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    Some statistical techniques for analysing Bluetooth tracking data in traffic modelling : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University, Palmerston North, New Zealand
    (Massey University, 2021) Aslani, Ghazaleh
    The economy and the environment are both affected by traffic congestion. People spend time stuck in traffic, which limits their free time. Every city's road infrastructure is under increased pressure, particularly in large cities, due to population growth and vehicle ownership patterns. Therefore, traffic control and management are crucial to reducing traffic congestion problems and effectively using existing road infrastructure. Bluetooth is a commonly used wireless technology for short-distance data exchange. This technology allows all mobile phones, GPS systems, and in-vehicle applications such as navigation systems to connect with the personal devices of drivers and passengers. A Media Access Control (MAC) address is a unique electronic identifier used by each Bluetooth device. The concept is that, while a Bluetooth-equipped device travels along a road, its MAC address, detection time, and location can be detected anonymously at different locations. Bluetooth technology can be integrated into Intelligent Transportation Systems (ITS) to enable better and more effective traffic monitoring and management, hence reducing traffic congestion. This thesis aims to develop some statistical methods for analysing Bluetooth tracking data in traffic modelling. One of the challenges of using Bluetooth data, particularly for travel time estimation, is multiple Bluetooth detections, which occur when a Bluetooth sensor records a Bluetooth device several times while it passes through the detection zone. We employ cluster analysis to look at the possibility of extracting meaningful traffic information from multiple detections, and the observed gap distribution, which is the time difference between records when multiple detections occur. We also develop a novel regression method to investigate the relationship between data from Bluetooth and Automatic Traffic Counts (ATCs) through weighted regression analysis, in order to explore potential causes of bias in the representativeness of Bluetooth detections. Finally, we seek the practical objective of recovering ATC from Bluetooth data as a statistical calibration problem, following the development of a new time-varying coefficients Poisson regression model.
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    Modelling and inference for dynamic traffic networks : a thesis presented for the degree of Doctor of Philosophy in Statistics at Massey University, Palmerston North, New Zealand
    (Massey University, 2020) Maahmoodjanlou, Ahmad
    Nowadays, traffic congestion is a significant problem in the world. With the noticeable rise in vehicle usage in recent years and therefore congestion, there has been a wealth of study into possible ways that this congestion can be eased and the flow of traffic on the road improved. Controlling traffic congestion relies on good mathematical models of traffic systems. Creating accurate and reliable traffic control systems is one of the crucial steps for active congestion control. These traffic systems generally use algorithms that depend on mathematical models of traffic. Day-to-day dynamic assignment models play a critical role in transport management and planning. These models can be either deterministic or stochastic and can be used to describe the day-to-day evolution of traffic flow across the network. This doctoral research is dedicated to understanding the difference between deterministic models and stochastic models. Deterministic models have been studied well, but the properties of stochastic models are less well understood. We investigate how predictions of the long term properties of the system differ between deterministic models and stochastic models. We find that in contrast to systems with a unique equilibrium where the deterministic model can be a good approximation for the mean of the stochastic model, for a system with multiple equilibria, the situation is more complicated. In such a case even when deterministic and stochastic models appear to have comparable properties over a significant time frame, they may still behave very differently in the long-run. Markov models are popular for stochastic day-to-day assignment. Properties of such models are difficult to analyse theoretically, so there has been an interest in approximations which are more mathematically tractable. However, it is di cult to tell when approximation will work well, both in a stationary state and during transient periods following a network disruption. The coefficient of reactivity introduced by Hazelton (2002) measures the degree to which a system reacts to a disruption. We propose that it can be used as a guide to when approximation models will work well. We study this issue through a raft of numerical experiments. We find that the value of the coefficient of reactivity is useful in predicting the accuracy of approximation models. However, the detailed interpretation of the coefficient of reactivity depends to a modest degree on properties of the network such as its size and number of routes.
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    Traffic flow modeling and forecasting using cellular automata and neural networks : a thesis presented in partial fulfillment of the requirements for the degree of Master of Science in Computer Science at Massey University, Palmerston North, New Zealand
    (Massey University, 2006) Liu, Mingzhe
    In This thesis fine grids are adopted in Cellular Automata (CA) models. The fine-grid models are able to describe traffic flow in detail allowing position, speed, acceleration and deceleration of vehicles simulated in a more realistic way. For urban straight roads, two types of traffic flow, free and car-following flow, have been simulated. A novel five-stage speed-changing CA model is developed to describe free flow. The 1.5-second headway, based on field data, is used to simulate car-following processes, which corrects the headway of 1 second used in all previous CA models. Novel and realistic CA models, based on the Normal Acceptable Space (NAS) method, are proposed to systematically simulate driver behaviour and interactions between drivers to enter single-lane Two-Way Stop-Controlled (TWSC) intersections and roundabouts. The NAS method is based on the two following Gaussian distributions. Distribution of space required for all drivers to enter intersections or roundabouts is assumed to follow a Gaussian distribution, which corresponds to heterogeneity of driver behaviour. While distribution of space required for a single driver to enter an intersection or roundabout is assumed to follow another Gaussian distribution, which corresponds to inconsistency of driver behavior. The effects of passing lanes on single-lane highway traffic are investigated using fine grids CA. Vehicles entering, exiting from and changing lanes on passing lane sections are discussed in detail. In addition, a Genetic Algorithm-based Neural Network (GANN) method is proposed to predict Short-term Traffic Flow (STF) in urban networks, which is expected to be helpful for traffic control. Prediction accuracy and generalization ability of NN are improved by optimizing the number of neurons in the hidden layer and connection weights of NN using genetic operations such as selection, crossover and mutation.
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    Dynamic programming based coordinated ramp metering algorithms : a thesis presented in partial fulfillment of the requirements for the degree of PhD of Englineeriing in Mechatronics at Massey University, Auckland, New Zealand
    (Massey University, 2014) Yu, Xuefeng
    Motorway congestion can be classified into two types, recurrent congestion and non-recurrent congestion. Recurrent congestion happens during peak hours. Non-recurrent congestion occurs due to car accidents, weather conditions or public events. Negative impacts of traffic congestion include wasted fuel, pollution, travel delay and spillover effects caused by slow traffic. Ramp metering, as an only way to regulate traffic amount accessing to the motorway, is considered as the most cost-effective way to prevent the recurrent congestion. Coordinated ramp metering was developed to control a number of on-ramps simultaneously to improve traffic conditions on busy motorways. The existing coordinated ramp metering algorithms were normally established on macroscopic traffic flow models based on Payne’s wok, the performances of which were measured by the employed macroscopic model themselves, and the released metering rates of which tended to be continuous. Implementations in microscopic traffic simulators were few. This thesis presents DP (Dynamic Programming) based online control approaches for the optimal coordination of ramp metering and evaluates its performances in both macroscopic and microscopic traffic simulation environment. DP decision networks were proposed, where a traffic system can be modeled as a number of discrete traffic states and separated by time stages, and the control problem of coordinated ramp metering was treated as the minimization problem to search the optimal trajectory of discrete decision variables (ramp metering rates) that minimized a cost criterion in terms of TTS (total time spent) along the time horizon. Experiments conducted in the macroscopic simulation environment demonstrated the full potential of proposed algorithms with precise queue constrains in an ideal deterministic environment, and experiments conducted in the microscopic simulation environment indicated the performances of the proposed algorithms in a stochastic environment and revealed the feasibility in the real world. The implementation of DP ramp metering was proposed under the framework of receding horizon control. A 6.7km stretch of motorway in Auckland, New Zealand, was chosen as a study location and constructed by a microscopic simulator as a simulation scenario and by a macroscopic traffic model as a prediction model. The simulation results indicated that the proposed algorithms were able to eliminate motorway queues under high traffic demands and manage queue lengths at metered on-ramps when queue constrains were not overstrict. The simulation results also revealed that 9 discrete metering rates for each ramp meter were adequate to prevent motorway queues. Such feature not only proved that the optimal trajectory converged very fast in the proposed DP decision networks, but also made on-line control system possible due to less computational load.
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    Statistical modelling and inference for traffic networks : a thesis submitted for the degree of Doctor of Philosophy
    (Massey University, 2012) Parry, Katharina
    There are two facets that are important in providing reliable forecasts from observed traffi c data. The first is that the model used should describe and represent as many characteristics of the system as possible. The second is that the estimates of the model parameters need to be accurate. We begin with improved methods of statistical inference for various types of models and using various types of data; and then move onto the development of new models that describe the day-to-day dynamics of traffic systems. Calibration of transport models for traffic systems gives rise to a variety of statistical inference problems, such as estimation of travel demand parameters. Once the ways in which vehicles move through the network are known, statistical inference becomes straightforward, however, at present, the data available are predominantly vehicle counts from a set of links in the network. The fundamental problem is that these vehicle counts do not uniquely determine the route ows, as there are a large number of possible route ows that could have led to a given set of observed link counts. A solution to this problem is to simulate the latent route ows conditional on the observed link counts in a Markov Chain Monte Carlo sampling algorithm. This is challenging because the set of feasible route ows will typically be far too large to enumerate in practice, meaning that we must simulate from a set that we cannot fully specify. An innovative piece of work here was the extension of an existing sampling methodology that works only for linear networks to be applicable for tree networks. In simulation studies where we use the sample to estimate average route ows, we show that our method provides more reliable estimates than generalised least squares methods. This is to be expected given that our method exploits information available via second order properties of the link counts. We provide another demonstration of how this generalised sampler can be applied whenever the need to sample from the set of latent route ows is pivotal for making statistical inference. We use the sampler to estimate travel demand parameters for day-to-day dynamic process models, an important class of model where the data has been collected on successive days and hence allows for inference using the evolution of the traffic flows over time. A new type of data, route flows from tracked vehicles, is becoming increasingly available through emerging technologies. Our contribution was to develop a statistical likelihood model that incorporates this routing information into currently used link-count data only models. We derive some tractable normal approximations thereof and perform likelihood-based inference for these normal models under the assumption that the probability of vehicle tracking is known. In our analysis we find that the likelihood shows irregular behaviour due to boundary effects, and provide conditions under which such behaviour will be observed. For regular cases we outline connections with existing generalised least squares methods. The theoretical analysis are complemented by simulation studies where we consider the tracking probability to be unknown and the effects on the accuracy in estimation of origin-destination matrices under estimated and/or misspecified models for this parameter. Real link flow count data observed on a sequence of days can exhibit considerable day-to-day variability. A better understanding of such variability has increasing policy-relevance in the context of network reliability assessment and the design of intelligent transport systems. Conventional day-to-day dynamic traffic assignment models are limited in terms of the extent to which non-stationary changes in traffic flows can be represented. In this thesis we introduce and develop an advanced class of models by replacing a subset of the fixed parameters in currently used traffic models with random processes. These resulting models are analogous to Cox process models. They are conditionally non-stationary given any realisation of the parameter processes. Numerical examples demonstrate that this new class of doubly stochastic day-to-day traffic assignment models is able to reproduce features such as the heteroscedasticity of traffic flows observed in real-life settings.
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    Intelligent driver agent model for autonomous navigation in a computer simulated vehicular traffic network : a thesis presented in total fulfillment of the requirements for the degree of Master of Science in Computer Science at Massey University, Albany (Auckland), New Zealand
    (Massey University, 2010) Kotushevski, Gligor; Kotusheveski, Gligor
    The purpose of this study was to investigate the possibilities of automating vehicular traffic and decrease traffic congestion by developing an intelligent driver agent model that autonomously navigates through a computer simulated traffic network. The aim was to examine various path nding algorithms and cost evaluation functions through di erent traffic conditions so that a basic intelligent driver agent model is designed using the best combination of algorithms and cost functions found. A computer simulation of vehicular traffic has been implemented to study different agent models. The intelligent driver agents developed act as independent entities with their own emergent properties and individual behaviours. Each simulated vehicle was navigated through the traffic network to its destination using a user defined algorithm and cost function. The case studies conducted focused on measuring the travel times of each driver agent from the starting to the destination point. The results indicated that the agents traveled at higher average speeds under low density traffic conditions, while lowering their average speed as the traffic density increased. It was also discovered that hybrid cost evaluation functions (designed by combining two or more basic cost functions) perform better in low and medium density traffic, while basic cost functions perform better under high density traffic conditions. Finally, the results revealed that Dijkstra pathfinding using a hybrid combination of time and length cost functions should be used under low and medium density traffic conditions and D* pathfinding using congestion cost evaluation function under high density traffic conditions. The conclusion was that the intelligent driver agent model implemented is suitable to be used as a navigation model for self-driving vehicles in traffic simulation software, but also given the right technology and social acceptance it is suitable to be implemented as a navigation model for robot vehicles and deployed in real world traffic situations.
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    Theoretical investigation of traffic flow : inhomogeneity induced emergence : a dissertation presented in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Auckland, New Zealand
    (Massey University, 2010) Liu, Mingzhe
    This research work is focused on understanding the effects of inhomogeneity on traffic flow by theoretical analysis and computer simulations. Traffic has been observed at almost all levels of natural and manmade systems (e.g., from microscopic protein motors to macroscopic objects like cars). For these various traffic, basic and emer- gent phenomena, modelling methods, theoretical analysis and physical meanings are normally concerned. Inhomogeneity like bottlenecks may cause traffic congestions or motor protein crowding. The crowded protein motors may lead to some human diseases. The congested traffic patterns have not been understood well so far. The modelling method in this research is based on totally asymmetric simple exclusion process (TASEP). The following TASEP models are developed: TASEP with single inhomogeneity, TASEP with zoned inhomogeneity, TASEP with junction, TASEP with site sharing and different boundary conditions. These models are motivated by vehicular traffic, pedestrian trafficc, ant traffic, protein motor traffic and/or Internet traffic. Theoretical solutions for the proposed models are obtained and verified by Monte Carlo simulations. These theoretical results can be used as a base for further developments. The emergent properties such as phase transitions, phase separations and spontaneous symmetry breaking are observed and discussed. This study has contributed to a deeper understanding of generic traffic dynamics, particularly, in the presence of inhomogeneity, and has important implications for explanation or guidance of future traffic studies.
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    Genetic fuzzy logic approach to local ramp metering control using microscopic traffic simulation : a thesis presented in partial fulfillment of the requirements for the degree of Master of Engineering in Mechatronics at Massey University, Auckland, New Zealand
    (Massey University, 2009) Yu, Xue Feng
    Ramp metering, one of the most effective solutions for improving motorway traffic flows, is playing increasingly important role in traffic management systems. Because of its capability to handle nonlinear and non-stationary problems, fuzzy logic based ramp metering algorithms have been always considered as an extremely suitable control measures to handle a complex nonlinear traffic system. This thesis proposes a genetic fuzzy approach to design a traffic-responsive ramp control algorithm for an isolated onramp. For a local ramp meter algorithm, the problem could be described as the inflow optimization of on-ramp, based on the evaluation of motorway traffic condition. If the inflow of on-ramp is considered as the decision variable, the ramp control problem could be treated as a nonlinear optimization problem of maximizing the evaluation function. The adaptive genetic fuzzy approach is actually a control approach to maximize the inflow of on-ramp under the restriction of evaluation function. In this thesis, a well-known fuzzy logic based ramp metering algorithms developed by Bogenberger is introduced and implemented with an on-ramp congestion model of Constellation Drive Interchange in a stochastic microscopic traffic simulator, Aimsun. To improve the performance of fuzzy control system, genetic algorithm is applied to tune the parameterized membership function of each fuzzy input to maintain the flow density of motorway blow the estimated congestion density. The performances of the genetic fuzzy logic control ramp metering are compared with FLC (fuzzy logic control) ramp metering by means of the percentage change of TTT (Total Travel Time) based on no control condition in Aimsun. The simulation results show the genetic fuzzy ramp metering has a more significant improvement on TTT and more strong stability to maintain system flow density than FLC ramp metering.
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    Signalized fuzzy logic for diamond interchanges incorporating with fuzzy ramp system : a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering in Mechatronics at Massey University, Auckland, New Zealand
    (Massey University, 2009) Pham, Cao Van
    New dynamic signal control methods such as fuzzy logic and artificial intelligence developed recently mainly focused on isolated intersection. In this study, a Fuzzy Logic Control for a Diamond Interchange incorporating with Fuzzy Ramp System (FLDI) has been developed. The signalization of two closely spaced intersections in a diamond interchange is a complicated problem that includes both increasing the diamond interchange capacity and reduce delays at the same time. The model comprises of three main modules. The Fuzzy Phase Timing module controls the current phase green time extension, the Phase Selection module select the next phase based on the pre-defined phase sequence or phase logics and the Fuzzy Ramp module determines the cycle time of the ramp meter bases on current traffic volumes and conditions of the interchanges and the motorways. The developed FLDI model has been compared with the traffic actuated simulation with respects to flow rates and the average delays of the vehicles. The model of an actual diamond interchange is described and simulated by using AIMSUN (Advanced Interactive Microscopic Simulator for Urban and Non-Urban Network) software. Simulation results show the FLDI model outperformed the traffic actuated models with lower system total travel time, average delay and improvements in downstream average speed and average delay.