Simulating Congestion Dynamics of Train Rapid Transit Using Smart Card Data

Othman, Nasri Bin; Legara, Erika Fille; Selvam, Vicknesh; Monterola, Christopher

doi:10.1016/j.procs.2014.05.146

Cited by 9 publications

(3 citation statements)

References 13 publications

(19 reference statements)

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“…De Nadi et al used cell phone location data to extract the population activity in six Italian cities, which can be used as a proxy for activity to reconfirm the four conditions of mixed spatial functions, small-scale neighborhoods, rich historical spaces, and dense population proposed by Jacobs to promote urban vibrancy [32]. Smart card data are widely used in traffic studies to obtain more accurate personal travel profiles, monitor traffic congestion, and plan transportation interchange routes [33,34].…”

Section: Measurement Of Urban Vibrancymentioning

confidence: 99%

Portraying the Influence Factor of Urban Vibrancy at Street Level Using Multisource Urban Data

Lu,

Wu,

Chu

2023

IJGI

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Exploring the factors influencing urban vibrancy can help policy development and advance urban planning and sustainable development. Previous studies have typically focused on the effects of physical environmental factors (e.g., built environment, urban landscape) on urban vibrancy, ignoring the role of non-physical environmental factors (e.g., urban psychological perceptions). In addition, these studies remain focused on relatively coarse spatial units and lack the exploration of finer-grained spatial structures. In this study, a novel framework is proposed to analyze urban vibrancy and its influencing factors at a more fine-grained street level. Firstly, two types of urban sensing data, POIs and Weibo check-ins, are integrated to portray the spatial distribution patterns of urban vibrancy on the streets. Secondly, a full convolutional network (FCN-8s) is used to segment the streetscape images of Beijing and use them as a basis to extract potential visual–spatial features and urban psychological perceptual features that influence urban vibrancy. Thirdly, we reveal the deeper causes of the impact of psychological perception on urban vibrancy. Finally, an improved ridge regression model is proposed to model the relationship between features and vibrancy, reducing the covariance between features while avoiding the reduction of important features. Satisfactory regression model performances were attained with adjusted R2 values of 0.706, 0.743, and 0.807 at each characteristic level. The results of the study show that: Urban vibrancy is highly dependent on the proposed visual–spatial and urban psychological perception characteristics at the street level. In particular, positive urban psychological perceptions (safety, lively, wealthy) are positively correlated with urban vibrancy, while negative street perceptions (boring) are negatively correlated with urban vibrancy. Unlike previous research scales, our study shows that urban vibrancy portrayal based on the street scale has a greater potential to demonstrate fine-grained vibrancy distribution compared to the neighborhood scale. These findings may provide important insights for people-oriented urban development and planning.

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Section: Measurement Of Urban Vibrancymentioning

confidence: 99%

Portraying the Influence Factor of Urban Vibrancy at Street Level Using Multisource Urban Data

Lu,

Wu,

Chu

2023

IJGI

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“…ABM has been applied in different fields such as e.g. train logistics [ 32 ], air traffic management [ 40 ] and evacuation in case of fire [ 13 ] and respective languages and platforms have been developed [ 19 , 46 ]. In this paper, we present an ABM approach especially developed in SATIE to model and simulate the behaviour of passengers in an airport.…”

Section: Approachmentioning

confidence: 99%

Impact Propagation in Airport Systems

Köpke

Srivastava

König

et al. 2021

Cyber-Physical Security for Critical Infrastructures Protection

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The effective protection of critical infrastructure against cyber and physical security threats involves many different steps from initially the identification of risks to finally the implementation of counter measures in the infrastructure. To derive counter measures and to come to intelligent decisions facing the identified risks, the impact calculation plays a central role. The impact of a specific threat can propagate through the systems of the infrastructure and thus needs to be analysed carefully. In this paper, the role of impact propagation of cyber-physical threats for infrastructure protection is discussed, exemplified for airport systems. In the ongoing EU-H2020 project SATIE (Security of Air Transport Infrastructure of Europe) a toolkit is developed containing two tools for impact propagation, namely the Business Impact Assessment (BIA) and the Impact Propagation Simulation (IPS). Both tools are described and for a small test case the propagation of a cyber threat and the transformation into a physical threat is demonstrated in a network representation as well as an agent-based model of the airport’s systems employing the IPS.

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“…Understanding human mobility is especially consequential in urban land-use and transportation planning [12,13]. Gaining insights on where people go and what activities they engage in, or even inferring what drives them to travel from one place to another, can help in designing smart cities that can sufficiently address the needs of their citizens from their environment [14,15]; thereby improving their overall well-being.…”

Section: Introductionmentioning

confidence: 99%

Inferring passenger types from commuter eigentravel matrices

Legara

Monterola

2017

Transportmetrica B: Transport Dynamics

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A sufficient knowledge of the demographics of a commuting public is essential in formulating and implementing more targeted transportation policies, as commuters exhibit different ways of traveling-including time in the day of travel, the duration of travel, and even the choice of transport mode. With the advent of the Automated Fare Collection system (AFC), probing the travel patterns of commuters has become less invasive and more accessible. Consequently, numerous transport studies related to human mobility have shown that these observed patterns allow one to pair individuals with locations and/or activities at certain times of the day. However, classifying commuters using their travel signatures is yet to be thoroughly examined.Here, we contribute to the literature by demonstrating a procedure to characterize passenger types (Adult, Child/Student, and Senior Citizen) based on their three-month travel patterns taken from a smart fare card system. We first establish a method to construct distinct commuter matrices, which we refer to as eigentravel matrices, that capture the characteristic travel routines of individuals. From the eigentravel matrices, we build classification models that predict the type of passengers traveling. Among the models explored, the gradient boosting method (GBM) gives the best prediction accuracy at 76%, which is 84% better than the minimum model accuracy (41%) required vis-à-vis the proportional chance criterion. In addition, we find that travel features generated during weekdays have greater predictive power than those on weekends. This work should not only be useful for transport planners, but for market researchers as well. With the awareness of which commuter types are traveling, ads, service announcements, and surveys, among others, can be made more targeted spatiotemporally. Finally, our framework should be effective in creating synthetic populations for use in real-world simulations that involve a metropolitan's public transport system.

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Simulating Congestion Dynamics of Train Rapid Transit Using Smart Card Data

Cited by 9 publications

References 13 publications

Portraying the Influence Factor of Urban Vibrancy at Street Level Using Multisource Urban Data

Portraying the Influence Factor of Urban Vibrancy at Street Level Using Multisource Urban Data

Impact Propagation in Airport Systems

Inferring passenger types from commuter eigentravel matrices

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