LIVIVO - The Search Portal for Life Sciences

zur deutschen Oberfläche wechseln
Advanced search

Search results

Result 1 - 8 of total 8

Search options

  1. Book ; Online: Pedestrian wayfinding behavior in a multi-story building

    Feng, Yan / Duives, Dorine C.

    a comprehensive modeling study featuring route choice, wayfinding performance, and observation behavior

    2023  

    Abstract: This paper proposes a comprehensive approach for modeling pedestrian wayfinding behavior in complex buildings. This study employs two types of discrete choice models (i.e., MNL and PSL) featuring pedestrian route choice behavior, and three multivariate ... ...

    Abstract This paper proposes a comprehensive approach for modeling pedestrian wayfinding behavior in complex buildings. This study employs two types of discrete choice models (i.e., MNL and PSL) featuring pedestrian route choice behavior, and three multivariate linear regression (MLR) models featuring the overall wayfinding performance and observation behavior (e.g., hesitation behavior and head rotation). Behavioral and questionnaire data featuring pedestrian wayfinding behavior and personal information were collected using a Virtual Reality experiment. Four wayfinding tasks were designed to determine how personal, infrastructure, and route characteristics affect indoor pedestrian wayfinding behavior on three levels, including route choice, wayfinding performance, and observation behavior. We find that pedestrian route choice behavior is primarily influenced by route characteristics, whereas wayfinding performance is also influenced by personal characteristics. Observation behavior is mainly influenced by task complexity, personal characteristics, and local properties of the routes that offer route information. To the best of our knowledge, this work represents the first attempt to investigate the impact of the same comprehensive set of variables on various metrics feature wayfinding behavior simultaneously.
    Keywords Computer Science - Human-Computer Interaction ; Computer Science - Computers and Society
    Subject code 380
    Publishing date 2023-04-20
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  2. Article ; Online: Bicycle Data-Driven Application Framework: A Dutch Case Study on Machine Learning-Based Bicycle Delay Estimation at Signalized Intersections Using Nationwide Sparse GPS Data.

    Yuan, Yufei / Wang, Kaiyi / Duives, Dorine / Hoogendoorn, Serge / Hoogendoorn-Lanser, Sascha / Lindeman, Rick

    Sensors (Basel, Switzerland)

    2023  Volume 23, Issue 24

    Abstract: Data-driven approaches are helpful for quantitative justification and performance evaluation. The Netherlands has made notable strides in establishing a national protocol for bicycle traffic counting and collecting GPS cycling data through initiatives ... ...

    Abstract Data-driven approaches are helpful for quantitative justification and performance evaluation. The Netherlands has made notable strides in establishing a national protocol for bicycle traffic counting and collecting GPS cycling data through initiatives such as the Talking Bikes program. This article addresses the need for a generic framework to harness cycling data and extract relevant insights. Specifically, it focuses on the application of estimating average bicycle delays at signalized intersections, as this is an essential variable in assessing the performance of the transportation system. This study evaluates machine learning (ML)-based approaches using GPS cycling data. The dataset provides comprehensive yet incomplete information regarding one million bicycle rides annually across The Netherlands. These ML models, including random forest, k-nearest neighbor, support vector regression, extreme gradient boosting, and neural networks, are developed to estimate bicycle delays. The study demonstrates the feasibility of estimating bicycle delays using sparse GPS cycling data combined with publicly accessible information, such as weather information and intersection complexity, leveraging the burden of understanding local traffic conditions. It emphasizes the potential of data-driven approaches to inform traffic management, bicycle policy, and infrastructure development.
    Language English
    Publishing date 2023-12-07
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s23249664
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  3. Article ; Online: Enhancing Crowd Monitoring System Functionality through Data Fusion: Estimating Flow Rate from Wi-Fi Traces and Automated Counting System Data.

    Duives, Dorine C / van Oijen, Tim / Hoogendoorn, Serge P

    Sensors (Basel, Switzerland)

    2020  Volume 20, Issue 21

    Abstract: Crowd monitoring systems (CMSs) provide a state-of-the-art solution to manage crowds objectively. Most crowd monitoring systems feature one type of sensor, which severely limits the insights one can simultaneously gather regarding the crowd's traffic ... ...

    Abstract Crowd monitoring systems (CMSs) provide a state-of-the-art solution to manage crowds objectively. Most crowd monitoring systems feature one type of sensor, which severely limits the insights one can simultaneously gather regarding the crowd's traffic state. Incorporating multiple functionally complementary sensor types is expensive. CMSs are needed that exploit data fusion opportunities to limit the number of (more expensive) sensors. This research estimates a data fusion algorithm to enhance the functionality of a CMS featuring Wi-Fi sensors by means of a small number of automated counting systems. Here, the goal is to estimate the pedestrian flow rate accurately based on real-time Wi-Fi traces at one sensor location, and historic flow rate and Wi-Fi trace information gathered at other sensor locations. Several data fusion models are estimated, amongst others, linear regression, shallow and recurrent neural networks, and Auto Regressive Moving Average (ARMAX) models. The data from the CMS of a large four-day music event was used to calibrate and validate the models. This study establishes that the RNN model best predicts the flow rate for this particular purpose. In addition, this research shows that model structures that incorporate information regarding the average current state of the area and the temporal variation in the Wi-Fi/count ratio perform best.
    Language English
    Publishing date 2020-10-23
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s20216032
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  4. Book ; Online: Development of a VR tool to study pedestrian route and exit choice behaviour in a multi-story building

    Feng, Yan / Duives, Dorine / Hoogendoorn, Serge

    2021  

    Abstract: Although route and exit choice in complex buildings are important aspects of pedestrian behaviour, studies predominantly investigated pedestrian movement in a single level. This paper presents an innovative VR tool that was designed to investigate ... ...

    Abstract Although route and exit choice in complex buildings are important aspects of pedestrian behaviour, studies predominantly investigated pedestrian movement in a single level. This paper presents an innovative VR tool that was designed to investigate pedestrian route and exit choice in a multi-story building. This tool supports free navigation and collects pedestrian walking trajectories, head movements and gaze points automatically. An experiment was conducted to evaluate the VR tool from objective standpoints (i.e., pedestrian behaviour) and subjective standpoints (i.e., the feeling of presence, system usability, simulation sickness). The results show that the VR tool allows for accurate collection of pedestrian behavioural data in the complex building. Moreover, the results of the questionnaire report high realism of the virtual environment, high immersive feeling, high usability, and low simulator sickness. This paper contributes by showcasing an innovative approach of applying VR technologies to study pedestrian behaviour in complex and realistic environments.
    Keywords Computer Science - Human-Computer Interaction
    Subject code 380
    Publishing date 2021-03-02
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  5. Article ; Online: Forecasting Pedestrian Movements Using Recurrent Neural Networks: An Application of Crowd Monitoring Data.

    Duives, Dorine C / Wang, Guangxing / Kim, Jiwon

    Sensors (Basel, Switzerland)

    2019  Volume 19, Issue 2

    Abstract: Currently, effective crowd management based on the information provided by crowd monitoring systems is difficult as this information comes in at the moment adverse crowd movements are already occurring. Up to this moment, very little forecasting ... ...

    Abstract Currently, effective crowd management based on the information provided by crowd monitoring systems is difficult as this information comes in at the moment adverse crowd movements are already occurring. Up to this moment, very little forecasting techniques have been developed that predict crowd flows a longer time period ahead. Moreover, most contemporary state estimation methods apply demanding pre-processing steps, such as map-matching. The objective of this paper is to design, train and benchmark a data-driven procedure to forecast crowd movements, which can in real-time predict crowd movement. This procedure entails two steps. The first step comprises of a cell sequence derivation method that allows the representation of spatially continuous GPS traces in terms of discrete cell sequences. The second step entails the training of a Recursive Neural Network (RNN) with a Gated Recurrent Unit (GRU) and six benchmark models to forecast the next location of pedestrians. The RNN-GRU is found to outperform the other tested models. Some additional tests of the ability of the RNN-GRU to forecast illustrate that the RNN-GRU preserves its predictive power when a limited amount of data is used from the first few hours of a multi-day event and temporal information is incorporated in the cell sequences.
    MeSH term(s) Algorithms ; Crowding ; Forecasting ; Geographic Information Systems ; Humans ; Movement ; Netherlands ; Neural Networks (Computer) ; Pedestrians ; Reproducibility of Results
    Language English
    Publishing date 2019-01-18
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s19020382
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  6. Article: Teleworking during COVID-19 in the Netherlands: Understanding behaviour, attitudes, and future intentions of train travellers.

    Ton, Danique / Arendsen, Koen / de Bruyn, Menno / Severens, Valerie / van Hagen, Mark / van Oort, Niels / Duives, Dorine

    Transportation research. Part A, Policy and practice

    2022  Volume 159, Page(s) 55–73

    Abstract: With the arrival of COVID-19 in the Netherlands in Spring 2020 and the start of the "intelligent lockdown", daily life changed drastically. The working population was urged to telework as much as possible. However, not everyone had a suitable job for ... ...

    Abstract With the arrival of COVID-19 in the Netherlands in Spring 2020 and the start of the "intelligent lockdown", daily life changed drastically. The working population was urged to telework as much as possible. However, not everyone had a suitable job for teleworking or liked teleworking. From a mobility perspective, teleworking was considered a suitable means to alleviate travel. Even after the pandemic it can (continue to) reduce pressure on the mobility system during peak hours, thereby improving efficiency and level of service of transport services. Additionally, this could reduce transport externalities, such as emissions and unsafety. The structural impact from teleworking offers opportunities, but also challenges for the planning and operations of public transport. The aim of this study is to better understand teleworking during and after COVID-19 among train travellers, to support operators and authorities in their policy making and design. We study the telework behaviour, attitude towards teleworking, and future intentions through a longitudinal data collection. By applying a latent class cluster analysis, we identified six types of teleworkers, varying in their frequency of teleworking, attitude towards teleworking, intentions to the future, socio-demographics and employer policy. In terms of willingness-to-telework in the future, we distinguish three groups: the high willingness-to-telework group (71%), the low willingness-to-telework group (16%), and the least-impacted self-employed (12%). Those with high willingness are expected to have lasting changes in their travel patterns, where especially public transport is impacted. For this group, policy is required to ensure when (which days) and where (geographical) telework takes place, such that public transport operators can better plan and operate their services. For those with low willingness, it is essential that the government provides tools to companies (especially in education and vital sector) such that they can be better prepared for teleworking (mostly during but also after the pandemic). Employers on the other hand need to better support their employees, such that they stay in contact with colleagues and their concentration and productivity can increase.
    Language English
    Publishing date 2022-03-16
    Publishing country England
    Document type Journal Article
    ZDB-ID 2015887-7
    ISSN 0965-8564 ; 0191-2607
    ISSN 0965-8564 ; 0191-2607
    DOI 10.1016/j.tra.2022.03.019
    Database MEDical Literature Analysis and Retrieval System OnLINE

    Full text online

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  7. Article ; Online: The multi-dimensional challenges of controlling respiratory virus transmission in indoor spaces: Insights from the linkage of a microscopic pedestrian simulation and SARS-CoV-2 transmission model.

    Atamer Balkan, Büsra / Chang, You / Sparnaaij, Martijn / Wouda, Berend / Boschma, Doris / Liu, Yangfan / Yuan, Yufei / Daamen, Winnie / de Jong, Mart C M / Teberg, Colin / Schachtschneider, Kevin / Sikkema, Reina S / van Veen, Linda / Duives, Dorine / Ten Bosch, Quirine A

    PLoS computational biology

    2024  Volume 20, Issue 3, Page(s) e1011956

    Abstract: SARS-CoV-2 transmission in indoor spaces, where most infection events occur, depends on the types and duration of human interactions, among others. Understanding how these human behaviours interface with virus characteristics to drive pathogen ... ...

    Abstract SARS-CoV-2 transmission in indoor spaces, where most infection events occur, depends on the types and duration of human interactions, among others. Understanding how these human behaviours interface with virus characteristics to drive pathogen transmission and dictate the outcomes of non-pharmaceutical interventions is important for the informed and safe use of indoor spaces. To better understand these complex interactions, we developed the Pedestrian Dynamics-Virus Spread model (PeDViS), an individual-based model that combines pedestrian behaviour models with virus spread models incorporating direct and indirect transmission routes. We explored the relationships between virus exposure and the duration, distance, respiratory behaviour, and environment in which interactions between infected and uninfected individuals took place and compared this to benchmark 'at risk' interactions (1.5 metres for 15 minutes). When considering aerosol transmission, individuals adhering to distancing measures may be at risk due to the buildup of airborne virus in the environment when infected individuals spend prolonged time indoors. In our restaurant case, guests seated at tables near infected individuals were at limited risk of infection but could, particularly in poorly ventilated places, experience risks that surpass that of benchmark interactions. Combining interventions that target different transmission routes can aid in accumulating impact, for instance by combining ventilation with face masks. The impact of such combined interventions depends on the relative importance of transmission routes, which is hard to disentangle and highly context dependent. This uncertainty should be considered when assessing transmission risks upon different types of human interactions in indoor spaces. We illustrated the multi-dimensionality of indoor SARS-CoV-2 transmission that emerges from the interplay of human behaviour and the spread of respiratory viruses. A modelling strategy that incorporates this in risk assessments can help inform policy makers and citizens on the safe use of indoor spaces with varying inter-human interactions.
    MeSH term(s) Humans ; SARS-CoV-2 ; COVID-19/prevention & control ; Pedestrians ; Respiratory Aerosols and Droplets ; Ventilation
    Language English
    Publishing date 2024-03-28
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2193340-6
    ISSN 1553-7358 ; 1553-734X
    ISSN (online) 1553-7358
    ISSN 1553-734X
    DOI 10.1371/journal.pcbi.1011956
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  8. Article ; Online: The multi-dimensional challenges of controlling SARS-CoV-2 transmission in indoor spaces: Insights from the linkage of a microscopic pedestrian simulation and virus transmission models

    Duives, Dorine / Chang, You / Sparnaaij, Martijn / Wouda, Berend / Boschma, Doris / Liu, Yangfan / Yuan, Yufei / Daamen, Winnie / de Jong, Mart C.M. / Teberg, Colin / Schachtschneider, Kevin / Sikkema, Reina S / van Veen, Linda / ten Bosch, Quirine

    medRxiv

    Abstract: Since its introduction in December of 2019, SARS-CoV-2, the virus that causes COVID-19 disease, has rapidly spread across the world. Whilst vaccines are being rolled out, non-pharmaceutical interventions remain the most important tools for mitigating the ...

    Abstract Since its introduction in December of 2019, SARS-CoV-2, the virus that causes COVID-19 disease, has rapidly spread across the world. Whilst vaccines are being rolled out, non-pharmaceutical interventions remain the most important tools for mitigating the spread of SARS-CoV-2. Quantifying the impact of these measures as well as determining what settings are prone to instigating (super)spreading events is important for informed and safe reopening of spaces and the targeting of interventions. Mathematical models can help decipher the complex interactions that underlie virus transmission. Currently, most mathematical models developed during the COVID-19 epidemic evaluate interventions at national or subnational levels. Smaller scales of transmission, such as at the level of indoor spaces, have received less attention, despite the central role they play in both transmission and control. Models that do act on this scale use simplified descriptions of human behavior, impeding a valid quantitative analysis of the impact of interventions on transmission in indoor spaces, particularly those that aim for physical distancing. To more accurately predict the transmission of SARS-CoV-2 through a pedestrian environment, we introduce a model that links pedestrian movement and choice dynamics with SARS-CoV-2 spreading models. The objective of this paper is to investigate the spread of SARS-CoV-2 in indoor spaces as it arises from human interactions and assess the relative impact of non-pharmaceutical interventions thereon. We developed a world-wide unique combined Pedestrian Dynamics - Virus Spread model (PeDViS model), which combines insights from pedestrian modelling, epidemiology, and IT-design. In particular, an expert-driven activity assignment model is coupled with the microscopic simulation model (Nomad) and a virus spread model (QVEmod). We first describe the non-linear relationships between the risks of exposure to the virus and the duration, distance, and context of human interactions. We compared virus exposure relative to a benchmark contact (1.5meters for 15 minutes): a threshold often used by public health agencies to determine at risk contacts. We discuss circumstances under which individuals that adhere to common distancing measures may nevertheless be at risk. Specifically, we illustrate the stark increase in exposure at shorter distances, as well as longer contact durations. These risks increase when the infected individual was present in the space before the interaction occurred, as a result of buildup of virus in the environment. The latter is particularly true in poorly ventilated spaces and highlights the importance of good ventilation to prevent potential virus exposure through indirect transmission routes. Combining intervention tools that target different routes of transmission can aid in accumulating impact. We use face masks as an example, which are particularly effective at reducing virus spread that is not affected by ventilation. We then demonstrate the use of PeDViS using a simple restaurant case study, focussing on transmission between guests. In this setting the exposure risk to individuals that are not seated at the same table is limited, but guests seated at nearby tables are estimated to experience exposure risks that surpass that of the benchmark contact. These risks are larger in low ventilation scenarios. Lastly, we illustrate that the impact of intervention measures on the number of new infections heavily depends on the relative efficiency of the direct and indirect transmission routes considered. This uncertainty should be considered when assessing the risks of transmission upon different types of human interactions in indoor spaces. The PeDViS case study shows the multi-dimensionality of SARS-CoV-2 that emerges from the interplay of human behaviour and the spread of respiratory viruses in indoor spaces. A modelling strategy that incorporates this in risk assessments can be an important tool to inform policy makers and citizens. It can empower them to make better design and policy decisions pertaining to the most effective use of measures to limit the spread of SARS-CoV-2 and safely open up indoor spaces.
    Keywords covid19
    Language English
    Publishing date 2021-04-19
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2021.04.12.21255349
    Database COVID19

    Full text online

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

To top