Traffic prediction, as a core component of intelligent transportation systems (ITS), has been investigated thoroughly in the literature. Nevertheless, timely accurate traffic prediction still remains an open challenge due to the nonlinearities and complex patterns of traffic flows. In addition, most of the existing traffic prediction methods focus on grid-based computing problems (e.g., crowd in-out flow prediction) and point-based computing problems (e.g., traffic detector data prediction), ignoring the segment-based traffic prediction tasks. In this study, we propose an attention-based spatiotemporal graph attention network (AST-GAT) for segment-level traffic speed prediction. In particular, a multi-head graph attention block is designed to capture the spatial dependencies among road segments. Then, a component fusion block is built for speed, volume, and weather information integration. Finally, an attention-based Long short-term memory (LSTM) block is constructed for temporal dependency learning as well as segment-based speed prediction. Experiments on a real-world dataset from Highways England demonstrate that the proposed AST-GAT model outperforms the state-of-the-art baselines, providing an efficient tool for segment-based traffic prediction and therefore filling the gap between point-based and grid-based predictions.

Objective: Pedestrians are the most vulnerable road users due to the lack of mass, speed, and protection compared to other types of road users. Adverse weather conditions may reduce road friction and visibility and thus increase crash risk. There is limited evidence and considerable discrepancy with regard to impacts of weather conditions on injury severity in the literature. This article investigated factors affecting pedestrian injury severity level under different weather conditions based on a publicly available accident database in Great Britain. Method: Accident data from Great Britain that are publicly available through the STATS19 database were analyzed. Factors associated with pedestrian, driver, and environment were investigated using a novel approach that combines a classification and regression tree with random forest approach. Results: Significant severity predictors under fine weather conditions from the models included speed limits, pedestrian age, light conditions, and vehicle maneuver. Under adverse weather conditions, the significant predictors were pedestrian age, vehicle maneuver, and speed limit. Conclusions: Elderly pedestrians are associated with higher pedestrian injury severities. Higher speed limits increase pedestrian injury severity. Based on the research findings, recommendations are provided to improve pedestrian safety.

Gradual penetration of automated vehicles (AVs) into current motorway systems will usher the stage of mixed traffic in which AVs will coexist with human driven vehicles. Thus, there is an urgent need to identify the possible impacts of this mixed traffic on motorway operation. To investigate the potential benefits or losses due to introducing AVs into existing motorway systems, this study conducts a comprehensive evaluation based on simulation using a 5.3 km stretch on Auckland Motorway and traffic data provided by New Zealand Traffic Agent (NZTA). We analyze the impacts of different AV shares on mobility, safety, emissions and fuel consumption. The motorway with and without traffic control are tested under four scenarios of traffic conditions, namely, heavily congested traffic (> 0.95*capacity), lightly congested traffic (≈0.7*capacity), free-flow traffic (≈0.5*capacity), and future traffic (3*heavily congested traffic volume). The outcomes of the research can provide motorway designers and operators a reasonable range of influences contributed by AV penetration so as to better prepare for AVs’ arrival.

In recent years, there has been rapid development in autonomous vehicle techniques due to the increasing demands in traffic management and travel planning. Accurate spatiotemporal traffic forecasting is crucial for autonomous vehicles. However, most existing methods are task-specific and tailored for specific cities, making them hard to apply for more downstream applications. These methods fail to accurately simulate the common variation of traffic states at different locations based on their spatial distance and time interval. Compared with most existing spatiotemporal prediction baselines, this paper proposes a joint pre-training framework, named JointSTNet, to further enhance the traffic spatiotemporal prediction accuracy while reducing model complexity for traffic forecasting tasks. Firstly, unlike treating traffic spatiotemporal data on roads as independent patterns, the spatial graph capsule module constructs inter-class traffic patterns based on shared dynamics within the dynamic graph structure. Additionally, the temporal encoding gated module proposed in our work expands the temporal receptive field along the time axis. Finally, we maximize reconstruction losses to handle incomplete connections within data involving intra-cluster and inter-cluster regional semantic relationships that can be enhanced through pre-training. Experimental results on four benchmarking datasets from the real world demonstrate that JointSTNet outperforms various types of state-of-the-art baselines.

Massive bike-sharing systems (BSS) usage and performance data have been collected for years over various locations. Nevertheless, researchers encountered several challenges while dealing with massive BSS data. The challenges that could be enhanced in the previous studies are 1) reducing high dimensionality and noise of BSS time series data and 2) extracting informative usage patterns out of massive BSS data. This paper extracts patterns and reduce data dimensions of BSS usage by exploring time series representation and clustering of BSS usage data. A reduced dimension allows us to efficiently approximate the BSS usage with reasonable accuracy, which can be further used for bike usage clustering, classification and prediction. We employ a non-data adaptive representation technique -Discrete Wavelet Transform (DWT) to reduce dimensionality and filter out random errors of the raw time series. Time series are clustered using k-means based on similarities measured by Dynamic Time Warping (DTW) and prototypes computed using DTW barycenter averaging (DBA). The proposed approaches are applied on a 3-month bike usage dataset acquired on the BSS of Chicago. The analysis results show that DWT can effectively reduce dimensionality, filter out random errors and reveal the main characteristics of the raw time series. The clustering approach offers the ability to differentiate and discover bike usage patterns across different stations.

With the booming adoption of Electric Vehicles (EVs) globally, the need for reliable and resilient EV Charging Monitoring (EVCM) systems has become crucial. A major challenge in real-time EVCM is the handling of missing data caused by unexpected events, which can impair both real-time monitoring and its downstream applications. To address this vital yet underexplored issue, we propose a curriculum guided multi-feature fusion transformer (CurriFusFormer) learning framework – a novel approach designed to enhance the resilience of EVCM systems against real-time information omissions. Our framework integrates curriculum learning with a multi-feature fusion transformer model, capable of handling various patterns and rates of missing data, ranging from random to block omissions. This innovative approach leverages spatial, temporal, and static features to generate accurate real-time estimations for missing values in diverse scenarios. Extensive experiments on a real-world EVCM dataset demonstrate that CurriFusFormer can perform well with $R^2$ ranging from 0.92 to 0.83 given the rising missing rate from 30-90%, outperforming seven popular and state-of-the-art methods, especially in scenarios with high missing rates and complex patterns, such as, at 90% missing rate, kNN ( $R^2$ $=$ 0.65), XGBoost ( $R^2$ $=$ 0.78), BRITS ( $R^2$ $=$ 0.79), TFT ( $R^2$ $=$ 0.80), and GRIN ( $R^2$ $=$ 0.82). All results suggest that the proposed framework could be a promising solution for developing future resilient EVCM networks.