85,316 research outputs found

    Asset Management for Mobility and ITS

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    MoDOT project # TR202117The Federal Highway Administration currently requires pavement and bridge National Highway System assets to be managed through a formal plan at the statewide level regardless of ownership. The methods for Transportation Asset Management planning for pavements and bridges are relatively well defined and mature. To incorporate evolving technologies and new industry standards, methods and practices need to continue to progress and evolve. New Intelligent Transportation System and Transportation Systems Management and Operations assets must be managed and maintained, with similar methods to bridges and pavements, to keep the asset management processes consistent. This project outlined a literature review, a state of the practice survey, an assessment of several state-published Transportation Asset Management Plans, and the development and application of a tool for use in varied settings across the Missouri to manage Intelligent Transportation Systems and other new mobility assets and highlights the importance of a common asset management approach, risk management, and quality data management plans. Future recommendations and strategies are provided to the Missouri Department of Transportation to improve and incorporate new mobility assets into existing transportation asset management plans

    Privacy-Preserving Vehicle Assignment for Mobility-on-Demand Systems

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    Urban transportation is being transformed by mobility-on-demand (MoD) systems. One of the goals of MoD systems is to provide personalized transportation services to passengers. This process is facilitated by a centralized operator that coordinates the assignment of vehicles to individual passengers, based on location data. However, current approaches assume that accurate positioning information for passengers and vehicles is readily available. This assumption raises privacy concerns. In this work, we address this issue by proposing a method that protects passengers' drop-off locations (i.e., their travel destinations). Formally, we solve a batch assignment problem that routes vehicles at obfuscated origin locations to passenger locations (since origin locations correspond to previous drop-off locations), such that the mean waiting time is minimized. Our main contributions are two-fold. First, we formalize the notion of privacy for continuous vehicle-to-passenger assignment in MoD systems, and integrate a privacy mechanism that provides formal guarantees. Second, we present a scalable algorithm that takes advantage of superfluous (idle) vehicles in the system, combining multiple iterations of the Hungarian algorithm to allocate a redundant number of vehicles to a single passenger. As a result, we are able to reduce the performance deterioration induced by the privacy mechanism. We evaluate our methods on a real, large-scale data set consisting of over 11 million taxi rides (specifying vehicle availability and passenger requests), recorded over a month's duration, in the area of Manhattan, New York. Our work demonstrates that privacy can be integrated into MoD systems without incurring a significant loss of performance, and moreover, that this loss can be further minimized at the cost of deploying additional (redundant) vehicles into the fleet.Comment: 8 pages; Submitted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 201

    Performance Boundary Identification for the Evaluation of Automated Vehicles using Gaussian Process Classification

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    Safety is an essential aspect in the facilitation of automated vehicle deployment. Current testing practices are not enough, and going beyond them leads to infeasible testing requirements, such as needing to drive billions of kilometres on public roads. Automated vehicles are exposed to an indefinite number of scenarios. Handling of the most challenging scenarios should be tested, which leads to the question of how such corner cases can be determined. We propose an approach to identify the performance boundary, where these corner cases are located, using Gaussian Process Classification. We also demonstrate the classification on an exemplary traffic jam approach scenario, showing that it is feasible and would lead to more efficient testing practices.Comment: 6 pages, 5 figures, accepted at 2019 IEEE Intelligent Transportation Systems Conference - ITSC 2019, Auckland, New Zealand, October 201

    A State-of-the-art Integrated Transportation Simulation Platform

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    Nowadays, universities and companies have a huge need for simulation and modelling methodologies. In the particular case of traffic and transportation, making physical modifications to the real traffic networks could be highly expensive, dependent on political decisions and could be highly disruptive to the environment. However, while studying a specific domain or problem, analysing a problem through simulation may not be trivial and may need several simulation tools, hence raising interoperability issues. To overcome these problems, we propose an agent-directed transportation simulation platform, through the cloud, by means of services. We intend to use the IEEE standard HLA (High Level Architecture) for simulators interoperability and agents for controlling and coordination. Our motivations are to allow multiresolution analysis of complex domains, to allow experts to collaborate on the analysis of a common problem and to allow co-simulation and synergy of different application domains. This paper will start by presenting some preliminary background concepts to help better understand the scope of this work. After that, the results of a literature review is shown. Finally, the general architecture of a transportation simulation platform is proposed
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