Analysis of Road Safety Perception and Influencing Factors in a Complex Urban Environment—Taking Chaoyang District, Beijing, as an Example
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Data Source
2.1.1. Study Area
2.1.2. Data Source
2.2. Research Method
2.2.1. Research Framework
2.2.2. Safety Perception Score and Street View Element Extraction
- (1)
- Safety perception score
- (2)
- Street view element extraction
2.2.3. LightGBM and SHAP
- (1)
- LightGBM
- (2)
- SHAP
3. Results
3.1. Spatial Distribution of Street View Safety Perception
3.2. Analysis of Influencing Factors of Street View Safety Perception
3.3. Interaction of Street View Elements
- (1)
- Interaction of different street view elements on safety perception
- (2)
- Interaction between the proportion and quantity of street view elements
3.4. Local Interpretation of Street View Environment
4. Conclusions
4.1. Conclusions
- (1)
- The overall street view safety perception level of Chaoyang District in Beijing is relatively high. The region with the highest level of safety perception is the western central region with dense road networks, which has a high economic level and dense population. The overall level of safety perception in the eastern region is relatively low, and the road network in this region is relatively sparse, indicating that the level of economic development is relatively low compared with the western region, and the street construction is not as good as the western region as a whole. The rich and diverse functional layout increases accessibility and watchability, which has a positive impact on enhancing safety perception [63].
- (2)
- The four most influential street view elements are the number of motor vehicles and the proportion of the area of roads, skies, and sidewalks. Among them, the greater the number of motor vehicles and the higher the proportion of roads and sidewalks, the greater the positive impact on safety perception and the higher the degree of safety perception; the higher the proportion of sky area, the greater the negative impact on safety perception and the lower the degree of safety perception. Human activities can increase informal monitoring, thereby enhancing residents’ safety perception.
- (3)
- There is interaction between street view elements on safety perception: when the number of motor vehicles is fixed, the greater the proportion of road area is, the less conducive to the improvement of safety perception; when the number of motor vehicles is fixed, the larger the proportion of building area, the more conducive to the improvement of safety perception. When there are fewer roads and more buildings, it is more conducive to the improvement of safety perception. When the proportion of sky area is fixed, the more trees, the less conducive to the improvement of safety perception. The proportion and number of street view elements interact with safety perception: when the number of pedestrians is less than two, the area proportion of fewer pedestrians will make people feel safer. When the number of motor vehicles is less than four, the area proportion of motor vehicles is relatively small, which makes people feel safer; when the number of motor vehicles is large, the larger the proportion of motor vehicles, the more people feel safe. This discovery is consistent with the findings of psychological research, and the impact of human behavioral activities on environmental safety perception is also consistent with CPTED.
- (4)
- In the sections with the lowest, moderate, and highest level of safety perception, the influence of street view elements on street view safety perception is inconsistent. In the place with the lowest safety perception, sidewalks have a significant negative impact on safety perception, while roads and pedestrians can significantly have a positive impact on safety perception. In places with moderate safety perception scores, the number of motor vehicles and sidewalks have significant negative effects on safety perception scores, while pedestrians, the sky, and roads have significant positive effects. For the places with the highest score of safety perception, the number of motor vehicles and the proportion of sidewalks and roads have the most significant impact, which has a significant negative impact on safety perception. The ground, traffic signs, and pedestrians have a certain positive impact, while the wall has a certain negative impact. It can be seen that the contribution of various street view elements to safety perception varies in different regions.
4.2. Implications for Practice
- (1)
- Appropriate monitoring can significantly enhance people’s safety perception. In areas with low safety perception, emphasis should be placed on improving the infrastructure for security prevention and security control mechanisms and configuring security infrastructure such as installing surveillance cameras.
- (2)
- Strengthen the deployment of police resources and patrols in areas with low safety perception; invest in public security human resources and improve the level of public safety service management.
- (3)
- In addition, building a good community environment can enhance residents’ safety perception. By promoting comprehensive management of spatial blind spots, making efficient use of the Internet to innovate police work, and strengthening cooperation between the police and the residents, residents’ safety perception will be improved.
4.3. Research Deficiency
- (1)
- In view of the limited resources, the safety perception dataset of this study is the domestic dataset constructed by the existing research. Although they are all domestic cities, there will be some differences between different cities. Therefore, in the follow-up study, we can consider building a local safety perception dataset in the study area to obtain more objective and real safety perception data.
- (2)
- Due to the large amount of data and limited computing resources, the image processing in this paper selects a relatively lightweight semantic segmentation and target detection model, and the processing results have some errors. In the future, more accurate models and more powerful computing resources can be used to process data.
- (3)
- The research on the interaction of street view elements in this article is at a macro level, and the interaction of street view elements may vary in different regions. We will conduct more detailed research on this issue in the future. In addition, due to the limitations of street view image data, only daytime data can be obtained on a large scale. Therefore, we have not yet considered the performance differences in different time periods.
- (4)
- The research in this article revolves around street view images, and the analysis is also based on the information obtained from street view images. For variables such as the surrounding built environment and social variables that cannot be directly obtained from street view images, we will conduct further analysis in subsequent research.
- (5)
- Existing studies have shown that there is a deviation between safety perception and actual safety (crime risk), and this deviation will be further studied in the future.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Name | Configuration |
---|---|
Num_estimators | 100 |
Boosting_type | Gbdt |
Objective | Regression |
Metric | L1, L2 |
Num_leaves | 46 |
Max_depth | 7 |
Learning_rate | 0.05 |
Feature_fraction | 0.6 |
Bagging_fraction | 0.9 |
Bagging_freq | 2 |
Min_data_in_leaf | 86 |
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Share and Cite
Hou, X.; Chen, P. Analysis of Road Safety Perception and Influencing Factors in a Complex Urban Environment—Taking Chaoyang District, Beijing, as an Example. ISPRS Int. J. Geo-Inf. 2024, 13, 272. https://doi.org/10.3390/ijgi13080272
Hou X, Chen P. Analysis of Road Safety Perception and Influencing Factors in a Complex Urban Environment—Taking Chaoyang District, Beijing, as an Example. ISPRS International Journal of Geo-Information. 2024; 13(8):272. https://doi.org/10.3390/ijgi13080272
Chicago/Turabian StyleHou, Xinyu, and Peng Chen. 2024. "Analysis of Road Safety Perception and Influencing Factors in a Complex Urban Environment—Taking Chaoyang District, Beijing, as an Example" ISPRS International Journal of Geo-Information 13, no. 8: 272. https://doi.org/10.3390/ijgi13080272
APA StyleHou, X., & Chen, P. (2024). Analysis of Road Safety Perception and Influencing Factors in a Complex Urban Environment—Taking Chaoyang District, Beijing, as an Example. ISPRS International Journal of Geo-Information, 13(8), 272. https://doi.org/10.3390/ijgi13080272