178 resultados para Demand-Responsive Transportation Systems.


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Dedicated Short Range Communication (DSRC) is the emerging key technology supporting cooperative road safety systems within Intelligent Transportation Systems (ITS). The DSRC protocol stack includes a variety of standards such as IEEE 802.11p and SAE J2735. The effectiveness of the DSRC technology depends on not only the interoperable cooperation of these standards, but also on the interoperability of DSRC devices manufactured by various manufacturers. To address the second constraint, the SAE defines a message set dictionary under the J2735 standard for construction of device independent messages. This paper focuses on the deficiencies of the SAE J2735 standard being developed for deployment in Vehicular Ad-hoc Networks (VANET). In this regard, the paper discusses the way how a Basic Safety Message (BSM) as the fundamental message type defined in SAE J2735 is constructed, sent and received by safety communication platforms to provide a comprehensive device independent solution for Cooperative ITS (C-ITS). This provides some insight into the technical knowledge behind the construction and exchange of BSMs within VANET. A series of real-world DSRC data collection experiments was conducted. The results demonstrate that the reliability and throughput of DSRC highly depend on the applications utilizing the medium. Therefore, an active application-dependent medium control measure, using a novel message-dissemination frequency controller, is introduced. This application level message handler improves the reliability of both BSM transmissions/receptions and the Application layer error handling which is extremely vital to decentralized congestion control (DCC) mechanisms.

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This paper assesses Intelligent Transportation Systems (ITS) to identify safety systems that are most likely to reduce driver errors at railway crossings. ITS technologies have been integrated in order to develop improved evaluation tools to reduce crashes at railway crossings. Although emerging technologies, knowledge, innovative interventions have been introduced to change driver behaviour, there is a lack of research on the impact of integrating ITS technologies and transportation simulation on drivers. The outcomes of ITS technologies for complementing traditional signage were compared with those of current safety systems (passive and active) at railway crossings. Three ITS technologies are compared with current treatments, in terms of compliance rate and vehicle speed profiles. It is found that ITS technologies improve compliance rate by 17~30% and also encourage drivers to slow down earlier compared to current passive and active crossings when there is a train approaching the railway crossings.

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Most individuals travel in order to participate in a network of activities which are important for attaining a good standard of living. Because such activities are commonly widely dispersed and not located locally, regular access to a vehicle is important to avoid exclusion. However, planning transport system provisions that can engage members of society in an acceptable degree of activity participation remains a great challenge. The main challenges in most cities of the world are due to significant population growth and rapid urbanisation which produces increased demand for transport. Keeping pace with these challenges in most urban areas is difficult due to the widening gap between supply and demand for transport systems which places the urban population at a transport disadvantage. The key element in mitigating the issue of urban transport disadvantage is to accurately identify the urban transport disadvantaged. Although wide-ranging variables and multi-dimensional methods have been used to identify this group, variables are commonly selected using ad-hoc techniques and unsound methods. This poses questions of whether the current variables used are accurately linked with urban transport disadvantage, and the effectiveness of the current policies. To fill these gaps, the research conducted for this thesis develops an operational urban transport disadvantage framework (UTDAF) based on key statistical urban transport disadvantage variables to accurately identify the urban transport disadvantaged. The thesis develops a methodology based on qualitative and quantitative statistical approaches to develop an urban transport disadvantage framework designed to accurately identify urban transport disadvantage. The reliability and the applicability of the methodology developed is the prime concern rather than the accuracy of the estimations. Relevant concepts that impact on urban transport disadvantage identification and measurement and a wide range of urban transport disadvantage variables were identified through a review of the existing literature. Based on the reviews, a conceptual urban transport disadvantage framework was developed based on the causal theory. Variables identified during the literature review were selected and consolidated based on the recommendations of international and local experts during the Delphi study. Following the literature review, the conceptual urban transport disadvantage framework was statistically assessed to identify key variables. Using the statistical outputs, the key variables were weighted and aggregated to form the UTDAF. Before the variable's weights were finalised, they were adjusted based on results of correlation analysis between elements forming the framework to improve the framework's accuracy. The UTDAF was then applied to three contextual conditions to determine the framework's effectiveness in identifying urban transport disadvantage. The development of the framework is likely to be a robust application measure for policy makers to justify infrastructure investments and to generate awareness about the issue of urban transport disadvantage.

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Recent road safety statistics show that the decades-long fatalities decreasing trend is stopping and stagnating. Statistics further show that crashes are mostly driven by human error, compared to other factors such as environmental conditions and mechanical defects. Within human error, the dominant error source is perceptive errors, which represent about 50% of the total. The next two sources are interpretation and evaluation, which accounts together with perception for more than 75% of human error related crashes. Those statistics show that allowing drivers to perceive and understand their environment better, or supplement them when they are clearly at fault, is a solution to a good assessment of road risk, and, as a consequence, further decreasing fatalities. To answer this problem, currently deployed driving assistance systems combine more and more information from diverse sources (sensors) to enhance the driver's perception of their environment. However, because of inherent limitations in range and field of view, these systems' perception of their environment remains largely limited to a small interest zone around a single vehicle. Such limitations can be overcomed by increasing the interest zone through a cooperative process. Cooperative Systems (CS), a specific subset of Intelligent Transportation Systems (ITS), aim at compensating for local systems' limitations by associating embedded information technology and intervehicular communication technology (IVC). With CS, information sources are not limited to a single vehicle anymore. From this distribution arises the concept of extended or augmented perception. Augmented perception allows extending an actor's perceptive horizon beyond its "natural" limits not only by fusing information from multiple in-vehicle sensors but also information obtained from remote sensors. The end result of an augmented perception and data fusion chain is known as an augmented map. It is a repository where any relevant information about objects in the environment, and the environment itself, can be stored in a layered architecture. This thesis aims at demonstrating that augmented perception has better performance than noncooperative approaches, and that it can be used to successfully identify road risk. We found it was necessary to evaluate the performance of augmented perception, in order to obtain a better knowledge on their limitations. Indeed, while many promising results have already been obtained, the feasibility of building an augmented map from exchanged local perception information and, then, using this information beneficially for road users, has not been thoroughly assessed yet. The limitations of augmented perception, and underlying technologies, have not be thoroughly assessed yet. Most notably, many questions remain unanswered as to the IVC performance and their ability to deliver appropriate quality of service to support life-saving critical systems. This is especially true as the road environment is a complex, highly variable setting where many sources of imperfections and errors exist, not only limited to IVC. We provide at first a discussion on these limitations and a performance model built to incorporate them, created from empirical data collected on test tracks. Our results are more pessimistic than existing literature, suggesting IVC limitations have been underestimated. Then, we develop a new CS-applications simulation architecture. This architecture is used to obtain new results on the safety benefits of a cooperative safety application (EEBL), and then to support further study on augmented perception. At first, we confirm earlier results in terms of crashes numbers decrease, but raise doubts on benefits in terms of crashes' severity. In the next step, we implement an augmented perception architecture tasked with creating an augmented map. Our approach is aimed at providing a generalist architecture that can use many different types of sensors to create the map, and which is not limited to any specific application. The data association problem is tackled with an MHT approach based on the Belief Theory. Then, augmented and single-vehicle perceptions are compared in a reference driving scenario for risk assessment,taking into account the IVC limitations obtained earlier; we show their impact on the augmented map's performance. Our results show that augmented perception performs better than non-cooperative approaches, allowing to almost tripling the advance warning time before a crash. IVC limitations appear to have no significant effect on the previous performance, although this might be valid only for our specific scenario. Eventually, we propose a new approach using augmented perception to identify road risk through a surrogate: near-miss events. A CS-based approach is designed and validated to detect near-miss events, and then compared to a non-cooperative approach based on vehicles equiped with local sensors only. The cooperative approach shows a significant improvement in the number of events that can be detected, especially at the higher rates of system's deployment.

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Besides responding to challenges of rapid urbanization and growing traffic congestion, the development of smart transport systems has attracted much attention in recent times. Many promising initiatives have emerged over the years. Despite these initiatives, there is still a lack of understanding about an appropriate definition of smart transport system. As such, it is challenging to identify the appropriate indicators of ‘smartness’. This paper proposes a comprehensive and practical framework to benchmark cities according to the smartness in their transportation systems. The proposed methodology was illustrated using a set of data collected from 26 cities across the world through web search and contacting relevant transport authorities and agencies. Results showed that London, Seattle and Sydney were among the world’s top smart transport cities. In particular, Seattle and Paris ranked high in smart private transport services while London and Singapore scored high on public transport services. London also appeared to be the smartest in terms of emergency transport services. The key value of the proposed innovative framework lies in a comparative analysis among cities, facilitating city-to-city learning.

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In this paper, an integrated inter-vehicles wireless communications and positioning system supporting alternate positioning techniques is proposed to meet the requirements of safety applications of Cooperative Intelligent Transportation Systems (C-ITS). Recent advances have repeatedly demonstrated that road safety problems can be to a large extent addressed via a range of technologies including wireless communications and positioning in vehicular environments. The novel communication stack utilizing a dedicated frequency spectrum (e.g. at 5.9 GHz band), known as Dedicated Short-Range Communications (DSRC), has been particularly designed for Wireless Access in Vehicular Environments (WAVE) to support safety applications in highly dynamic environments. Global Navigation Satellite Systems (GNSS) is another essential enabler to support safety on rail and roads. Although current vehicle navigation systems such as single frequency Global Positioning System (GPS) receivers can provide route guidance with 5-10 meters (road-level) position accuracy, positioning systems utilized in C-ITS must provide position solutions with lane-level and even in-lane-level accuracies based on the requirements of safety applications. This article reviews the issues and technical approaches that are involved in designing a vehicular safety communications and positioning architecture; it also provides technological solutions to further improve vehicular safety by integrating the DSRC and GNSS-based positioning technologies.

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One of the concerns about the use of Bluetooth MAC Scanner (BMS) data, especially from urban arterial, is the bias in the travel time estimates from multiple Bluetooth devices being transported by a vehicle. For instance, if a bus is transporting 20 passengers with Bluetooth equipped mobile phones, then the discovery of these mobile phones by BMS will be considered as 20 different vehicles, and the average travel time along the corridor estimated from the BMS data will be biased with the travel time from the bus. This paper integrates Bus Vehicle Identification system with BMS network to empirically evaluate such bias, if any. The paper also reports an interesting finding on the uniqueness of MAC IDs.

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Addressing the Crew Scheduling Problem (CSP) in transportation systems can be too complex to capture all details. The designed models usually ignore or simplify features which are difficult to formulate. This paper proposes an alternative formulation using a Mixed Integer Programming (MIP) approach to the problem. The optimisation model integrates the two phases of pairing generation and pairing optimisation by simultaneously sequencing trips into feasible duties and minimising total elapsed time of any duty. Crew scheduling constraints in which the crew have to return to their home depot at the end of the shift are included in the model. The flexibility of this model comes in the inclusion of the time interval of relief opportunities, allowing the crew to be relieved during a finite time interval. This will enhance the robustness of the schedule and provide a better representation of real-world conditions.

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Traffic accidents often cause lane closure, and diminish stability of travel time as well as the level of road services. On the other hand, research on the implementation of ITS services aiming at the reduction of traffic accidents has made considerable progress lately. However there has been little discussion on the benefits obtained by traffic accident reduction from the view point of travel time reliability. Therefore, in this research, relationships between traffic accidents and travel time reliability are examined, and the benefit of traffic accident reduction is calculated based on the scheduling model under travel time uncertainties. The results show the significance of traffic accident reduction for the improvement of travel time reliability.

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This paper addresses the topic of real-time decision making for autonomous city vehicles, i.e., the autonomous vehicles' ability to make appropriate driving decisions in city road traffic situations. The paper explains the overall controls system architecture, the decision making task decomposition, and focuses on how Multiple Criteria Decision Making (MCDM) is used in the process of selecting the most appropriate driving maneuver from the set of feasible ones. Experimental tests show that MCDM is suitable for this new application area.

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Loop detectors are widely used on the motorway networks where they provide point speed and traffic volumes. Models have been proposed for temporal and spatial generalization of speed for average travel time estimation. Advancement in technology provides complementary data sources such as Bluetooth MAC Scanner (BMS), detecting the MAC ID of the Bluetooth devices transported by the traveller. Matching the data from two BMS stations provides individual vehicle travel time. Generally, on the motorways loops are closely spaced, whereas BMS are placed few kilometres apart. In this research, we fuse BMSs and loops data to define the trajectories of the Bluetooth vehicles. The trajectories are utilised to estimate the travel time statistics between any two points along the motorway. The proposed model is tested using simulation and validated with real data from Pacific motorway, Brisbane. Comparing the model with the linear interpolation based trajectory provides significant improvements.

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This project is a breakthrough in developing new scientific approaches for the design, development and evaluation of inter-vehicle communications, networking and positioning systems as part of Cooperative Intelligent Transportation Systems ensuring the safety of both roads and rail networks. This research focused on the elicitation, specification, analysis and validation of requirements for Vehicle-to-Vehicle communications and networking, and Vehicle-to-Vehicle positioning, which are accomplished with the research platform developed for this study. A number of mathematical models for communications, networking and positioning were developed from which simulations and field experiments were conducted to evaluate the overall performance of the platform. The outcomes of this research significantly contribute to improving the performance of the communications and positioning components of Cooperative Intelligent Transportation Systems.

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This study aims to examine the severe consequences of traffic crashes related to a special group of drivers in China, which is called the “Second Rich & Powerful Generation” (SRPG). The unique driving behaviors and attitudes of this special group are intertwined with the general cultural and social environment in China. To investigate the difference of traffic crash consequences between drivers who belong to SRPG and general driver population, injuries and fatalities in 2009, 2010, 2011 and 2012 were compared. Results consistently showed that while no significant difference was detected between these two groups in terms of injuries, fatalities per crash caused by SRPG were significantly larger than that caused by general driver population. Findings from our study clearly demonstrate the complexity of road safety issues in developing countries, and can also be used to develop road safety improvement strategies tailored to SRPG.

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Driver training is one of the interventions aimed at mitigating the number of crashes that involve novice drivers. Our failure to understand what is really important for learners, in terms of risky driving, is one of the many drawbacks restraining us to build better training programs. Currently, there is a need to develop and evaluate Advanced Driving Assistance Systems that could comprehensively assess driving competencies. The aim of this paper is to present a novel Intelligent Driver Training System (IDTS) that analyses crash risks for a given driving situation, providing avenues for improvement and personalisation of driver training programs. The analysis takes into account numerous variables acquired synchronously from the Driver, the Vehicle and the Environment (DVE). The system then segments out the manoeuvres within a drive. This paper further presents the usage of fuzzy set theory to develop the safety inference rules for each manoeuvre executed during the drive. This paper presents a framework and its associated prototype that can be used to comprehensively view and assess complex driving manoeuvres and then provide a comprehensive analysis of the drive used to give feedback to novice drivers.

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In recent decades, highly motorised countries, such as Australia, have witnessed significant improvements in population health through reductions in fatalities and injuries from road traffic crashes. In Australia, concerted efforts have been made to reduce the road trauma burden since road fatalities reached their highest level in in the early 1970s. Since that time, many improvements have been made drawing on various disciplines to reduce the trauma burden (e.g., road and vehicle design, road user education, traffic law enforcement practices and enforcement technologies). While road fatalities have declined significantly since the mid-1970s, road trauma remains a serious public health concern in Australia. China has recently become the largest car market in the world (Ma, Li, Zhou, Duan, & Bishai, 2012). This rapid motorisation has been accompanied by substantial expansion of the road network as well as a large road trauma burden. Road traffic injuries are a major cause of death in China, reported as accounting for one third of all injury-deaths between 2002 and 2006 (Ma et al., 2012). In common with Australia, China has experienced a reported decline in fatalities since 2002 (see Hu, Wen & Baker, 2008). However, there remains a strong need for action in this area as rates of motorisation continue to climb in China. In Australia, a wide range of organisations have contributed to the improvements in road safety including government agencies, professional organisations, advocacy groups and research centres. In particular, Australia has several highly regarded and multi-disciplinary, university-based research centres that work across a range of road safety fields, including engineering, intelligent transportation systems, the psychology of road user behaviour, and traffic law enforcement. Besides conducting high-quality research, these centres fulfil an important advocacy role in promoting safer road use and facilitating collaborations with government and other agencies, at both the national and international level. To illustrate the role of these centres, an overview will be provided of the Centre for Accident Research and Road Safety-Queensland (CARRS-Q), which was established in 1996 and has gone on to become a recognised world-leader in road safety and injury prevention research. The Centre’s research findings are used to provide evidence-based recommendations to government and have directly contributed to promoting safer road use in Australia. Since 2006, CARRS-Q has also developed strong collaborative links with various universities and organisations in China to assist in building understanding, connections and capacity to assist in reducing the road trauma burden. References Hu, G., Wen, M., Baker, T. D., & Baker, S. P. (2008). Road-traffic deaths in China, 1985–2005: threat and opportunity. Injury Prevention, 14, 149-153. Ma, S., Li, Q., Zhou, M., Duan, L., & Bishai, D. (2012). Road Traffic Injury in China: A Review of National Data Sources. Traffic Injury Prevention, 13(S1), 57-63.