Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction (1610.00081v2)

Abstract: Forecasting the flow of crowds is of great importance to traffic management and public safety, yet a very challenging task affected by many complex factors, such as inter-region traffic, events and weather. In this paper, we propose a deep-learning-based approach, called ST-ResNet, to collectively forecast the in-flow and out-flow of crowds in each and every region through a city. We design an end-to-end structure of ST-ResNet based on unique properties of spatio-temporal data. More specifically, we employ the framework of the residual neural networks to model the temporal closeness, period, and trend properties of the crowd traffic, respectively. For each property, we design a branch of residual convolutional units, each of which models the spatial properties of the crowd traffic. ST-ResNet learns to dynamically aggregate the output of the three residual neural networks based on data, assigning different weights to different branches and regions. The aggregation is further combined with external factors, such as weather and day of the week, to predict the final traffic of crowds in each and every region. We evaluate ST-ResNet based on two types of crowd flows in Beijing and NYC, finding that its performance exceeds six well-know methods.

• The paper introduces ST-ResNet, which employs residual learning to effectively predict citywide crowd flows by modeling short-term, periodic, and trending temporal dependencies.
• The model integrates convolutional units with dynamic aggregation of external factors, achieving significantly lower RMSE than traditional and state-of-the-art methods on Beijing and NYC datasets.
• Empirical results demonstrate that ST-ResNet can enhance urban traffic management and planning by accurately forecasting crowd inflows and outflows under varying real-world conditions.

Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction

The paper "Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction" by Junbo Zhang, Yu Zheng, and Dekang Qi presents a deep-learning-based approach to forecasting crowd flows within urban regions. The unique complexity of urban crowd behavior, influenced by various spatial, temporal, and external factors, necessitates a sophisticated predictive method. This work introduces the ST-ResNet model, which leverages the strengths of residual learning to predict inflow and outflow of crowds across multiple city regions effectively.

Model Architecture and Methodology

The ST-ResNet model is designed as an end-to-end deep residual network that addresses three key temporal properties: closeness, period, and trend. These properties are captured through three separate branches of residual convolutional units, each modeling different aspects of temporal dependencies. The model structure includes:

1. Closeness: Recent time intervals immediately preceding the prediction point are modeled to capture short-term dependencies.
2. Period: Daily periodic patterns are captured by considering similar time intervals from previous days.
3. Trend: Weekly trends are accounted for by examining time intervals from previous weeks.

These branches integrate spatial dependencies of crowd flows over both nearby and distant regions and utilize convolutional neural networks (ConvNets) structures without subsampling to maintain spatial resolution. Residual learning is employed to manage the depth of the network effectively, ensuring that the model captures citywide dependencies.

External Factors and Dynamic Aggregation

In addition to the temporal branches, ST-ResNet incorporates external factors such as weather, events, and contextual metadata, which are inputted into a two-layer fully connected neural network. This inclusion helps adjust predictions based on real-world conditions that influence crowd movements.

Dynamic aggregation is a critical component of the model, wherein outputs from the branches representing temporal properties (closeness, period, trend) are combined using parameter matrices assigning different weights to different spatio-temporal branches and regions. This dynamic weighting approach is further fused with external influences to refine the final prediction, ensuring a highly adaptive predictive capability.

Empirical Validation

Experiments conducted on crowd flow data from Beijing and New York City (NYC) demonstrated the efficacy of ST-ResNet. The model outperformed six well-known methods, including traditional statistical models (ARIMA, SARIMA), vector auto-regressive models (VAR), and deep learning-based approaches (ST-ANN, DeepST variants).

Especially notable are the model's performance metrics. In the case of the Beijing taxi GPS dataset, a variant of the ST-ResNet incorporating twelve residual units and batch normalization (L12-E-BN) achieved an RMSE of 16.69, significantly lower than the best-performing baseline, DeepST-CPTM, which had an RMSE of 18.18. Similarly, for the NYC bike rental data, the model achieved an RMSE of 6.33, outperforming the state-of-the-art by a margin of up to 37.1%.

Implications and Speculative Future Developments

The practical implication of the ST-ResNet model is substantial for urban traffic management and public safety. Accurate predictions of crowd inflows and outflows can help mitigate risks associated with overcrowding and optimize traffic control measures. Theoretical implications include advancing the methodology of modeling spatio-temporal data by incorporating deep residual learning, which shows promise for other complex spatio-temporal predictive tasks beyond crowd flow.

Future developments could involve enhancing the ST-ResNet model to handle multiple types of flow data simultaneously, such as combining taxi, bus, and metro trajectory data, among others. Exploring more sophisticated dynamic weighting mechanisms and real-time adaptive models could further improve the robustness and accuracy of predictions. These advancements would extend the applicability of deep learning models in intelligent urban planning and smart city initiatives.