Vehicle detection in aerial images is an important and challenging task in the field of remote sensing. Recently, deep learning technologies have yielded superior performance for object detection in remote sensing images. However, the detection results of the existing methods are horizontal bounding boxes that ignore vehicle orientations, thereby having limited applicability in scenes with dense vehicles or clutter backgrounds. In this article, we propose a one-stage, anchor-free detection approach to detect arbitrarily oriented vehicles in high-resolution aerial images. The vehicle detection task is transformed into a multitask learning problem by directly predicting high-level vehicle features via a fully convolutional network. That is, a classification subtask is created to look for vehicle central points and three regression subtasks are created to predict vehicle orientations, scales, and offsets of vehicle central points. First, coarse and fine feature maps outputted from different stages of a residual network are concatenated together by a feature pyramid fusion strategy. Upon the concatenated features, four convolutional layers are attached in parallel to predict high-level vehicle features. During training, task uncertainty learned from the training data is used to weight loss function in the multitask learning setting. For inferencing, oriented bounding boxes are generated using the predicted vehicle features, and oriented nonmaximum suppression (NMS) postprocessing is used to reduce redundant results. Experiments on two public aerial image data sets have shown the effectiveness of the proposed approach.

Road link speed is often employed as an essential measure of traffic state in the operation of an urban traffic network. Not only real-time traffic demand but also signal timings and other local planning factors are major influential factors. This paper proposes a short-term traffic speed prediction approach, called PL-WGAN, for urban road networks, which is considered an important part of a novel parallel learning framework for traffic control and operation. The proposed method applies Wasserstein Generative Adversarial Nets (WGAN) for robust data-driven traffic modeling using a combination of generative neural network and discriminative neural network. The generative neural network models the road link features of the adjacent intersections and the control parameters of intersections using a hybrid graph block. In addition, the spatial-temporal relations are captured by stacking a graph convolutional network (GCN), a recurrent neural network (RNN), and an attention mechanism. A comprehensive computational experiment was carried out including comparing model prediction and computational performances with several state-of-the-art deep learning models. The proposed approach has been implemented and applied for predicting short-term link traffic speed in a large-scale urban road network in Hangzhou, China. The results suggest that it provides a scalable and effective traffic prediction solution for urban road networks.

Accurate estimation of missing traffic data is one of the essential components in intelligent transportation systems (ITS). The non-Euclidean data structure and complex missing traffic flow patterns make it challenging to capture nonlinear spatiotemporal correlations of missing traffic flow, which are critical for the imputation of missing traffic data. In this study, we propose a novel multi-view bidirectional spatiotemporal graph network called Multi-BiSTGN to impute urban traffic data with complex missing patterns. First, three spatiotemporal graph sequences are constructed to comprehensively describe traffic conditions from different temporal correlation views, i.e. temporal closeness view, daily periodicity view, and weekly periodicity view. Then, three bidirectional spatiotemporal graph networks are fused by a parametric-matrix-based method to obtain the final imputation results. To train the Multi-BiSTGN model, a novel loss function that considers the interactions between three temporal correlation views is designed to optimize the parameters of the Multi-BiSTGN model. The proposed model was validated on real-world traffic datasets collected in Wuhan, China. Experimental results showed that Multi-BiSTGN outperformed ten existing baselines under different missing types (random missing, block missing, and mixed missing) and missing rates.

The temporal dimensions of public transit accessibility have recently garnered an increasing amount of interest. However, the existing literature on transit accessibility is heavily based on oversimplified assumptions that transit services operate at deterministic speeds using predetermined timetables. These measurements may overestimate transit accessibility, especially for large metropolitan areas where inter- and intra-modal transfers are frequent. To handle travel time uncertainty, a multi-modal transit accessibility modeling approach is proposed to account for realistic variations in travel time and service reliability. The proposed approach is applied to the mapping of transit accessibility in Shenzhen (China), where transit services exhibit significant travel time variations over space and time. Compared to traditional transit accessibility measures, our method has been demonstrated to better capture intrinsic spatial and temporal accessibility variations with complex multi-modal transit networks. Normal distribution of inter-stop travel times and constant travel speed between GPS sampling points are assumed to simply the computation, which we consider to adjust in future studies to better quantify the dynamics of transit accessibility across space and time.

Accurate traffic flow prediction on the urban road network is an indispensable function of Intelligent Transportation Systems (ITS), which is of great significance for urban traffic planning. However, the current traffic flow prediction methods still face many challenges, such as missing values and dynamic spatial relationships in traffic flow. In this study, a dynamic temporal graph neural network considering missing values (D-TGNM) is proposed for traffic flow prediction. First, inspired by the Bidirectional Encoder Representations from Transformers (BERT), we extend the classic BERT model, called Traffic BERT, to learn the dynamic spatial associations on the road structure. Second, we propose a temporal graph neural network considering missing values (TGNM) to mine traffic flow patterns in missing data scenarios for traffic flow prediction. Finally, the proposed D-TGNM model can be obtained by integrating the dynamic spatial associations learned by Traffic BERT into the TGNM model. To train the D-TGNM model, we design a novel loss function, which considers the missing values problem and prediction problem in traffic flow, to optimize the proposed model. The proposed model was validated on an actual traffic dataset collected in Wuhan, China. Experimental results showed that D-TGNM achieved good prediction results under four missing data scenarios (15% random missing, 15% block missing, 30% random missing, and 30% block missing), and outperformed ten existing state-of-the-art baselines.

Predicting traffic flow is critical in efficient maritime transportation management, coordination, and planning. Scientists have proposed many prediction methods, most of which are designed for specific locations or for short-term prediction. For the purpose of management, methods that enable long-term prediction for large areas are highly desirable. Therefore, we propose developing a spatiotemporal approach that can describe and predict traffic flows within a region. We designed the model based on a multiple hexagon-based convolutional neural network (mh-CNN) model that takes both the flow dynamics and environmental conditions into account. This model is highly flexible in that it predicts zonal traffic flow within variable time windows. We applied the method to measure and predict the daily and hourly traffic flows in the South Atlantic States region by taking the impacts of extreme weather events into consideration. Results show that our method outperformed other methods in daily prediction during normal days and hourly prediction during hurricane events. Based on the results, we also provide some recommendations regarding the future usage and customization of the model.