It is difficult for a single model to simultaneously capture the nonlinear, correlation, and periodicity of data series in the passenger flow prediction of urban rail transit (URT). To better predict the short-term passenger flow of URT, based on the long short-term memory network (LSTM) model, a deep learning model prediction method combining the time convolution network (TCN) and the long short-term memory network (LSTM) based on machine learning is proposed. The model couples the external factors such as date attributes, weather conditions, and air quality, to improve the overall prediction performance and solve the difficulty of accurate prediction due to the large fluctuation and randomness of short-term passenger flow in rail transit. Using the swiping data and related weather information of some stations of Chongqing Rail Transit Line 3, the TCN-LSTM model is verified by an example, and the prediction results of the single LSTM model are given for comparison. The results show that the TCN-LSTM model can better predict the passenger flow characteristics of different stations at different times. Compared with the single LSTM model, the TCN-LSTM model has better prediction accuracy and data generalization ability.

Traditional station passenger flow prediction can no longer meet the application needs of urban rail transit vehicle scheduling. Station passenger flow can only predict station distribution, and the passenger flow distribution in general sections is unknown. Accurate short-term travel origin and destination (OD) passenger flow prediction is the main basis for formulating urban rail transit operation organization plans. To simultaneously consider the spatiotemporal characteristics of passenger flow distribution and achieve high precision estimation of origin and destination (OD) passenger flow quickly, a predictive model based on a temporal convolutional network and a long short-term memory network (TCN–LSTM) combined with an attention mechanism was established to process passenger flow data in urban rail transit. Firstly, according to the passenger flow data of the urban rail transit section, the existing data characteristics were summarized, and the impact of external factors on section passenger flow was studied. Then, a temporal convolutional network and long short-term memory (TCN–LSTM) deep learning model based on an attention mechanism was constructed to predict interval passenger flow. The model combines some external factors such as time, date attributes, weather conditions, and air quality that affect passenger flow in the interval to improve the shortcomings of the original model in predicting origin and destination (OD) passenger flow. Taking Chongqing Rail Transit as an example, the model was validated, and the results showed that the deep learning model had significantly better prediction results than other baseline models. The applicability analysis in scenarios such as high/medium/low passenger flow could achieve stable prediction results.

In this paper, aiming at the detection of fatigue driving scene of drivers, a diagnostic model based on machine learning is proposed under the scene of long-time driving. The validity of the model is verified by simulation experiments. The simulation result shows that the model can effectively fit the fatigue condition of drivers under long-time driving, and accurately judge and warn the fatigue state of drivers. At the same time, the model also extends the application of fatigue classification detection.

This paper aims at the control decision of the compound-fault scene in urban rail transit Fully Automatic Operating (FAO) system. Under the compound-fault scene of vehicle fire and station fire occurring simultaneously, a bi-level optimization model is proposed for the operation control model of urban rail transit FAO system, and the validity of the model is verified by the simulation experiment. The simulation results show that the decision model can effectively find the optimal control points for the compound-fault occurrence of urban rail transit FAO system out, so as to carry out the active control and improve the operation efficiency of the urban rail transit system.

The flexible pantograph of straddle-type monorail has influence on the dynamics performance of pantograph-catenary system. This paper established a rigid-flexible pantograph-catenary-vehicle coupling dynamics model of monorail. By embedded the rigid-flexible pantograph into the vehicle dynamic analysis model, the rigid-flexible pantograph-catenary coupling dynamics model that could consider the random irregularity of track beam ultimately established. The validity of model has been approved by the pantograph dynamics test of Chongqing monorail line 3. The dynamic performance of the pantograph-catenary system, calculated by the dynamics models with rigid and rigid-flexible pantograph are compared in detail. The results show that the pantograph flexibility affects the vibration characteristics and the current collection quality of pantograph-catenary system.

With the continuous advancement of urban construction, inertial sensors are increasingly used in the detection of underground pipelines. However, inertial measurement units (IMUs) are susceptible to a variety of error sources, leading to the accumulation of position estimation errors over time, which severely restricts their positioning accuracy. This paper provides a systematic review of IMU calibration and drift suppression error compensation methods applicable to underground pipeline environments. Building upon this foundation, it innovatively proposes a three-tiered review framework based on “error characteristics–compensation mechanisms–application scenarios”. The framework begins with the characterization of error factors, maps them to corresponding compensation mechanisms, and then applies them to specific operating conditions. It identifies current research limitations in real-time performance, robustness, experimental validation, and standardized evaluation. Future efforts should focus on designing lightweight fusion algorithms, integrating deep learning with sensor fusion, and establishing standardized testing platforms. This paper aims to summarize the current state and development trends of inertial sensor error compensation methods, providing a reference for advancing related technologies while offering beginners a clear and systematic learning path.