Perumal Vedagiri's research while affiliated with Indian Institute of Technology Bombay and other places

The accurate measurement of pavement distresses is essential for setting preservation and rehabilitation priorities. The manual measurement of surface distresses is the most commonly used method for evaluating the severity of pavement distresses. However, the manual method has less accuracy and consistency. So that in the present study, the images of different pavement distresses, potholes, cracking, patching, and ravelling for the selected sections were captured using terrestrial laser scanner (TLS). The surface area of these distresses was measured in Leica Cyclone software. The obtained results were validated by collecting a similar distress pattern at a different location using the same equipment. The measured surface area is executed in a GIS platform to develop an automated database for the prioritization of selected pavement sections of Mumbai city for pavement maintenance. It was observed that the TLS technique could be used effectively for safer and more accurate surface distress measurement.

Motorized Two Wheelers (MTWs) possess specific driving patterns which increase their crash risk, especially in heterogeneous traffic, typically found in developing countries. Understanding the driving behavior of MTWs in such conditions is crucial for transportation researchers and vehicle manufacturers to improve driving behavior modeling, vehicle design, and risk evaluation. This study aims to model single-leader following behavior of MTWs in mixed traffic, by using Generalized Linear Modelling (GLM) technique to develop a safe-distance following model considering various factors specific to mixed traffic. Time to Collision (TTC) and Potential Index for Collision with Urgent Deceleration (PICUD) were employed for the safety assessment. Results of the study present a typical MTW following behavior and associated crash risk for heterogeneous traffic. Apart from theoretical applications such as microsimulation modeling, this study is useful for smart systems such as collision avoidance systems, in-vehicle warning and autonomous driving systems.

Moving through lateral gaps defined by slower vehicles, or “filtering,” is a common characteristic of motorized two wheelers (MTWs) in mixed traffic conditions. Despite its potential benefits such as reduced journey time and emissions, filtering is a critical maneuver, owing to the lesser conspicuity of MTWs and their almost non-existent physical protection from crashes. An in-depth knowledge of filtering behavior is necessary for various theoretical and practical applications. In an attempt to model this distinct driving characteristic of MTWs, this study investigates the filtering interactions of MTWs in urban mixed traffic conditions. Specifically, the behavioral differences between following and filtering interactions were investigated while considering the conditions found in heterogeneous traffic. The choice of filtering was modeled based on multiple factors including spatial parameters and classification parameters. This paper employs a utility-based binary logit model (BLM) and two machine learning (ML) based models: random forest (RF); and adaptive neuro fuzzy inference system (ANFIS) to predict the filtering choice of MTWs in urban mixed traffic. A comparative analysis revealed that, generally if not always, the ML based models (prediction accuracy of RF and ANFIS was 90.07% and 96.02%, respectively) performed better than utility-based models (prediction accuracy of BLM was 80.05%). Owing to the better performance measures of the ANFIS technique, it can be considered a powerful tool for predicting following and filtering choices of MTWs, useful for policy makers designing intelligent transportation systems and microsimulation models.

Children are one of the most vulnerable road user groups, experiencing a higher rate of crashes and injuries in India. Children under the age of 18 account for 37.30% of fatalities in traffic accidents. Thus, the objectives of this study are to examine how various factors affect safe road crossing behaviour of Children and propose models for estimating inattentive behaviour, sight behaviour, and collision severity that will be useful to regulate child pedestrian behaviour. The data were collected through a self-reported perceptions survey at two selected higher secondary schools in Chennai, India. The difference between safe road crossing behaviour with respect to personal characteristics, safety knowledge, unsafe crossing behaviour, and collision severity among Children was identified by conducting descriptive statistics, bivariate analysis, and correlation tests. The results showed that older boys (12–16 years old) walk alone to or from school, intentionally engage in risky behaviour, and have more collisions with two-wheelers. Further, the multiple linear regression technique was adopted to develop three different models, such as collision severity, inattentive behaviour, and sight behaviour in children. Models were validated, and statistical results showed that models accurately predicted safe road crossing behaviour of Children in Indian conditions. The developed models and outcomes of this study can help transport planners and designers to understand road safety knowledge and crossing behaviour among children and paves way to create safe crossing infrastructure and environment for them.

The traffic scenario found in most developing countries including India is mixed traffic, wherein a significant proportion is composed of motorized two-wheelers (MTWs). The irregular driving patterns of MTWs increase the complexity of driver behavior modeling as compared to homogeneous car-based traffic. In this direction, this study aims at investigating the single leader following behavior of MTWs in mixed traffic conditions based on field data. Multiple linear regression (MLR) models were developed for quantifying the following behavior, and support vector machines (SVMs) were used to investigate the discrepancies and highlight variations with longitudinal and lateral parameters. The findings highlight clear differences in the following behavior between cases with different leader vehicle classes and an aggressive driving behavior by MTW drivers. Results from this study can be useful for developing accurate microsimulation models, vehicle-to-vehicle (V2V) communication systems, etc.KeywordsMixed trafficMotorized two-wheelersVehicle followingDriving behavior

The ownership of motorised two wheelers (MTWs) has been on the rise across various countries across the globe. The growth has been especially higher in developing countries which have typical traffic characteristics and higher populations. This steady rise has resulted in increased accident and fatalities. This abrupt increase warranted attention from the researchers to carry out specific studies for MTWs, which have a very different behaviour as compared to cars in terms of physical and dynamic parameters. Moreover, the unique traffic patterns usually found in the developing countries pose an additional challenge to the researchers, since the conventional focus of transportation safety researchers was a homogeneous car-based traffic. Many such studies have been attempted, especially in the recent decades, which have considered various risk factors related to MTW safety. However, the studies have considered different sets of risk factors and have given surprising and even conflicting results. Therefore, a comprehensive review of the diverse studies needs to be carried out which incorporates all the risk factors considered in previous research. This study reviews such research papers which have analyzed various risk factors related to safety of MTWs, especially in heterogeneous, non-lane based traffic. Specifically, this paper aims to incorporate results from those studies and highlight the conclusions from state of the art. The paper also discusses about the research gaps that are crucial for MTW safety in mixed traffic conditions. The review will be useful for researchers working in the field of MTW safety and for policy implementation and analysis.

Crashes take place at intersections even when signalized due to the presence of dilemma zone, where the driver feels uncertain whether to cross or stop at the trigger of yellow signal. Erroneous decisions taken by the driver leads to rear-end or right-angle crashes, causing safety implications. Accurate position of the dilemma zone can help in implementing various safety measures and enhance the safety at signalized intersections. This study evaluates the dilemma boundaries for mixed traffic conditions using Support Vector Machine (SVM), a supervised machine learning technique. The study results indicate that the position of the dilemma zone is influenced by traffic and geometric characteristics. Results show that trucks and motorized three-wheelers have dilemma zone closer to the intersection compared to other vehicle types. The dilemma zone for mixed traffic conditions is found to be in the range of 30 m to 80 m from the stop line. The outcome of this study contributes to better understand the dilemma boundaries in developing nations with mixed traffic conditions, like India.

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.