In order to improve the efficiency of overpass and the safety level of pedestrian, this paper aims to investigate the contributing factors for selective preference of overpass. Eight overpasses were investigated in Xi...In order to improve the efficiency of overpass and the safety level of pedestrian, this paper aims to investigate the contributing factors for selective preference of overpass. Eight overpasses were investigated in Xi＇an, and a questionnaire was conducted by the pedestrians near the overpass. Totally, 1131 valid samples （873 used of overpasses and 258 non-used of overpasses） were collected. Based on the data, a binary logit （BL） model was developed to identify what and how the factors affect the selective preference of overpass. The BL model was calibrated by the maximum likelihood method. Likelihood ratio test and McFadden-R2 were used to analyze the goodness-of-fit of the model. The results show that the BL model has a reasonable goodness-of-fit, and the prediction accuracy of the BL model can reach 81.9%. The BL model showed that the selective preference of overpass was signifi- cantly influenced by eight factors, including gender, age, career, education level, license, detour wishes, detour distance, and crossing time. Besides, the odds ratios of significant factors were also analyzed to explain the impacts of the factors on selective preference of overpass.展开更多
基金sponsored by the National Natural Science Foundation of China(No.51178108)the Scientific Research Foundation of Graduate School of Southeast University(No.YBJJ1458)the Fundamental Research Funds for the Central Universities and the Scientific Innovation Research of College Graduates in Jiangsu Province(Nos.KYLX_0173,KYLX_0174)
文摘In order to improve the efficiency of overpass and the safety level of pedestrian, this paper aims to investigate the contributing factors for selective preference of overpass. Eight overpasses were investigated in Xi＇an, and a questionnaire was conducted by the pedestrians near the overpass. Totally, 1131 valid samples （873 used of overpasses and 258 non-used of overpasses） were collected. Based on the data, a binary logit （BL） model was developed to identify what and how the factors affect the selective preference of overpass. The BL model was calibrated by the maximum likelihood method. Likelihood ratio test and McFadden-R2 were used to analyze the goodness-of-fit of the model. The results show that the BL model has a reasonable goodness-of-fit, and the prediction accuracy of the BL model can reach 81.9%. The BL model showed that the selective preference of overpass was signifi- cantly influenced by eight factors, including gender, age, career, education level, license, detour wishes, detour distance, and crossing time. Besides, the odds ratios of significant factors were also analyzed to explain the impacts of the factors on selective preference of overpass.
