Public space accessibility and machine learning tools for street vending spatial categorization

Barreda-Luna, Antonio A.; Kuri, Gonzálo Hatch; Rodríguez‐Reséndiz, Juvenal; Zamora-Antuñano, Marco Antonio; Altamirano-Corro, Antonio; Paredes-García, Wilfrido J.

doi:10.1080/17445647.2022.2035836

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…Machine-learning algorithms are used in various classification, prediction, clustering applications [15][16][17][18]. The step-by-step methodology followed before feeding the data to machine-learning algorithms is highlighted in Figure 9.…”

Section: Machine-learning-based Methodology and Analysismentioning

confidence: 99%

A Machine-Learning-Based Analysis of the Relationships between Loneliness Metrics and Mobility Patterns for Elderly

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Vasudevan

Afolaranmi

et al. 2022

Sensors

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Loneliness and social isolation are subjective measures associated with the feeling of discomfort and distress. Various factors associated with the feeling of loneliness or social isolation are: the built environment, long-term illnesses, the presence of disabilities or health problems, etc. One of the most important aspect which could impact feelings of loneliness is mobility. In this paper, we present a machine-learning based approach to classify the user loneliness levels using their indoor and outdoor mobility patterns. User mobility data has been collected based on indoor and outdoor sensors carried on by volunteers frequenting an elderly nursing house in Tampere region, Finland. The data was collected using Pozyx sensor for indoor data and Pico minifinder sensor for outdoor data. Mobility patterns such as the distance traveled indoors and outdoors, indoor and outdoor estimated speed, and frequently visited clusters were the most relevant features for classifying the user’s perceived loneliness levels.Three types of data used for classification task were indoor data, outdoor data and combined indoor-outdoor data. Indoor data consisted of indoor mobility data and statistical features from accelerometer data, outdoor data consisted of outdoor mobility data and other parameters such as speed recorded from sensors and course of a person whereas combined indoor-outdoor data had common mobility features from both indoor and outdoor data. We found that the machine-learning model based on XGBoost algorithm achieved the highest performance with accuracy between 90% and 98% for indoor, outdoor, and combined indoor-outdoor data. We also found that Lubben-scale based labelling of perceived loneliness works better for both indoor and outdoor data, whereas UCLA scale-based labelling works better with combined indoor-outdoor data.

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Section: Machine-learning-based Methodology and Analysismentioning

confidence: 99%

A Machine-Learning-Based Analysis of the Relationships between Loneliness Metrics and Mobility Patterns for Elderly

Site

Vasudevan

Afolaranmi

et al. 2022

Sensors

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“…The variables selected by the PSAT indicator have a value based on 100 points. It was made with a series of focus groups totaling 50 students from architecture and geography universities made by [29]. The students determined the categories that will be used as the qualifications for every street segment of the network.…”

Section: Public Space Accessibility Tool Data and Methodsmentioning

confidence: 99%

“…Therefore, an indicator that better predicts and helps locate streets with potentially the most activity, like [23] uses for predicting demand for e-scooters, is needed. Some benefits of the PSAT are the characterization of the urban environment with these AC and HA variables, which helps inform other urban phenomena related to sustainable mobility, such as road incidence [24,25], sustainable travel behavior [26,27], new electric modes [28], or street vending [29]. Finally, this tool elaborates the term accessibility of the public space to provide a paradigm in which the street network of a city is a type of public space, therefore a habitat, so it needs measurable AC and HA characteristics.…”

Section: Literature Review About Spatial Models For Accessibility And...mentioning

confidence: 99%

Neural Network and Spatial Model to Estimate Sustainable Transport Demand in an Extensive Metropolitan Area

et al. 2022

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Urban renewal projects worldwide focus mainly on resolving motorized, personal, and low occupancy problems instead of sustainable mobility. As part of the process, traditional field audits have a high cost in time and resources. This paper reviews a spatial model of accessibility and habitability of the streets, oriented to the location of the volume of people moving sustainably out of an extensive street network. The exercise site is in the Monterrey Metropolitan Area, the second largest in Mexico. Here, the population that moves sustainably as the collective (public and enterprise transportation) and the active (cycling, walking, and others) represents a considerable portion (49%) of travelers, thus, confirming the need for intervention. The spatial model is elaborated in a Geographical Information System (GIS), and the main results are compared with the actual public transport demand using a neural networks process. The results of the tool as a predictor have a 91% efficiency, making it possible to determine the location of urban renewal projects related to the volume of people moving sustainably.

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How about finding alternative livelihood sources to relocate in-lane street hawkers from the urban road network?

Alimo,

Sarfo,

Yeboah

et al. 2024

Habitat International

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Public space accessibility and machine learning tools for street vending spatial categorization

Cited by 4 publications

References 22 publications

A Machine-Learning-Based Analysis of the Relationships between Loneliness Metrics and Mobility Patterns for Elderly

A Machine-Learning-Based Analysis of the Relationships between Loneliness Metrics and Mobility Patterns for Elderly

Neural Network and Spatial Model to Estimate Sustainable Transport Demand in an Extensive Metropolitan Area

How about finding alternative livelihood sources to relocate in-lane street hawkers from the urban road network?

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