Metro Emergency Research Articles

Coordinative dispatching of shared and public transportation under passenger flow outburst

In the passenger flow outburst area (e.g., metro emergency, dispersal of sporting events, and concerts), passengers put forward large demand for ride-sourcing services. Insufficient ride-sourcing supply of the area induces increasing matching time, and the ride-sourcing influx from outside picking up passengers leads to heavier road traffic congestion in the flow outburst area. To solve this problem, we propose a novel coordinative dispatching method of ride-sourcing and multi-modal transit, in which subsidies and buses are provided for passengers in flow outburst areas to encourage taking other public transportation to low passenger flow areas first and then taking ride-sourcing to their destinations. In this paper, there are two options for passengers: (a) wait for ride-sourcing to pick them up; (b) take other public transportation first and then take ride-sourcing. To obtain the optimal dispatching and subsidy schemes, we develop a bi-level mixed integer programming model based on network flow theory and design the corresponding iterative algorithm to solve it. Considering the high uncertainty of ride-sourcing demand in the area, we further develop a robust optimization model to obtain more reliable schemes. Case studies based on a real-problem-scale dataset are conducted. The results demonstrate that our approach can be carried out in real time, and encouraging passengers to take multi-modal transit shows great potential in reducing affected users’ delays. The robust optimization model offers a more reliable and competitive solution when demand varies widely. Our method offers a win–win–win way for passengers, ride-sourcing service providers, and public transportation systems.

Exploring the evolutionary characteristics of social media data in metro emergencies: A case study of Zhengzhou Metro flood

Exploring the evolutionary characteristics of social media data in metro emergencies: A case study of Zhengzhou Metro flood

A game model and numerical simulation of risk communication in metro emergencies under the influence of emotions

To study the influence of subject emotions on risk communication under emergencies, this paper adopts the Rank Dependent Expected Utility (RDEU) theory to define sentiment functions. Using a metro emergency as an example, a game model of risk communication between the public and the management is constructed, then a Nash equilibrium solution analysis is conducted, and finally a numerical simulation using MATLAB is carried out to investigate the optimal strategy for risk communication in different situations. The results show that emotions can affect the risk communication behaviour of the emergency to a certain extent, but are not a determining factor. When both sides of the game have emotions, the pessimistic emotional state will have a greater impact on decision-making compared to the optimistic emotional state. In addition, it is found that opinion leaders significantly influence the choice of risk communication strategies of game players. This study provides theoretical and methodological support for risk communication in emergencies.

Clustering Patterns and Hot Spots of Opioid Overdoses in Louisville, Kentucky

Using data obtained from the Louisville Metro Emergency Medical Services, this article examined the spatial and temporal patterns of opioid overdoses in Louisville, Kentucky. We aggregated opioid overdoses to street segments and applied the optimized hot spot analysis to identify areas with significant high overdose rates. Multiple spatial regression models were used to explore the ecological risk factors potentially associated with the spatial variations of the epidemic. The results suggest an overall clustered pattern of opioid overdoses with all overdose incidents concentrated in less than 8% of all the street segments. The consecutive hot spots largely overlapped with the most disadvantaged inner-city neighborhoods in Louisville. Regression results provided statistical evidence regarding the effects of socioeconomic correlates including uninsured, vacancy rates, and criminal activity. The spatial discrepancy between the overdose hot spots and lack of medical facilities or hospitals in the disadvantaged neighborhoods points to the critical issue of health inequity.

Metro Emergency Passenger Flow Prediction on Transfer Learning and LSTM Model

The metro transportation system will have emergency passenger flow for various reasons, resulting in passenger flow congestion, affecting efficiency and risks. In this paper, the LSTM network is applied to predict the normal passenger flow and emergency passenger flow of metro transportation based on transfer learning to solve the imbalanced data set problem when the amount of emergency samples is too small. The results show that under normal and emergency conditions, the average prediction error is less than 5%, which provides an alarm for the operating company to take preventive measures in advance. Compared with the strategy without transfer learning, it proves that the strategy proposed in this paper has advantages in predicting emergency conditions.

Discrete-event simulations for metro train operation under emergencies: A multi-agent based model with parallel computing

Emergencies of metro systems have become more frequent in rush hours, which have significant consequences for metro planning, designing, operating, and even the passengers’ daily travel. The motivation of this paper is to establish a hybrid metro simulation method with high efficiency and sufficient precision. To this end, a discrete-event simulation method based on a multi-agent model with parallel computing is proposed to estimate the effects of emergencies efficiently. Firstly, the trains’ motion algorithms are developed to compute the train speed profile for normal operation and metro emergency operation, respectively. Moreover, three types of agents (passenger, station, and train agents) are classified for rescheduling calculation, and six types of events are defined to discretize the emergency simulation process. Furthermore, a parallel computing method is proposed to accelerate the simulation process. Finally, a case study of the Yizhuang Line in Beijing metro is conducted to verify the effectiveness of the proposed simulation methodology. The results have proved the effectiveness and practicality of the proposed simulation method and the influence of the positions where emergencies occur and the emergency durations upon delays of trains and passengers.

Metro emergency resource distribution model

A novel station-level vulnerability algorithm that takes both passenger flow and network topology into account to reveal critical stations in a metro network is proposed. A vulnerability-based emergency resource distribution model (V-ERDM) is proposed in which a given metro station's vulnerability is measured by a combined index of network topology and passenger demand. The paper compares the proposed V-ERDM with a coverage-oriented model (i.e. a similar model without the vulnerability measure) to evaluate its performance. The models were implemented on two Chinese metro networks (Shaoxing and Shanghai), and these applications validated the feasibility of the proposed models. The unique contributions of this work are two-fold: network vulnerability in a metro system based on the characteristics of the metro network is proposed and the paper introduces a vulnerability index into the emergency resource distribution problem.

Scenario-Based Allocation of Emergency Resources in Metro Emergencies: A Model Development and a Case Study of Nanjing Metro

As metro systems are becoming more and more widely used, all kinds of emergencies happen from time to time. A series of cases indicate that inefficient emergency response is a dominating cause of tremendous casualties and losses. The fast and valid allocation of emergency resources after the occurrence of metro emergencies has become a key point in improving the sustainability of metro operations. However, few studies have attempted to determine the allocation of emergency resources in metro emergencies. In this study, considering the unpredictability of different emergency scenarios in the metro system, the scenario-response mode was applied in the resource allocation decision. In this mode, a metro emergency scenario framework was first constructed through the identification of metro emergency elements. Next, a multi-objective model was established for the allocation of emergency resources in the metro emergency rescue process using a scenario-based analysis. The model aims to minimize both the penalty costs due to delays and the sum of allocation costs. The particle swarm optimization algorithm was adopted to solve the model. Eventually, a fire accident scenario at Nanjing Metro was applied to verify the feasibility and validity of the presented model and algorithm. The research results not only enrich and improve metro emergency management theoretically, but also enhance metro emergency rescue ability in practice.

Evaluating the Learning Performance of Emergency Procedures for Operators in Metro Based on a Cognition Model

With the rapid development of the automated metro, valid emergency procedures play a significant role for operators in metro emergency response and recovery. Also, the operators have a challenge to learn different emergency procedures under different automation grades of the metro. Hence, the paper aims to evaluate the learning performance of emergency procedures with regard to the operator. Based on the ACT-R cognitive theory, two decision patterns of the operators are proposed to predict the operator’s learning process for emergency procedures, and a cognition model including 16 production rules and 32 chunks to realize the perceptual encoding and the corresponding determining parts is built. After that, an experiment is further implemented to validate the model results.

Simulation of Metro Congestion Propagation Based on Route Choice Behaviors Under Emergency-Caused Delays

Generally, metro emergencies could lead to delays and seriously affect passengers’ trips. The dynamic congestion propagation process under metro emergency-caused delays could be regarded as the aggregation of passengers’ individual travel choices. This paper aims to simulate the congestion propagation process without intervention measures under the metro emergency-caused delays, which is integrated with passengers’ route choice behaviors. First, using a stated preference survey data collected from Guangzhou Metro (GZM) passengers, route choice models are developed based on random regret minimization (RRM) theory under metro emergency conditions. Then, a simulation environment is established using graph cellular automata (graph-CA) with augmented GZM network structure, where an ASEIR (advanced susceptible-exposed-infectious-recovered) model with time delay is proposed as the evolution rule in graph-CA. Furthermore, considering passengers’ routing preferences, a quantified method for the congestion propagation rate is proposed, and the congestion propagation process on a subnetwork of the GZM network is simulated. The simulation results show that metro congestion during peak periods has a secondary increase after the end of the emergency-caused delays, while the congestion during nonpeak hours has a shorter duration and a smaller influence range. The proposed simulation model could clearly reflect the dynamic process of congestion propagation under metro emergencies.

Travel Choice Analysis under Metro Emergency Context: Utility? Regret? Or Both?

With the continuous expansion of the network scale and increasing of passengers, metro emergencies such as operational equipment failure are happening more frequently. Due to the narrow space and crowds of people, metro emergencies always have more of an impact than road traffic emergencies. In order to adopt appropriate measures to ensure passenger safety and avoid risks, we need to get a better understanding of passengers’ travel choice behaviors under emergencies. Most of the existing research studies related to travel choice behaviors took the random utility maximization (RUM) principle for granted, but failed to realize the potential of different decision-making processes and changes to the decision-making environment. In this research, we aim to analyze metro passengers’ travel choice behaviors under metro network emergency contexts. Based on the data collected from a survey about travel choices under metro emergencies in the Guangzhou Metro, we compared the performances of models that follow the RUM and random regret minimization (RRM) principles, and established a hybrid RUM-RRM model as well as a nested logit model following RRM (NL-RRM) to estimate the effects of various factors on passengers’ travel choice behaviors. Comparisons illustrate that the hybrid model and NL-RRM model can improve model fit, and the combination of RUM and RRM outperforms either of them respectively.

Communication effect of passengers on information diffusion in metro emergency

Information diffusion is significant for emergency management as it can decide the severity of accidents. In this paper, we set up a communication model of passengers for the metro emergency. In the model, four categories of passengers are defined as unknown passengers, supportive passengers, neutral passengers and opposed passengers. Three passengers’ characteristics are taken into account, such as spreading desire, the trustworthiness and the passengers’ uncertainty about their opinions. From the simulation results, we can see that the passengers’ uncertainty about their opinions has a positive correlation with the time of passengers’ opinions reaching consensus, while other two factors both have a negative correlation. The result is useful for metro officials to guide and control emergency information.

Hybrid Decision-making Method for Emergency Response System of Unattended Train Operation Metro

Suitable selection of the emergency alternatives is a critical issue in emergency response system of Unattended Train Operation (UTO) metro system of China. However, there is no available method for dispatcher group in Operating Control Center (OCC) to evaluate the decision under emergency situation. It was found that the emergency decision making in UTO metro system is relative with the preferences and the importance of multi-dispatcher in emergency. Regarding these factors, this paper presents a hybrid method to determinate the priority weights of emergency alternatives, which aggregates the preference matrix by constructing the emergency response task model based on the Weighted Ordered Weighted Averaging (WOWA) operator. This calculation approach derives the importance weights depending on the dispatcher emergency tasks and integrates it into the Ordered Weighted Averaging (OWA) operator weights based on a fuzzy membership relation. A case from train fire is given to demonstrate the feasibility and practicability of the proposed methods for Group Multi-Criteria Decision Making (GMCDM) in emergency management of UTO metro system. The innovation of this research is paving the way for a systematic emergency decision-making solution which connects the automatic metro emergency response system with the GMCDM theory.

Research for the Metro Emergency Based on the Ontology Model

Currently, with the rapid development of subway in China, many cities are gradually creating a trend of metro network-operation. In this paper, the metro emergency plans are analyzed and compared, with the result on which generate a general pattern. Then the ontology model to describe the emergency events is established. By the multi-dimensional description, the semantic representation for the emergency events was optimized.

城市地铁应急管理的研究

城市地铁应急管理的研究

Metro Emergency Management Capacity Assessment Research Based on AHP-FCE Model

Based on the characteristics of the Metro Operation System and the types of accidents, linked with Metro Operation Emergency Management System, this thesis develops a System of Metro Emergency Management Capacity Evaluation Index. Whats more, with the application of Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation (FCE), it also sets up the Metro Emergency Management Capacity Evaluation Model AHP - FCE Comprehensive Evaluation Model. Finally, the model comprehensively evaluates the ability of the domestic Metro Operation Emergency Management System. By analyzing the evaluation results, the author finds out the vulnerable spots of the system and puts forward his own opinions for improvement.