5,448 research outputs found
Weak nodes detection in urban transport systems: Planning for resilience in Singapore
The availability of massive data-sets describing human mobility offers the
possibility to design simulation tools to monitor and improve the resilience of
transport systems in response to traumatic events such as natural and man-made
disasters (e.g. floods terroristic attacks, etc...). In this perspective, we
propose ACHILLES, an application to model people's movements in a given
transport system mode through a multiplex network representation based on
mobility data. ACHILLES is a web-based application which provides an
easy-to-use interface to explore the mobility fluxes and the connectivity of
every urban zone in a city, as well as to visualize changes in the transport
system resulting from the addition or removal of transport modes, urban zones,
and single stops. Notably, our application allows the user to assess the
overall resilience of the transport network by identifying its weakest node,
i.e. Urban Achilles Heel, with reference to the ancient Greek mythology. To
demonstrate the impact of ACHILLES for humanitarian aid we consider its
application to a real-world scenario by exploring human mobility in Singapore
in response to flood prevention.Comment: 9 pages, 6 figures, IEEE Data Science and Advanced Analytic
Mobility: a double-edged sword for HSPA networks
This paper presents an empirical study on the performance of mobile High Speed Packet Access (HSPA, a 3.5G cellular standard) networks in Hong Kong via extensive field tests. Our study, from the viewpoint of end users, covers virtually all possible mobile scenarios in urban areas, including subways, trains, off-shore ferries and city buses. We have confirmed that mobility has largely negative impacts on the performance of HSPA networks, as fast-changing wireless environment causes serious service deterioration or even interruption. Meanwhile our field experiment results have shown unexpected new findings and thereby exposed new features of the mobile HSPA networks, which contradict commonly held views. We surprisingly find out that mobility can improve fairness of bandwidth sharing among users and traffic flows. Also the triggering and final results of handoffs in mobile HSPA networks are unpredictable and often inappropriate, thus calling for fast reacting fallover mechanisms. We have conducted in-depth research to furnish detailed analysis and explanations to what we have observed. We conclude that mobility is a double-edged sword for HSPA networks. To the best of our knowledge, this is the first public report on a large scale empirical study on the performance of commercial mobile HSPA networks
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BEAM: The Modeling Framework for Behavior, Energy, Autonomy & Mobility
This report outlines the concepts, mechanisms and inner dynamics of the BEAM
(Behavior, Energy, Autonomy, and Mobility) modeling framework. BEAM is an
open-source large-scale high-resolution transportation model that harnesses the
principles of the actor model of computation to build a powerful and efficient
agent-based model of travel behavior. It allows a detailed microscopic view of
how people make travel choices and interact with the transportation system,
enabling more accurate simulations of human mobility and urban transport
networks. It also allows the analysis of numerous spatially defined but
interacting layers, and integrates them into a cohesive representation of a
regional transportation system. This integrated picture provides invaluable
insights to policy makers and other stakeholders about how changes to the
transportation system result in changes to traffic congestion, mode share,
energy use, and emissions throughout a modeled region. These capabilities are
demonstrated with a case study of New York City that showcase BEAM's
application in a very large and intricate urban transportation system, without
relying on existing travel demand models. The unique ability of BEAM to
simulate individual behaviors, integrate with other models, and adapt to
different real-world scenarios underscores its importance in the rapidly
evolving field of transportation and emphasizes its potential as a valuable
proof-of-concept tool to contribute to more informed and effective policy and
planning decisions
Exploring Large Language Models for Human Mobility Prediction under Public Events
Public events, such as concerts and sports games, can be major attractors for
large crowds, leading to irregular surges in travel demand. Accurate human
mobility prediction for public events is thus crucial for event planning as
well as traffic or crowd management. While rich textual descriptions about
public events are commonly available from online sources, it is challenging to
encode such information in statistical or machine learning models. Existing
methods are generally limited in incorporating textual information, handling
data sparsity, or providing rationales for their predictions. To address these
challenges, we introduce a framework for human mobility prediction under public
events (LLM-MPE) based on Large Language Models (LLMs), leveraging their
unprecedented ability to process textual data, learn from minimal examples, and
generate human-readable explanations. Specifically, LLM-MPE first transforms
raw, unstructured event descriptions from online sources into a standardized
format, and then segments historical mobility data into regular and
event-related components. A prompting strategy is designed to direct LLMs in
making and rationalizing demand predictions considering historical mobility and
event features. A case study is conducted for Barclays Center in New York City,
based on publicly available event information and taxi trip data. Results show
that LLM-MPE surpasses traditional models, particularly on event days, with
textual data significantly enhancing its accuracy. Furthermore, LLM-MPE offers
interpretable insights into its predictions. Despite the great potential of
LLMs, we also identify key challenges including misinformation and high costs
that remain barriers to their broader adoption in large-scale human mobility
analysis
Using Data From the Web to Predict Public Transport Arrivals Under Special Events Scenarios
The Internet has become the preferred resource to announce, search, and comment about social events such as concerts, sports games, parades, demonstrations, sales, or any other public event that potentially gathers a large group of people. These planned special events often carry a potential disruptive impact to the transportation system, because they correspond to nonhabitual behavior patterns that are hard to predict and plan for. Except for very large and mega events (e.g., Olympic games, football world cup), operators seldom apply special planning measures for two major reasons: The task of manually tracking which events are happening in large cities is labor-intensive; and, even with a list of events, their impact is hard to estimate, especially when more than one event happens simultaneously. In this article, we utilize the Internet as a resource for contextual information about special events and develop a model that predicts public transport arrivals in event areas. In order to demonstrate the feasibility of this solution for practitioners, we apply off-the-shelf techniques both for Internet data collection and for the prediction model development. We demonstrate the results with a case study from the city-state of Singapore using public transport tap-in/tap-out data and local event information obtained from the Internet. Keywords: Data mining; Demand Prediction; Public Transport; Smartcard; Urban Computing; Web Minin
The discrete dynamics of small-scale spatial events: agent-based models of mobility in carnivals and street parades
Small-scale spatial events are situations in which elements or objects vary in such away that temporal dynamics is intrinsic to their representation and explanation. Someof the clearest examples involve local movement from conventional traffic modelingto disaster evacuation where congestion, crowding, panic, and related safety issue arekey features of such events. We propose that such events can be simulated using newvariants of pedestrian model, which embody ideas about how behavior emerges fromthe accumulated interactions between small-scale objects. We present a model inwhich the event space is first explored by agents using ?swarm intelligence?. Armedwith information about the space, agents then move in an unobstructed fashion to theevent. Congestion and problems over safety are then resolved through introducingcontrols in an iterative fashion and rerunning the model until a ?safe solution? isreached. The model has been developed to simulate the effect of changing the route ofthe Notting Hill Carnival, an annual event held in west central London over 2 days inAugust each year. One of the key issues in using such simulation is how the processof modeling interacts with those who manage and control the event. As such, thischanges the nature of the modeling problem from one where control and optimizationis external to the model to one where this is intrinsic to the simulation
Index-based Approach to Evaluate City Resilience in Flooding Scenarios
Intense rainfall events combined with high tide levels frequently result in urban floods in riverine or coastal cities. Their increasing variability and uncertainty demand urgent but sustained responses. Thus, resilience-driven approaches are emerging in contrast to the traditional technical-economic frameworks, as urban resilience reflects the overall capacity of a city to survive, adapt and thrive when experiencing stresses and shocks. This paper presents a simplified index-based methodology for the evaluation and quantification of urban resilience to flooding, based on the works developed in the EU H2020 RESCCUE project. A set of five indicators are proposed to compute the Integrated Urban Resilience Index (IURI), allowing to classify resilience according to a proposed range of rankings. This methodology considers simultaneously a multisectoral approach, reflecting services interdependences, and a sectorial approach, applying 1D/2D computational modelling of the urban drainage network. It was applied to the study case of Lisbon downtown, involving the analysis of interdependencies between 124 infrastructures of 10 urban services. Two scenarios were considered, respecting the current and future situations, considering climate changes. Results enhance the usefulness, practicability, and potential of the proposed approach, and improvement opportunities were also identified for future developments. Doi: 10.28991/cej-2021-03091647 Full Text: PD
A Real-time Information System for Public Transport in Case of Delays and Service Disruptions
AbstractPromoting the use of public transportation and Intelligent Transport Systems, as well as improving transit accessibility for all citizens, may help in decreasing traffic congestion and air pollution in urban areas. In general, poor information to customers is one of the main issues in public transportation services, which is an important reason for allocating substantial efforts to implement a powerful and easy to use and access information tool. This paper focuses on the design and development of a real time mobility information system for the management of unexpected events, delays and service disruptions concerning public transportation in the city of Milan. Exploiting the information on the status of urban mobility and on the location of citizens, commuters and tourists, the system is able to reschedule in real time their movements. The service proposed stems from the state of the art in the field of travel planners for public transportation, available for Milan. Peculiarly, we built a representation of the city transit based on a time-expanded graph that considers the interconnections among all the stops of the rides offered during the day. The structure distinguishes the physical stations and the get on/get off stops of each ride, representing them with two different types of nodes. Such structure allows, with regard to the main focus of the project, to model a wide range of service disruptions, much more meaningful than those possible with approaches currently proposed by transit agencies. One of the most interesting point lies in the expressive capability in describing the different disruptions: with our model it is possible, for instance, to selectively inhibit getting on and/or off at a particular station, avoid specific rides, and model temporary deviations
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