Showing 1 - 20 results of 218 for search 'computing trips', query time: 0.07s Refine Results
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    Edge Computing-Aided Dynamic Wireless Charging and Trip Planning of UAVs by Palwasha W. Shaikh, Hussein T. Mouftah

    Published 2025-01-01
    “…To address these issues, this study proposes an integrated system design combining dynamic wireless charging (DWC), intelligent trip planning, and intelligent edge computing (IEC). …”
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    Examining trip-level errors in passively collected mobile device data for data quality assurance. by Peiqi Zhang, Kathleen Stewart, Aref Darzi

    Published 2025-01-01
    “…We designed a distributed-computing workflow to quantify the errors by comparing the number of trips on closed road segments during road closures with time periods before and after. …”
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    Hybrid subQUBO Annealing With a Correction Process for Multi-Day Intermodal Trip Planning by Tatsuya Noguchi, Keisuke Fukada, Siya Bao, Nozomu Togawa

    Published 2025-01-01
    “…The multi-day intermodal trip planning problem (MITPP) is an optimization problem that seeks to create the optimal route to visit Point-of-Interest (POI) and hotels over days. …”
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    Trip mode inference from mobile phone signaling data using Logarithm Gaussian Mixture Model by Xiaoxu Chen, Chao Yang, Xiangdong Xu

    Published 2020-11-01
    “…While these methods can achieve relatively high accuracy, they also have drawbacks in data quantity, coverage, and computational complexity. This paper develops a trip mode inference method based on mobile phone signaling data. …”
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    Bike sharing systems: The impact of precise trip demand forecasting on operational efficiency in different city structures by Selin Ataç, Nikola Obrenović, Michel Bierlaire

    Published 2025-03-01
    “…We employ clustering in the optimization module to manage larger case studies, dividing the problem into smaller sub-problems. Our computational experiments compare two main scenarios, perfect demand forecast and unknown future demand, as well as several intermediate scenarios where partial future trip demand information is available. …”
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