Issues and concerns of microscopic calibration process at different network levels : case study of Pacific Motorway

Calibration process in micro-simulation is an extremely complicated phenomenon. The difficulties are more prevalent if the process encompasses fitting aggregate and disaggregate parameters e.g. travel time and headway. The current practice in calibration is more at aggregate level, for example travel time comparison. Such practices are popular to assess network performance. Though these applications are significant there is another stream of micro-simulated calibration, at disaggregate level. This study will focus on such microcalibration exercise-key to better comprehend motorway traffic risk level, management of variable speed limit (VSL) and ramp metering (RM) techniques. Selected section of Pacific Motorway in Brisbane will be used as a case study. The discussion will primarily incorporate the critical issues encountered during parameter adjustment exercise (e.g. vehicular, driving behaviour) with reference to key traffic performance indicators like speed, lane distribution and headway; at specific motorway points. The endeavour is to highlight the utility and implications of such disaggregate level simulation for improved traffic prediction studies. The aspects of calibrating for points in comparison to that for whole of the network will also be briefly addressed to examine the critical issues such as the suitability of local calibration at global scale. The paper will be of interest to transport professionals in Australia/New Zealand where micro-simulation in particular at point level, is still comparatively a less explored territory in motorway management

Nonminimal state space approach to multivariable ramp metering control of motorway bottlenecks

The paper discusses the automatic control of motorway traffic flows utilising ramp metering, i.e. traffic lights on the on-ramp entrances. A multivariable ramp metering system is developed, based on the nonminimal state space (NMSS) approach to control system design using adaptive proportional-integral-plus, linear quadratic (PIP–LQ) optimal controllers. The controller is evaluated on a nonlinear statistical traffic model (STM) simulation of the Amsterdam motorway ring road near the Coen Tunnel

Environmental screening tools for assessment of infrastructure plans based on biodiversity preservation and global warming (PEIT, Spain).

Most Strategic Environmental Assessment (SEA) research has been concerned with SEA as a procedure, and there have been relatively few developments and tests of analytical methodologies. The first stage of the SEA is the ‘screening’, which is the process whereby a decision is taken on whether or not SEA is required for a particular programme or plan. The effectiveness of screening and SEA procedures will depend on how well the assessment fits into the planning from the early stages of the decision-making process. However, it is difficult to prepare the environmental screening for an infrastructure plan involving a whole country. To be useful, such methodologies must be fast and simple. We have developed two screening tools which would make it possible to estimate promptly the overall impact an infrastructure plan might have on biodiversity and global warming for a whole country, in order to generate planning alternatives, and to determine whether or not SEA is required for a particular infrastructure plan

Statistical modelling and analysis of traffic: a dynamic approach

In both developed and emerging-economies, major cities continue to experience increasing traffic congestion. To address this issue, complex Traffic Management Systems (TMS) are employed in recent years to help manage traffic. These systems fuse traffic-surveillance-related information from a variety of sensors deployed across traffic networks. A TMS requires real-time information to make effective control decisions and to deliver trustworthy information to users, such as travel time, congestion level, etc. There are three fundamental inputs required by TMS, namely, traffic volume, vehicular speed, and traffic density. Using conventional traffic loop detectors one can directly measure flow and velocity. However, traffic density is more difficult to measure. The situation becomes more difficult for multi-lane motorways due to drivers lane-change behaviour. This research investigates statistical modelling and analysis of traffic flow. It contributes to the literature of transportation and traffic management and research in several aspects. First, it takes into account lane-changes in traffic modelling through incorporating a Markov chain model to describe the drivers lane-change behaviour. Secondly, the lane change probabilities between two adjacent lanes are not assumed to be fixed but rather they depend on the current traffic condition. A discrete choice model is used to capture drivers lane choice behaviour. The drivers choice probabilities are modelled by several traffic-condition related attributes such as vehicle time headway, traffic density and speed. This results in a highly nonlinear state equation for traffic density. To address the issue of high nonlinearity of the state space model, the EKF and UKF is used to estimate the traffic density recursively. In addition, a new transformation approach has been proposed to transform the observation equation from a nonlinear form to a linear one so that the potential approximation in the EKF & UKF can be avoided. Numerical studies have been conducted to investigate the performance of the developed method. The proposed method outperformed the existing methods for traffic density estimation in simulation studies. Furthermore, it is shown that the computational cost for updating the estimate of traffic densities for a multi-lane motorway is kept at a minimum so that online applications are feasible in practice. Consequently the traffic densities can be monitored and the relevant information can be fed into the traffic management system of interest

A framework for smart traffic management using heterogeneous data sources

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.Traffic congestion constitutes a social, economic and environmental issue to modern cities as it can negatively impact travel times, fuel consumption and carbon emissions. Traffic forecasting and incident detection systems are fundamental areas of Intelligent Transportation Systems (ITS) that have been widely researched in the last decade. These systems provide real time information about traffic congestion and other unexpected incidents that can support traffic management agencies to activate strategies and notify users accordingly. However, existing techniques suffer from high false alarm rate and incorrect traffic measurements. In recent years, there has been an increasing interest in integrating different types of data sources to achieve higher precision in traffic forecasting and incident detection techniques. In fact, a considerable amount of literature has grown around the influence of integrating data from heterogeneous data sources into existing traffic management systems. This thesis presents a Smart Traffic Management framework for future cities. The proposed framework fusions different data sources and technologies to improve traffic prediction and incident detection systems. It is composed of two components: social media and simulator component. The social media component consists of a text classification algorithm to identify traffic related tweets. These traffic messages are then geolocated using Natural Language Processing (NLP) techniques. Finally, with the purpose of further analysing user emotions within the tweet, stress and relaxation strength detection is performed. The proposed text classification algorithm outperformed similar studies in the literature and demonstrated to be more accurate than other machine learning algorithms in the same dataset. Results from the stress and relaxation analysis detected a significant amount of stress in 40% of the tweets, while the other portion did not show any emotions associated with them. This information can potentially be used for policy making in transportation, to understand the users��� perception of the transportation network. The simulator component proposes an optimisation procedure for determining missing roundabouts and urban roads flow distribution using constrained optimisation. Existing imputation methodologies have been developed on straight section of highways and their applicability for more complex networks have not been validated. This task presented a solution for the unavailability of roadway sensors in specific parts of the network and was able to successfully predict the missing values with very low percentage error. The proposed imputation methodology can serve as an aid for existing traffic forecasting and incident detection methodologies, as well as for the development of more realistic simulation networks

The state of the art of cooperative and connected autonomous vehicles from the future mobility management perspective:a systematic review

© 2022 The Authors. Published by MDPI. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.3390/futuretransp2030032Cooperative and connected autonomous vehicles (CCAVs) are considered to be a promising solution for addressing congestion and other operational deficiencies, as part of a holistic future mobility management framework. As a result, a significant number of studies have recently been published on this topic. From the perspective of future mobility management, this review paper discusses three themes, which are traffic management, network performance, and mobility management, including congestion, and incident detection using the PRISMA methodology. Three databases were considered for this study, and peer-reviewed primary studies were selected that were published within the last 10 years in the English language, focusing on CCAV in the context of the future transportation and mobility management perspective. For synthesis and interpretation, like-for-like comparisons were made among studies; it was found that extensive research-supported information is required to ensure a smooth transition from conventional vehicles to the CCAVs regime, to achieve the projected traffic and environmental benefits. Research investigations are ongoing to optimize these benefits and associated goals via the setting of different models and simulations. The tools and technologies for the testing and simulation of CCAV were found to have limited capacity. Following the review of the current state-of-the-art, recommendations for future research have been discussed. The most notable is the need for large-scale simulations to understand the impact of CCAVs beyond corridor-based and small-scale networks, the need for understanding the interactions between the drivers of CCAVs and traffic management centers, and the need to assess the technological transition, as far as infrastructure systems are concerned, that is necessary for the progressive penetration of CCAVs into traffic streams.This research was funded by European Union’s Horizon 2020 research and innovation program, grant number 955317.Published onlin

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

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