metro networks at higher passenger densities. The model is aimed at providing metro operators with a tool to predict the likely occurrences of a problem in the network well in advance and materialise appropriate control measures to minimise the impact of delays and improve the overall system reliability. The empirical results from this thesis have important implications for appraisal of transportation investment projects.Open Acces

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

Modelling and management of multi-modal urban traffic

This research aims to model and manage bus regularity with consideration of the interaction between buses and surrounding traffic in an integrated multi-modal system. A parsimonious macroscopic simulation framework is first developed to estimate multi-modal road traffic conditions and the bus-traffic interaction based on the variational formulation of kinematic waves. The proposed simulation platform can capture shocks, dispersion of vehicle platoons, moving bottlenecks and traffic characteristics effectively with data collected from Central London. Second, different bus holding strategies are implemented on the proposed simulation platform in order to evaluate and compare their performance on improving bus service regularity and impact on transport system efficiency. It is shown that the two-way holding strategy performs the best in terms of regulating headway at low-traffic level. At high-traffic level, the two-way holding strategy and the forward holding strategy have a similar performance. However, the efficiency of buses and road traffic can be severely compromised due to bus holding at stops and the consequential delay on road traffic, especially under heavy traffic conditions. In order to mitigate these challenges, the third part of this thesis presents a range of signal-based bus holding strategies which are responsive to road traffic dynamics. Proposed control strategies are implemented on the proposed simulation platform to evaluate their performance. They are also compared with traditional stop-based holding strategies and numerical results suggest improved bus service regularity and transport efficiency

Resilience in Transportation Networks

The functionality of transportation networks is greatly challenged by risk factors such as increasing climate-related hazards, rising population exposure, and greater city vulnerability. Inevitably, the transportation network cannot withstand the impact of an overwhelming disaster, which results in rapid declines in the performance of road net-work. As a next step, the authorities need to restore the performance of the road net-work to an acceptable state as soon as possible and rebalance the conflict between the capacity of the road network and travel demand. Resilience is defined as the process of system performance degradation followed by recovery. To improve the transportation network resilience and maintain regular traffic, it is crucial to identify which factors are related to the resilience and investigate how these factors impact resilience. In this thesis, four factors, i.e., road networks, evacuees, disruption types and au-thorities, are identified to analyze resilience mechanisms. Firstly, the change in vehicle speed during a disaster is used as a measure of resilience, and we analyze the quantita-tive relationship between resilience and the structural characteristics and properties of the road network in multiple disruptions in multiple cities. The results show that the connectivity of the road network, the predictability of disruption, and the population density affect the resilience of the road network in different ways. Secondly, as the road connectivity plays a crucial role during the evacuation pe-riod and considering more frequent and extensive bushfires, we explore a practical and challenging problem: are bushfire fatalities related to road network characteristics? Con-nectivity index (CI), a composite metric that takes into account redundancy, connectivi-ty, and population exposure is designed. The statistical analysis of real-world data sug-gests that CI is significantly negatively correlated with historical bushfire fatalities. This parsimonious and simple graph-theoretic measure can provide planners a useful metric to reduce vulnerability and increase resilience among areas that are prone to bushfires. Finally, a modelling framework for optimizing road network pre-disaster invest-ment strategy under different disaster damage levels is proposed. A bi-level multi-objective optimization model is formulated, in which the upper-level aims to maximize the capacity-based functionality and robustness of the road network, and the lower-level is the user equilibrium problem. To efficiently solve the model, the Shapley value is used to select candidate edges and obtain a near-optimal project order. For more reality, the heterogeneity of road segments to hazards and the correlation of road segments in dif-ferent hazard phases are considered. Realistic speed data is used to explore the depend-ency between different disaster states with copula functions. The numerical results illus-trate that the investment strategy is significantly influenced by the road edge character-istics and the level of disaster damage. Critical sections that can significantly improve the overall functionality of the network are identified. Overall, the core contribution of this thesis is to provide insights into the evalua-tion and analysis of resilience in transportation networks, as well as develop modelling frameworks to promote resilience. The results of this work can provide a theoretical ba-sis for road network design, pre-disaster investment and post-disaster emergency rescue

Information and Dynamics in Urban Traffic Networks

The study of complex systems has intensified in recent years. Researchers from many different disciplines have realised that the study of systems possessing a large number of degrees of freedom interacting in a non-linear way can offer insights into problems in engineering, biology, economics and many other fields besides. Among the themes in complexity, we focus here the issues of congestion and congestion emergence in the context of urban networks, with particular reference to the effects of dissemination of information about the system’s status. This topic is of great relevance today, due to the increasing availability of real-time information about traffic conditions and the large diffusion of personal devices that allow travellers to access such information. Through the analysis of a few simple models of information propagation in urban environment, we uncover that, contrarily to the naïve expectation, complete information is often detrimental to the global performance of the urban traffic network. Indeed, global or long-range dissemination induces correlations in the systems that become a source for spatial disorder, making the system more prone to the emergence of congested states and pushing it away from its Wardrop equilibrium. The models we study range from simple flow models on network to complete agent-based simulations on real-world networks with interacting agents and dynamical information. We then analyse real data, coming from London’s network of traffic detectors. We confirm that the heterogeneity in the distribution of traffic flow and occupancies across the network reduces its performances, consistently with the results obtained for the information propagation models. In addition, we find a rich phenomenology strikingly similar to the one found in critical self-organised systems. Indeed, we measure power-law correlation functions and 1/f power spectra, hinting to long spatial and temporal effects in the traffic flow, and confirm this result through the community detection analysis of the detectors’ correlation network, which showing that the whole urban area behaves as a single large chunk. We conclude discussing the origin of these features and how they can be used to improve the network performances