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<p>The mismatch between the supply and demand in the ride-hailing market often results in operational inefficiencies, such as low vehicle occupancy rates and prolonged passenger waiting times. This paper proposes a novel multi-step spatio-temporal prediction model, termed the ConvLSTM + model, which integrates a convolutional long short-term memory (ConvLSTM) network within a dual-layer architecture enhanced by a residual correction mechanism. The proposed model is specifically designed to predict passenger pick-ups and drop-offs. Using taxi datasets from New York City, Chengdu, and Beijing, the experimental results demonstrate that the ConvLSTM + model significantly outperforms several widely used passenger flow prediction models in terms of multi-step prediction accuracy. This study not only provides valuable decision-making support for ride-hailing drivers but also offers actionable insights for improving service quality and operational efficiency within the ride-hailing market.</p>

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Multi-step spatio-temporal passenger flow prediction based on a dual-layer convolutional long short-term memory model with residual correction

  • 期刊论文

  • 发表日期：

    2025年7月9日

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  • Zhi-Wei Chen,
  • Xiao-Lan Jiang,
  • Li-Jun Tian,
  • Peng Wu

摘要

The mismatch between the supply and demand in the ride-hailing market often results in operational inefficiencies, such as low vehicle occupancy rates and prolonged passenger waiting times. This paper proposes a novel multi-step spatio-temporal prediction model, termed the ConvLSTM + model, which integrates a convolutional long short-term memory (ConvLSTM) network within a dual-layer architecture enhanced by a residual correction mechanism. The proposed model is specifically designed to predict passenger pick-ups and drop-offs. Using taxi datasets from New York City, Chengdu, and Beijing, the experimental results demonstrate that the ConvLSTM + model significantly outperforms several widely used passenger flow prediction models in terms of multi-step prediction accuracy. This study not only provides valuable decision-making support for ride-hailing drivers but also offers actionable insights for improving service quality and operational efficiency within the ride-hailing market.