Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2007. / With the rapid advancement of communication technologies and broadband communication, an era is starting to emerge where everything and everyone is always connected, regardless of geography. No other technology has made this more possible than over-the-air data communications technologies such as Wi-Fi, WiMAX and cellular technologies. With the possibility of connecting more devices to a common communications network, more and more applications become available and necessary. One such application is a concept designed to manage a different type of network; the traffic networks of large metropolitan areas. These networks carry more traffic with each passing year and the need to manage them fficiently has become essential. A system to manage traffic networks is an intelligent transport system (ITS), which integrates all methods of transportation into a single manageable resource. Information about the current status of the traffic network can be relayed to road users, allowing them to make informed decisions about alternative routes, or to emergency personnel to inform them of accidents that occurred on the traffic networks. In order to implement an ITS, a communication network is required. This thesis investigates different communication technologies, discussing their merits and shortcomings in an ITS implementation. A suitable technology is selected and a communications system is conceptualised. The communications system is an ad hoc wireless network and a routing protocol used to manage the network, is designed and tested through simulation. The TH(O)RP routing protocol was developed with a focus on scalability, stability and low latency in an ad hoc network. TH(O)RP was designed to operate in an ITS environment, where traffic intersection controllers (TIC) are monitored from a central entity, with optimal routes between the central entity and the TICs, that can be automatically configured and repaired.

The aim of this study is to evaluate an alternative socio-economical valuation method (i.e., Hybrid Real Options, HRO) to the traditional benefit cost method (CBA) for the evaluation of investments within Intelligent Transportation Systems (ITS). The proposed alternative method will be evaluated by the use of a case study where it is applied and compared to the results of the traditional method. The case study evaluates the socio-economical effects of an investment in Variable Speed Limits along a section of the motorway E18. The results of the study shows that the choice of evaluation methods affects both the investment strategy and the estimated socio-economical benefits of the investment. Using the HRO method yields twice as high socio-economical benefits compared to the CBA method. The main reason for this being that HRO account for risk and uncertainties wheras CBA only accounts for the most probable outcome of the investment. The choice of method is a complex task that involves many stakeholders however a more critical approach to the choice of socio-economical evaluation method is advocated based on the results of this study.

Les systèmes de transport intelligents (STI) sont conçus pour améliorer les transports, réduire les accidents, le temps de transport et la consommation de carburant, tout en augmentant la sécurité, le confort et l'efficacité de conduite. L'objectif final de ITS est de développer ADAS pour faciliter les tâches de conduite, jusqu'au développement du véhicule entièrement automatisé. Les systèmes actuels ne sont pas assez robustes pour atteindre un niveau entièrement automatisé à court terme. Les environnements urbains posent un défi particulier, car le dynamisme de la scène oblige les algorithmes de navigation à réagir en temps réel aux éventuels changements, tout en respectant les règles de circulation et en évitant les collisions avec les autres usagers de la route. Sur cette base, cette thèse propose une approche de la planification locale en deux étapes pour apporter une solution au problème de la navigation en milieu urbain. Premièrement, les informations statiques des contraintes de la route et du véhicule sont considérées comme générant la courbe optimale pour chaque configuration de virage réalisable, où plusieurs bases de données sont générées en tenant compte de la position différente du véhicule aux points de début et de fin des courbes, permettant ainsi une analyse réaliste. planificateur de temps pour analyser les changements de concavité en utilisant toute la largeur de la voie. Ensuite, la configuration réelle de la route est envisagée dans le processus en temps réel, où la distance disponible et la netteté des virages à venir et consécutifs sont étudiées pour fournir un style de conduite à la manière humaine optimisant deux courbes simultanément, offrant ainsi un horizon de planification étendu. Par conséquent, le processus de planification en temps réel recherche le point de jonction optimal entre les courbes. Les critères d’optimalité minimisent à la fois les pics de courbure et les changements abrupts, en recherchant la génération de chemins continus et lisses. Quartic Béziers est l'algorithme d'interpolation utilisé en raison de ses propriétés, permettant de respecter les limites de la route et les restrictions cinématiques, tout en permettant une manipulation facile des courbes. Ce planificateur fonctionne à la fois pour les environnements statiques et dynamiques. Les fonctions d'évitement d'obstacles sont présentées en fonction de la génération d'une voie virtuelle qui modifie le chemin statique pour effectuer chacune des deux manoeuvres de changement de voie sous la forme de deux courbes, convertissant le problème en un chemin statique. Ainsi, une solution rapide peut être trouvée en bénéficiant du planificateur local statique. Il utilise une discrétisation en grille de la scène pour identifier l'espace libre nécessaire à la construction de la route virtuelle, où le critère de planification dynamique consiste à réduire la pente pour les changements de voie. Des essais de simulation et des tests expérimentaux ont été réalisés pour valider l'approche dans des environnements statiques et dynamiques, adaptant la trajectoire en fonction du scénario et du véhicule, montrant la modularité du système. / Intelligent Transportation Systems (ITS) developments are conceived to improve transportation reducing accidents, transport time and fuel consumption, while increasing driving security, comfort and efficiency. The final goal of ITS is the development of ADAS for assisting in the driving tasks, up to the development of the fully automated vehicle. Despite last ADAS developments achieved a partial-automation level, current systems are not robust enough to achieve fully-automated level in short term. Urban environments pose a special challenge, since the dynamism of the scene forces the navigation algorithms to react in real-time to the eventual changes, respecting at the same time traffic regulation and avoiding collisions with other road users. On this basis, this PhD thesis proposes a two-staged local planning approach to provide a solution to the navigation problem on urban environments. First, static information of both road and vehicle constraints is considered to generate the optimal curve for each feasible turn configuration, where several databases are generated taking into account different position of the vehicle at the beginning and ending points of the curves, allowing the real-time planner to analyze concavity changes making use of the full lane width.Then, actual road layout is contemplated in the real-time process, where both the available distance and the sharpness of upcoming and consecutive turns are studied to provide a human-like driving style optimizing two curves concurrently, offering that way an extended planning horizon. Therefore, the real-time planning process searches the optimal junction point between curves. Optimality criteria minimizes both curvature peaks and abrupt changes on it, seeking the generation of continuous and smooth paths. Quartic Béziers are the interpolating-based curve algorithm used due to their properties, allowing compliance with road limits and kinematic restrictions, while allowing an easy manipulation of curves. This planner works both for static and dynamic environments. Obstacle avoidance features are presented based on the generation of a virtual lane which modifies the static path to perform each of the two lane change maneuvers as two curves, converting the problem into a static-path following. Thus, a fast solution can be found benefiting from the static local planner. It uses a grid discretization of the scene to identify the free space to build the virtual road, where the dynamic planning criteria is to reduce the slope for the lane changes. Both simulation and experimental test have been carried out to validate the approach, where vehicles performs path following on static and dynamic environments adapting the path in function of the scenario and the vehicle, testing both with low-speed cybercars and medium-speed electic platforms, showing the modularity of the system.

No description available.

The United States has been going through a road accident crisis for many years. The National Safety Council estimates 40,000 people were killed and 4.57 million injured on U.S. roads in 2017. Direct and indirect loss from tra c congestion only is more than $140 billion every year. Vehicular Ad-hoc Networks (VANETs) are envisioned as the future of Intelligent Transportation Systems (ITSs). They have a great potential to enable all kinds of applications that will enhance road safety and transportation efficiency. In this dissertation, we have aggregated seven years of real-life tra c and incidents data, obtained from the Florida Department of Transportation District 4. We have studied and investigated the causes of road incidents by applying machine learning approaches to this aggregated big dataset. A scalable, reliable, and automatic system for predicting road incidents is an integral part of any e ective ITS. For this purpose, we propose a cloud-based system for VANET that aims at preventing or at least decreasing tra c congestions as well as crashes in real-time. We have created, tested, and validated a VANET traffic dataset by applying the connected vehicle behavioral changes to our aggregated dataset. To achieve the scalability, speed, and fault-tolerance in our developed system, we built our system in a lambda architecture fashion using Apache Spark and Spark Streaming with Kafka. We used our system in creating optimal and safe trajectories for autonomous vehicles based on the user preferences. We extended the use of our developed system in predicting the clearance time on the highway in real-time, as an important component of the traffic incident management system. We implemented the time series analysis and forecasting in our real-time system as a component for predicting traffic flow. Our system can be applied to use dedicated short communication (DSRC), cellular, or hybrid communication schema to receive streaming data and send back the safety messages. The performance of the proposed system has been extensively tested on the FAUs High Performance Computing Cluster (HPCC), as well as on a single node virtual machine. Results and findings confirm the applicability of the proposed system in predicting traffic incidents with low processing latency. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

Cheong Chi Hong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 81-86). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Problem statement --- p.1 / Chapter 1.2 --- Major contributions --- p.4 / Chapter 1.3 --- Thesis organization --- p.4 / Chapter 2 --- Smart Card System --- p.6 / Chapter 2.1 --- Introduction --- p.7 / Chapter 2.2 --- Related Work --- p.11 / Chapter 2.2.1 --- Mining Customer Behaviors --- p.11 / Chapter 2.2.2 --- Privacy Preserving Data Mining --- p.12 / Chapter 2.2.3 --- Definitions of Privacy --- p.13 / Chapter 2.3 --- Model --- p.14 / Chapter 2.4 --- Algorithms --- p.17 / Chapter 2.4.1 --- Baseline Algorithm --- p.17 / Chapter 2.4.2 --- Privacy Equation --- p.19 / Chapter 2.4.3 --- Random Subsequence Selection Algorithm (RSSA) --- p.20 / Chapter 2.4.4 --- Popular Item Selection Algorithm (PISA) --- p.21 / Chapter 2.5 --- Analysis --- p.26 / Chapter 2.5.1 --- Accuracy --- p.26 / Chapter 2.5.2 --- Analysis of RSSA: Determine te values --- p.27 / Chapter 2.5.3 --- Analysis of RSSA: Accuracy --- p.30 / Chapter 2.5.4 --- Analysis of PISA --- p.33 / Chapter 2.5.5 --- Theoretical Proof of PISA --- p.41 / Chapter 2.5.6 --- Privacy Protection --- p.42 / Chapter 2.6 --- Simulations --- p.45 / Chapter 3 --- Transportation System --- p.48 / Chapter 3.1 --- Introduction --- p.49 / Chapter 3.2 --- Related Work --- p.51 / Chapter 3.3 --- Model --- p.56 / Chapter 3.4 --- Algorithms --- p.60 / Chapter 3.5 --- Simulations --- p.63 / Chapter 4 --- Enhanced Features in a Smart Card System --- p.67 / Chapter 4.1 --- Adding Time Information --- p.68 / Chapter 4.1.1 --- Motivation --- p.68 / Chapter 4.1.2 --- Time Intervals --- p.68 / Chapter 4.1.3 --- Original Graph --- p.69 / Chapter 4.1.4 --- New Graph --- p.70 / Chapter 4.1.5 --- Adding the New Graph into the Model --- p.72 / Chapter 4.1.6 --- Rewriting the Definition of a Path --- p.73 / Chapter 4.1.7 --- Drawback of Adding Time Information --- p.73 / Chapter 4.2 --- Generalization --- p.75 / Chapter 4.2.1 --- Motivation --- p.75 / Chapter 4.2.2 --- Generalization --- p.75 / Chapter 4.3 --- Specialization vs. Generalization --- p.75 / Chapter 5 --- Conclusion --- p.79 / Bibliography --- p.81

Li Xiang. / "September 2004." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (p. 164-172) / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

As an example, the thesis proposes a real-time route guidance system to show how it supports other transportation services, which can then automatically guide vehicles by voice. It illustrates the system architecture and describes the establishment of each part. The concept of agent network is introduced to build up the system. Furthermore, a dynamic route algorithm is presented in brief. A communication system integrating the existing infrastructure is discussed and simulation results are provided to testify the applicability of the proposed wireless data communication system. / Finally, the thesis sums up the contributions achieved and proposes some future works. / For the communication network, the main challenging problems are the large scale of the network, the movement of vehicles that may cause the levity of the network structure, and the large demands on communication capacity. In order to solve these problems, the performance optimization technique is accredited as one of the most important techniques for such a large scale wireless sensor network. This thesis focuses on the research in the following aspects. First, the optimal combination of the duty cycle, one of the most important parameters, is introduced to optimize the system performance. A duty cycle optimization model is put forward based on calculating n-times reachable matrix. Now that the parameter optimization model can be boiled down to a NP-hard problem, an improved genetic algorithm is introduced to solve the problem. The computational procedure and efficiency are discussed, and simulation study based on a practical road network is given to illustrate the validity of the proposed method. Second, the topological structure optimization problem is formulated as a graph problem, while fulfilling random node-to-node communication demands. A new optimization method, called un-detour optimization, is proposed to optimize the topological structure based on the improved genetic algorithm. In addition, the approach is evaluated quantitatively by simulating community wireless sensor networks. The comparison results demonstrate that some significant performance advantages can be achieved by this approach. / In addition, two important techniques required to build the new surveillance system are discussed in this thesis. (1) the sensors to collect traffic information; (2) the communication network to transmit information among all sensors and vehicles. / In order to detect and track the moving objects, this thesis presents a creative background updating method, which can works effectively even for some complex circumstances. The image processing results show that this method can realize the segmentation of the moving objects. Due to the simple model and fast calculation speed, the method can satisfy the requirements of detecting and tracking traffic objects in real time and at a high speed. Additionally, the thesis designs a new kind of object detection and tracking algorithm based on the attributive combination of contour and color in order to deal with the occlusion problem to some extent. Some experiments have testified to the robustness and practicability of the proposed system. / Nowadays, with the rapid development of economics and societies, transportation is playing a very important role in the balanced running of social and economic systems. However, urban traffic problems such as traffic accidents and traffic congestions are becoming more and more serious in almost all large cities in the world. / This thesis is focused on a traffic surveillance system which collects and transmits real-time traffic information in a large city, which is one of the most important steps in solving the transportation problems above. Considering the drawbacks of current traffic surveillance system, a brand-new system with a distributed architecture is proposed based on the concept of sensor networks. Then, an intelligent sensor node using an embedded ARM chip and MCU is developed and software system is built up accordingly, including Linux operating system, hardware drivers, and so on. Finally, a simulation program proves the validity of the system. / Shi, Xi. / "September 2007." / Adviser: YangShong Xu. / Source: Dissertation Abstracts International, Volume: 69-08, Section: B, page: 4946. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (p. 120-130). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

Today transportation systems are facing big transitions all over the world. We created fly overs, roads under the ground, bridges over the river and ocean to get efficient access and to increase the road connectivity. Our transportation system is more intelligent than ever. Our traffic signaling system became adaptive. Our vehicles equipped with new gadgets and we developed new tools for more efficient analysis of traffic. Our research relies on existing traffic infrastructure to generate better understanding of traffic. More specifically, this research focused on traffic and UAV cameras to extract information about the traffic. Our first goal was to create an automatic system to count the cars using traffic cameras. To achieve this goal, we implemented Background Subtraction Method (BSM) and OverFeat Framework. BSM compares consecutive frames to detect the moving objects. Because BSM only works for ideal lab conditions, therefor we implemented a Convolutional Neural Network (CNN) based classification algorithm called OverFeat Framework. We created different segments on the road in various lanes to tabulate the number of passing cars. We achieved 96.55% accuracy for car counting irrespective of different visibility conditions of the day and night. Our second goal was to find out traffic density. We implemented two CNN based algorithms: Single Shot Detection (SSD) and MobileNet-SSD for vehicle detection. These algorithms are object detection algorithms. We used traffic cameras to detect vehicles on the roads. We utilized road markers and light pole distances to determine distances on the road. Using the distance and count information we calculated density. SSD is a more resource intense algorithm and it achieved 92.97% accuracy. MobileNet-SSD is a lighter algorithm and it achieved 79.30% accuracy. Finally, from a moving platform we estimated the velocity of multiple vehicles. There are a lot of roads where traffic cameras are not available, also traffic monitoring is necessary for special events. We implemented Faster R-CNN as a detection algorithm and Discriminative Correlation Filter (with Channel and Spatial Reliability Tracking) for tracking. We calculated the speed information from the tracking information in our study. Our framework achieved 96.80% speed accuracy compared to manual observation of speeds. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2019. / FAU Electronic Theses and Dissertations Collection

Automatic Incident Detection Algorithms (AIDA) have been part of freeway management system software from the beginnings of ITS deployment. These algorithms introduce the capability of detecting incidents on freeways using traffic operations data. Over the years, several approaches to incident detection have been studied and tested. However, the size and scope of the urban transportation networks under direct monitoring by transportation management centers are growing at a faster rate than are staffing levels and center resources. This has entailed a renewed emphasis on the need for reliability and accuracy of AIDA functionality. This study investigates a new approach to incident detection that promises a significant improvement in operational performance. This algorithm is formulated on the premise that the current conditions facilitate the prediction of future traffic conditions, and deviations of observations from the predictions beyond a calibrated level of tolerance indicate the occurrence of incidents. This algorithm is specifically designed for easy implementation and calibration at any site. Offline tests with data from the Georgia-Navigator system indicate that this algorithm realizes a substantial improvement over the conventional incident detection algorithms. This algorithm not only achieves a low rate of false alarms but also ensures a high detection rate.