Passenger Flows in Underground Railway Stations and Platforms, MTI Report 12-43

Urban rail systems are designed to carry large volumes of people into and out of major activity centers. As a result, the stations at these major activity centers are often crowded with boarding and alighting passengers, resulting in passenger inconvenience, delays, and at times danger. This study examines the planning and analysis of station passenger queuing and flows to offer rail transit station designers and transit system operators guidance on how to best accommodate and manage their rail passengers. The objectives of the study are to: 1) Understand the particular infrastructural, operational, behavioral, and spatial factors that affect and may constrain passenger queuing and flows in different types of rail transit stations; 2) Identify, compare, and evaluate practices for efficient, expedient, and safe passenger flows in different types of station environments and during typical (rush hour) and atypical (evacuations, station maintenance/ refurbishment) situations; and 3) Compile short-, medium-, and long-term recommendations for optimizing passenger flows in different station environments

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

Exploring Boarding Strategies for High-Speed Railway

In light of the increasing demand for passenger transportation on high-speed railway (HSR), the pedestrian flow at HSR stations has become quite crowded in many countries, which has attracted researchers to study the HSR boarding behavior. In this paper, we propose three boarding strategies based on the features of the boarding behavior at an origin HSR station; we then use a cellular automaton (CA) model to study the impacts of boarding strategies on each passenger’s motion during the boarding process at HSR station. The simulation results indicate that some of the three strategies can optimize some passengers’ boarding time and relieve the congestion degree, and the positive impacts on the boarding process are the most prominent when the three strategies are used simultaneously. The results can help administrators to effectively organize the boarding process at the origin HSR station

Application of shape grammar theory to underground rail station design and passenger evacuation

This paper outlines the development of a computer design environment that generates station ‘reference’ plans for analysis by designers at the project feasibility stage. The developed program uses the theoretical concept of shape grammar, based upon principles of recognition and replacement of a particular shape to enable the generation of station layouts. The developed novel shape grammar rules produce multiple plans of accurately sized infrastructure faster than by traditional means. A finite set of station infrastructure elements and a finite set of connection possibilities for them, directed by regulations and the logical processes of station usage, allows for increasingly complex composite shapes to be automatically produced, some of which are credible station layouts at ‘reference’ block plan level. The proposed method of generating shape grammar plans is aligned to London Underground standards, in particular to the Station Planning Standards and Guidelines 5th edition (SPSG5 2007) and the BS-7974 fire safety engineering process. Quantitative testing is via existing evacuation modelling software. The prototype system, named SGEvac, has both the scope and potential for redevelopment to any other country’s design legislation

Modeling passenger flows in public transport stations

There are many architectural design parameters for public transport stations which include urban and station level studies. Each station must be designed in accordance with the basic passenger requirements such as accessibility, safety, comfort, satisfaction and etc. Circulation spaces must be formed and sized to meet the minimum movement needs of passengers. For an underground station; main entrance region, position of gates, location and number of turnstiles, escalators, stairs, ramps, passageways, intermediate concourses and platforms must be arranged to minimize walking distances and to prevent congestion. In this study, circulation of passengers is simulated in a quantitatively verifiable manner, taking into account how individuals interact with each other and with the physical obstacles in their environment in a metro station. Virtual experiments are performed to see the continuity and density of pedestrian flow at different levels of Haram Area East Metro Station of the first metro line of Madinah Al-Munawwarah, Saudi Arabia. According to the predictions, more than 40.000 passengers are expected to use this station in one hour after a Friday prayer during Ramadan period in the year of 2040. That means a critically high travel demand and it is really significant to design the most convenient underground station for these passengers to fulfil the necessary requirements

Study on the Impact of Health Condition Registration and Temperature Check on Inbound Passenger Flow and Optimisation Measures in a Metro Station during the COVID-19 Pandemic

The Guangzhou Metro Authority implemented health condition registration and temperature checks to curb the spread of the virus during the COVID-19 pandemic. However, it is important to investigate how these measures may have impacted the get-through efficiency and whether they caused the increased crowding at entrances and the station hall. To address these questions, simulation models based on the T Station were developed using AnyLogic. The model compared the get-through efficiencies with and without the anti-epidemic measures, while also analysing the risk of crowding at entrances and within the station hall after their implementation. Results revealed an increase in the number of passengers unsuccessfully passing through the check-in gate machines from 15% to 53% within 5 minutes, and 10% to 45% within 10 minutes when the anti-epidemic measures were in place. It was also observed that some entrances experienced significant crowding. Three measures were simulated to find effective ways to increase the get-through efficiency and mitigate the crowding – increasing the distance between security and health checks, utilising automatic infrared thermometers, and arranging volunteers or staff to assist with the registration process. The results demonstrated that using automatic infrared thermometers instead of handheld forehead thermometers proved to be effective in improving passenger efficiency and alleviating crowding at entrances and within the station hall

Train Dwell Time Evaluation at High Passenger Volume Stations

Train dwell time is complicated and depends on many factors, one of the dominant ones being passenger volume. High passenger volume on a platform always causes trains to stop longer and consequently delays the service. This research used London Underground’s actual train movement data to evaluate train dwell times on the Victoria line, which is one of the most crowded lines in the London Underground system. In the morning peak of the northbound service of the Victoria line, Victoria station becomes a critical station that determines the line capacity due to the extended dwell time at the platform. This research introduces the Data Envelopment Analysis to benchmark dwell times at each station on the line in relation to passenger volume at that station, and it suggests that stations be classified based on their demand profile. Stations with the same demand profile as Victoria station (defined as high-passenger-volume stations in this research) would be looked into further. The dwell times at these high-passenger-volume stations are highly variable and dependent on passenger movements. Many studies have found that dwell times at high-passenger-volume stations are difficult to predict accurately using dwell time models. The current method for calculating a dwell time in a train schedule uses the value calculated from the prediction model or the calculation of historical data. Considering the perspective of the uncertainty inherent in dwell time evaluation, this research proposes a new dwell time evaluation approach to evaluate the likelihood and consequences of dwell time delays at different passenger volume levels. The research contributed to the evaluation framework to evaluate the risk of dwell time delays and found that the best-case scenario with the lowest risk of dwell time delays occurs when the dwell time margin is 20 seconds, and the train is loaded to 70 percent of its maximum capacity prior to arriving at the critical station

Great cities look small

Great cities connect people; failed cities isolate people. Despite the fundamental importance of physical, face-to-face social-ties in the functioning of cities, these connectivity networks are not explicitly observed in their entirety. Attempts at estimating them often rely on unrealistic over-simplifications such as the assumption of spatial homogeneity. Here we propose a mathematical model of human interactions in terms of a local strategy of maximising the number of beneficial connections attainable under the constraint of limited individual travelling-time budgets. By incorporating census and openly-available online multi-modal transport data, we are able to characterise the connectivity of geometrically and topologically complex cities. Beyond providing a candidate measure of greatness, this model allows one to quantify and assess the impact of transport developments, population growth, and other infrastructure and demographic changes on a city. Supported by validations of GDP and HIV infection rates across United States metropolitan areas, we illustrate the effect of changes in local and city-wide connectivities by considering the economic impact of two contemporary inter- and intra-city transport developments in the United Kingdom: High Speed Rail 2 and London Crossrail. This derivation of the model suggests that the scaling of different urban indicators with population size has an explicitly mechanistic origin.Comment: 19 pages, 8 figure

Stations as Nodes

At the main point of intersection between the railway and the city, stations are key elements in the organization of the intermodal transport as well as catalysts of urban developments in metropolises, medium and small cities. The focus of this publication is to explore the enrichment of a renewed approach of railway stations as intermodal nodes, therefore acting as breeding grounds for both urban and social developments. This book has been initiated and built upon several activities currently running at the Amsterdam Institute for Advanced Metropolitan Solutions (AMS Institute), Delft University of Technology (DIMI, Delft Deltas Infrastructure Mobility Initiative and Department of Architecture of the Faculty of Architecture and the Built Environment) and University of Paris-Est (l’École d’Urbanisme de Paris). These activities have been framed within the context of two rapidly developing metropolitan areas: Randstad in the Netherlands and Métropole du Grand Paris in the Ile de France. This volume forms the basis for a research on the ‘role of stations in future metropolitan areas’ with the ambition to link the two countries, learning from their different cities and distinct geographical context through comparable mobility challenges on the levels of the inner city, suburban and peripheral areas. In line with these considerations, in 2018 AMS Institute, TU Delft/ DIMI and the Dutch Embassy in Paris with Atelier Néerlandais organized a successful workshop: ‘Stations of the Future’, in collaboration with La Fabrique de la Cité. Together with Dutch and French planning entities, involving mass transit operators and railway companies, this workshop focused on several case studies in both metropolitan areas to understand the role of station hubs as intermodal nodes. During this joint French-Dutch event that took place in Paris, we spoke on topics like Station as intermodal node, Station as destination and Station as data center, including a debate on the relation between public space and architecture, densification and programming of station areas, pedestrian flows management and the integration of data. Following the Paris workshop, the summer school ‘Integrated Mobility Challenges in Future Metropolitan Areas’ was organised by AMS Institute and Delft University of Technology/DIMI with the collaboration of the ARENA architectural research network, University of Paris-Est and the City of Amsterdam. This 8-day workshop extended the debate among international young professionals, academics and master students by looking at an important rail-metro node in the metropolitan area of the city Amsterdam: Sloterdijk Station – a crucial hub in a bigger urban area for mobility and exchange, and for urban growth. The main question was: which approaches and scenarios can be tested and applied to these intermodal nodes, particularly when dealing with lack of space and growing number of users? The results were four very different plans to improve the Sloterdijk Station area and to make the station a ‘future proof’ intermodal hub. In this publication, invited experts from practice and knowledge institutes in France and the Netherlands share their common experience and draw on specific aspects and problems of conception, management and development of stations. A brief overview of the results of the two initiatives ‘Stations of the Future’ and the summer school ‘Integrated Mobility Challenges in Future Metropolitan Areas’ is here illustrated, accompanied by photo reportages of both events and by a curated reportage of the Amsterdam Sloterdijk station area