Understanding the determinants of transfer ridership is important for providing insights into improving the attractiveness of transit systems and building reliable and resilient metro stations. This study focuses on the transfer ridership between bus and metro systems under different dates and severe weather conditions to quantify the impacts of various attributes on the transfer ridership of different transfer modes (metro-to-bus and bus-to-metro). A multivariate generalized Poisson regression (GPR) model is applied to investigate the effects of critical factors on the transfer ridership of different transfer modes on weekdays, holidays, and typhoon days, respectively. The results indicate that the transfer-related variables, real-time weather, socioeconomic characteristics, and built environment significantly affect the transfer ridership. Concretely, the influence of socioeconomic and demographic factors on transfer ridership is the most significant on different types of dates, which is approximately 1.19 to 9.28 times that of the other variables. Weather variables have little effect on transfer ridership on weekdays, but they have a more significant impact on the transfer ridership on holidays and typhoon days. Specifically, during typhoons, transfer ridership is more affected by the weather factors: the coefficients are about 2.36 to 4.74 times higher than that in the other periods. Moreover, under strong wind speed, heavy rain, and high-temperature conditions, transfer ridership of the metro-to-bus mode significantly increases. In contrast, transfer ridership of the bus-to-metro mode rapidly decreases. Additionally, the peak hours have a strong positive influence on the transfer ridership, and the average hourly transfer ridership during peak hours is 1.16 to 4.02 times higher than that during the other periods. These findings indicate that the effect of each factor on transfer ridership varies with dates and transfer modes. This can also provide support for improving metro stations and increasing the attractiveness of public transport.

Despite the extensive efforts, accurate traffic time series forecasting remains challenging. By taking into account the non-linear nature of traffic in-depth, we propose a novel ST-CRMF model consisting of the Compensated Residual Matrix Factorization with Spatial-Temporal regularization for graph-based traffic time series forecasting. Our model inherits the benefits of MF and regularizer optimization and further carries out the compensatory modeling of the spatial-temporal correlations through a well-designed bi-directional residual structure. Of particular concern is that MF modeling and later residual learning share and synchronize iterative updates as equal training parameters, which considerably alleviates the error propagation problem that associates with rolling forecasting. Besides, most of the existing prediction models have neglected the difficult-to-avoid issue of missing traffic data; the ST-CRMF model can repair the possible missing value while fulfilling the forecasting tasks. After testing the effects of key parameters on model performance, the numerous experimental results confirm that our ST-CRMF model can efficiently capture the comprehensive spatial-temporal dependencies and significantly outperform those state-of-the-art models in the short-to-long terms (5-/15-/30-/60-min) traffic forecasting tasks on the open Seattle-Loop and METR-LA traffic datasets.

The Industrial Internet of Things (IIoT) has found extensive applications in intelligent transportation. However, as the number of vehicles increases, the issue of traffic congestion becomes more prominent, emphasizing the need for accurate congestion propagation prediction to enhance traffic conditions. Current methods for predicting congestion propagation lack consideration for the influence of communication networks and do not incorporate the path characteristics of congestion propagation. Therefore, we propose a path-based congestion propagation model, UAU_SIS_Path, employing multigrain abstraction of traffic congestion and information propagation. Specifically, UAU_SIS_Path effectively captures the propagation dynamics of congestion in IIoT by leveraging the interaction of two-layer networks and the path propagation characteristics of traffic congestion. Subsequently, we validate the effectiveness of the UAU_SIS_Path model through theoretical analysis, establishing tight upper and lower bounds of the propagation threshold and elucidating the relationship between the propagation of congestion information in the information network and the diffusion of congestion in the road network. Finally, based on theoretical analysis, we examine the impact of our model on congestion control strategies, utilizing path replanning, and traffic restriction as congestion control strategies. Experimental results in simulated road network BA and real road networks of varying sizes (GC, TA, and As) demonstrate the stability and scalability of our model. In comparison to the traditional contact-based propagation model, our model achieves a reduction in congestion propagation rates of 58.2%, 66.9%, 32.6%, and 48.6%, respectively.

Accurate forecasting of the future transfer passenger flow from historical data is essential for helping travelers to adjust their trips, optimal resource allocation and alleviating traffic congestion. However, current studies have mainly emphasized predicting traffic parameters for a single type of transport, while lacking research into transfer passenger flow influenced by multiple factors across different transport modes. Additionally, efficient traffic prediction relies on high-quality traffic data, yet data loss issues are inevitable but often ignored. To fill these gaps, we present for the first time a reliable joint long short-term memory with matrix factorization deep learning model (i.e., Joint-IF) for accurate imputation and forecasting of transfer passenger flow between metro and bus. This hybrid Joint-IF model uses a repair-before-prediction strategy to deliver the final high-quality outputs. In particular, we simulate a variety of missing combinations under the natural conditions and apply a low-rank matrix factorization to infer those lost values. In addition, we investigate the effects of crucial parameters and spatiotemporal features on transfer flow prediction. To validate the effectiveness of Joint-IF, a large series of experiments are carried out for models’ comparison and validation on the real-world transfer passenger flow dataset of the Shenzhen public transport system, and the results show that the proposed Joint-IF performs better for both imputation and forecasting of transfer passenger flow relative to the baseline models in terms of accuracy and stability.