Clustering pedestrians’ perceptions towards road infrastructure and traffic characteristics

Saxena, Aditya; Yadav, Ankit

doi:10.1080/17457300.2022.2112234

Cited by 18 publications

(6 citation statements)

References 65 publications

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“…Because there is no engine noise to warn pedestrians or cyclists of oncoming EVs, pedestrians and cyclists face safety issues (66,67). In this context, various studies on pedestrian safety have been performed in India (68)(69)(70)(71)(72)(73). However, there is a paucity of specific literature on the effect of EVs on pedestrian and cyclist safety.…”

Section: Limitations and Future Scopementioning

confidence: 99%

Adopting a Multi-Criteria Decision-Making Approach to Identify Barriers to Electrification of Urban Freight in India

Saxena

Yadav

2023

Transportation Research Record: Journal of the Transportation R

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The economics and social development of cities depend heavily on urban freight. But, despite its obvious benefits, freight transportation has substantial negative externalities. The electrification of urban freight could help mitigate these. However, there exists a paucity of literature on barriers to the adoption of electric freight in the context of developing countries such as India. The present study intends to bridge this gap. It aims to identify the barriers to the adoption of electric freight vehicles (light commercial vehicles) in urban areas by using a fuzzy analytical hierarchy process (FAHP), a multi-criteria decision-making (MCDM) technique. Based on the opinion of 32 experts, it was found that high capital costs, low driving range, and long charging times are the major obstacles to the adoption of electric freight vehicles in India. The results from the present study will assist government officials in identifying bottlenecks and prioritizing strategies for the promotion of electric freight vehicles in India.

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Section: Limitations and Future Scopementioning

confidence: 99%

Adopting a Multi-Criteria Decision-Making Approach to Identify Barriers to Electrification of Urban Freight in India

Saxena

Yadav

2023

Transportation Research Record: Journal of the Transportation R

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“…The main purpose is to divide observations in a dataset into subsets, where objects in the same subset are similar to each other with respect to a certain similarity measure [28]. Over the past decades, a large number of clustering methods such as hierarchical clustering, K-means clustering, spectral clustering, etc., have been developed and successfully applied in a wide range of fields, such as biology, geography, climate, psychology, medicine, and business (see e.g., [29,30]).…”

Section: Indicator Selection and Data Collectionmentioning

confidence: 99%

Assessing Road Safety Development in European Countries: A Cross-Year Comparative Analysis of a Safety Performance Index

2022

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The development of a road safety performance index has been widely accepted as a supportive instrument to evaluate and compare the safety performance in different countries. However, most of the current studies concentrate on the index development for only one year. In other words, there is still a lack of cross-year comparison based on the constructed safety performance index, so as to assess the progress of road safety performance in different countries over time. In this study, by collecting data on four background indicators and seven safety performance indicators of 21 European countries for both 2008 and 2015, the hierarchical clustering analysis is first utilized to identify country groups based on the background indicators. Then, the principal component analysis (PCA) is applied for each group to construct a safety performance index, and a cross-year comparative analysis on country grouping, index ranking, and weight allocation is conducted. The results show that the members in the two country groups remain the same, implying that there was no dramatic change with respect to these countries’ road safety policy context in these two years. However, the gaps between these two country groups with respect to their overall socioeconomic development, as well as their road safety performance, enlarged over this period. Moreover, by comparing the indicator weights assigned for each country group in different years and examining the changes in indicator values of each country, a useful insight into the areas of underperformance is gained, which cannot be revealed in single year index evaluation. All these findings provide policy makers with valuable guidance to prioritize their actions to improve the level of road safety.

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“…Many studies traditionally apply their assessment of the road and urban environment around physical data, primarily relying on GIS-based or POI-based datasets, as well as investigating people's perceptions of these environments [5,7]. Nevertheless, recent technological advancements have seen an increase in the application of artificial intelligence analysis in transportation studies, particularly in the evaluation of road and urban environments.…”

Section: Introductionmentioning

confidence: 99%

Assessing Subjective and Objective Road Environment Perception in the Bangkok Metropolitan Region, Thailand: A Deep Learning Approach Utilizing Street Images

Iamtrakul,

Chayphong,

Kantavat

et al. 2024

Sustainability

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Recently, deep learning techniques, specifically semantic segmentation, have been employed to extract visual features from street images, a dimension that has received limited attention in the investigation of the connection between subjective and objective road environment perception. This study is dedicated to exploring and comprehending the factors influencing commuters’ perceptions of the road environment, with the aim of bridging the gap in interpreting environmental quality in Thailand. Semantic segmentation was applied to identify visual objects, expressed as a percentage of pixels represented in 14,812 street images from the Bangkok Metropolitan Region. Subjective road environment perception was assessed through a questionnaire, with a total of 3600 samples collected. Both sets of data were converted to average values per grid, with a grid size of 500 × 500 square meters, resulting in a total of 631 grids with data points. Finally, a multiple linear regression model was employed to analyze the relationship between the ratios of objects obtained from street images via semantic segmentation and human sensory perception of the road environment. The findings from this analysis indicate that the attributes of distinct object classes have a notable impact on individuals’ perceptions of the road environment. Visual elements such as infrastructure, construction, nature, and vehicles were identified as influential factors in shaping the perception of the road environment. However, human and object features did not exhibit statistical significance in this regard. Furthermore, when examining different road environments, which can be categorized into urban, community, and rural contexts, it becomes evident that these contexts distinctly affect the perceptions of various road environments. Consequently, gaining a comprehensive understanding of how street environments are perceived is crucial for the design and planning of neighborhoods and urban communities, facilitating the creation of safer and more enjoyable living environments.

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Clustering pedestrians’ perceptions towards road infrastructure and traffic characteristics

Cited by 18 publications

References 65 publications

Adopting a Multi-Criteria Decision-Making Approach to Identify Barriers to Electrification of Urban Freight in India

Adopting a Multi-Criteria Decision-Making Approach to Identify Barriers to Electrification of Urban Freight in India

Assessing Road Safety Development in European Countries: A Cross-Year Comparative Analysis of a Safety Performance Index

Assessing Subjective and Objective Road Environment Perception in the Bangkok Metropolitan Region, Thailand: A Deep Learning Approach Utilizing Street Images

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