Assessing Subjective and Objective Road Environment Perception in the Bangkok Metropolitan Region, Thailand: A Deep Learning Approach Utilizing Street Images
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1. Introduction
Over the past few decades, the rapid urbanization in developing countries has led to significant changes in the structural and functional complexity of cities as they strive to accommodate a growing population [1]. This transformation, particularly evident in the transportation sector, is a result of the urbanization process and encompasses various aspects, including the choice of transportation mode, urban traffic demand, and trip purposes. Numerous travel options have been developed to address these challenges, such as public transport, active transport, and para-transport. However, it is essential to note that the primary transportation system that has seen significant development is the road network, which serves as the most widely used mode of transportation, facilitating vehicle travel more extensively than any other mode [2]. The transportation network plays a vital role in providing essential connections that allow individuals access to a wide range of activities necessary for their daily lives. Moreover, it increasingly contributes to fostering social networks and promoting sustainable inclusive growth within society and the economy [3,4]. Beyond its primary function of connecting people, goods, and services, the perspective of urban road environment design and planning highlights the significance of designing road elements and the overall environment. This aspect becomes a vital consideration in enhancing the quality and value of the surrounding living neighborhoods and facilitating various dimensions, e.g., vitality, safety, comfort, aesthetics, etc. [5,6].
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. Deep learning technology has emerged as a powerful tool for assessing these environments, employing semantic segmentation of street imagery to enhance the comprehension of road and urban settings [2,8]. Significantly, addressing the limitations of street image analysis is crucial, especially given its gaps in accurately capturing and visually representing real on-site perceptions, which necessitate a sense of realism at the human scale. Consequently, this study aims to bridge these gaps by integrating the analysis of both subjective and objective factors, taking into account people’s perceptions. By incorporating street view images into the study, we seek to confirm the connection between people’s perceptions and the road environment [9,10]. However, existing studies still encounter challenges in terms of object detection within images, as the specific objects of interest may vary in different urban contexts or countries. Hence, our research is dedicated to exploring and comprehending the impact of the road environment on people’s perceptions, with the ultimate goal of narrowing the gap in interpreting environmental quality in Thailand. This region is characterized by its unique and diverse activities, making it an area of particular interest. The findings from this study will provide recommendations for environmental development that can better meet the needs of the people and enhance the quality of the road environment in the neighborhood, contributing to sustainable development.
5. Discussion
The analysis of the relationship between objective road environment factors and individuals’ perceptions of the road environment can be discussed in two parts. Firstly, the overall image is considered at the area context level, taking into account different spatial contexts. When examining distinct groups of road environments, which can be classified into urban, community, and rural contexts, these settings significantly influence perceptions of various road environments both positive and negative based on subjective and objective road environment considerations. The research highlights that different road environments, categorized as urban, community, and rural, exert a notable impact on individuals’ perceptions, encompassing both positive aspects (wealth, safety, vitality, and beauty) and negative aspects (depression and boredom). These perceptions are shaped by both subjective experiences and objective factors tied to the distinctive characteristics inherent in each context. Secondly, consider the object visual feature classes at a more granular level. The findings indicate that the characteristics of visual feature objects in distinct classes significantly impact individuals’ perceptions of the road environment. Visual features that impact road environment perceptions include “infrastructure”, “construction”, “nature”, and “vehicle”, while “human” and “object” features did not demonstrate statistical significance. The findings concerning ‘infrastructure’ (e.g., road and sidewalk) and “nature” (i.e., trees) are consistent with the study conducted by Li et al. [44], which emphasizes the significance of these visual feature objects in shaping perceptions of environmental safety.
Addressing the matter of travel safety perception, the study revealed a negative association with vehicles, aligning with the findings of Rita et al. (2023) [40]. Their study, utilizing street view imagery to assess safety among various user groups, found statistically significant negative correlations between cars and buses, cars and cyclists, and cars and pedestrians in a similar context. Few studies have explored the research on the effects of road environments on safety aspects, despite the notable impact that the design of roads and urban areas has on individuals’ perceptions. Examining the situation from a driver’s standpoint, the environment can significantly influence their behavior, manifesting in actions such as speeding, overtaking violations, and altered reaction times [45,46]. Shifting the focus to residents, residing in an unsuitable road environment can adversely affect their perception of safety, encompassing both travel and daily living experiences. The perception of unsafe conditions in the environment is likely to have a negative impact on the well-being of the residents.
With respect to depression, upon examining the environmental image data, it was observed that the absence of beautiful shady scenery and higher road traffic density were correlated with an increased perception of depression and boredom. This indicates another cognitive factor with a negative relationship, warranting attention from planners. The stress associated with travel can significantly impact individuals’ mental health, particularly during extended travel times in congested urban areas. Xu et al.’s [47] study, aiming to integrate street view images and deep learning to investigate the correlation between human perceptions of the built environment and cardiovascular disease, revealed an association between depression and vitality perceptions and the risk of cardiovascular disease. Thoughtfully designed urban spaces foster social cohesion, physical health, economic opportunities, and environmental sustainability. Conversely, poorly planned areas may contribute to issues such as social isolation, health risks, economic disparities, and the loss of green spaces. Striking a balance through inclusive sustainable urban planning is vital for creating environments that enhance well-being and community resilience. Notably, it is intriguing to note that “nature” exhibits a positive relationship with perceptions of beauty, while “vehicle” is negatively associated with safety perceptions.
Overall, recognizing this diversity is crucial for effective research, policy development, and urban planning tailored to the specific needs and challenges of different road environments. Consequently, comprehending how individuals perceive the urban environment is vital for designing and planning neighborhoods and urban communities that foster safer and more pleasant living environments, ultimately enhancing the well-being of city residents. This aligns with the findings of Long and Tang [22], which emphasize the strong positive impact of the quality of urban streets on residents’ well-being. While numerous studies have verified the effectiveness of street images and deep learning techniques, which propose the fusion of street view data and semantic segmentation methods for the accurate identification of objects within images and precise measurement on a human scale [9,13,24,48], it is crucial to recognize that the efficacy of deep learning tools is contingent upon the quantity of data. Therefore, future studies employing larger image databases are expected to enhance the accuracy of determinations. The perception of a place by individuals is a highly subjective process that objective indicators alone cannot fully encapsulate. However, by concurrently considering both dimensions, a more comprehensive understanding of perception can be achieved [49]. Nevertheless, this study has limitations due to the constraints associated with the available large databases. It delves into people’s subjective perception of the objective road environment, specifically the visual features, from a mechanistic standpoint, focusing on the correlation between visual feature classes of the road environment and people’s perception of it. It is essential to acknowledge that individuals’ perception of their surroundings may encompass numerous objective urban environmental factors.
However, other objective data, such as physical information from geographic information systems (GIS), including road structures and land use activities, are not updated in these extensive metropolitan area databases. Consequently, road environment image data from current road images are utilized to investigate the perceptions of the road environment. Future studies in Thailand or countries within the Asian region, or nations with comparable road environment contexts, will need to analyze these factors collectively. While this study specifically examines people’s subjective perception of the objective environment (visual features), the findings underline the significance of prioritizing transportation design and planning, particularly in terms of scenery, mobility, and patterns of activities along roadways, to enhance the overall travel experience.
6. Conclusions
This study focuses on exploring and comprehending road environment perceptions, employing both subjective and objective measurement techniques that utilize street images and deep learning. We propose the use of deep convolutional neural networks (CNNs) for street image classification, incorporating scene classification and image semantic segmentation. The use of deep convolutional neural networks (CNNs) allows for a detailed analysis of visual features in street images. This, in turn, provides a comprehensive understanding of the road environment, capturing a wide range of elements and their impact on perceptions. Furthermore, we quantify the relationship between the visual features of street images and human perceptions of the road environment. The study recognizes that perceptions (both positive and negative) can change based on individual differences and urban contexts (urban, suburban, and rural). The recognition of this insight appeals to the imperative for adopting a context-specific approach in urban planning, particularly in the integration of land use and transportation. This emphasizes the necessity of tailoring planning strategies to the unique characteristics of each environment, acknowledging that a one-size-fits-all approach may not be suitable.
By doing so, urban planners can effectively address the intricate interplay between land use and transportation within specific contexts. This approach leads to more nuanced and effective solutions for the diverse needs and challenges of different urban areas. The insights gained can be applied to urban planning and design, contributing to the creation of safer and more livable communities. This is particularly valuable for city planners and policymakers seeking evidence-based guidance. The utilization of deep learning tools and image databases provides scalability. This implies that the approach can be applied to larger datasets and extended to encompass a broader geographical area, yielding even more accurate and representative results. Finally, it is crucial to acknowledge that people’s perceptions may vary due to individual differences and alterations in urban contexts. Hence, it is imperative to comprehend these variations in diverse contexts. This understanding serves as a valuable resource for informing urban planning decisions, ultimately contributing to the development of sustainable living environments.
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