Delay recovery model for high-speed trains with compressed train dwell time and running time

Modeling the application of train operation adjustment actions to recover from delays is of great importance to supporting the decision-making of dispatchers.In this study,the effects of two train operation adjustment actions on train delay recovery were explored using train operation records from scheduled and actual train timetables.First,the modeling data were sorted to extract the possible influencing factors under two typical train operation adjustment actions,namely the compression of the train dwell time at stations and the compression of the train running time in sections.Stepwise regression methods were then employed to determine the importance of the influencing factors corresponding to the train delay recovery time,namely the delay time,the scheduled supplement time,the running interval,the occurrence time,and the place where the delay occurred,under the two train operation adjustment actions.Finally,the gradient-boosted regression tree(GBRT)algorithm was applied to construct a delay recovery model to predict the delay recovery effects of the train operation adjustment actions.A comparison of the prediction results of the GBRT model with those of a random forest model confirmed the better performance of the GBRT prediction model.

Simulation of fire emergency evacuation in a large-passenger-flow subway station based on the on-site measured data of Shenzhen Metro

Because of its large capacity,high efficiency and energy savings,the subway has gradually become the primary mode of transportation for citizens.A high density of passengers exists within a large-passenger-flow subway station,and the number of casualties and injuries during a fire emergency is substantial.In this paper,Pathfinder software and on-site measured data of Pingzhou station in Shenzhen(China)were utilized to simulate a fire emergency evacuation in a large-passenger-flow subway station.The Required Safe Egress Time(RSET),number of passengers and flow rates of stairs and escalators were analysed for three fire evacuation scenarios:train fire,platform fire and hall fire.The evacuation time of the train fire,which was 1173 s,was the longest,and 3621 occupants needed to evacuate when the train was fully loaded.Occupants could not complete the evacuation within 6 mins in all three fire evacuation scenarios,which does not meet the current standard requirements and codes.By changing the number of passengers and the number of stairs for evacuation,the flow rate capacity and evacuation time were explored,which have reference values for safety management and emergency evacuation plan optimization during peak hours of subway operation.

Machine learning noise exposure detection of rail transit drivers using heart rate variability

Previous studies have found that drivers’physiological conditions can deteriorate under noise conditions,which poses a potential hazard when driving.As a result,it is crucial to identify the status of drivers when exposed to different noises.However,such explo-rations are rarely discussed with short-term physiological indicators,especially for rail transit drivers.In this study,an experiment involving 42 railway transit drivers was conducted with a driving simulator to assess the impact of noise on drivers’physiological responses.Considering the individuals’heterogeneity,this study introduced drivers’noise annoyance to measure their self-noise-adaption.The variances of drivers’heart rate variability(HRV)along with different noise adaptions are explored when exposed to different noise conditions.Several machine learning approaches(support vector machine,K-nearest neighbour and random forest)were then used to classify their physiological status under different noise conditions according to the HRV and drivers’self-noise adaptions.Results indicate that the volume of traffic noise negatively affects drivers’performance in their routines.Drivers with different noise adaptions but exposed to a fixed noise were found with discrepant HRV,demonstrating that noise adaption is highly associated with drivers’physiological status under noises.It is also found that noise adaption inclusion could raise the accuracy of classifications.Overall,the random forests classifier performed the best in identifying the physiological status when exposed to noise conditions for drivers with different noise adaptions.