Ride-hailing has become a vital component of urban transportation due to its flexibility and efficiency in meeting diverse passenger needs. Accurate demand estimation, particularly in identifying origin-destination (OD) pairs, is crucial for optimizing services for ride-hailing providers and drivers and ensuring efficient resource allocation. This study proposes an alternative approach to demand estimation using public transportation card data, addressing privacy concerns associated with directly collecting OD data from ride-hailing services. By analyzing hourly recorded departure and arrival data between administrative districts, the proposed statistical model estimates ride-hailing demand patterns, capturing both spatial and temporal dynamics across urban areas. Transportation data from selected urban districts in Seoul, Korea, was analyzed to validate this approach. Results demonstrate significant daily and hourly variations in demand, with distinct weekday commuting patterns and more balanced weekend flows, reflecting shifts between work-related and leisure activities. These findings highlight the model’s potential for accurately estimating demand without direct OD data, providing valuable insights for improving ride-hailing operations and supporting urban transportation planning. This approach can assist service providers in optimizing driver allocation and enhancing operational efficiency, contributing to the broader objectives of urban mobility management.

With global EV sales projected to reach 3.5 million units by 2023 and public charging stations increasing by 40%, effective management and optimization of charging infrastructure have become critical. While current research primarily focuses on models that address either users or charging stations, these approaches often overlook external factors such as weather and traffic conditions, which significantly impact driving patterns and energy consumption. The integration of these external variables into forecasting models is thus critical for enhancing prediction accuracy and infrastructure optimization. In this study, we conducted an in-depth analysis of EV charging patterns at highway rest areas. This study employed deep learning models, GRU, and LSTM architectures, trained using various data combinations. The experiments revealed that the inclusion of traffic data notably improves forecasting precision. In particular, the LSTM model demonstrated a 10.5% reduction in mean absolute percentage error(MAPE), decreasing the standard deviation from 4.95 to 3.87 when external factors were included.

In this paper, we investigate a localization performance based on channel state information (CSI) of separated antenna. In multi-carrier transmission system, CSI has been considered as one of the promising signatures of radio frequency (RF) channel and employed to device localization. However, CSI can be correlated in typical indoor environment which may limit the unique representations of the signature for RF fingerprinting. To solve this, we propose a joint CSI-based device localization, exploiting the diversity of RF signatures. Even if the one CSI loses the reliability as a the fingerprint due to spatial correlation, the other CSI can support the device localization performance. For experimental evaluation, we construct the test-bed and confirmed the proposed scheme can support single CSI-based localization even if highly correlated CSI are reproduced occasionally at many locations. The test results in indoor environment show that the proposed scheme complements the single-CSI in terms of the reliability of authentication with localization of the devices.

In this paper, lossless time-series data compression scheme with bit-back asymmetric numeral systems (BB-ANS) is proposed for massive smart meter environment. As smart meters increase in deployment and connectivity, an efficient compression method is needed to transmit and save big data. Bit-back coding was introduced as a novel compression method using bayesian inference modeling. Recently, bit-back coding is combined with asymmetric numeral systems (ANS) which are stack-like structures, showing significant compression gains on several cases. ANS is an approach for entropy coding combining Huffman coding and arithmetic coding which has a first-in-last-out (FILO) form suited for bit-back coding. Compared to other compression methods, bit-back coding effectively shares priorly learned probabilistic models for encoder and decoder. In this study, variational auto-encoder (VAE) is customized to jointly learn approximate posterior and likelihood between message and latent variables. Thus, the smart meter data can be efficiently updated within finite length time-intervals. The proposed scheme is evaluated with the actual smart meter dataset and the results demonstrate the superiority of BB-ANS in compression ratios over other state-of-the-art lossless compression schemes. To the best of our knowledge, this study is the first attempt to apply bit-back coding to time-series smart metering data, enabling efficient data compression with deep generative models.

For machine type communication with random access (RA) protocol, finding optimal policy using deep reinforcement learning (DRL) is being actively investigated for various quality of service requirements. In particular, it was shown that throughput-based reward function in DRL can derive policies that outperform conventional exponential backoff (EB)-based algorithms in terms of throughput and fairness. However, age of information (AoI), which is a measure of the freshness of data, was not addressed in the process of training the DRL agent and only used as a measure of fairness. It has been theoretically proven that, even in the simplest queuing system, maximizing throughput does not guarantee minimizing AoI. In this paper, we proposed a novel DRL scheme to directly optimize AoI in a slotted ALOHA RA channel. By taking into account urgency and packet age as extra local information for a reward, AoI could be improved while preserving the throughput. Numerical simulations showed that the proposed DRL approach could achieve an improvement of 22.04% in AoI performance, with a marginal throughput loss of around 3.95%, compared to the existing throughput-based DRL method.