Power of User Perception on Pedestrian Quality of Service

Rodríguez-Valencia, Álvaro; Barrero, German A.; Ortiz-Ramirez, Hernan Alberto; Vallejo-Borda, Jose Agustin

doi:10.1177/0361198120914611

Cited by 30 publications

(16 citation statements)

References 34 publications

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“…From a larger perspective, results presented herein constitute a building block to the planning of urban renewal interventions. The latter, in synchrony with other urban structures analyses (e.g., trees, highways, cycling paths, or sidewalks [Rodriguez-Valencia, Barrero, Ortiz-Ramirez, & Vallejo-Borda, 2020;Vallejo-Borda, Cantillo, & Rodriguez-Valencia, 2020]), represent an opportunity for urban renewal, while decreasing the risk of failure and improving the perception of sewer system quality of service (Vallejo-Borda, Ortiz-Ramirez, Rodriguez-Valencia, Hurtubia, & de D. Ortúzar, 2020).…”

Section: Discussionmentioning

confidence: 95%

A Two‐Stage Data‐Driven Spatiotemporal Analysis to Predict Failure Risk of Urban Sewer Systems Leveraging Machine Learning Algorithms

et al. 2021

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Risk-informed asset management is key to maintaining optimal performance and efficiency of urban sewer systems. Although sewer system failures are spatiotemporal in nature, previous studies analyzed failure risk from a unidimensional aspect (either spatial or temporal), not accounting for bidimensional spatiotemporal complexities. This is owing to the insufficiency of good-quality data, which ultimately leads to under-/overestimation of failure risk. Here, we propose a generalized methodology/framework to facilitate a robust spatiotemporal analysis of urban sewer system failure risk, overcoming the intrinsic challenges of data imperfections-e.g., missing data, outliers, and imbalanced information. The framework includes a two-stage data-driven modeling technique that efficiently models the highly rightskewed sewer system failure data to predict the failure risk, leveraging a bidimensional spacetime approach. We implemented our analysis for Bogotá, the capital city of Colombia. We train, test, and validate a battery of machine learning algorithms-logistic regression, decision trees, random forests, and XGBoost-and select the best model in terms of goodnessof-fit and predictive accuracy. Finally, we illustrate the applicability of the framework in planning/scheduling sewer system maintenance operations using state-of-the-art optimization techniques. Our proposed framework can help stakeholders to analyze the failure-risk models' performance under different discrimination thresholds, and provide managerial insights on the model's adequate spatial resolution and appropriateness of decentralized management for sewer system maintenance.

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Section: Discussionmentioning

confidence: 95%

A Two‐Stage Data‐Driven Spatiotemporal Analysis to Predict Failure Risk of Urban Sewer Systems Leveraging Machine Learning Algorithms

et al. 2021

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“…e concept of quality of service (QOS) can be considered as an extension of LOS since it takes into account the viewpoint of the road users [29]. Rodriguez-Valencia et al [38] proposed a pedestrian QOS methodology by using the data of 30 infrastructures and carrying out surveys to consider user perception in Bogota, Colombia, and the results showed that including user perceptions improves the assessment capability of the QOS model. erefore, QOS can be considered as a combination of LOS and road users' perception of infrastructure.…”

Section: Perception Of Infrastructurementioning

confidence: 99%

Modelling Road User Perceptions towards Safety, Comfort, and Chaos at Shared Space: The via Maqueda Case Study, Italy

Akgün-Tanbay

Campisi

Tanbay

et al. 2022

Journal of Advanced Transportation

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This study aims to investigate the impacts of perception of infrastructure, sociodemographic characteristics, frequency of road use, and road user perception on safety, comfort, and chaos with respect to shared spaces. The case study area was the Via Maqueda, a shared street in Palermo, Italy. A face-to-face survey was conducted and the answers of 200 of the participants, who use three active travel modes, namely, walking, cycling, and micromobility, were analysed. The results obtained from the ordered logit models suggest that one-unit higher perception of infrastructure will increase safety and comfort perceptions for both walking and cycling. Females feel less safe while walking and less comfortable while cycling at shared spaces compared to males. Increasing the age group by one unit will decrease the safety and comfort perceptions for walking. The participants who use the shared spaces more frequently perceive that they feel more comfortable with cycling. Participants, particularly males, find the shared spaces less chaotic for walking when the perception of infrastructure is higher. Regarding the micromobility use at shared spaces, females feel less comfortable compared to males while using micromobility.

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“…Level of service perception of any pedestrian facility depends on condition of the facility. The importance of various factors that affect perceptions of level of service of pedestrian facilities vary with socio-demographic conditions of pedestrians [2][3][4][5][6][7][8]. Significant perception variation is observed with age [9][10] and gender [11][12].…”

Section: Introductionmentioning

confidence: 99%

Factor Structure Analysis for Pedestrian Level of Service Assessment using Structural Equation Modelling

Ujjwal¹

2021

TURCOMAT

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Pedestrian facilities are aimed at providing proper walking infrastructure and environment to promote walking in cities and needs to serve the desired requirements of users. It is important to assess the level of service (LOS) provided by such facilities. Various guidelines are available for assessing LOS of sidewalks, midblock crossings and pedestrians walking along the carriageway. However, the guidelines and factors considered for assessment are diverse and no single guideline is available that comprehensively takes into account all variables relevant for both sidewalks and crossings and for mixed land use settings. This work thus attempts to identify factors and their underlying variables and also the relative importance of the factors in determining LOS of pedestrian facilities in mixed land use areas. For this purpose, the study systematically identifies a comprehensive list of 20 important variables associated with LOS assessment for pedestrian facilities from literature. A survey was conducted of a heterogeneous group of 720 pedestrians from Patna, India to find importance perceptions of all the above identified variables. The important factors and their significant underlying variables were then identified using Exploratory Factor Analysis and the factor structure was confirmed using Structural Equation Model using survey data. Five factors namely Infrastructure, Crossing issues, Security, Sidewalk encroachments and Safety were identified with 13 significant underlying variables. It was observed that the crossing facility conditions predominantly influence the perception of LOS of pedestrian facilities. The findings of the study can be used by urban planners in focusing their improvement efforts for pedestrian facilities.

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Power of User Perception on Pedestrian Quality of Service

Cited by 30 publications

References 34 publications

A Two‐Stage Data‐Driven Spatiotemporal Analysis to Predict Failure Risk of Urban Sewer Systems Leveraging Machine Learning Algorithms

A Two‐Stage Data‐Driven Spatiotemporal Analysis to Predict Failure Risk of Urban Sewer Systems Leveraging Machine Learning Algorithms

Modelling Road User Perceptions towards Safety, Comfort, and Chaos at Shared Space: The via Maqueda Case Study, Italy

Factor Structure Analysis for Pedestrian Level of Service Assessment using Structural Equation Modelling

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