2,375 research outputs found

    Mobile Map Browsers: Anticipated User Interaction for Data Pre-fetching

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    When browsing a graphical display of geospatial data on mobile devices, users typically change the displayed maps by panning, zooming in and out, or rotating the device. Limited storage space on mobile devices and slow wireless communications, however, impede the performance of these operations. To overcome the bottleneck that all map data to be displayed on the mobile device need to be downloaded on demand, this thesis investigates how anticipated user interactions affect intelligent pre-fetching so that an on-demand download session is extended incrementally. User interaction is defined as a set of map operations that each have corresponding effects on the spatial dataset required to generate the display. By anticipating user interaction based on past behavior and intuition on when waiting for data is acceptable, it is possible to device a set of strategies to better prepare the device with data for future use. Users that engage with interactive map displays for a variety of tasks, whether it be navigation, information browsing, or data collection, experience a dynamic display to accomplish their goal. With vehicular navigation, the display might update itself as a result of a GPS data stream reflecting movement through space. This movement is not random, especially as is the case of moving vehicles and, therefore, this thesis suggests that mobile map data could be pre-fetched in order to improve usability. Pre-fetching memory-demanding spatial data can benefit usability in several ways, but in particular it can (1) reduce latency when downloading data over wireless connections and (2) better prepare a device for situations where wireless internet connectivity is weak or intermittent. This thesis investigates mobile map caching and devises an algorithm for pre-fetching data on behalf of the application user. Two primary models are compared: isotropic (direction-independent) and anisotropic (direction-dependent) pre-fetching. A prefetching simulation is parameterized with many trajectories that vary in complexity (a metric of direction change within the trajectory) and it is shown that, although anisotropic pre-fetching typically results in a better pre-fetching accuracy, it is not ideal for all scenarios. This thesis suggests a combination of models to accommodate the significant variation in moving object trajectories. In addition, other methods for pre-fetching spatial data are proposed for future research

    A Primal-Dual Algorithm for Link Dependent Origin Destination Matrix Estimation

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    Origin-Destination Matrix (ODM) estimation is a classical problem in transport engineering aiming to recover flows from every Origin to every Destination from measured traffic counts and a priori model information. In addition to traffic counts, the present contribution takes advantage of probe trajectories, whose capture is made possible by new measurement technologies. It extends the concept of ODM to that of Link dependent ODM (LODM), keeping the information about the flow distribution on links and containing inherently the ODM assignment. Further, an original formulation of LODM estimation, from traffic counts and probe trajectories is presented as an optimisation problem, where the functional to be minimized consists of five convex functions, each modelling a constraint or property of the transport problem: consistency with traffic counts, consistency with sampled probe trajectories, consistency with traffic conservation (Kirchhoff's law), similarity of flows having close origins and destinations, positivity of traffic flows. A primal-dual algorithm is devised to minimize the designed functional, as the corresponding objective functions are not necessarily differentiable. A case study, on a simulated network and traffic, validates the feasibility of the procedure and details its benefits for the estimation of an LODM matching real-network constraints and observations

    Exploring Potentials in Mobile Phone GPS Data Collection and Analysis

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    In order to support efficient transportation planning decisions, household travel survey data with high levels of accuracy are essential. Due to a number of issues associated with conventional household travel surveys, including high cost, low response rate, trip misreporting, and respondents’ self-reporting bias, government and private agencies are desperately searching for alternative data collection methods. Recent advancements in smart phones and Global Positioning System (GPS) technologies present new opportunities to track travelers’ trips. Considering the high penetration rate of smartphones, it seems reasonable to use smartphone data as a reliable source of individual travel diary. Many studies have applied GPS-Based data in planning and demand analysis but mobile phone GPS data has not received much attention. The Google Location History (GLH) data provide an opportunity to explore the potential of these data. This research presents a study using GLH data, including the data processing algorithm in deriving travel information and the potential applications in understanding travel patterns. The main goal of this study is to explore the potential of using cell phone GPS data to advance the understanding in mobility and travel behavior. The objectives of the study include: a) assessing the technical feasibility of using smartphones in transportation planning as a substitute of traditional household survey b) develop algorithms and procedures to derive travel information from smartphones; and c) identify applications in mobility and travel behavior studies that could take advantage of these smartphones GPS data, which would not have been possible with conventional data collection methods. This research aims to demonstrate how accurate travel information can be collected and analyzed with lower cost using smartphone GPS data and what analysis applications can be made possible with this new data source. Moreover, the framework developed in this study can provide valuable insights for others who are interested in using cell phone data. GLH data are obtained from 45 participants in a two-month period for the study. The results show great promise of using GLH data as a supplement or complement to conventional travel diary data. It shows that GLH provides sufficient high resolution data that can be used to study people’s movement without respondent burden, and potentially it can be applied to a large scale study easily. The developed algorithms in this study work well with the data. This study supports that transportation data can be collected with smartphones less expensively and more accurately than by traditional household travel survey. These data provide the opportunity to facilitate the investigation of various issues, such as less frequent long-distance travel, hourly variations in travel behavior, and daily variations in travel behavior

    Intersection SPaT Estimation by means of Single-Source Connected Vehicle Data

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    The file attached to this record is the author's final peer reviewed version.Current traffic management systems in urban networks require real-time estimation of the traffic states. With the development of in-vehicle and communication technologies, connected vehicle data has emerged as a new data source for traffic measurement and estimation. In this work, a machine learning-based methodology for signal phase and timing information (SPaT) which is highly valuable for many applications such as green light optimal advisory systems and real-time vehicle navigation is proposed. The proposed methodology utilizes data from connected vehicles travelling within urban signalized links to estimate the queue tail location, vehicle accumulation, and subsequently, link outflow. Based on the produced high-resolution outflow estimates and data from crossing connected vehicles, SPaT information is estimated via correlation analysis and a machine learning approach. The main contribution is that the single-source proposed approach relies merely on connected vehicle data and requires neither prior information such as intersection cycle time nor data from other sources such as conventional traffic measuring tools. A sample four-leg intersection where each link comprises different number of lanes and experiences different traffic condition is considered as a testbed. The validation of the developed approach has been undertaken by comparing the produced estimates with realistic micro-simulation results as ground truth, and the achieved simulation results are promising even at low penetration rates of connected vehicles
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