Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction (1802.08714v2)

Abstract: Taxi demand prediction is an important building block to enabling intelligent transportation systems in a smart city. An accurate prediction model can help the city pre-allocate resources to meet travel demand and to reduce empty taxis on streets which waste energy and worsen the traffic congestion. With the increasing popularity of taxi requesting services such as Uber and Didi Chuxing (in China), we are able to collect large-scale taxi demand data continuously. How to utilize such big data to improve the demand prediction is an interesting and critical real-world problem. Traditional demand prediction methods mostly rely on time series forecasting techniques, which fail to model the complex non-linear spatial and temporal relations. Recent advances in deep learning have shown superior performance on traditionally challenging tasks such as image classification by learning the complex features and correlations from large-scale data. This breakthrough has inspired researchers to explore deep learning techniques on traffic prediction problems. However, existing methods on traffic prediction have only considered spatial relation (e.g., using CNN) or temporal relation (e.g., using LSTM) independently. We propose a Deep Multi-View Spatial-Temporal Network (DMVST-Net) framework to model both spatial and temporal relations. Specifically, our proposed model consists of three views: temporal view (modeling correlations between future demand values with near time points via LSTM), spatial view (modeling local spatial correlation via local CNN), and semantic view (modeling correlations among regions sharing similar temporal patterns). Experiments on large-scale real taxi demand data demonstrate effectiveness of our approach over state-of-the-art methods.

• The paper introduces DMVST-Net to integrate spatial, temporal, and semantic views for enhanced taxi demand prediction.
• It employs a local CNN for spatial, LSTM for temporal, and graph embedding for semantic analysis to capture urban mobility patterns.
• Experimental results show a MAPE of 0.1616 and an RMSE of 9.642, outperforming traditional methods significantly.

Overview of "Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction"

The paper "Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction" by Huaxiu Yao et al. presents an advanced framework designed to tackle the problem of predicting taxi demand in urban environments. The authors propose a comprehensive model that considers multiple dimensions of data through the integration of spatial, temporal, and semantic views. This multi-faceted approach addresses the limitations of existing methods that often fail to capture the complex interplay of spatial and temporal relations.

Introduction

Accurate demand prediction models are imperative for the efficient allocation of resources in smart cities. With the surge in popularity of ride-hailing services, such as Didi Chuxing and Uber, large-scale data is now available, presenting an opportunity to improve predictive models. Traditional models, which predominantly employ time series forecasting techniques like ARIMA, often fall short in capturing non-linear spatial-temporal dependencies. Leveraging advances in deep learning, this paper introduces the Deep Multi-View Spatial-Temporal Network (DMVST-Net) to concurrently model these relationships for enhanced prediction accuracy.

Model Architecture

DMVST-Net consists of three distinct views:

1. Spatial View: This view employs a local Convolutional Neural Network (CNN) to capture spatial dependencies among nearby regions. Unlike global CNN approaches, local CNN focuses on spatially adjacent areas, adhering to the First Law of Geography which states that "near things are more related than distant things." This approach mitigates the risk of incorporating weakly related distant regions, thus enhancing prediction accuracy.
2. Temporal View: For temporal relationships, the model uses Long Short-Term Memory (LSTM) networks to account for sequential dependencies in taxi demand data. The temporal view integrates outputs from the spatial view with contextual features, enabling the LSTM to model the complex temporal interactions effectively.
3. Semantic View: This view constructs a region graph based on functional similarity (e.g., residential vs. commercial areas), with edges representing similarities in demand patterns. A graph embedding method, such as LINE, encodes these regions into low-dimensional vectors. This semantic information helps in modeling correlations among functionally similar but spatially distant regions.

Experimental Validation

The proposed model was validated using a real-world dataset from Didi Chuxing, collected in Guangzhou, China. The data included approximately 300,000 daily taxi requests over two months, providing a substantial test bed for the model. The performance of DMVST-Net was compared with several baseline methods, including historical average, ARIMA, linear regression, XGBoost, and the ST-ResNet model.

Results

The experiments demonstrated that DMVST-Net outperforms baseline approaches, achieving a significant reduction in both Mean Average Percentage Error (MAPE) and Root Mean Square Error (RMSE). Specifically, DMVST-Net achieved a MAPE of 0.1616 and an RMSE of 9.642, indicating a 12.17% improvement in MAPE and a 3.70% improvement in RMSE compared to the best baseline method. These results underscore the efficacy of integrating spatial, temporal, and semantic views in capturing the intricate patterns in taxi demand data.

Implications and Future Directions

The success of DMVST-Net has several practical implications. For urban planners and policymakers, the enhanced prediction accuracy can facilitate better resource allocation, traffic management, and energy conservation. Theoretically, this work contributes to the growing body of literature on integrating multiple data perspectives using deep learning.

Looking ahead, future research could explore the interpretability of such complex models to provide insights into what factors contribute to the performance gains. Additionally, incorporating more explicit semantic information, such as Points of Interest (POI), could further refine the predictive accuracy and applicability of the model.

In conclusion, the "Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction" represents a significant step forward in urban transportation management, leveraging cutting-edge deep learning techniques to address a critical problem in smart city initiatives.