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Developing countries such as India need to have the proper pedestrian level of service (PLOS) criteria for various facilities to help in planning, designing, and maintaining pedestrian facilities. Thus, the objective of this study was to develop a suitable method for estimating the PLOS model under mixed traffic conditions and also to define threshold values for PLOS classification at signalized intersections. First, the data were collected with video and a user perceptions survey at eight selected signalized intersections in Mumbai, India. Second, pedestrian crossing behaviors were modeled according to arrival pattern, crossing speed, noncompliance behavior, and pedestrian–vehicular interaction. Third, a pedestrian delay model was proposed by considering crossing behavior variations and subsequent validation with field data. Fourth, significant variables were identified on the basis of the Pearson’s correlation test with user’s perceptions score. Fifth, the conventional linear regression (CLR) technique was explored to determine the PLOS. To overcome the limitations of the CLR technique, fuzzy linear regression (FLR) was done to develop a PLOS model that fits mixed traffic conditions in India. Two models were validated, and their statistical performance results indicate that the FLR model predicts the PLOS score more precisely. Finally, k-means and fuzzy C-means (FCM) clustering techniques were applied to classify the PLOS score, and the results were compared by time complexity value and field values. The performance evaluation results indicate that the k-means method saves time but fails to produce more reliable threshold values, and the FCM method produces more accurate and efficient threshold values for the PLOS score at signalized intersections under mixed traffic conditions.

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Pedestrian noncompliance behavior is one of the most critical causes of pedestrian involved traffic crashes at intersections in India. Thus, the objectives of this study are to examine how various factors affect pedestrian crossing behavior and to propose models for pedestrian crossing behavior and level of safety at signalized intersections which will be useful to regulate pedestrian flow. The data were collected with video and a user perceptions survey at six selected signalized intersections in Mumbai, India. The differences between pedestrian crossing behavior with respect to personal characteristics, socioeconomic attributes, and existing crossing facilities were identified using Pearson correlation and odd ratio tests. Furthermore, the major reasons for noncompliance behavior were obtained by analysis of field data to prevent noncompliance behavior and enhance pedestrian safety. The results showed that a significant number of the pedestrians violated the traffic signal to save time and for convenience (46%). A binary logit model was developed to evaluate the impacts of contributing factors on pedestrian crossing behavior. Further, an ordered probability model was established to evaluate and estimate the pedestrian level of safety at signalized intersections. Two models were validated, and their statistical results show that the models predict the pedestrian crossing behavior and safety level more precisely. Developed models and study outcomes can help transport planners and designers understand pedestrian crossing behavior on crosswalks at signalized intersections and thus create a safer crossing environment for all pedestrians.