## 3 Analysis and Results  
### Analysis of Interview Data  
The data analysis method followed [36]. After each interview, we compared the interview notes and transcribed the recorded content into text materials. Transcription relies on being faithful to the interview, and any missing information should be supplemented by contacting the interviewees as soon as possible. On the other hand, it is necessary to adjust the interview outline, summarize the interview skills, and ensure the accurate and complete data collected during the interview and the subsequent data compilation. The respondents' answers were closely related to their own driving experience. They preferred to list the events they had encountered and express their views. The interview materials of drivers with 2 degrees of concern are rich and complementary to each other. Therefore, the different degrees of concern of the interviewees in this study had little impact on the research results. The 22 interview materials were used for the grounded theory analysis. The sample size met the requirements of research. After the text transcription was completed, the entire interview material was integrated, and the text material was more than 11,000 words. With the help of NVivo11 coding software, the textual material was open-ended and coded line by line [37], and the text material was cut into nodes and  
Figure 2: The logical relationship between the three codes and the research categories. sub-nodes [36]. The process is also called inductive coding [38], and 316 original codes were obtained. The new concepts and categories are summarized and finally summarized in a first-level code, resulting in 79 concepts. An in-depth understanding of the concepts' similarities and differences reduces the overlap and redundancy between categories. The associated primary codes are formed into secondary codes (primary categories), 13 in total, and 7 secondary categories. Based on this, this study will use these 13 main categories to build a model of pedestrian crossing behavior and construct the internal relationship between the concepts. Four main categories: crossing state, crossing speed, attention shifting, and mental state, form the pedestrian characteristics. The three main categories of older people, young and children form the age elements. The three main categories of mandatory restraint, public education, and traffic facilities form suggestions for improvement. The three subcategories of unsafe behavior, external factors, and internal requirements form the driver behavior. Two subcategories, traffic environment, and traffic participants, form driver concerns. The two subcategories of information sufficiency and information form safety awareness. Due to the richness of the driver behavior section, the axial code level is further divided into primary and secondary categories. The three main categories of driver behavior, driver concerns, and safety awareness constitute the main categories of driver characteristics. Based on this, this paper will use these 13 main categories to build a model of pedestrian crossing behavior and construct the internal relationship between the concepts. In particular, in the axial coding of driver characteristics, the relationship between concepts could not be well expressed by only one level, so the axial coding was divided into two categories. The final conceptual model was divided into Tables 3 and 4. According to the interviewees' descriptions, some pedestrian violations are shown in Figure 3.