Browsing by Author "Arathi, A.R."

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Analysis of Critical Gap and Capacity at Skewed Uncontrolled Intersections
(Institute for Transport Studies in the European Economic Integration, 2023) Arathi, A.R.; Harikrishna, M.; Mohan, M.
Critical gaps and capacity of movements at uncontrolled intersections are influenced by intersection geometry, especially in mixed traffic conditions. However, existing models to compute the capacity of uncontrolled base intersections are only suitable for intersections with 00 to 100 skew angles. This study aims to bridge the gap by evaluating the effect of skew angle on the critical gap and capacity of uncontrolled intersections. The critical gap models are developed for different vehicle types. The capacity of uncontrolled intersections is determined for different skew angles (00 to 270) using simulation and Indo-HCM models. The comparison reveals that the Indo-HCM model over-predicts the capacities. Thus, new capacity models are proposed, and it is observed that the capacity varies as a quadratic function of the skew angle, where the constant indicates base capacity. This study also provides the adjustment factors for Indo-HCM capacity models to deduce the capacity of any skew-angled intersections. © 2023 Institute for Transport Studies in the European Economic Integration. All rights reserved.
Correction to: Simulation-based Performance Evaluation of Skewed Uncontrolled Intersections (International Journal of Intelligent Transportation Systems Research, (2023), 21, 2, (349-360), 10.1007/s13177-023-00360-6)
(Springer, 2023) Arathi, A.R.; Harikrishna, M.; Mohan, M.
Upon further review, the authors wish to make the following correction to the article: After reviewing the online print, it was observed that the first and second authorâ€™s have initials in their last name. Therefore, their names appear in citation as A. R., A., M., H. & Mohan, M. In this case, it will be fully of initials in place of 1st and 2nd author names. So we would like to change the appearance of first and second name of authors so that it will appear in citation as Arathi, A. R., Harikrishna, M., Mohan, M. We humbly request you to correct the author names appearance in online print as A. R. Arathi, M. Harikrishna & Mithun Mohan so that the citation (bibtex) will become Arathi, A. R., Harikrishna, M. & Mohan, M. First Author: First Name: A. R. Last Name: Arathi Co Author 1: First Name: M. Last Name: Harikrishna Co Author 2: First Name: Mithun Last Name: Mohan The authors wish to apologize for any inconvenience. Â© 2023, The Author(s), under exclusive licence to Intelligent Transportation Systems Japan.
Development of lag size-based safety thresholds for skewed uncontrolled intersections
(Aracne Editrice, 2024) Arathi, A.R.; Harikrishna, M.; Mohan, M.
Gap/Lag acceptance is the primary basis for analysing uncontrolled intersections. Misjudgement in gap/lag acceptance imparts high risk to the drivers. The gap refers to the temporal difference between consecutive vehicles on a major road, whereas lag is a part of the available gap, occasionally coinciding with the first gap. Even though both are different in real scenarios, studies do not consider them separate. The lag acceptance behaviour of drivers must be studied thoroughly because the acceptance of shorter lags is more in developing countries due to the aggressive behaviour of drivers, which might lead to road crashes. However, such studies are very scarce compared to gap acceptance studies. A study of the lag acceptance process is essential for improved traffic safety and operational efficiency at skewed uncontrolled intersections. This study adopted machine-learning techniques to predict the lag acceptance decision of drivers to examine how it performs compared to commonly used methods. Data were collected at six intersections from various cities in Kerala, India, during peak hours. Artificial Neural Network (ANN), Logistic Regression (LR) and Support Vector Machine (SVM) models were developed, and their performance was compared. The occupancy time approach was used to determine the critical lag. The goodness of fit measures shows that the ANN model outperforms the LR and SVM models, with an accuracy of 93.6%. Furthermore, goodness-of-fit measures such as F1 score and R2 values are 0.964 and 0.892, indicating that the prediction of the ANN model is excellent. Lag sizes of less than 2.7, 3.5, and 3.0 seconds were shown to be less safe, corresponding to right-turn from major, right-turn from minor and through from major roads. © 2024, Aracne Editrice. All rights reserved.
Influence of Geometric and Traffic Characteristics on Priority Violations at Uncontrolled Intersections
(Springer Science and Business Media Deutschland GmbH, 2025) Arathi, A.R.; Harikrishna, H.; Mohan, M.
Most of the intersections in developing nations are uncontrolled. The traffic movements at such intersections are very complex because of aggressive driving behaviour, lane indiscipline, and lack of priority rules. Priority violations are frequent at uncontrolled intersections due to the lack of traffic control, making the gap acceptance model less effective. The increase in priority violations may increase the capacity, delay and vehicular conflicts, thereby affecting the safety at intersections. Therefore, it is necessary to study the rate of occurrence of priority violations and the influence of road geometrics and traffic conditions on priority violations at uncontrolled intersections. This paper describes the influence of traffic and geometric characteristics on the overall priority violations, including priority reversals and limited priority. Moreover, this study provides separate models for predicting the occurrence rate of priority reversals and limited priority, which will be helpful for design engineers. In addition, this study also shows that an increase in priority violations increases capacity. Â© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Machine Learning-Based Gap Acceptance Model for Uncontrolled Intersections Under Mixed Traffic Conditions
(Springer Science and Business Media Deutschland GmbH, 2023) Arathi, A.R.; Harikrishna, M.; Mohan, M.
Uncontrolled intersections are the most common type of intersections in a transportation network. The study modeled minor road driverâ€™s decision of accepting or rejecting a gap at four-legged uncontrolled intersections having similar geometric characteristics using Artificial Neural Network (ANN) model, Logistic Regression (LR) model, and Support Vector Machine (SVM) model. The results reveal that the performance of LR and SVM models are somewhat similar, while the performance of ANN model exceeds the performance of both LR and SVM models with a correct prediction of about 96.2%. Also, the higher values of the goodness of fit measures like F1 score and R2 value together with a lower value of MSE show that ANN model is better in distinguishing between the classes. The variable gap duration has a major influence on model prediction comparing to other variables. The effect of the critical gap, occupancy time, conflicting volume, and vehicle type are also found remarkable. Â© 2023, Transportation Research Group of India.
Simulation-based Performance Evaluation of Skewed Uncontrolled Intersections
(Springer, 2023) Arathi, A.R.; Harikrishna, M.; Mohan, M.
This study has developed simulation-based models for evaluating the performance of skewed uncontrolled intersections since the existing models do not consider the effect of skew angle. The calibration parameters for simulating four-legged uncontrolled intersections are suggested. The results indicate that the developed simulation model can predict the capacity and level of service more accurately than the existing Indo-HCM. Moreover, different scenarios were also analysed to study the influence of speed breaker, temporary median, widening, and change in vehicle proportions on capacity. The study also proposes skew-angle-based volume warrants for the capacity of approach roads, which will be helpful for design engineers. © 2023, The Author(s), under exclusive licence to Intelligent Transportation Systems Japan.