Xingming Wu

Bio: Xingming Wu is an academic researcher from Beihang University. The author has contributed to research in topics: Computer science & Feature extraction. The author has an hindex of 13, co-authored 102 publications receiving 1253 citations.

LSTM network: a deep learning approach for short-term traffic forecast

Abstract: Short-term traffic forecast is one of the essential issues in intelligent transportation system. Accurate forecast result enables commuters make appropriate travel modes, travel routes, and departure time, which is meaningful in traffic management. To promote the forecast accuracy, a feasible way is to develop a more effective approach for traffic data analysis. The availability of abundant traffic data and computation power emerge in recent years, which motivates us to improve the accuracy of short-term traffic forecast via deep learning approaches. A novel traffic forecast model based on long short-term memory (LSTM) network is proposed. Different from conventional forecast models, the proposed LSTM network considers temporal-spatial correlation in traffic system via a two-dimensional network which is composed of many memory units. A comparison with other representative forecast models validates that the proposed LSTM network can achieve a better performance.

Intelligent Detail Enhancement for Exposure Fusion

Abstract: Multiscale exposure fusion is a fast approach to fuse several differently exposed images captured at the same high dynamic range (HDR) scene into a high-quality low-dynamic range (LDR) image. The fused image is expected to include all details of the input images. However the details in the brightest and darkest regions are usually not well preserved. Adding details that are extracted from the input images to the fused image is an efficient approach to overcome the problem. In this paper a new gradient domain weighted least square based image smoothing algorithm is proposed to extract the details in the brightest and darkest regions of the HDR scene. The extracted details are then added to an image that is produced using an edge-preserving smoothing pyramid based multiscale exposure fusion algorithm. Experimental results show that the proposed detail enhanced exposure fusion algorithm can preserve details in saturated regions especially the brightest regions better than the state-of-the-art multiscale exposure fusion algorithms.

Detail-Enhanced Multi-Scale Exposure Fusion in YUV Color Space

Abstract: It is recognized that existing multi-scale exposure fusion algorithms can be improved using edge-preserving smoothing techniques. However, the complexity of edge-preserving smoothing-based multi-scale exposure fusion is an issue for mobile devices. In this paper, a simpler multi-scale exposure fusion algorithm is designed in YUV color space. The proposed algorithm can preserve details in the brightest and darkest regions of a high dynamic range (HDR) scene and the edge-preserving smoothing-based multi-scale exposure fusion algorithm while avoiding color distortion from appearing in the fused image. The complexity of the proposed algorithm is about half of the edge-preserving smoothing-based multi-scale exposure fusion algorithm. The proposed algorithm is thus friendlier to the smartphones than the edge-preserving smoothing-based multi-scale exposure fusion algorithm. In addition, a simple detail-enhancement component is proposed to enhance fine details of fused images. The experimental results show that the proposed component can be adopted to produce an enhanced image with visibly enhanced fine details and a higher MEF-SSIM value. This is impossible for existing detail enhancement components. Clearly, the component is attractive for PC-based applications.

Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting

Abstract: Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due to the time-varying traffic patterns and the complicated spatial dependencies on road networks. To address this challenge, we learn the traffic network as a graph and propose a novel deep learning framework, Traffic Graph Convolutional Long Short-Term Memory Neural Network (TGC-LSTM), to learn the interactions between roadways in the traffic network and forecast the network-wide traffic state. We define the traffic graph convolution based on the physical network topology. The relationship between the proposed traffic graph convolution and the spectral graph convolution is also discussed. An L1-norm on graph convolution weights and an L2-norm on graph convolution features are added to the model’s loss function to enhance the interpretability of the proposed model. Experimental results show that the proposed model outperforms baseline methods on two real-world traffic state datasets. The visualization of the graph convolution weights indicates that the proposed framework can recognize the most influential road segments in real-world traffic networks.

Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach

Abstract: Short-term passenger demand forecasting is of great importance to the on-demand ride service platform, which can incentivize vacant cars moving from over-supply regions to over-demand regions. The spatial dependencies, temporal dependencies, and exogenous dependencies need to be considered simultaneously, however, which makes short-term passenger demand forecasting challenging. We propose a novel deep learning (DL) approach, named the fusion convolutional long short-term memory network (FCL-Net), to address these three dependencies within one end-to-end learning architecture. The model is stacked and fused by multiple convolutional long short-term memory (LSTM) layers, standard LSTM layers, and convolutional layers. The fusion of convolutional techniques and the LSTM network enables the proposed DL approach to better capture the spatio-temporal characteristics and correlations of explanatory variables. A tailored spatially aggregated random forest is employed to rank the importance of the explanatory variables. The ranking is then used for feature selection. The proposed DL approach is applied to the short-term forecasting of passenger demand under an on-demand ride service platform in Hangzhou, China. The experimental results, validated on the real-world data provided by DiDi Chuxing, show that the FCL-Net achieves the better predictive performance than traditional approaches including both classical time-series prediction models and state-of-art machine learning algorithms (e.g., artificial neural network, XGBoost, LSTM and CNN). Furthermore, the consideration of exogenous variables in addition to the passenger demand itself, such as the travel time rate, time-of-day, day-of-week, and weather conditions, is proven to be promising, since they reduce the root mean squared error (RMSE) by 48.3%. It is also interesting to find that the feature selection reduces 24.4% in the training time and leads to only the 1.8% loss in the forecasting accuracy measured by RMSE in the proposed model. This paper is one of the first DL studies to forecast the short-term passenger demand of an on-demand ride service platform by examining the spatio-temporal correlations.