As app-based ride-hailing services have been widely adopted within existing traditional taxi markets, researchers have been devoted to understand the important factors that influence the demand of the new mobility. Econometric models (EMs) are mainly utilized to interpret the significant factors of the demand, and deep neural networks (DNNs) have been recently used to improve the forecasting performance by capturing complex patterns in the large datasets. However, to mitigate possible (induced) traffic congestion and balance utilization rates for the current taxi drivers, an effective strategy of proactively managing a quota system for both emerging services and regular taxis is still critically needed. This paper aims to systematically design an explainable deep learning model capable of assessing the quota system balancing the demand volumes between two modes. A two-stage interpretable machine learning modeling framework was developed by a linear regression (LR) model, coupled with a neural network layered by long short-term memory (LSTM). The first stage investigates the correlation between the existing taxis and on-demand ride-hailing services while controlling for other explanatory variables. The second stage fulfills the long short-term memory (LSTM) network structure, capturing the residuals from the first estimation stage in order to enhance the forecasting performance. The proposed stepwise modeling approach (LR-LSTM) forecasts the demand of taxi rides, and it is implemented in the application of pick-up demand prediction using New York City (NYC) taxi data. The experiment result indicates that the integrated model can capture the inter-relationships between existing taxis and ride-hailing services as well as identify the influence of additional factors, namely, the day of the week, weather, and holidays. Overall, this modeling approach can be applied to construct an effective active demand management (ADM) for the short-term period as well as a quota control strategy between on-demand ride-hailing services and traditional taxis.

由于基于应用程序的打车服务已在现有的传统出租车市场中广泛采用，因此研究人员一直致力于了解影响新出行需求的重要因素。计量经济学模型（EMs）主要用于解释需求的重要因素，近来深度神经网络（DNN）已用于通过捕获大型数据集中的复杂模式来提高预测性能。然而，为了减轻当前出租车司机可能的（诱发的）交通拥堵并平衡利用率，仍然迫切需要一种主动管理新兴服务和常规出租车配额系统的有效策略。本文旨在系统地设计一种可解释的深度学习模型，该模型能够评估在两种模式之间的需求量之间取得平衡的配额系统。通过线性回归（LR）模型以及通过长短期记忆（LSTM）分层的神经网络，开发了一个两阶段可解释的机器学习建模框架。第一阶段研究现有出租车与按需乘车服务之间的相关性，同时控制其他解释变量。第二阶段满足长短期记忆（LSTM）网络结构，捕获第一估计阶段的残差以增强预测性能。拟议的逐步建模方法（LR-LSTM）可以预测出租车的需求，并在使用纽约市（NYC）出租车数据的接机需求预测的应用中实现。实验结果表明，该集成模型可以捕获现有出租车与乘车服务之间的相互关系，并确定其他因素的影响，例如星期几，天气和假日。总体而言，该建模方法可用于构建短期有效的主动需求管理（ADM）以及按需乘车服务和传统出租车之间的配额控制策略。和假期。总体而言，该建模方法可用于构建短期有效的主动需求管理（ADM）以及按需乘车服务和传统出租车之间的配额控制策略。和假期。总体而言，该建模方法可用于构建短期有效的主动需求管理（ADM）以及按需乘车服务和传统出租车之间的配额控制策略。