Abstract: With the advent of mobile internet and the evolution of intelligent transportation systems, online car-hailing services have become increasingly popular as a primary mode of transportation. These platforms, such as Didi Chuxing and Uber, have revolutionized the way people travel by reducing information asymmetry and enhancing the efficiency of resource allocation. They achieve this by effectively matching the supply and demand between passengers and drivers. However, as the frequency of usage has grown, the problems with unreasonable vehicle scheduling and mismatch between supply and demand arise, including extended waiting times for passengers and high empty load rates for drivers. To address these issues, numerous scholars have proposed various deep learning methods to predict car-hailing demand more accurately. Despite these efforts, current forecasting methods still fall short in fully exploiting the features of time series data.
In light of this gap, our paper introduces a novel hybrid prediction model known as Convolutional Attention mechanism Bi-directional Long Short-Term Memory (CNN-ATTBiLSTM). This model innovatively captures time series features from both macro and micro perspectives, utilizing both forward and reverse directions to enhance the accuracy of car-hailing demand forecasts. This approach aims to mitigate the problems with suboptimal vehicle scheduling and the imbalance between supply and demand.
We validate our model using a dataset of car-hailing orders from HaiKou in June 2017, provided by Didi Chuxing’s Gaia Plan. The process begins with data cleansing to remove invalid entries, followed by resampling the data at ten-minute intervals. We then employ the K-means algorithm to identify passenger hot spots, which are used to define the unit regions for analysis. The model, CNN-ATTBiLSTM, is fed with external factors such as weather conditions and the demand for unit areas and time slots to forecast demand for the next time slot. To demonstrate the superiority of our model, we compare it with LSTM and CNN-LSTM models using evaluation metrics like Coefficient of Determination (R²), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Symmetric Mean Absolute Percentage Error (SMAPE). Additionally, we visually compare the forecasting and actual demand curves to analyze the models’ fitting degrees during peak and off-peak times.
The experimental results indicate that CNN-ATTBiLSTM outperforms both LSTM and CNN-LSTM across all evaluation metrics. Our visual analysis reveals that while the LSTM and CNN-LSTM models exhibit a degree of lag in their predictions, our CNN-ATTBiLSTM model more effectively captures the dynamics of time series data during both peak and off-peak periods. Theoretically, the CNN component extracts local features, the Attention mechanism assigns varying weights to these features to model their global relationships, and the BiLSTM component provides robust temporal sequence feature extraction capabilities. Collectively, these elements enable the CNN-ATTBiLSTM to model time series features comprehensively, enhancing demand prediction accuracy and providing valuable insights for car-hailing platforms to devise policies for efficient vehicle scheduling.
However, there are still some improvements that can be made in the future. Currently, our study focuses solely on passenger travel demand, neglecting the potential for drivers to be assigned to restricted areas so as to subsequently cancel orders, leading to a resource waste. Additionally, our spatial analysis does not account for the geographical locations and semantic significance of Points of Interest (POIs). In future research, we plan to incorporate the influence of passenger destinations and consider spatial similarities and POI semantic information as crucial factors to further enhance forecasting accuracy.

Key words: online car-hailing demand prediction, deep learning, convolutional neural networks, attention mechanism, bi-directional long short-term memory

摘要： 网约车的需求预测对于实现网约车平台高效调度车辆具有十分重要的指导意义。为了提高网约车需求的预测准确率,结合卷积神经网络(Convolutional Neural Network,CNN)、Attention机制和双向长短期记忆网络(Bi-directional Long Short-Term Memory,BiLSTM)三种深度学习算法的优点,提出了一种从宏、微观角度和正反两个方向充分提取时间序列特征的CNN-ATTBiLSTM组合神经网络预测模型。首先,利用CNN对网约车需求数据进行局部特征提取;其次,运用Attention机制对局部细节进行宏观特征建模;最后,使用BiLSTM方法从正反两个方向提取时序特征,实现对网约车需求数据的预测。实验选取了滴滴出行盖亚计划提供的网约车订单数据集以验证模型的有效性。实验结果表明,CNN-ATTBiLSTM组合神经网络预测模型在网约车需求预测中的表现优于当前主流的预测模型,能够为网约车平台高效调度车辆提供方法指导与技术支持。

关键词: 网约车需求预测, 深度学习, 卷积神经网络, 注意力机制, 双向长短期记忆