Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item Modeling free-flow speeds on undivided roads in mixed traffic withweak lane discipline(SAGE Publications Ltd, 2018) Chathoth, V.; Asaithambi, G.In developing countries like India, transportation systems are characterized by limited roadway infrastructure and lack of operation and management experience. Hence, there exists a need to evaluate a performance indicator that reflects the current level of service (LOS) of a road facility. Free-flow speed (FFS) is a key parameter used to express LOS assessment. The objective of this study is to develop FFS prediction models for undivided roads with mixed traffic conditions in both urban and rural settings in India. Traffic data were collected from two-way two-lane undivided roads in southern India during freeflow traffic conditions using videographic method. Various class-specific and site-specific characteristics, such as vehicle class, subclass, carriageway width, link length, number of side roads, lateral clearance, land use type, and area type, were investigated and their influence on FFS evaluated. Statistical tests assessed the variations of obtained FFS with different vehiclespecific and site-specific factors. Free-flow prediction models were developed using linear regression method. The developed models show that FFS increases with greater carriageway width, lateral clearance, and link length, and decreases with increase in number of side roads. In general, FFS is higher in rural areas than urban areas. Similarly, open areas have higher FFS than residential, institutional, and commercial areas. The model can be used to predict FFS of undivided roads if site-specific and vehicle-specific data are known. This study finds interesting applications in capacity and LOS analysis, accident analysis, and before-and-after studies of road improvement schemes. © National Academy of Sciences: Transportation Research Board 2018.Item Design of Collision Detection System for Smart Car Using Li-Fi and Ultrasonic Sensor(Institute of Electrical and Electronics Engineers Inc., 2018) Krishnan, K.The 21st century is defined as the era of technological development. With drastic increase in population, automation is becoming the need of the hour in order to make life more comfortable and easy. Due to the advancement and development in the field of automation and embedded system, the notion of smart car has become very popular. Smart cars are modernizing trends in the traditional automobile industry. Companies across the globe have been investing a huge amount of resources on the production and design of smart cars. Every technological development needs to overcome certain obstacles, and hence, in this paper, a design of a collision detection system for smart car using light fidelity (Li-Fi) and ultrasonic sensor on the Arduino platform is proposed. This design consists of an ultrasonic sensor, an Arduino processor, and a Li-Fi circuit. The ultrasonic is used for measurement of distance between vehicles, and Arduino processes the data and makes decisions accordingly. Data transmission between vehicles is ensued using a Li-Fi transmitter circuit and a Li-Fi receiver circuit. The transmitter circuit is mounted on the tail lights of the leading car and the receiver circuit is mounted on the front side of the car that follows. Using visible light communication, the transmitter circuit transmits the calculated speed and the information is received by the receiver circuit of the second car. On the basis of the information received, the speed of the second car is changed in order to avoid collision. In this paper, a system that can detect and thus avoid collision between vehicles and prevent accidents is proposed and studied. © 1967-2012 IEEE.Item Context Aware Trust Management Scheme for Pervasive Healthcare(Springer New York LLC barbara.b.bertram@gsk.com, 2019) Karthik, N.; Ananthanarayana, V.S.Medical sensor nodes are used in pervasive healthcare applications like remote patient monitoring, elderly care to collect patients vital signs for identifying medical emergency. These resource restricted sensor nodes are prone to various malicious attacks, data faults and data losses. Presence of faulty data, data loss in collected patient data may lead to incorrect analysis of patient condition, which decreases the reliability of pervasive healthcare system. The aim of this work is to alert the caregiver and raise the alarm only when the patient enters into medical emergency situation. The proposed scheme also reduces the false alarms and alerts caused by data fault and misbehaving sensor nodes. To achieve this, we introduce a context aware trust management scheme for data fault detection, data reconstruction and event detection in pervasive healthcare systems. It employs heuristic functions, data correlation and contextual information based algorithms to identify the data faults and events. It also reconstructs the data faults and data loss for identifying patient condition. Performance of this approach is evaluated with the help of real data samples collected by medical sensor network prototype of remote patient monitoring application. The experimental results show that the proposed trust scheme outperforms state-of-the-art techniques and achieves good detection accuracy in data fault detection and event detection. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.Item Performance evaluation of cement mortar compositions at elevated temperatures(Associated Cement Companies Ltd., 2019) Yaragal, S.C.; Vivek, S.; Kumar, B.Natural river sand is becoming scarce day by day due to rapid growth in construction sector. There is need for alternatives to be used in place of river sand. The performance of alternatives to river sand at elevated temperatures is also important in the likely event of fire accidents. In this study, the effect of elevated temperatures on the compressive strength of mortars containing Crushed Rock Fines (CRF) and Lateritic Sand (LS) is investigated. Cement mortar cubes were cast for varied proportion of lateritic soil and quarry dust as fine aggregate. Lateritic content was varied from 25%-100%, and 50% quarry dust was adopted. After 28 days of water curing, specimens were exposed to temperatures of 200, 400, 600, and 800°C. At room temperature, the compressive strength decreases with increase in level of lateritic fine aggregate. The lateritic mortar mixes (50, 75, and 100%) have exhibited superior elevated temperature endurance characteristics at 400, 600, and 800°C when compared to control mix. Even the 25% laterized mortar has performed equally well as that of control mix. At elevated temperatures, CRF blended mix has performed very poorly. Mortar containing lateritic sand has potential for utilization in buildings and other structures, for better fire endurance in the likely event of fire accidents. © 2019 Associated Cement Companies Ltd.. All rights reserved.Item Assessment of Efficiency and Effectiveness of Bus Transport Organizations Using DEA Incorporating Emissions and Accidents(Institute for Transport Studies in the European Economic Integration, 2023) Praveen Kumar, P.; George, V.; Mulangi, R.H.Environmental pollution due to vehicular emissions and accidents reflect upon the sustainability, and social responsibility of transport organizations. However, it is also necessary to attain higher levels of performance by ensuring higher transit ridership measured in terms of passengers carried per day. The present work is focused on the analysis of performance efficiency and service effectiveness of 25 selected State Road Transport Undertakings (SRTUs) in India for the year 2004-05, 2009-10 and 2014-15. Here, it was proposed to use a hybrid output-oriented Data Envelopment Analysis (DEA) approach developed by Seiford and Zhu in 2002 to handle undesirable outputs such as annual Carbon-di-oxide (CO2) emitted per passenger-km, and total accidents per year in addition to overall productivity. The results of the analysis provided details on targets that could be achieved for the available input resources allocated. Transport organizations can adopt similar approaches in performance evaluation and benchmarking considering sustainability, and social responsibility along with efficiency. © 2023 Institute for Transport Studies in the European Economic Integration. All rights reserved.Item Modelling personal safety perceptions at bus stop: employing hierarchical confirmatory factor analysis and structural equation approach(Aracne Editrice, 2023) Sethulakshm, G.; Mohan, M.Vehicle-pedestrian interactions occurring within a limited space are quite common at bus stops, making it essential to comprehend passengers' perception of safety near bus stops. Since the sense of perceived safety is subjective, developing a standardized tool to measure travellers' perception of safety, especially of bus users, is complex. The first part of this study aims to identify the indicators for measuring the perceived safety at bus stops, and the second part focuses on modelling the overall perceived safety of users’ at bus stops using structural equation modelling. The research examined the safety factors according to 14 indicators which were further reduced to five latent constructs using exploratory factor analysis. Perceived safety is taken as a second-order latent construct, and the second-order confirmatory factor analysis found that safety derived from five latent variables, namely bus stop facility, bus stop location, bus operator behaviour, other users' behaviour, and pedestrian facility, are potential indicators of overall perceived safety at the bus stop. The results recommend that providing night light facilities, adopting measures to avoid improper stopping of buses and left-side overtaking, avoiding bus stops on curves and junctions, and ensuring better sidewalk facilities could improve perceived safety. Structural equation modelling revealed that safety perceptions are negatively influenced by previous accident victimization and witnessing, age, educational qualification and total household vehicles. The results conclude that female respondents perceive less safety than males, and no effects can be attributed to the frequency of travel and trip length. The research findings will be helpful for the planning agencies to prioritize measures to improve travellers' feeling of safety. © 2023, Aracne Editrice. All rights reserved.Item An analytical hierarchy approach for studying the impact of human error, environmental factors, and equipment failure on mine accidents: a case study in India(Springer, 2024) Kar, M.B.; Mangalpady, M.; Kunar, B.M.This paper presents a study using the Analytical Hierarchy Process (AHP) to understand and prioritize the accidents that have occurred in the Indian mining industry. The data for the study was collected from accident reports submitted to the Directorate General of Mines Safety from 2011 to 2020. The accident information was divided into six categories (i.e., accidents due to ground movement, transport machinery, machinery other than transport, explosives, electricity shock, and fall-of-person). These accidents were considered alternatives in the AHP analysis. Three risk factors (i.e., environment, equipment fault, and human error) that caused the accident were considered as criteria in the AHP analysis. The safety expert carefully examined the pattern of accidents and ranked the relative importance of the alternatives with respect to each criterion. This rank was used to build the AHP model using the R programming language and the AHP library (version 0.2.8). The results revealed that the highest number of accidents occurred due to the transport machinery (0.306), followed by accidents due to ground movement (0.232), falls of individuals (0.206), machinery other than transportation (0.122), electricity (0.082), and explosives (0.048). In order to identify the contributing risk factors for each type of mining accident, the weight and the rank of the criteria were determined. The result showed that the most accidents in the six accident categories are due to human error (0.26), followed by environmental (0.25) and equipment faults. The finding of the study provides valuable insights for the mining industry to develop effective strategies to mitigate mine accidents. © The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2024.Item Designing safe and accessible bus stops: an exploration of the interplay between perceived safety at crosswalk and transit ridership(Routledge, 2025) Sethulakshmi, G.; Mohan, M.Measuring personal safety perception is inherently complex, involving a multifaceted array of factors. This research advances the field of knowledge by developing a novel factor structure to assess pedestrian safety perceptions and modelling overall safety as a latent construct through a second-order Confirmatory Factor Analysis. Data were collected from 568 pedestrian interviews on safety perceptions near bus stops. The study concluded that perceived safety can be measured using four latent constructs: crosswalk infrastructure, crossing environment, management measures, and driver behavior, which collectively contribute to overall crosswalk safety. Using Structural Equation Modelling, the study confirms that as perceived safety while accessing bus stops via crosswalks decreases, bus ridership also declines. Findings also reveal demographic differences, with women, older individuals, and prior accident victims perceiving bus stop environments as less safe. Results suggest that policymakers should prioritize dedicated crosswalks and control speed and aggressive driving to maximize perceived safety at bus stops. © 2025 Informa UK Limited, trading as Taylor & Francis Group.Item Human-in-the-Loop Data Analytics for Classifying Fatal Mining Accident Causes Using Natural Language Processing and Machine Learning Techniques(Springer Science and Business Media Deutschland GmbH, 2025) Sharma, A.; Kumar, A.; Vardhan, H.; Mangalpady, A.; Mandal, B.B.; Senapati, A.; Akhil, A.; Saini, S.Mining remains one of the most hazardous industries globally, marked by frequent fatalities resulting from complex operational risks. While accident investigation reports hold valuable insights for improving safety practices, the manual coding of fatality narratives remains labor-intensive, inconsistent, and impractical for large datasets. Although natural language processing (NLP) and machine learning (ML) techniques have gained traction for automating the analysis of safety narratives in other high-risk industries, their application to mining accident data, particularly within the Indian context, remains limited. Addressing this gap, the present study proposes a ML framework for the semi-automated classification of fatal accident causes from unstructured text narratives reported by the Directorate General of Mines Safety (DGMS) between 2016 and 2022. A total of 401 fatal accident descriptions were pre-processed and vectorized using Bag-of-Words, TF-IDF, and Word2Vec techniques, followed by model evaluation across multiple algorithms. A semi-automated classification scheme was developed to balance efficiency with expert oversight, where high-confidence predictions were assigned automatically and uncertain cases were flagged for manual review. Logistic regression combined with TF-IDF unigram features achieved the highest performance, with an F1 score of 0.78 and an accuracy of 0.81. Overall, the developed framework successfully auto-coded 68.75% of cases with 94% accuracy, 0.93 recall, and 0.91 precision. Word cloud visualizations were also employed to capture dominant words associated with different cause categories. The proposed framework offers a practical and operationally feasible solution for assigning fatality causes in the mining sector, contributing to active safety management, surveillance, and policy formulation. © Society for Mining, Metallurgy & Exploration Inc. 2025.Item An uncertainty-aware decision support system: Integrating text narratives and conformal prediction for trustworthy accident code classification(Institution of Chemical Engineers, 2025) Kumar, A.; Senapati, A.; Upadhyay, R.; Chatterjee, S.; Bhattacherjee, A.; Samanta, B.It is imperative to assign accident classification codes to the Mine Safety and Health Administration (MSHA) accident data for effective data analysis and risk assessment. Although trained personnel are capable of performing this task, the manual process is both time-consuming and resource-intensive. Automating the classification process with machine learning (ML) algorithms promises to expedite code assignment. However, ML predictions typically lack uncertainty metrics. This study proposes an uncertainty-aware hierarchical classification framework that assists human experts in efficiently and accurately assigning accident codes. Several text representation techniques combined with different ML algorithms were employed within a hierarchical architecture to assign classification codes. Low-frequency codes were consolidated into a single category, with a primary classifier distinguishing between these and a secondary classifier further classifying the grouped categories. Regularized Adaptive Prediction Sets (RAPS) was integrated to quantify uncertainty. Highly confident predictions yielding single-class sets were automatically classified, whereas multi-class sets were flagged for manual review. Primary Classifier with XGBoost with word2vec text representation achieved the best performance, with 95.12 % coverage, 37.02 % single-class prediction sets at 96.11 % accuracy, and an average prediction set size of 2.39. Whereas the secondary classifier, a logistic regression model with TF-IDF representation, yielded 96.19 % coverage, an average set size of 1.80, and 53.66 % single-class prediction sets with 98.90 % accuracy. Additionally, sensitivity analysis determined that a 95 % coverage guarantee offers the best trade-off between prediction set size and coverage. The framework effectively integrates conformal prediction to quantify uncertainty and aid human experts in improving the decision-making process in safety management. Although the framework is broadly applicable across different sectors, it needs to be retrained on domain-specific data for effective use. © 2025 The Institution of Chemical Engineers