Not wearing a helmet, not properly strapping the helmet on, or wearing a substandard helmet increases the risk of fatalities and injuries in motorcycle crashes. This research examines the differences in motorcycle crash injury severity considering crashes involving the compliance with and defiance of helmet use by motorcycle riders and highlights the temporal variation in their impact. Three-year (2017–2019) motorcycle crash data were collected from RESCUE 1122, a provincial emergency response service for Rawalpindi, Pakistan. The available crash data include crash-specific information, vehicle, driver, spatial and temporal characteristics, roadway features, and traffic volume, which influence the motorcyclist’s injury severity. A random parameters logit model with heterogeneity in means and variances was evaluated to predict critical contributory factors in helmet-wearing and non-helmet-wearing motorcyclist crashes. Model estimates suggest significant variations in the impact of explanatory variables on motorcyclists’ injury severity in the case of compliance with and defiance of helmet use. For helmet-wearing motorcyclists, key factors significantly associated with increasingly severe injury and fatal injuries include young riders (below 20 years of age), female pillion riders, collisions with another motorcycle, large trucks, passenger car, drivers aged 50 years and above, and drivers being distracted while driving. In contrast, for non-helmet-wearing motorcyclists, the significant factors responsible for severe injuries and fatalities were distracted driving, the collision of two motorcycles, crashes at U-turns, weekday crashes, and drivers above 50 years of age. The impact of parameters that predict motorcyclist injury severity was found to vary dramatically over time, exhibiting statistically significant temporal instability. The results of this study can serve as potential motorcycle safety guidelines for all relevant stakeholders to improve the state of motorcycle safety in the country.

In order to improve the accuracy of short-term travel time prediction in an urban road network, a hybrid model for spatio-temporal feature extraction and prediction of urban road network travel time is proposed in this research, which combines empirical dynamic modeling (EDM) and complex networks (CN) with an XGBoost prediction model. Due to the highly nonlinear and dynamic nature of travel time series, it is necessary to consider time dependence and the spatial reliance of travel time series for predicting the travel time of road networks. The dynamic feature of the travel time series can be revealed by the EDM method, a nonlinear approach based on Chaos theory. Further, the spatial characteristic of urban traffic topology can be reflected from the perspective of complex networks. To fully guarantee the reasonability and validity of spatio-temporal features, which are dug by empirical dynamic modeling and complex networks (EDMCN), for urban traffic travel time prediction, an XGBoost prediction model is established for those characteristics. Through the in-depth exploration of the travel time and topology of a particular road network in Guiyang, the EDMCN-XGBoost prediction model’s performance is verified. The results show that, compared with the single XGBoost, autoregressive moving average, artificial neural network, support vector machine, and other models, the proposed EDMCN-XGBoost prediction model presents a better performance in forecasting.

Accurate prediction of passenger demand is essential for the efficient operation and management of metro systems. In practical scenarios, strategies to enhance metro service quality often require passenger demand information on multiple fronts, such as inflow to a station, outflow from a station, as well as transition flow between entry/exit stations. While predictions for a single type of passenger demand have been extensively studied, limited attention was paid to jointly predicting multiple demands. This problem is challenging due to the complex relationships among multiple demands (e.g., inflow is only correlated with historical inflow, while the outflow is not only correlated with outflow but also determined by the inflow) and the imbalanced training issue of multiple prediction tasks. To address these challenges, this paper proposes a deep multi-task learning (MTL) model called MTLMetro to co-predict multiple demands in metro systems. More specifically, we deploy the message-passing schemes in graph neural networks (GNNs) as the knowledge-sharing mechanisms in the MTL model to capture the inherent relationships among multiple demands. To balance the training of multiple tasks, we introduce a novel weighting scheme named dynamic weight average (DWA), which can dynamically adapt relative weight for each task. In addition, the partial observability problem of transition flow is also considered in MTLMetro in an end-to-end manner. Empirical evaluation on a real-world dataset demonstrates MTLMetro’s superior performance across the different demand prediction tasks when compared to several benchmarks. Further ablation experiments verify the effectiveness of the proposed modules and the weighting method.