Research on Passenger Flow Control Plans for a Metro Station Based on Social Force Model

To better utilise the service capacity of the limited facilities of a metro station, as well as ensure safety and transport efficiency during peak hours, a large passenger flow control plan is studied through theoretical analysis and numerical simulation. Firstly, by passenger data collection and data analysis, the characteristics of the inbound and outbound passenger flow of a T metro station are analysed. Secondly, AnyLogic evacuation simulation models for the T Station during peak hours, peak hours without/with passenger flow control are established based on real passenger flow data as well as the station structures and layouts by using the AnyLogic software. The results show that there are no obvious congestions in the station hall, and the travel delay is significantly reduced when effective passenger flow control measures are taken. By controlling the speed, direction and movement path of passengers, as well as adjusting the operation of escalators, entrances and automatic ticket-checking machines, passenger flow can become more orderly, transport efficiency can also be improved, and congestion in the station can be well mitigated

Modeling, Evaluation, and Scale on Artificial Pedestrians: A Literature Review

Modeling pedestrian dynamics and their implementation in a computer are challenging and important issues in the knowledge areas of transportation and computer simulation. The aim of this article is to provide a bibliographic outlook so that the reader may have quick access to the most relevant works related to this problem. We have used three main axes to organize the article's contents: pedestrian models, validation techniques, and multiscale approaches. The backbone of this work is the classification of existing pedestrian models; we have organized the works in the literature under five categories, according to the techniques used for implementing the operational level in each pedestrian model. Then the main existing validation methods, oriented to evaluate the behavioral quality of the simulation systems, are reviewed. Furthermore, we review the key issues that arise when facing multiscale pedestrian modeling, where we first focus on the behavioral scale (combinations of micro and macro pedestrian models) and second on the scale size (from individuals to crowds). The article begins by introducing the main characteristics of walking dynamics and its analysis tools and concludes with a discussion about the contributions that different knowledge fields can make in the near future to this exciting area

Survey of detection techniques, mathematical models and simulation software in pedestrian dynamics

The study of pedestrian dynamics has become in the latest years an increasing field of research. A relevant number of technicians have been looking for improving technologies able to detect walking people in various conditions. Several researchers have dedicated their works to model walking dynamics and general laws. Many studiers have developed interesting software to simulate pedestrian behavior in all sorts of situations and environments. Nevertheless, till nowadays, no research has been carried out to analyze all the three over-mentioned aspects. The remarked lack in literature of a complete research, pointing out the fundamental features of pedestrian detection techniques, pedestrian modelling and simulation and their tight relationships, motivates the draft of this paper. Aim of the paper is, first, to provide a schematic summary of each topic. Secondly, a more detailed description of the subjects is displayed, pointing out the advantages and disadvantages of each detection technology, the working logic of each model, outlining the inputs and the provided outputs, and the main features of the simulation software. Finally, the obtained results are summarized and discussed, in order to outline the correlation among the three explained themes

Developing standard pedestrian-equivalent factors: passenger car–equivalent approach for dealing with pedestrian diversity

Similar to vehicular traffic, pedestrians, despite having diverse capabilities and body sizes, can be classified as heterogeneous. The use of vehicular traffic resolves the diversity issue with a conversion of heterogeneous vehicle flow into an equivalent flow with the use of passenger car–equivalent (PCE) factors. Analysis of pedestrian flow has yet to incorporate pedestrian diversity analysis implicitly into the design of pedestrian facilities, although some form of adjustment has been suggested. This paper introduces the concept of PCE-type factors for mixed pedestrian traffic called standard pedestrian-equivalent (SPE) factors. Estimates of SPE factors are made relative to the average commuter. The equivalent total travel time approach for PCE estimation was adapted to consider the effects of the differences in physical and operational characteristics of pedestrians, particularly walking speed and body size. Microsimulation of pedestrians was employed to evaluate hypothetical pedestrian proportions so as to generate corresponding flow relationships. Walking speeds and body sizes were varied across different flow conditions, walkway widths, and proportions of other pedestrian types. The first part of this paper explores how the two pedestrian characteristics (walking speed and body size) influence estimated SPE factors. The second part is a case study in which field-collected data illustrate SPE factors calculated for older adults, obese pedestrians, and their combination. An application of SPE factors demonstrates the robustness of the methodology in bridging the gap between pedestrian compositions and planning practice

Utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) for overcrowding level risk assessment in railway stations

The railway network plays a significant role (both economically and socially) in assisting the reduction of urban traffic congestion. It also accelerates the decarbonization in cities, societies and built environments. To ensure the safe and secure operation of stations and capture the real-time risk status, it is imperative to consider a dynamic and smart method for managing risk factors in stations. In this research, a framework to develop an intelligent system for managing risk is suggested. The adaptive neuro-fuzzy inference system (ANFIS) is proposed as a powerful, intelligently selected model to improve risk management and manage uncertainties in risk variables. The objective of this study is twofold. First, we review current methods applied to predict the risk level in the flow. Second, we develop smart risk assessment and management measures (or indicators) to improve our understanding of the safety of railway stations in real-time. Two parameters are selected as input for the risk level relating to overcrowding: the transfer efficiency and retention rate of the platform. This study is the world’s first to establish the hybrid artificial intelligence (AI) model, which has the potency to manage risk uncertainties and learns through artificial neural networks (ANNs) by integrated training processes. The prediction result shows very high accuracy in predicting the risk level performance, and proves the AI model capabilities to learn, to make predictions, and to capture risk level values in real time. Such risk information is extremely critical for decision making processes in managing safety and risks, especially when uncertain disruptions incur (e.g., COVID-19, disasters, etc.). The novel insights stemmed from this study will lead to more effective and efficient risk management for single and clustered railway station facilities towards safer, smarter, and more resilient transportation systems

Moving Risk of Crowds in the Entrance Confluence Area in the Presence of Channelizing Facilities

In recent years, the measures to interfere the crowds movement with physical facilities (such as channelizing, separation railing) have become more and more common, but how they affect the crowd movement and what moving risks exist in the entrance confluence area have not been fully revealed. Therefore, this paper analyzes the moving risk of the crowds before the bottleneck entrance area, in the presence of the channelizing barriers by controllable laboratory experiments. The visual color cloud charts of the local density, speed and confusion degree of moving directions within the entrance confluence area are analyzed in the presence of different gaps (1.05m and 0.7m) channelizing barriers, to further quantify the motion risk of the crowds. The study finds that the narrower gaps of the channelizing railings, the larger area of high-risk zones, and they have clear ‘lane formation’ effect in shaping the risk zones. The both ends of the channelizing barriers are higher moving risk zones for multi-entry sides conditions, but the area before the middle channels also needs to be closely concerned when the participants entering from two opposite entering sides. The study will provide theoretical basis for evaluating the safety of the setting conditions of the channelizing barriers and conducting scientific crowd management decisions

Detection and Simulation of Dangerous Human Crowd Behavior

Tragically, gatherings of large human crowds quite often end in crowd disasters such as the recent catastrophe at the Loveparade 2010. In the past, research on pedestrian and crowd dynamics focused on simulation of pedestrian motion. As of yet, however, there does not exist any automatic system which can detect hazardous situations in crowds, thus helping to prevent these tragic incidents. In the thesis at hand, we analyze pedestrian behavior in large crowds and observe characteristic motion patterns. Based on our findings, we present a computer vision system that detects unusual events and critical situations from video streams and thus alarms security personnel in order to take necessary actions. We evaluate the system’s performance on synthetic, experimental as well as on real-world data. In particular, we show its effectiveness on the surveillance videos recorded at the Loveparade crowd stampede. Since our method is based on optical flow computations, it meets two crucial prerequisites in video surveillance: Firstly, it works in real-time and, secondly, the privacy of the people being monitored is preserved. In addition to that, we integrate the observed motion patterns into models for simulating pedestrian motion and show that the proposed simulation model produces realistic trajectories. We employ this model to simulate large human crowds and use techniques from computer graphics to render synthetic videos for further evaluation of our automatic video surveillance system