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

VANET Applications: Hot Use Cases

Current challenges of car manufacturers are to make roads safe, to achieve free flowing traffic with few congestions, and to reduce pollution by an effective fuel use. To reach these goals, many improvements are performed in-car, but more and more approaches rely on connected cars with communication capabilities between cars, with an infrastructure, or with IoT devices. Monitoring and coordinating vehicles allow then to compute intelligent ways of transportation. Connected cars have introduced a new way of thinking cars - not only as a mean for a driver to go from A to B, but as smart cars - a user extension like the smartphone today. In this report, we introduce concepts and specific vocabulary in order to classify current innovations or ideas on the emerging topic of smart car. We present a graphical categorization showing this evolution in function of the societal evolution. Different perspectives are adopted: a vehicle-centric view, a vehicle-network view, and a user-centric view; described by simple and complex use-cases and illustrated by a list of emerging and current projects from the academic and industrial worlds. We identified an empty space in innovation between the user and his car: paradoxically even if they are both in interaction, they are separated through different application uses. Future challenge is to interlace social concerns of the user within an intelligent and efficient driving

From systems to patterns and back - Exploring the spatial role of dynamic time and direction patterns in the area of regional planning

This master thesis presents a data-driven framework to explore the role of dynamic time and direction patterns in the area of Finnish Lapland in order to improve decision-making in urban planning and design tasks. The Arctic Ocean Railway project is chosen as a case study. In an era marked by dramatic environmental, political and societal changes, the Arctic region becomes more global and complex. An increasing number of actors are involved in its spatial transformations. Due to melting ice, the Northern Sea Route gains attention from the shipping and trade industries that are manifested in new port and infrastructure projects. Eco-tourism is booming in the Arctic due to its imaginary remoteness, while local Indigenous People try to preserve traditional livelihoods. In order to cope with the increasing complexity of such dynamic urban and regional challenges, Systems Thinking, dynamic patterns, modelling and use of simulation are researched to open up novel ways for complex regional planning methods. This is achieved by designing an agent-based model and using different representation and abstraction features for different dynamic data packages. The project is integrated within the GAMA simulation platform (a modelling and simulation development environment for building spatially explicit agent-based simulations) and embedded in the MIT CityScope framework - a medium for both, analyzing agent’s behavioural patterns and displaying them to the relevant stakeholders. The project attempts to address the necessity to handle the increasing complexity by presenting a dynamic, evidence-based planning and decision support tool called CityScope Lapland. The main goal of CityScope Lapland is to use digital technologies to incorporate variables like time and direction in urban spatial analysis and methodology; secondly, to improve the accessibility of the decision-making process for non-experts through a tangible user interface, and third, to help users evaluate their decisions by creating a feedback through real-time visualization of urban simulation results when facing less and less predictable futures. The project provides an alternative design approach, introducing new forms of urban imagination and different ways of perceiving and measuring complex spatial transformations

A tram-train system to connect the urban area of Cosenza to its province: A simulation model of transport demand modal split and a territorial analysis to identify adapted transit oriented development prospects

International audienceThe purpose of this paper is to study possible prospects of regional development and of public transport demand evolution, resulting in the implementation of a new tramtrain service to suburban and a tramway for urban area of Cosenza and Rende and for municipalities of Savuto valley, in the southern Italian region of Calabria. This is an area that in recent decades has seen significant phenomena of urban de‐population, with consequent problems of urban sprawl into neighbouring small cities and land consumption. The mobility system is heavily focused on the use of private cars as the main and often the only way to travel; causing obvious problems of traffic congestionand poor urban quality of life for citizens. The modern tramway system project, next to be realized, will connect the urban area of Cosenza and Rende with the University of Calabria. It is a first important structural intervention that will hopefully help to significantly increase public transport modal share and to promote implementation of Transit Oriented Development policies, properly adapted to that specific territory. The decision to adopt such a narrow gauge tramway line, allows to consider the prospect of actually integrating this service with a tramtrain system linking Rogliano and municipalities of Savuto valley, with the urban area, using existing narrow gauge railways of Ferrovie della Calabria (main regional train operators). With this purpose was developed a transport demand simulation model, using the Tranus system, to estimate the evolution of the transport demand modal split in that area, caused by such changes in the mobility system. Through a spatial analysis were showed bsome areas that might be interested by interventions of urban renewal and regeneration, with greater access to public transport services and Transit Oriented Development policies. Results of this analysis and the simulation model will be presented and discussed in detail in this paper

The Effects of Carry-on Baggage on Aircraft Evacuation Efficiency

The most frequent obstacle of an aircraft evacuation is the passengers carrying baggage while evacuating. Passengers who insist on taking their carry-on baggage during an emergency evacuation not only slow down the evacuation process but also act as a significant risk to the safety of other passengers. This study investigated the factors that affect passengers’ behavioral intention to evacuate with carry-on baggage and the effects of evacuating with carry-on baggage on the total evacuation time. Overall, two studies were conducted to provide an outline of the factors that affect and affected by carry-on baggage. Study 1 used an agent-based model, AnyLogic, to simulate the aircraft evacuation model of an A380. The model was validated, and a two-way Analysis of Variance (ANOVA) was conducted to examine the effects of the percentage of passengers evacuating with carry-on baggage and exit selection choices on the total evacuation time. The simulation results suggested that the mean evacuation time for 0% was significantly lower than 50% and 80%. The mean evacuation time for the shortest queue choice was also lower than the closest exit choice. Study 2 used an expanded theory of planned behavior (TPB) to determine the factors that affect passengers’ intentions to evacuate with carry-on baggage. The total sample size was 281 after data cleaning. The confirmatory factor analysis (CFA) and structural equation model (SEM) were used to analyze the data. The results indicated that attitude was the significant determinant of passengers’ intention to evacuate with carryon baggage. The factor of ‘perceived risk’ was not supported, but the results showed that the opposite effect of the hypothesis was significant. The results of this study fill a gap in the research regarding passengers’ behavior of evacuating with carry-on baggage. Potential applications of this study will also help the federal regulations, airlines, and aircraft manufacturers by providing a better understanding of carry-on baggage at aircraft emergency

Potential Explosive Device on a Commuter Train: What drives train drivers to deviate from the security procedure?

Explosives pose a major threat to urban metro rail systems. Train drivers are therefore expected to regularly perform security procedures in response to reports of suspicious items on the train. This study was conducted to develop a multi-factorial account of deviation from one such security procedure by train drivers. By analysing data from focus group interviews with 30 train drivers, observation in a rail simulator, actual cab rides, and training material four major themes emerged to explain why drivers may deliberately deviate from following normative procedures designed by their managers. This included perceived pressure from safety and service goals, stress and fatigue during peak hours of operation, and workload created by security tasks. The results are organised in a succinct model that draws a link between drivers’ perceived pressure from multiple goals, and the changing driving conditions in which they perform. The study proposes ways for managers of urban commuter rail networks to understand the pressures that their drivers face in performing security tasks that are not part of their conventional job profile. The findings can inform changes in training methods, encourage drivers to discuss their reasons for deliberate rule violation, and support the design of security procedures more likely to be implemented

Systemic risk approach to mitigate delay cascading in railway networks

In public railway systems, minor disruptions can trigger cascading events that lead to delays in the entire system. Typically, delays originate and propagate because the equipment is blocking ways, operational units are unavailable, or at the wrong place at the needed time. The specific understanding of the origins and processes involved in delay-spreading is still a challenge, even though large-scale simulations of national railway systems are becoming available on a highly detailed scale. Without this understanding, efficient management of delay propagation, a growing concern in some Western countries, will remain impossible. Here, we present a systemic risk-based approach to manage daily delay cascading on national scales. We compute the {\em systemic impact} of every train as the maximum of all delays it could possibly cause due to its interactions with other trains, infrastructure, and operational units. To compute it, we design an effective impact network where nodes are train services and links represent interactions that could cause delays. Our results are not only consistent with highly detailed and computationally intensive agent-based railway simulations but also allow us to pinpoint and identify the causes of delay cascades in detail. The systemic approach reveals structural weaknesses in railway systems whenever shared resources are involved. We use the systemic impact to optimally allocate additional shared resources to the system to reduce delays with minimal costs and effort. The method offers a practical and intuitive solution for delay management by optimizing the effective impact network through the introduction of new cheap local train services.Comment: 27 pages, 14 figure

Development and application of dynamic models for predicting transit arrival times

Stochastic variations in traffic conditions and ridership often have a negative impact in transit operations resulting in the deterioration of schedule/headway adherence and lengthening of passenger wait times. Providing accurate information on transit vehicle arrival times is critical to reduce the negative impacts on transit users. In this study, models for dynamically predicting transit arrival times in urban settings are developed, including a basic model, a Kalman filtering model, link-based and stop-based artificial neural networks (ANNs) and Neural/Dynamic (ND) models. The reliability of these models is assessed by enhancing the microscopic simulation program CORSIM which can calculate bus dwell and passenger wait times based on time-dependent passenger demands and vehicle inter-departure times (headways) at stops. The proposed prediction models are integrated with the enhanced CORSIM individually to predict bus arrival times while simulating the operations of a bus transit route in New Jersey. The reliability analysis of prediction results demonstrates that ANNs are superior to the basic and Kalman filtering models. The stop-based ANN generally predicts more accurately than the link-based ANN. By integrating an ANN (either link-based or stop-based) with the Kalman filtering algorithm, two ND models (NDL and NDS) are developed to decrease prediction error. The results show that the performance of the ND models is fairly close. The NDS model performs better than the NDL model when stop-spacing is relatively long and the number of intersections between a pair of stops is relatively large. In the study, an application of the proposed prediction models to a real-time headway control model is also explored and experimented through simulating a high frequency light rail transit route. The results show that with the accurate prediction of vehicle arrival information from the proposed models, the regularity of headways between any pair of consecutive operating vehicles is improved, while the average passenger wait times at stops are reduced significantly