Analyzing dilemma driver behavior at signalized intersection under mixed traffic conditions

Author(s):  
Bharat Kumar Pathivada ◽  
Vedagiri Perumal
2017 ◽  
Vol 45 (1) ◽  
pp. 12 ◽  
Author(s):  
Gowri Asaithambi ◽  
Hayjy Sekar Mourie ◽  
Ramaswamy Sivanandan

In India, traffic on roads is mixed in nature with widely varying static and dynamic characteristics of vehicles. At intersections, vehicles do not follow ordered queue and lane discipline. Different vehicle types occupy different spaces on the road, move at different speeds, and start at different accelerations. The problem of measuring volume of such mixed traffic has been addressed by converting different vehicles categories into equivalent passenger cars and expressing the volume in terms of Passenger Car Unit (PCU) per hour. The accurate estimation of PCU values for different roadway and traffic conditions is essential for better operation and management of roadway facilities. Hence, the objective of the present study is to estimate the PCU values at signalized intersection in mixed traffic and to study the influence of traffic volume, traffic composition and road width on PCU values.For this purpose, a mixed traffic simulation model developed specifically for a signalized intersection was used. The model was calibrated and validated with the traffic data collected from a signalized intersection in Chennai city. Simulation runs were carried out for various combinations of vehicular composition, volume levels and road width. It was observed that presence of heavy vehicles and increase in road width affects the PCU values. The obtained PCU values were statistically checked for accuracy and proven to be satisfied. The PCU values obtained in this study can be used as a guideline for the traffic engineers and practitioners in the design and analysis of signalized intersections where mixed traffic conditions exist.


Transport ◽  
2020 ◽  
Vol 35 (1) ◽  
pp. 48-56
Author(s):  
Sankaran Marisamynathan ◽  
Perumal Vedagiri

The large proportions of pedestrian fatalities led researchers to make the improvements of pedestrian safety at intersections. Thus, this paper proposes a methodology to evaluate crosswalk safety at signalized intersections using Surrogate Safety Measures (SSM) under mixed traffic conditions. The required pedestrian, traffic, and geometric data were extracted based on the videographic survey conducted at signalized intersections in Mumbai (India). Post Encroachment Time (PET) for each pedestrian were segregated into three categories for estimating pedestrian–vehicle interactions and Cumulative Frequency Distribution (CDF) was plotted to calculate the threshold values for each interaction severity level. The Cumulative Logistic Regression (CLR) model was developed to predict the pedestrian mean PET values in the cross-walk at signalized intersections. The proposed model was validated with a new signalized intersection and the results were shown that the proposed PET ranges and model appropriate for Indian mixed traffic conditions. To assess the suitability of model framework, model transferability was carried out with data collected at signalized intersection in Kolkata (India). Finally, this study can be helpful to rank the severity level of pedestrian safety in the crosswalk and improve the existing facilities at signalized intersections.


Author(s):  
Xiaofei Ye ◽  
Jun Chen ◽  
Guiyan Jiang ◽  
Xingchen Yan

The objectives of this study were to identify the factors affecting the pedestrian level of service (LOS) at signalized intersection crosswalks under mixed traffic conditions and to develop a suitable method for estimating pedestrian LOS. The important factors influencing pedestrian LOS at crosswalks were summarized: turning traffic, through traffic, number of pedestrians, and pedestrian delay. In the Highway Capacity Manual method, pedestrian delay can be calculated by Webster's delay model, which assumes that pedestrians arrive at a uniform rate and comply with the signal at an intersection. However, that assumption is not suitable for the Chinese scenario. A pedestrian delay model was developed by considering nonuniform arrival rates and noncompliant behavior under mixed traffic conditions. The data collected by video and a questionnaire survey include information on 1,257 participants' real-time sense of comfort and safety when crossing five selected intersections and on the operational characteristics of the intersections. With perceived LOS as a dependent variable, Pearson correlation analysis and linear regression techniques were explored to determine the significant factors affecting LOS. To overcome the limitations of linear regression techniques, cumulative logistic regression was done to develop a model that fits mixed traffic conditions in China—a model that can predict the probability of responses within each LOS on the basis of a combination of explanatory variables. The results showed that the cumulative logistic model fit the survey data better than the linear regression model and produces LOS A for the crosswalks.


2021 ◽  
Vol 147 (4) ◽  
pp. 04021006
Author(s):  
Bhargav Naidu Matcha ◽  
Sivakumar Sivanesan ◽  
K. C. Ng

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