Efficient time–frequency approach for prediction of subway train-induced tunnel and ground vibrations

In this paper, an efficient time–frequency approach is presented for the prediction of subway train-induced tunnel and ground vibrations. The proposed approach involves two steps. In the first step, a time domain simulation of the vehicle–track subsystem is used to determine the track–tunnel interaction forces and, in the second step, the resulting forces are then applied to a 2.5 D FEM–PML model of the tunnel–soil system. There are two main aspects to the novelty and contribution of this work: First, the errors of the linearized Hertzian wheel–rail contact models in the calculation of the track–tunnel interaction forces are quantified by a comparison with the nonlinear Hertzian contact model. The results show that the relative errors are less than 2%. Second, an efficient time–frequency analysis framework is proposed, including the use of a strongly coupled model in the time domain solution and a 2.5 D FEM–PML model in the frequency–wavenumber domain solution. Finally, the accuracy and efficiency of the proposed approach are verified by comparison with a time-dependent 3 D approach, where three types of soil, i.e. soft, medium, and hard, are considered.

Reducing the nanoparticles generated at the wheel–rail contact by applying tap water lubricant at subway train operational velocities

The formation characteristics and the reduction of nanoparticles emitted from wheel–rail contacts at subway-train velocities of 73, 90, and 113 km/h under dry and water-lubricated conditions (using tap water) were studied using a twin-disk rig. The resulting number concentration (NC) of ultrafine and fine particles increased with train velocity under both conditions. Particle generation varied with slip rate under both conditions in both the particle categories. Furthermore, the formation characteristics at 113 km/h under dry conditions showed a notable deviation from those under water-lubricated conditions in three aspects: (i) The maximum NC of ultrafine particles was higher than that of fine particles, (ii) the predominant peak diameter was in the ultrafine particles category, and (iii) the proportion of ultrafine particles was much higher than those of the fine particles. Applying water decreased the NC of ultrafine and fine particles significantly at all tested velocities (by 54–69% and 87–91%, respectively). Adding water increased the NC of particles ≤ 35 nm in diameter, possibly owing to the increase in water vapor and mineral crystals from tap water. Overall, this study provides a reference for researchers aiming to minimize nanoparticle formation at the wheel–rail contacts by applying a lubricant.

Subway Obstacle Perception and Identification Method Based on Cloud Edge Collaboration

The traditional analysis method of train obstacle uses isomorphic sensors to obtain the state information and completes detection and identification analysis at the remote end of a network. A single data sample and more processing links will reduce the accuracy and speed analysis for subway encountering obstacles. To solve this problem, this paper proposes a subway obstacle perception and identification method based on cloud edge cooperation. The subway monitoring cloud platform realizes the training and construction of a detection model, and the network edge side completes the situation awareness of track state and real-time action when the train encounters obstacles. Firstly, the railroad track position is detected by cameras, and subway running track is identified by Mask RCNN algorithm to determine the detection area of obstacles in the process of subway train running. At the edge of network, the feature-level fusion of data collected by sensor cluster is carried out to provide reliable data support for detection work. Then, based on the DeepSort and YOLOv3 network models, the subway obstacle detection model is constructed on the subway monitoring cloud platform. Moreover, a trained model is distributed to the network edge side, so as to realize the fast and efficient perception and action of obstacles. Finally, the simulation verification is implemented based on actual collected datasets. Experimental results show that the proposed method has good detection accuracy and efficiency, which maintains 98.9% and 1.43 s for obstacle detection accuracy and recognition time in complex scenes.

Subway Embedded Track Geometric Irregularity Safety Limits

A coupling dynamic model of a subway train and an embedded track is established to study the safety limits of track irregularities. The simulated vehicle system was a 74-degrees of freedom multi-rigid body model, and the rail was a Timoshenko beam. The slab was a three-dimensional solid finite element model. The sensitive wavelength irregularity was first studied, and then the safety limit of the sensitive wavelength was analyzed. The wheel-rail lateral force exhibited a substantial effect on the track alignment and gauge irregularity safety limit. The wheel-rail vertical force and the rate of wheel load reduction significantly affected the height and cross-level irregularity safety limit. The results demonstrate that the safety limits of the alignment, gauge, height, and cross-level embedded track geometric irregularity are 5.3 mm, [− 10.5, 8] mm, 5.6 mm, and 6 mm, respectively.

Reduction the Nanoparticles Generated at the Wheel–rail Contact by Applying Tap Water Lubricant at Subway Train Operational Velocities1

The formation characteristics and reduction of nanoparticles emitted from wheel–rail contacts at subway train velocities of 73, 90, and 113 km/h under dry and water-lubricated conditions (using tap water) were studied using a twin-disk rig. The resulting number concentration (NC) of ultrafine and fine particles increased with train velocity under both conditions. Particle generation varied with slip rate under both conditions in both the particle categories studied. Further, the formation characteristics at 113 km/h under dry conditions showed a notable deviation from those under water-lubricated conditions in three aspects: (i) the maximum NC of ultrafine particles was higher than that of fine particles, (ii) the predominant peak diameter was in the ultrafine particles category, and (iii) the proportion of ultrafine particles was much higher than those of fine particles. Applying water decreased the NC of ultrafine and fine particles significantly at all tested velocities (by 54%–69% and 87%–91%, respectively). Adding water increased the NC of particles ≤35 nm in diameter, possibly owing to the increase in water vapor and mineral crystals from tap water. Overall, this study provides a reference for researchers aiming to minimize nanoparticle formation at the wheel–rail contacts by applying a lubricant.

Evacuation Behavior in a Subway Train Emergency: A Video-based Analysis

How do people behave in the seconds after they become aware they have been caught up in a real-life transport emergency? This paper presents the first micro-behavioral, video-based analysis of the behavior of passengers during a small explosion and subsequent fire on a subway train. We analyzed the behavior of 40 passengers present in the same carriage as the explosion. We documented the first action of the passengers following the onset of the emergency and described evidence of pro- and anti-social behavior. Passengers’ first actions varied widely. Moreover, anti-social behavior was rare and displays of pro-sociality were more common. In a quantitative analysis, we examined spatial clustering of running behavior and patterns in passenger exit choices. We found both homogeneity and heterogeneity in the running behavior and exiting choices of passengers. We discuss the implications of these findings for the mass emergency literature and for evacuation modeling.