51 |
A strategic vision of AVCS maglev and its socioeconomic implicationsLee, Sang Hyup 05 October 2007 (has links)
The purpose of this research is to develop a conception of a transportation system called AVCS maglev which is the synergistic combination of two promising concepts, AVCS and Maglev, and to assess its potential as a transportation strategy to cope with the forthcoming challenge of the mobility, safety, environmental protection, and economic growth of the United States. The emphases are put on investigating suitable technological aspects, selecting suitable operational control concepts, assessing economic viability, and determining socioeconomic impacts of the system. Also, the National Development Model (NDM) is developed and analyzed to obtain a deeper understanding of the rational policy formation about the U.S. SOCioeconomic development of the next century, based on the premise that development means improving both quantity of life and quality of life. NDM is organized into six sectors: (1) Industrial Sector, (2) Environmental Sector, (3) Infrastructure Sector, (4) Social Development Sector, (5) Demographic Sector, and (6) Employment Sector. Four policy alternatives are identified, based on the key issues relevant to the future development patterns, and analyzed by computer simulation: (1) Social Development Policy, (2) Industrial Development Policy, (3) Infrastructure Development Policy, and (4) Environmental Protection Policy. / Ph. D.
|
52 |
Context aware pre-crash system for vehicular ad hoc networks using dynamic Bayesian modelAswad, Musaab Z. January 2014 (has links)
Tragically, traffic accidents involving drivers, motorcyclists and pedestrians result in thousands of fatalities worldwide each year. For this reason, making improvements to road safety and saving people's lives is an international priority. In recent years, this aim has been supported by Intelligent Transport Systems, offering safety systems and providing an intelligent driving environment. The development of wireless communications and mobile ad hoc networks has led to improvements in intelligent transportation systems heightening these systems' safety. Vehicular ad hoc Networks comprise an important technology; included within intelligent transportation systems, they use dedicated short-range communications to assist vehicles to communicate with one another, or with those roadside units in range. This form of communication can reduce road accidents and provide a safer driving environment. A major challenge has been to design an ideal system to filter relevant contextual information from the surrounding environment, taking into consideration the contributory factors necessary to predict the likelihood of a crash with different levels of severity. Designing an accurate and effective pre-crash system to avoid front and back crashes or mitigate their severity is the most important goal of intelligent transportation systems, as it can save people's lives. Furthermore, in order to improve crash prediction, context-aware systems can be used to collect and analyse contextual information regarding contributory factors. The crash likelihood in this study is considered to operate within an uncertain context, and is defined according to the dynamic interaction between the driver, the vehicle and the environment, meaning it is affected by contributory factors and develops over time. As a crash likelihood is considered to be an uncertain context and develops over time, any usable technology must overcome this uncertainty in order to accurately predict crashes. This thesis presents a context-aware pre-crash collision prediction system, which captures information from the surrounding environment, the driver and other vehicles on the road. It utilises a Dynamic Bayesian Network as a reasoning model to predict crash likelihood and severity level, whether any crash will be fatal, serious, or slight. This is achieved by combining the above mentioned information and performing probabilistic reasoning over time. The thesis introduces novel context aware on-board unit architecture for crash prediction. The architecture is divided into three phases: the physical, the thinking and the application phase; these which represent the three main subsystems of a context-aware system: sensing, reasoning and acting. In the thinking phase, a novel Dynamic Bayesian Network framework is introduced to predict crash likelihood. The framework is able to perform probabilistic reasoning to predict uncertainty, in order to accurately predict a crash. It divides crash severity levels according to the UK department for transport, into fatal, serious and slight. GeNIe version 2.0 software was used to implement and verify the Dynamic Bayesian Network model. This model has been verified using both syntactical and real data provided by the UK department for transport in order to demonstrate the prediction accuracy of the proposed model and to demonstrate the importance of including a large amount of contextual information in the prediction process. The evaluation of the proposed system delivered high-fidelity results, when predicting crashes and their severity. This was judged by inputting different sensor readings and performing several experiments. The findings of this study has helped to predict the probability of a crash at different severity levels, accounting for factors that may be involved in causing a crash, thereby representing a valuable step towards creating a safer traffic network.
|
53 |
Towards a non-intrusive traffic surveillance system using digital image processingLorio, Berino 12 1900 (has links)
Thesis (MScEng)--Stellenbosch University, 2001. / ENGLISH ABSTRACT: With the increased focus on the use of innovative and state-of-the-art technology in
Intelligent Transport Systems (ITS), the need for more accurate and more detailed road
traffic flow data has become apparent. Data obtained from vehicle detector loops, which
merely act as vehicle presence sensors, is neither reliable nor accurate enough anymore.
This type of sensor poses the problem that it has to be inserted into the road surface;
temporarily obstructing traffic flows, and has to be replaced after pavement
reconstruction. One of the solutions to this problem is to develop a traffic surveillance
system that uses video image processing.
In cities where Intelligent Transport Systems are used extensively, roadways are
monitored through Closed Circuit Television Cameras (CCTV) that are closely watched
by traffic control centre personnel. These cameras are mounted on posts on the roadside.
These cameras can serve a dual purpose, being used for both human monitoring and as
inputs to Video Image Processing Systems.
In this study some of the digital image processing techniques that could be used in a
traffic surveillance system were investigated. This report leads the reader through the
various steps in the processing of a scene by a traffic surveillance system based on
feature tracking, and discusses the pitfalls and problems that are experienced.
The tracker was tested using three image sequences and the results are presented in the
final chapter of this report. / AFRIKAANSE OPSOMMING: Met die toenemende fokus op die gebruik van innoverende oplossings en gevorderde
tegnologie in Intelligente Vervoerstelsels, het die noodsaaklikheid van akkurater en meer
gedetailleerde padverkeer vloeidata duidelik geword. Data wat verkry word d.m.v.
voertuig deteksie lusse, wat alleenlik voertuig teenwoordigheid/afwesigheid meet, is nie
meer akkuraat of betroubaar genoeg nie. Hierdie tipe sensors het egter die nadeel dat dit
in die plaveisel ingesny moet word, dus vloei tydelik kan belemmer, en moet vervang
word elke keer as plaveisel rekonstruksie gedoen word. Een van die oplossings vir hierdie
probleem is om 'n verkeers waarnemingstelsel te ontwikkel wat van videobeeldverwerking
gebruik maak.
In stede waar van uitgebreide intelligente verkeerstelsels gebruik gemaak word, word
paaie gemonitor d.m.v. geslote baan televisiekameras wat op pale langs die paaie
aangebring is. Personeellede van die verkeers beheer sentrum hou dan die inkomende
televisiebeelde dop. Hierdie kameras kan 'n dubelle rol vervul deurdat dit vir beide
menslike waarneming en as invoer in 'n video-beeldverwerking stelsel gebruik kan word.
In hierdie studie was verskeie digitale beeldverwerking tegnieke wat gebruik kan word in
'n verkeers waarnemingstelsel ondersoek. Hierdie verslag lei die leser deur die verskeie
stappe in die verwerking van 'n toneel deur 'n verkeers waarneming stelsel wat gebaseer
is op die volg van kenmerke. Die verslag beskryf ook die slaggate en probleme wat
ondervind word.
Die voertuig volger was getoets deur van drie reekse beelde gebruik te maak en die
resultate word weergegee in die finale hoodfstuk van hierdie verslag.
|
54 |
Using ad hoc wireless networks to enable intelligent transport systems: the design and analysis of the TH(O)RP routing protocolMorrison, Daniel Weich 03 1900 (has links)
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.
|
55 |
Intelligent Transportation Systems : Capturing the socio-economic value of uncertain and flexible investmentsAndersson, David, Robertsson, Simon January 2017 (has links)
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.
|
56 |
Bayesian-based Traffic State Estimation in Large-Scale Networks Using Big DataGu, Yiming 01 February 2017 (has links)
Traffic state estimation (TSE) aims to estimate the time-varying traffic characteristics (such as flow rate, flow speed, flow density, and occurrence of incidents) of all roads in traffic networks, provided with limited observations in sparse time and locations. TSE is critical to transportation planning, operation and infrastructure design. In this new era of “big data”, massive volumes of sensing data from a variety of source (such as cell phones, GPS, probe vehicles, and inductive loops, etc.) enable TSE in an efficient, timely and accurate manner. This research develops a Bayesian-based theoretical framework, along with statistical inference algorithms, to (1) capture the complex flow patterns in the urban traffic network consisting both highways and arterials; (2) incorporate heterogeneous data sources into the process of TSE; (3) enable both estimation and perdition of traffic states; and (4) demonstrate the scalability to large-scale urban traffic networks. To achieve those goals, a Hierarchical Bayesian probabilistic model is proposed to capture spatio-temporal traffic states. The propagation of traffic states are encapsulated through mesoscopic network flow models (namely the Link Queue Model) and equilibrated fundamental diagrams. Traffic states in the Hierarchical Bayesian model are inferred using the Expectation-Maximization Extended Kalman Filter (EM-EKF). To better estimate and predict states, infrastructure supply is also estimated as part of the TSE process. It is done by adopting a series of algorithms to translate Twitter data into traffic incident information. Finally, the proposed EM-EKF algorithm is implemented and examined on the road networks in Washington DC. The results show that the proposed methods can handle large-scale traffic state estimation, while achieving superior results comparing to traditional temporal and spatial smoothing methods.
|
57 |
Planification locale de trajectoires à deux étapes basée sur l’interpolation des courbes optimales pré-planifiées pour une conduite humaine en milieu urbain / Two-staged local trajectory planning based on optimal pre-planned curves interpolation for human-like driving in urban areasGarrido Carpio, Fernando José 04 December 2018 (has links)
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.
|
58 |
Deep Learning Metadata Fusion for Traffic Light to Lane AssignmentLangenberg, Tristan Matthias 26 July 2019 (has links)
No description available.
|
59 |
Incentives for user-generated content in intelligent transportation systems : Which incentives are useful for increasing quality content in the field of intelligent transportation system traffic applications?Kemppainen, Anton, Wikström Wirén, Arvid January 2018 (has links)
For applications that rely on User-Generated Content (UGC), there is a need to find what may motivate the applications user base to consistently contribute with quality content. One category of such applications is Intelligent Transportation Systems (ITS) traffic applications, which serve a specific goal; providing useful traffic-oriented content. By implementing useful incentives into Intelligent Transportation System traffic applications, the applications can better serve their purposes, and at the same time, improve their user's experience. Incentives are intrinsic or extrinsic, i.e., the motivation comes from internal- or external stimuli, which can motivate users in different ways and produce different incentive outcomes. To find the most useful incentives, and gain a better understanding of how to best stimulate active application participation, the research question addressed by this thesis is: Which incentives are useful for increasing quality content in the field of ITS traffic applications? The main method employed to address the research question was a survey. The survey was carried out to investigate what people thought was motivating in ITS traffic applications. In addition to the survey, an interview with the project manager of a Swedish ITS traffic application was done. Previous research concludes that the gain and the incentive for people or organizations hosting UGC are apparent, but the gain for the creators is not as clearly recognized and varies in which area the content is created. The findings of this study showed, from a user perspective, an interest in helping others and monetary gain, as potential incentives for implementation. The authors concluded that intrinsic inclined incentives should work better in-line with the goal of functionality and user long-term engagement, which the authors believe would be preferable for UGC based ITS traffic applications. These findings will be useful for understanding the optimal way to increase motivation for adequate quality UGC in ITS traffic applications.
|
60 |
Real-time traffic incidents prediction in vehicular networks using big data analyticsUnknown Date (has links)
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
|
Page generated in 0.1862 seconds