Impact of Traffic Park Use on Children’s Traffic Rule Awareness and Behavioral Intentions: Case Study in Toyohashi City
Abstract
:1. Introduction
2. Materials and Methods
2.1. Toyohashi Traffic Park (TP) as a Case Study
2.2. Questionnaire Design and Data Collection
2.3. Conceptual Structural Equation Modeling (SEM) and Hypothesis
2.4. Data Processing and SEM Model Fit Criteria
3. Results
3.1. Participants Frequency of TP Use
3.2. Aggregate Analysis
3.3. SEM for Traffic Safety Education
3.4. Result of the SEM Model
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Study | Traffic Safety Education Methods | Effect Evaluations | Advantages | Limitations |
---|---|---|---|---|
Tomoda et al. [15]; Meir et al. [21]; Feng et al. [22]; Purcell and Romijn, [23]; Schwebel et al. [24]; Meir and Oron-Gilad, [25]. | Virtual reality-based training intervention | Questionnaire and extensive analysis of children’s traffic safety awareness, road-crossing performance, and gaze behavior to identify hazard situations through before–after training. | Increase children’s safety awareness. | However, there is a lack of experiential learning in a real environment. |
Riaz et al. [26]; Arbogast et al. [27]; Purcell and Romijn, [28]; Lehtonen et al. [29]; Renaud and Suissa, [30]. | Computer-based video game | Questionnaire and extensive analysis of children’s traffic safety awareness, behaviors, and attitudes through before–after training. | Effective for child road safety awareness in specific road situations. | Comparative research is required in an actual road environment for long-term effect evaluation. |
Zeedyk and Wallace, [31]; Whitebread and Neilson, [32]. | Video simulation | Questionnaire and comparative analysis of children’s road safety knowledge and visual search behavior through pre- and post-test. | Improves children’s visual skills in specific road situations. | No evidence for diverse settings. |
Barton et al. [33]; Demetrem et al. [34]. | Pretend road crossing | Questionnaire, observation, and extensive analysis of children’s road-crossing performance through pre–post-training. | A short-term effect was observed in improving children’s road-crossing behavior. | Lack of long-term effects. |
Albert and Dolgin, [35]; Hatfield et al. [36]; Feenstra et al. [37]; Zare et al. [38]; Trifunović et al. [39]. | Classroom-oriented or school-based program | Questionnaire and extensive analysis of children’s traffic knowledge, awareness, and behaviors through before–after training. | A short-term effect was shown after the intervention on children’s road-crossing knowledge. | Lack of long-term effects. |
McLaughlin et al. [40]; Willyarto et al. [41]; Zare et al. [42]. | Interactive or active learning | Questionnaire and extensive analysis of children’s understanding of traffic signs, and road-crossing performance through before–after training. | Effective immediately before and after intervention. | The effect gradually declined after intervention and lack of long-term effect evaluation. |
Salducco et al. [43]. | Multimedia base program | Questionnaire, observation, and extensive analysis of children’s road safety perception and behavior. | Video projection may improve the ability to identify risky behaviors. | However, there is a lack of experiential learning in a real environment. |
Jiang et al. [44]. | Theory of behavior spectrums | Comparative analysis of children road-crossing gaze behavior through before and after intervention. | Unsafe crossing behavior was reduced immediately after the intervention. | However, their road-crossing skills gradually declined after one month. |
Fyhri et al. [45]. | Tabletop model | Questionnaire and extensive analysis of children’s behaviors through before and after observation. | Improved children’s behaviors in various traffic situations in urban settings. | However, unable to improve in a semi-urban environment. |
Alonso et al. [46]. | Road Safety Education Project | Questionnaire and extensive analysis of children’s road safety behavior. | Age and other factors affect children’s road safety education. | Traffic rule knowledge did not significantly predict road behavior. |
Koekemoer et al. [47]. | Safe Kids Project | Questionnaire survey and analysis of children’s knowledge and behaviors related to pedestrian safety. | Children who walk accompanied were found to exhibit negligent road-crossing behavior | An educational intervention is needed to assess changes in children’s behavior. |
Freitas et al. [48]. | Educational therapeutic method | Questionnaire and observation before and after an experimental treatment. | Improve children’s knowledge of traffic accidents. | Lack of long-term effects. |
Zeedyk et al. [49]. | Outdoor ‘treasure trail’ method | Video observation on children’s road-crossing behavior. | Improve children’s visual skills in a particular road situation. | Lack of evidence for visual search behavior across different settings. |
Questions | Answer Option |
---|---|
Q03_1. Is it better to ride a bicycle on the left side of a road? |
|
Q03_2. Do you ride a bicycle on the left side of a road? |
|
Elementary School A | |||
---|---|---|---|
School Year | Number of Students | Number of Responses | Response Rate (%) |
1st grade | 56 | 31 | 55% |
2nd grade | 59 | 36 | 61% |
3rd grade | 72 | 55 | 76% |
4th grade | 69 | 50 | 72% |
5th grade | 68 | 28 | 41% |
6th grade | 71 | 51 | 72% |
Number of respondents for elementary school A is 251 (64%), boys = 122, girls = 126, no answer = 3 | |||
Elementary School B | |||
1st grade | 40 | 22 | 55% |
2nd grade | 43 | 17 | 40% |
3rd grade | 39 | 16 | 41% |
4th grade | 47 | 19 | 40% |
5th grade | 41 | 19 | 46% |
6th grade | 52 | 15 | 29% |
Number of respondents for elementary school B is 108 (41%), boys = 58, girls = 50 | |||
Elementary School C | |||
1st grade | 59 | 27 | 45% |
2nd grade | 57 | 27 | 47% |
3rd grade | 67 | 44 | 65% |
4th grade | 65 | 38 | 58% |
5th grade | 76 | 37 | 48% |
6th grade | 65 | 34 | 52% |
Number of respondents for elementary school C is 207 (53%), boys = 95, girls = 110, no answer = 2 | |||
Elementary School D | |||
1st grade | 94 | 40 | 42% |
2nd grade | 73 | 32 | 43% |
3rd grade | 96 | 37 | 38% |
4th grade | 96 | 50 | 52% |
5th grade | 105 | 59 | 56% |
6th grade | 92 | 24 | 26% |
Number of respondents for elementary school D is 242 (44%), boys = 108, girls = 130, no answer = 4 | |||
Total | 1602 | 808 (boys: 383, girls: 416, no answer: 9) | 50.4% |
Model | CFI | GFI | AGFI | TLI | RMSEA | SRMR | Chi-Squ | DF | p-Value |
---|---|---|---|---|---|---|---|---|---|
Awareness | 0.88 | 0.95 | 0.92 | 0.85 | 0.04 | 0.03 | 214.43 | 100.00 | 0.00 |
Behavioral intention | 0.89 | 0.937 | 0.90 | 0.86 | 0.05 | 0.04 | 280.30 | 100.00 | 0.00 |
Integrated | 0.63 | 0.83 | 0.79 | 0.57 | 0.07 | 0.06 | 1594.47 | 297.00 | 0.00 |
SEM Model for Awareness | SEM Model for Behavioral Intention | ||||||
---|---|---|---|---|---|---|---|
Latent Variables: | Latent Variables: | ||||||
Awareness =~ | Unstandardized Estimate | Standardized Estimate | p-Value | Behavioral Intention =~ | Unstandardized Estimate | Standardized Estimate | p-Value |
Q01_1 | 0.051 | 0.065 | 0.156 | Q01_2 | 0.075 | 0.119 | 0.005 |
Q02_1 | 0.492 | 0.208 | 0.000 | Q02_2 | 0.186 | 0.092 | 0.030 |
Q03_1 | 1.023 | 0.479 | 0.000 | Q03_2 | 1.170 | 0.522 | 0.000 |
Q04_1 | 0.836 | 0.492 | 0.000 | Q04_2 | 1.087 | 0.574 | 0.000 |
Q05_1 | 0.418 | 0.389 | 0.000 | Q05_2 | 0.577 | 0.559 | 0.000 |
Q06_1 | 0.419 | 0.345 | 0.000 | Q06_2 | 0.810 | 0.585 | 0.000 |
Q07_1 | 1.056 | 0.470 | 0.000 | Q07_2 | 1.189 | 0.538 | 0.000 |
Q08_1 | 0.333 | 0.449 | 0.000 | Q08_2 | 1.128 | 0.655 | 0.000 |
Q09_1 | 0.629 | 0.423 | 0.000 | Q09_2 | 1.283 | 0.606 | 0.000 |
Q10_1 | 1.000 | 0.474 | Q10_2 | 1.000 | 0.443 | ||
Regressions: | Regressions: | ||||||
Awareness ~ | Behavioral intention ~ | ||||||
grd | 0.022 | 0.196 | 0.000 | grd | 0.017 | 0.130 | 0.002 |
gndr | 0.004 | 0.010 | 0.822 | gndr | 0.024 | 0.055 | 0.184 |
schl_dmy_m | −0.002 | −0.006 | 0.916 | schl_dmy_m | 0.024 | 0.046 | 0.356 |
schl_dmy_s | −0.018 | −0.034 | 0.488 | schl_dmy_s | 0.004 | 0.007 | 0.875 |
schl_dmy_y | 0.010 | 0.024 | 0.634 | schl_dmy_y | −0.024 | −0.050 | 0.295 |
tp00_dmy56 | 0.064 | 0.159 | 0.004 | tp00_dmy56 | 0.042 | 0.089 | 0.075 |
tp12_dmy56 | −0.011 | −0.024 | 0.671 | tp12_dmy56 | 0.021 | 0.039 | 0.457 |
tp00_dmy56 ~ | tp00_dmy56 ~ | ||||||
gndr | −0.003 | −0.003 | 0.935 | gndr | −0.003 | −0.003 | 0.935 |
schl_dmy_m | 0.246 | 0.228 | 0.000 | schl_dmy_m | 0.246 | 0.228 | 0.000 |
schl_dmy_s | −0.090 | −0.069 | 0.084 | schl_dmy_s | −0.090 | −0.069 | 0.084 |
schl_dmy_y | 0.022 | 0.021 | 0.610 | schl_dmy_y | 0.022 | 0.021 | 0.610 |
tp12_dmy56 ~ | tp12_dmy56 ~ | ||||||
gndr | 0.036 | 0.045 | 0.125 | gndr | 0.036 | 0.045 | 0.125 |
schl_dmy_m | 0.222 | 0.236 | 0.000 | schl_dmy_m | 0.222 | 0.236 | 0.000 |
schl_dmy_s | 0.000 | 0.000 | 0.993 | schl_dmy_s | 0.000 | 0.000 | 0.993 |
schl_dmy_y | −0.027 | −0.030 | 0.381 | schl_dmy_y | −0.027 | −0.030 | 0.381 |
tp00_dmy56 | 0.437 | 0.503 | 0.000 | tp00_dmy56 | 0.437 | 0.503 | 0.000 |
Latent Variables: | Latent Variables: | ||||||
---|---|---|---|---|---|---|---|
Awareness =~ | Unstandardized Estimate | Standardized Estimate | p-Value | Behavioral Intention=~ | Unstandardized Estimate | Standardized Estimate | p-Value |
Q01_1 | 0.075 | 0.092 | 0.036 | Q01_2 | 0.075 | 0.123 | 0.004 |
Q02_1 | 0.449 | 0.183 | 0.000 | Q02_2 | 0.227 | 0.116 | 0.006 |
Q03_1 | 1.134 | 0.513 | 0.000 | Q03_2 | 1.166 | 0.539 | 0.000 |
Q04_1 | 0.790 | 0.449 | 0.000 | Q04_2 | 1.035 | 0.566 | 0.000 |
Q05_1 | 0.382 | 0.344 | 0.000 | Q05_2 | 0.519 | 0.520 | 0.000 |
Q06_1 | 0.458 | 0.364 | 0.000 | Q06_2 | 0.751 | 0.561 | 0.000 |
Q07_1 | 1.232 | 0.530 | 0.000 | Q07_2 | 1.221 | 0.571 | 0.000 |
Q08_1 | 0.327 | 0.426 | 0.000 | Q08_2 | 1.081 | 0.649 | 0.000 |
Q09_1 | 0.638 | 0.414 | 0.000 | Q09_2 | 1.253 | 0.613 | 0.000 |
Q10_1 | 1.000 | 0.457 | Q10_1 | 1.000 | 0.458 | ||
Regressions: | Regressions: | ||||||
Awareness ~ | Behavioral intention~ | ||||||
grd | 0.022 | 0.205 | 0.000 | grd | −0.036 | −0.020 | 0.596 |
gndr | 0.003 | 0.008 | 0.850 | gndr | 0.022 | 0.049 | 0.185 |
schl_dmy_m | 0.000 | 0.000 | 0.996 | schl_dmy_m | 0.025 | 0.047 | 0.294 |
schl_dmy_s | −0.013 | −0.026 | 0.606 | schl_dmy_s | 0.017 | 0.027 | 0.504 |
schl_dmy_y | 0.010 | 0.026 | 0.616 | schl_dmy_y | −0.033 | −0.065 | 0.127 |
tp00_dmy56 | 0.062 | 0.160 | 0.004 | tp00_dmy56 | −0.015 | −0.030 | 0.512 |
tp12_dmy56 | −0.011 | −0.025 | 0.657 | tp12_dmy56 | 0.034 | 0.060 | 0.199 |
awareness | 0.931 | 0.737 | 0.000 | ||||
tp00_dmy56 ~ | tp12_dmy56 ~ | ||||||
gndr | −0.003 | −0.003 | 0.935 | gndr | 0.036 | 0.045 | 0.125 |
schl_dmy_m | 0.246 | 0.228 | 0.000 | schl_dmy_m | 0.222 | 0.236 | 0.000 |
schl_dmy_s | −0.090 | −0.069 | 0.084 | schl_dmy_s | 0.000 | 0.000 | 0.993 |
schl_dmy_y | 0.022 | 0.021 | 0.610 | schl_dmy_y | −0.027 | −0.030 | 0.381 |
tp00_dmy56 | 0.437 | 0.503 | 0.000 |
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Chakma, M.; Matsuo, K.; Sugiki, N. Impact of Traffic Park Use on Children’s Traffic Rule Awareness and Behavioral Intentions: Case Study in Toyohashi City. Sustainability 2025, 17, 937. https://doi.org/10.3390/su17030937
Chakma M, Matsuo K, Sugiki N. Impact of Traffic Park Use on Children’s Traffic Rule Awareness and Behavioral Intentions: Case Study in Toyohashi City. Sustainability. 2025; 17(3):937. https://doi.org/10.3390/su17030937
Chicago/Turabian StyleChakma, Mital, Kojiro Matsuo, and Nao Sugiki. 2025. "Impact of Traffic Park Use on Children’s Traffic Rule Awareness and Behavioral Intentions: Case Study in Toyohashi City" Sustainability 17, no. 3: 937. https://doi.org/10.3390/su17030937
APA StyleChakma, M., Matsuo, K., & Sugiki, N. (2025). Impact of Traffic Park Use on Children’s Traffic Rule Awareness and Behavioral Intentions: Case Study in Toyohashi City. Sustainability, 17(3), 937. https://doi.org/10.3390/su17030937