Macroscopic Traffic Model Validation of Large Networks and the Introduction of a Gradient Based Solver

Traffic models are important for the evaluation of various Intelligent Transport Systems and the development of new traffic infrastructure. In order for this to be done accurately and with confidence the correct parameter values of the model must be identified. The focus of this thesis is the identification and confirmation of these parameters, which is model validation. Validation is performed on two different models; the first-order CTM and the second-order METANET model. The CTM is validated for two UK sites of 7.8 and 21.9 km and METANET for the same two sites using a variety of meta-heuristic algorithms. This is done using a newly developed method to allow for the optimisation method to determine the number of parameters to be used and the spatial extent of their application. This allows for the removal of expert engineering knowledge and ad-hoc decomposition of networks. This thesis also develops a methodology by use of Automatic Differentiation to allow gradient based optimisation to be used. This approach successfully validated the METANET model for the 21.9 km site and also a large network surrounding the city of Manchester of 186.9 km. This proves that gradient based optimisation can be used for the macroscopic traffic model validation problem. In fact the performance of the developed gradient method is superior to the meta-heuristics tested for the same sites. The methodology defined also allows for more data to be obtained from the model such as its Jacobian and the sensitivity of the objective function being used relative to the individual parameters. Space-Time contour plots of this newly acquired data show structures and shock waves that are not visible in the mean speed contour diagrams

Traffic Flow Model Validation Using METANET, ADOL-C and RPROP

Macroscopic traffic flow model calibration is an optimisation problem typically solved by a derivative-free population based stochastic search methods. This paper reports on the use of a gradient based algorithm using automatic differentiation. The ADOL-C library is coupled with the METANET source code and this system is embedded within an optimisation algorithm based on RPROP. The result is a very efficient system which is able to be calibrate METANET's second order model by determining the density and speed equation parameters as well as the fundamental diagrams used. Information obtained from the system's Jacobian provides extra insight into the system dynamics. A 22 km site is considered near Sheffield, UK and the results of a typical calibration and validation process are reported

Uncertainty propagation from the cell transmission traffic flow model to emission predictions: a data-driven approach

Road traffic exhaust emission predictions are used to inform transport policy and investment decisions aimed at reducing emissions and achieving sustainable mobility. Emission predictions are also used as inputs when modeling air quality and human exposure to traffic-related air pollutants. To be effective, such policies and/or integration must be based on robust models that not only provide point-based predictions but also inform these with an interval of confidence that properly accounts for the propagation of uncertainties through the complex chain of models involved. This paper develops a data-driven methodological framework that enables calculating the uncertainty in average speed–based emission predictions induced by uncertainty in its traffic data inputs, which are most often predictions (or outputs) of traffic flow models. An ensemble-based optimisation approach is used to estimate both calibration and validation errors arising from uncertainty in the structure and parameterisation of the cell transmission model, a discretised first-order macroscopic traffic flow model that is often integrated with average speed–based emission models. A Monte Carlo sampling approach is proposed to propagate the uncertainty in traffic flow inputs to emission predictions. To ensure transferability of findings, this methodology has been tested using multiple real data sets on three motorway road networks, one of which operates under variable speed limits

Dispatching and Rescheduling Tasks and Their Interactions with Travel Demand and the Energy Domain: Models and Algorithms

Abstract The paper aims to provide an overview of the key factors to consider when performing reliable modelling of rail services. Given our underlying belief that to build a robust simulation environment a rail service cannot be considered an isolated system, also the connected systems, which influence and, in turn, are influenced by such services, must be properly modelled. For this purpose, an extensive overview of the rail simulation and optimisation models proposed in the literature is first provided. Rail simulation models are classified according to the level of detail implemented (microscopic, mesoscopic and macroscopic), the variables involved (deterministic and stochastic) and the processing techniques adopted (synchronous and asynchronous). By contrast, within rail optimisation models, both planning (timetabling) and management (rescheduling) phases are discussed. The main issues concerning the interaction of rail services with travel demand flows and the energy domain are also described. Finally, in an attempt to provide a comprehensive framework an overview of the main metaheuristic resolution techniques used in the planning and management phases is shown

Multi-objective Optimization in Traffic Signal Control

Traffic Signal Control systems are one of the most popular Intelligent Transport Systems and they are widely used around the world to regulate traffic flow. Recently, complex optimization techniques have been applied to traffic signal control systems to improve their performance. Traffic simulators are one of the most popular tools to evaluate the performance of a potential solution in traffic signal optimization. For that reason, researchers commonly optimize traffic signal timing by using simulation-based approaches. Although evaluating solutions using microscopic traffic simulators has several advantages, the simulation is very time-consuming. Multi-objective Evolutionary Algorithms (MOEAs) are in many ways superior to traditional search methods. They have been widely utilized in traffic signal optimization problems. However, running MOEAs on traffic optimization problems using microscopic traffic simulators to estimate the effectiveness of solutions is time-consuming. Thus, MOEAs which can produce good solutions at a reasonable processing time, especially at an early stage, is required. Anytime behaviour of an algorithm indicates its ability to provide as good a solution as possible at any time during its execution. Therefore, optimization approaches which have good anytime behaviour are desirable in evaluation traffic signal optimization. Moreover, small population sizes are inevitable for scenarios where processing capabilities are limited but require quick response times. In this work, two novel optimization algorithms are introduced that improve anytime behaviour and can work effectively with various population sizes. NS-LS is a hybrid of Non-dominated Sorting Genetic Algorithm II (NSGA-II) and a local search which has the ability to predict a potential search direction. NS-LS is able to produce good solutions at any running time, therefore having good anytime behaviour. Utilizing a local search can help to accelerate the convergence rate, however, computational cost is not considered in NS-LS. A surrogate-assisted approach based on local search (SA-LS) which is an enhancement of NS-LS is also introduced. SA-LS uses a surrogate model constructed using solutions which already have been evaluated by a traffic simulator in previous generations. NS-LS and SA-LS are evaluated on the well-known Benchmark test functions: ZDT1 and ZDT2, and two real-world traffic scenarios: Andrea Costa and Pasubio. The proposed algorithms are also compared to NSGA-II and Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D). The results show that NS-LS and SA-LS can effectively optimize traffic signal timings of the studied scenarios. The results also confirm that NS-LS and SA-LS have good anytime behaviour and can work well with different population sizes. Furthermore, SA-LS also showed to produce mostly superior results as compared to NS-LS, NSGA-II, and MOEA/D.Ministry of Education and Training - Vietna

Traffic Flow Forecasting for Road Tunnel Using PSO-GPR Algorithm with Combined Kernel Function

With the rapid development of long or extra-long highway tunnel, accurate and reliable methods and techniques to forecast traffic flow for road tunnel are urgently needed to improve the ventilation efficiency and saving energy. This paper presents a new hybrid Gaussian process regression (GPR) optimized by particle swarm optimization (PSO) for coping with the forecasting of the uncertain, nonlinear, and complex traffic flow for road tunnel. In this proposed coupling approach, the PSO algorithm is employed to overcome the disadvantages of too strong dependence of optimization effect on initial value and easy falling into local optimum of the traditional conjugate gradient algorithm and accurately search the optimal hyperparameters of the GPR method, and the GPR model simulates the internal uncertainties and dynamic feature of tunnel traffic flow. The predicted results indicate that the proposed PSO-GPR algorithm with different kernel function is able to predict traffic flow for road tunnel with a higher degree of accuracy. The PSO-GPR-CK is effective in boosting the forecasting accuracy in comparison with the single kernel function and is worth promoting in the field of traffic flow forecasting for road tunnel to improve the ventilation efficiency

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

Uncertainties and Errors in Predicting Vehicle Exhaust Emissions using Traffic Flow Models

Vehicle exhaust emissions predicted based on the outputs of traffic flow models are used directly to calculate traffic-related emissions, but also indirectly as input to 'air quality - human exposure' models. Both of which inform transport and environmental policies aimed at achieving sustainable mobility. To be effective, these must be based on robust modelling approaches that not only provide point-based emission predictions, but also inform these with an interval of confidence that properly accounts for the propagation of uncertainties and errors through the complex chain of models involved. This research develops a data-driven methodological framework to probabilistic average speed-based emission predictions using two widely deployed macroscopic traffic flow models. These are the Cell Transmission Model (CTM), a discretised first-order LWR-type model, and METANET, a discretised second-order Payne-type model. Studying both allows quantitative comparison in their application to predicting emissions. While this research discusses all potential sources of uncertainty in this modelling chain, it focusses on those arising from the traffic flow modelling output. The methodology starts with an ensemble-based optimisation approach to estimate both calibration and validation prediction errors in the traffic flow model, and then proposes a Monte Carlo sampling approach to propagate these to emission predictions. This allows predicting emissions alongside their upper and lower bounds for any time period and road network, at different levels of detail. To ensure transferability of findings, this methodology has been tested on three motorway road networks, one of which operates under Variable Speed Limits (VSL). This permits the quantitative assessment of VSL-modified traffic flow models. In the results of this research, emissions of Oxides of Nitrogen (NOx) and uncertainty associated with their prediction are specifically reported for each road network under study. Finally, this research argues that the methodological framework developed can (and should) be applied to any other (relatively) simple or complex integrated 'traffic flow - emission' modelling chain used as part of policy and decision making process