The collection and use of road and traffic information for road accident investigation and prevention

DUE TO COPYRIGHT RESTRICTIONS ONLY AVAILABLE FOR CONSULTATION AT ASTON UNIVERSITY LIBRARY AND INFORMATION SERVICES WITH PRIOR ARRANGEMENT

Mobile application as a tool for urban traffic data collection and generation to advanced traveler information systems using Wi-Fi networks available in urban centers

This paper presents an Advanced Traveler Information System (ATIS) developed on Android platform, which is open source and free. The developed application has as its main objective the free use of a Vehicle-to- Infrastructure (V2I) communication through the wireless network access points available in urban centers. In addition to providing the necessary information for an Intelligent Transportation System (ITS) to a central server, the application also receives the traffic data close to the vehicle. Once obtained this traffic information, the application displays them to the driver in a clear and efficient way, allowing the user to make decisions about his route in real time. The application was tested in a real environment and the results are presented in the article. In conclusion we present the benefits of this application. © 2012 IEEE.

An object-oriented neural network approach to short-term traffic forecasting

This paper discusses an object-oriented neural network model that was developed for predicting short-term traffic conditions on a section of the Pacific Highway between Brisbane and the Gold Coast in Queensland, Australia. The feasibility of this approach is demonstrated through a time-lag recurrent network (TLRN) which was developed for predicting speed data up to 15 minutes into the future. The results obtained indicate that the TLRN is capable of predicting speed up to 5 minutes into the future with a high degree of accuracy (90-94%). Similar models, which were developed for predicting freeway travel times on the same facility, were successful in predicting travel times up to 15 minutes into the future with a similar degree of accuracy (93-95%). These results represent substantial improvements on conventional model performance and clearly demonstrate the feasibility of using the object-oriented approach for short-term traffic prediction. (C) 2001 Elsevier Science B.V. All rights reserved.

An agent-based approach to modelling driver route choice behaviour under the influence of real-time information

This paper presents an agent-based approach to modelling individual driver behaviour under the influence of real-time traffic information. The driver behaviour models developed in this study are based on a behavioural survey of drivers which was conducted on a congested commuting corridor in Brisbane, Australia. Commuters' responses to travel information were analysed and a number of discrete choice models were developed to determine the factors influencing drivers' behaviour and their propensity to change route and adjust travel patterns. Based on the results obtained from the behavioural survey, the agent behaviour parameters which define driver characteristics, knowledge and preferences were identified and their values determined. A case study implementing a simple agent-based route choice decision model within a microscopic traffic simulation tool is also presented. Driver-vehicle units (DVUs) were modelled as autonomous software components that can each be assigned a set of goals to achieve and a database of knowledge comprising certain beliefs, intentions and preferences concerning the driving task. Each DVU provided route choice decision-making capabilities, based on perception of its environment, that were similar to the described intentions of the driver it represented. The case study clearly demonstrated the feasibility of the approach and the potential to develop more complex driver behavioural dynamics based on the belief-desire-intention agent architecture. (C) 2002 Elsevier Science Ltd. All rights reserved.

A Beacon based Localization System for Autonomous Mobile Robots and its Applications in Traffic and Transport Control

ACCURATE sensing of vehicle position and attitude is still a very challenging problem in many mobile robot applications. The mobile robot vehicle applications must have some means of estimating where they are and in which direction they are heading. Many existing indoor positioning systems are limited in workspace and robustness because they require clear lines-of-sight or do not provide absolute, driftfree measurements.The research work presented in this dissertation provides a new approach to position and attitude sensing system designed specifically to meet the challenges of operation in a realistic, cluttered indoor environment, such as that of an office building, hospital, industrial or warehouse. This is accomplished by an innovative assembly of infrared LED source that restricts the spreading of the light intensity distribution confined to a sheet of light and is encoded with localization and traffic information. This Digital Infrared Sheet of Light Beacon (DISLiB) developed for mobile robot is a high resolution absolute localization system which is simple, fast, accurate and robust, without much of computational burden or significant processing. Most of the available beacon's performance in corridors and narrow passages are not satisfactory, whereas the performance of DISLiB is very encouraging in such situations. This research overcomes most of the inherent limitations of existing systems.The work further examines the odometric localization errors caused by over count readings of an optical encoder based odometric system in a mobile robot due to wheel-slippage and terrain irregularities. A simple and efficient method is investigated and realized using an FPGA for reducing the errors. The detection and correction is based on redundant encoder measurements. The method suggested relies on the fact that the wheel slippage or terrain irregularities cause more count readings from the encoder than what corresponds to the actual distance travelled by the vehicle.The application of encoded Digital Infrared Sheet of Light Beacon (DISLiB) system can be extended to intelligent control of the public transportation system. The system is capable of receiving traffic status input through a GSM (Global System Mobile) modem. The vehicles have infrared receivers and processors capable of decoding the information, and generating the audio and video messages to assist the driver. The thesis further examines the usefulness of the technique to assist the movement of differently-able (blind) persons in indoor or outdoor premises of his residence.The work addressed in this thesis suggests a new way forward in the development of autonomous robotics and guidance systems. However, this work can be easily extended to many other challenging domains, as well.

A Novel Framework for Real-time Traffic Flow Parameter Estimation from Aerial Videos

Thesis (Master's)--University of Washington, 2016-06

An adaptive IEEE 802.15.4 MAC layer transmission scheme for applications with heterogeneous traffic

IEEE 802.15.4 networks has the features of low data rate and low power consumption. It is a strong candidate technique for wireless sensor networks and can find many applications to smart grid. However, due to the low network and energy capacities it is critical to maximize the bandwidth and energy efficiencies of 802.15.4 networks. In this paper we propose an adaptive data transmission scheme with CSMA/CA access control, for applications which may have heavy traffic loads such as smart grids. The adaptive access control is simple to implement. Its compatibility with legacy 802.15.4 devices can be maintained. Simulation results demonstrate the effectiveness of the proposed scheme with largely improved bandwidth and power efficiency. © 2013 International Information Institute.

Mitigating congestion by integrating time forecasting and realtime information aggregation in cellular networks

An iterative travel time forecasting scheme, named the Advanced Multilane Prediction based Real-time Fastest Path (AMPRFP) algorithm, is presented in this dissertation. This scheme is derived from the conventional kernel estimator based prediction model by the association of real-time nonlinear impacts that caused by neighboring arcs’ traffic patterns with the historical traffic behaviors. The AMPRFP algorithm is evaluated by prediction of the travel time of congested arcs in the urban area of Jacksonville City. Experiment results illustrate that the proposed scheme is able to significantly reduce both the relative mean error (RME) and the root-mean-squared error (RMSE) of the predicted travel time. To obtain high quality real-time traffic information, which is essential to the performance of the AMPRFP algorithm, a data clean scheme enhanced empirical learning (DCSEEL) algorithm is also introduced. This novel method investigates the correlation between distance and direction in the geometrical map, which is not considered in existing fingerprint localization methods. Specifically, empirical learning methods are applied to minimize the error that exists in the estimated distance. A direction filter is developed to clean joints that have negative influence to the localization accuracy. Synthetic experiments in urban, suburban and rural environments are designed to evaluate the performance of DCSEEL algorithm in determining the cellular probe’s position. The results show that the cellular probe’s localization accuracy can be notably improved by the DCSEEL algorithm. Additionally, a new fast correlation technique for overcoming the time efficiency problem of the existing correlation algorithm based floating car data (FCD) technique is developed. The matching process is transformed into a 1-dimensional (1-D) curve matching problem and the Fast Normalized Cross-Correlation (FNCC) algorithm is introduced to supersede the Pearson product Moment Correlation Co-efficient (PMCC) algorithm in order to achieve the real-time requirement of the FCD method. The fast correlation technique shows a significant improvement in reducing the computational cost without affecting the accuracy of the matching process.

SIAT:sistema de informações e alertas de trânsito

A evolução tecnológica e das sociedades permitiu que, hoje em dia, uma boa parte da população tenha acesso a dispositivos móveis com funcionalidades avançadas. Com este tipo de dispositivos, temos acesso a inúmeras fontes de informação em tempo-real, mas esta característica ainda não é, hoje em dia, aproveitada na sua totalidade. Este projecto tenta tirar partido desta realidade para, utilizando os diversos dispositivos móveis, criar uma rede de troca de informações de trânsito. O utilizador apenas necessita de servir-se do seu dispositivo móvel para, automaticamente, obter as mais recentes informações de trânsito enquanto, paralelamente, partilha com os outros utilizadores a sua informação. Apesar de existirem outras alternativas no mercado, com soluções que permitem usufruir do mesmo tipo de funcionalidades, nenhuma utiliza este tipo de dispositivos (GPS’s convencionais, por exemplo). Um dos requisitos necessário na implementação deste projecto é uma solução de geocoding. Após terem sido testadas várias soluções, nenhuma cumpria, na totalidade, os requisitos deste projecto, o que originou o desenvolvimento de uma nova solução que cumpre esses requisitos. A solução é, toda ela, muito modular, formada por vários componentes, cada um com responsabilidades bem identificadas. A arquitectura desta solução baseia-se nos padrões de desenvolvimento de uma Service Oriented Architecture. Todos os componentes disponibilizam as suas operações através de web services, e a sua descoberta recorre ao protocolo WS-Discovery. Estes vários componentes podem ser divididos em duas categorias: os do núcleo, responsáveis por criar e oferecer as funcionalidades requisitadas neste projecto e os módulos externos, nos quais se incluem as aplicações que apresentam as funcionalidades ao utilizador. Foram criadas duas formas de consumir a informação oferecida pelo serviço SIAT: a aplicação móvel e um website. No âmbito dos dispositivos móveis, foi desenvolvida uma aplicação para o sistema operativo Windows Phone 7.

A Smartphone-Based Prototype System for Incident/Work Zone Management Driven by Crowd-Sourced Data, 2015

This project develops a smartphone-based prototype system that supplements the 511 system to improve its dynamic traffic routing service to state highway users under non-recurrent congestion. This system will save considerable time to provide crucial traffic information and en-route assistance to travelers for them to avoid being trapped in traffic congestion due to accidents, work zones, hazards, or special events. It also creates a feedback loop between travelers and responsible agencies that enable the state to effectively collect, fuse, and analyze crowd-sourced data for next-gen transportation planning and management. This project can result in substantial economic savings (e.g. less traffic congestion, reduced fuel wastage and emissions) and safety benefits for the freight industry and society due to better dissemination of real-time traffic information by highway users. Such benefits will increase significantly in future with the expected increase in freight traffic on the network. The proposed system also has the flexibility to be integrated with various transportation management modules to assist state agencies to improve transportation services and daily operations.

Helsingin seudun liikenteenhallinnan kärkihankkeiden päivitys 2011

Työn tavoitteena oli kokonaisnäkemyksen luominen Helsingin seudun liikenteenhallinnan tilasta ja kehitysnäkymistä sekä seuraavien tärkeimpien kehittämispolkujen tunnistaminen ja konkretisointi toimenpiteiksi. Työssä keskityttiin seuraaviin painopistealueisiin 1. Liityntäpysäköinnin informaation ja maksujärjestelmien kehittäminen 2. Liikenteenhallintakeskuksen toiminnan kehittäminen ja laajentaminen 3. Liikennejärjestelmän reaaliaikainen tilannekuva ja lyhyen aikavälin ennusteet 4. Pääkaupunkiseudun pääväylien ruuhkien ja häiriöiden hallinta 5. Joukkoliikenteen ajantasaisen matkustajainformaation kehittäminen. Työssä laadittiin katsaus kullakin painopistealueella jo tapahtuneeseen sekä käynnissä olevaan kehitystyöhön, kuvattiin esiin nousseita ongelmia ja laadittiin esitys vuoteen 2020 ulottuvasta toiminnallisesta tavoitetilasta sekä konkreettisista kehittämistoimista. Työssä järjestettiin myös tulevaisuustyöpaja, johon pyydettiin alustuksia valikoiduilta viranomaistahoilta, tutkijoilta ja palveluntuottajilta. Näiden keskustelujen kautta kirkastettiin seudun toimijoiden näkemystä älyliikenteen toimialan kehitysnäkymistä ja –tarpeista. Työn lopputuloksena syntyneeseen toimenpideohjelmaan kirjattiin yhteensä 30 hanketta. Näistä viisi hanketta valittiin työpajatyöskentelyn kautta varsinaisiksi kärkihankkeiksi, joiden toteutus on kriittistä liikkujien ja viranomaistyön palvelutason kannalta ja joka edellyttää usean toimijan yhteistyötä. Lisäksi tunnistettiin joukko seurattavia hankkeita. Valitut kärkihankkeet, käynnistämisen vastuutaho sekä tavoitteellinen käynnistysvuosi ovat seuraavat: 1. Liityntäpysäköinnin dynaamisen informaatiojärjestelmän pilotointi Hämeenlinnanväylän käytävässä Kehäradan asemilla (HSL 2012) 2. HSL:n alueen joukkoliikenteen häiriönhallinnan uudelleenorganisointi (HSL, Liikennevirasto 2012) 3. Seudullisen liikenteenhallintasuunnitelman laadinta verkollisen operoinnin kehittämiseksi häiriötilanteissa (ELY-keskus, Liikennevirasto 2013) 4. Reaaliaikaisen sujuvuustiedon tuottaminen ruuhkautuvalta pääkatu- ja alempiasteiselta maantieverkolta (Liikennevirasto, kunnat 2012) 5. Liikenteen vaihtuvan ohjauksen ja tiedottamisen hyödyntäminen pääväylien ruuhkautumisen ja häiriöiden hallinnassa. (ELY-keskus 2012) Kärkihankkeisiin ei liity sellaisia riippuvuuksia, että niitä ei voitaisi käynnistää ennen päätöksentekoa jostakin muusta investoinnista. Merkittävimmät riippuvuudet liittyvät LIJ2014-järjestelmän ajoneuvojen paikannuksen ja muiden työkalujen valmistumiseen. Johtoryhmän linjauksen mukaan kärkihankkeissa ja muissakin toimijoiden omissa hankkeissa syntyvien tietojärjestelmien rajapinnat avataan soveltuvasti kaupallisten toimijoiden käyttöön, jolloin syntyvien kaupallisten loppukäyttäjäpalveluiden kautta voidaan parantaa tietopalvelujen tavoittavuutta. Kärkihankkeiden valinta ja sisältö on rakennettu pitkälti sen lähtökohdan varaan, että liikenteen tietopalveluissa julkistoimijoiden roolina on pääasiassa lähtötietojen tuottaminen ja jakaminen varsinaisille palveluntuottajille. Vuoteen 2016 mennessä uusien rajapintojen kautta jaettavien tietojen määrä on nykyiseen verrattuna huomattavasti laajempi, ja odotukset uusien ja innovatiivisten palvelujen syntymiselle ovat korkeat. HLH-johtoryhmä nimeää jokaisen kärkihankkeen läpiviennistä vastaavan työryhmän, joiden työskentelyä ohjataan nykyisen kaltaisessa johtoryhmätyössä. Johtoryhmätyön organisointi on todettu toimivaksi, sillä ryhmä koostuu seudun 14 kunnan alueen keskeisistä julkisista toimijoista ja sen kautta on olemassa vahvat kytkennät sekä seudun liikennejärjestelmätyöhön (HLJ) että valtakunnalliseen älyliikenteen kehitystyöhön.

Adaptive Routing Approaches for Networked Many-Core Systems

Through advances in technology, System-on-Chip design is moving towards integrating tens to hundreds of intellectual property blocks into a single chip. In such a many-core system, on-chip communication becomes a performance bottleneck for high performance designs. Network-on-Chip (NoC) has emerged as a viable solution for the communication challenges in highly complex chips. The NoC architecture paradigm, based on a modular packet-switched mechanism, can address many of the on-chip communication challenges such as wiring complexity, communication latency, and bandwidth. Furthermore, the combined benefits of 3D IC and NoC schemes provide the possibility of designing a high performance system in a limited chip area. The major advantages of 3D NoCs are the considerable reductions in average latency and power consumption. There are several factors degrading the performance of NoCs. In this thesis, we investigate three main performance-limiting factors: network congestion, faults, and the lack of efficient multicast support. We address these issues by the means of routing algorithms. Congestion of data packets may lead to increased network latency and power consumption. Thus, we propose three different approaches for alleviating such congestion in the network. The first approach is based on measuring the congestion information in different regions of the network, distributing the information over the network, and utilizing this information when making a routing decision. The second approach employs a learning method to dynamically find the less congested routes according to the underlying traffic. The third approach is based on a fuzzy-logic technique to perform better routing decisions when traffic information of different routes is available. Faults affect performance significantly, as then packets should take longer paths in order to be routed around the faults, which in turn increases congestion around the faulty regions. We propose four methods to tolerate faults at the link and switch level by using only the shortest paths as long as such path exists. The unique characteristic among these methods is the toleration of faults while also maintaining the performance of NoCs. To the best of our knowledge, these algorithms are the first approaches to bypassing faults prior to reaching them while avoiding unnecessary misrouting of packets. Current implementations of multicast communication result in a significant performance loss for unicast traffic. This is due to the fact that the routing rules of multicast packets limit the adaptivity of unicast packets. We present an approach in which both unicast and multicast packets can be efficiently routed within the network. While suggesting a more efficient multicast support, the proposed approach does not affect the performance of unicast routing at all. In addition, in order to reduce the overall path length of multicast packets, we present several partitioning methods along with their analytical models for latency measurement. This approach is discussed in the context of 3D mesh networks.

Generating descriptions that summarize geospatial and temporal data

Effective data summarization methods that use AI techniques can help humans understand large sets of data. In this paper, we describe a knowledge-based method for automatically generating summaries of geospatial and temporal data, i.e. data with geographical and temporal references. The method is useful for summarizing data streams, such as GPS traces and traffic information, that are becoming more prevalent with the increasing use of sensors in computing devices. The method presented here is an initial architecture for our ongoing research in this domain. In this paper we describe the data representations we have designed for our method, our implementations of components to perform data abstraction and natural language generation. We also discuss evaluation results that show the ability of our method to generate certain types of geospatial and temporal descriptions.