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Item Modeling acceleration and deceleration rates for two-lane rural highways using global positioning system data(Wiley, 2021-04) Malaghan, Vinayak DevendraSeveral past studies developed acceleration/deceleration rate models as a function of a single explanatory variable. Most of them were spot speed studies with speeds measured at specific locations on curves (usually midpoint of the curve) and tangents to determine acceleration and deceleration rates. Fewer studies adopted an estimated value of 0.85 m/s2 for both deceleration and acceleration rates while approaching and departing curves, respectively. In this study, instrumented vehicles with a high-end GPS (global positioning system) device were used to collect the continuous speed profile data for two-lane rural highways. The speed profiles were used to locate the speeds at the beginning and end of deceleration/acceleration on the successive road geometric elements to calculate the deceleration/acceleration rate. The influence of different geometric design variables on the acceleration/deceleration rate was analysed to develop regression models. This study also inspeced the assumption of constant operating speed on the horizontal curve. The study results indicated that mean operating speeds measured at the point of curvature (PC) or point of tangency (PT), the midpoint of curve (MC), and the end of deceleration in curve were statistically different. Acceleration/deceleration rates as a function of different geometric variables improved the accuracy of models. This was evident from model validation and comparison with existing models in the literature. The results of this study highlight the significance of using continuous speed profile data to locate the beginning and end of deceleration/acceleration and considering different geometric variables to calibrate acceleration/deceleration rate models.Item Exploring maximum and minimum operating speed positions on road geometric elements using continuous speed data(ASCE, 2021-05) Malaghan, Vinayak DevendraSpeed prediction models are commonly developed using maximum and minimum operating speeds measured at or within specific locations on the tangents and horizontal curves, respectively. However, the actual distribution of the maximum and minimum operating speed positions on the entire lengths of the geometric elements (tangents and curves) have not been rigorously studied and, therefore, present opportunities to refine further and develop robust speed prediction models. This paper presents the probability distributions (normal, lognormal, gamma, and Weibull) for speed positions on the entire tangent and curve lengths using continuous speed data recorded on two-lane rural highways in India. The findings showed that the data could be best approximated for a large number of horizontal curves and tangents using the normal and Weibull distributions. Also, the results showed that the operating speeds measured at the midpoint of the horizontal curves overestimate the actual minimum operating speeds measured over the entire curve length of horizontal curves between 1.2 and 1.9 km/h. Similarly, maximum operating speeds measured at or within a 200-m length from the point of curvature into the approach tangents underestimate the maximum operating speeds measured over the entire length of tangents between 1.33 and 1.77 km/h. The results of this study highlight the importance of considering the entire length of geometric elements in developing accurate speed prediction models for use in evaluating highway design consistency.Item Speed prediction models for heavy passenger vehicles on rural highways based on an instrumented vehicle study(Taylor & Francis, 2020-08) Malaghan, Vinayak DevendraThis study developed operating speed prediction models on tangents, curves, and tangent-to-curve transitions for heavy passenger vehicles (HPVs), using continuous speed profiles. Continuous speed profile data for HPVs were collected on two-lane rural highway sections, spanning a total length of 77 km. The curve radius, degree of curve, and preceding tangent length were found to be the influencing variables in predicting both the operating speed on the horizontal curves and the operating speed differential from tangent-to-curve transitions. The study also modeled the relationship between the differential of the 85th percentile operating speed (Δ𝑉85) and 85th percentile operating speed differential (Δ85𝑉). The analysis results from empirical data revealed that Δ𝑉85 underestimates Δ85𝑉 by 5.01 km/h. The reliability of the developed models was validated and compared with existing models from literature. The study highlights the significance of using continuous speed profile data to calibrate the operating speed models.Item A short-term naturalistic driving study on predicting comfort thresholds for horizontal curves on two-lane rural highways(ASCE, 2022-05) Malaghan, Vinayak DevendraThe comfort threshold is one of the basic controls in designing horizontal alignment. Some studies have implied that the recommended comfort thresholds are conservative because they were determined using the traditional approach for vehicle design and corresponding driver behavior during the 1930s and 1940s. Drivers exceed the recommended comfort thresholds (because they are conservative) when traversing horizontal curves, which results in overturning and lateral skidding of the vehicle, increasing the chances of crashes on the horizontal curves. Therefore, the design guidelines need to consider comfort thresholds determined using recent approaches in data collection for modern vehicle design and corresponding driver behavior. Limited studies have determined the comfort thresholds for horizontal curves. Specifically, no studies determined the comfort thresholds for the horizontal alignment design in India. Thus, this study attempted to estimate the comfort thresholds for horizontal curves using an advanced Global Positioning System (GPS) device instrumented in passenger cars for data collection on two-lane rural highways in India. An all-subset regression approach was used to model comfort thresholds, and statistics such as Akaike information criterion (AIC), coefficient of determination, and cross-validation were used in the model selection. The study results showed no significant difference in the comfort thresholds on the right-turn and left-turn curves. Among the various geometric design features of curves, the curve radius significantly influenced the variation in the comfort threshold. The estimated comfort thresholds were higher than the side-friction demand recommended in an Indian design guideline. This study highlights the importance of determining the comfort thresholds using an advanced data collection tool for modern vehicle design and corresponding driver behavior to avoid crashes on the horizontal curves.Item Micro-simulation insights into the safety and operational benefits of autonomous vehicles(IEEE, 2023-12) Malaghan, Vinayak DevendraSeveral past studies showed that Autonomous Vehicles (AVs) can reduce crash risk, stop-and-go traffic, and travel time. To analyze the safety benefits of AVs, most of the researchers proposed algorithms and simulation-based techniques. However, these studies have not assessed the safety benefits of AVs for different vehicle types under heterogeneous conditions. With this opportunity, this study focuses on the benefits of AVs in terms of safety for different penetration rates under heterogeneous conditions. This study considered three driving logics during peak hour conditions to assess the performance of AVs in terms of safety. In VISSIM, default driving behavior models for AVs were adopted to consider cautious and all-knowing driving logic and the third driving logic (Atkins) was modeled in VISSIM using parameters adopted from the previous studies. To this end, using VISSIM, the travel time output results were obtained. Also, using Surrogate Safety Assessment Model (SSAM), conflicts were extracted from output trajectory files (VISSIM). The results suggest that “cautious driving logic” reduced travel time and crash risk significantly when compared to the other two driving logics during peak hour conditions. Furthermore, the statistical analysis clearly demonstrated that “cautious driving logic” differs significantly from the other two driving logics. When Market Penetration Rates (MPR) were 50% or greater, the “cautious driving logic” significantly outperforms the other two driving logics.Item Understanding the operating speed profile patterns using unsupervised machine learning approach: short-term naturalistic driving study(ASCE, 2022-12) Malaghan, Vinayak DevendraSeveral studies have measured the minimum operating speed on horizontal curves to model the operating speed to assess the geometric design consistency. Most of these studies approximated equal lengths of deceleration and acceleration in the operating speed profiles for the curves and assumed the minimum operating speed position at the midpoint of the curve. In contrast, a few recent studies showed different percentages of deceleration lengths on the curve and measured the minimum operating speed at the deceleration end on the curve to model the operating speed. A defined pattern of the operating speed profile on the horizontal curve was not reported in the previous studies and therefore presents opportunities to determine the patterns of the operating speed profiles on curves. In this study, the operating speed profiles of different drivers for the given features of the horizontal curve were studied, and the clustering technique was used to categorize the different patterns in the operating speed profiles on horizontal curves. The optimal number of clusters was determined using four methods: silhouette, elbow, gap statistic, and NbClust function. The different patterns observed from the clustering results are as follows: (1) complete deceleration on the curve, (2) complete acceleration on the curve, (3) deceleration length slightly greater or lower than acceleration length, and (4) longer deceleration/acceleration lengths followed by shorter acceleration/deceleration lengths, respectively. The study results imply that all operating speed profiles are not symmetric around the midpoint of the curve (MC), and the group of drivers exhibited defined patterns of the operating speed profiles on the curves. This study helps in understanding the different patterns of operating speed profiles exhibited by the drivers and the measurement of the minimum operating speed at the deceleration end to model the operating speed to assess the geometric design consistency.Item Evaluating effectiveness and acceptance of advanced driving assistance systems using field operational test(IEEE, 2023-05) Malaghan, Vinayak DevendraA large number of reported road collisions are caused by driver inattention, and inappropriate driving behaviour. This study investigated the effectiveness and acceptance of Advanced Driving Assistance Systems (ADAS) for driver age groups, gender, occupation (professional/non-professional), and road type (expressway, urban roads, and semiurban road) based on the Field Operational Test (FOT). The ADAS is provided with assistance features, such as Lane Departure Warning (LDW), Forward Collision Warning (FCW), and Traffic Speed Recognition Warning (TSRW). In total, the FOT involved 30 participants who drove the test vehicle twice (once in the stealth phase and once in the active phase). The FOT included three sections: expressway (20.60 km), urban road (7.2 km), and semi-urban road (13.35 km). A questionnaire was used to determine user acceptance of the ADAS technology. In addition, parametric and non-parametric statistical tests were carried out to determine ADAS's significant effects. The FOT results showed statistically significant differences in the LDW's acceptance and effectiveness for gender, age group, occupation, and road type before and after exposure to ADAS. Male participants showed significant lateral behavior improvement compared to female participants. Old-aged drivers scored the highest acceptance score for the technology compared to middle and young-aged drivers. The subjective ratings ranked the assistance features in descending order as TSRW, LDW, and FCW. This study's findings can support policy development and induce trust in the public for the technology adoption to improve road traffic safety.Item Behavioral insights and hotspot identification: Integrating natural language processing, machine learning and geospatial analyses of cyclist crashes(Elsevier, 2025-08) Malaghan, Vinayak DevendraIn response to the rising trend in the promotion and adoption of cycling, ensuring cyclist safety is paramount. Understanding behavioural causes of crashes and identifying collision hotspots is important; however, the efforts are hindered by underreporting and limited data on all types of incidents, including near misses. Addressing these challenges, this study analyses text data reported on dedicated active travel collision platforms to categorize incidents and uncover behavioural patterns contributing to collisions. The reported text data is grouped into distinct themes applying Term Frequency-Inverse Document Frequency (TF-IDF) vectorization, and clustering. Additionally, the advanced geospatial technique Getis-Ord Gi* statistic is computed to identify spatial clustering of collisions and categorize geographical regions as hotspots and cold spots. Key themes contributing to collisions are grouped as follows: ‘close pass incidents,’ ‘blocked bicycle lanes,’ ‘cyclist incidents on tram tracks,’ ‘roundabout incidents,’ ‘left turn incidents,’ ‘incidents between buses and cyclists,’ ‘incidents involving cyclists and trucks,’ ‘incidents related to traffic lights and pedestrian crossings,’ and ‘turning incidents at intersections.’ Moreover, the hotspots from these incidents are located at or near the intersections of regional roads in the Central Business District (CBD) and on the peripheral regional roads encapsulating the CBD in Dublin, Ireland. This study advances the state of the art by utilizing an alternative data source, ‘crash descriptions’ from cyclist crashes, through the application of innovative machine learning techniques and advanced geospatial analyses. The insights from the unique themes and identified hotspots enhance understanding of risky behaviours and their spatial distribution, contributing to ongoing efforts to foster a safer cycling environment.Item Axial load prediction of circular hybrid double-skin tubular columns using interpretable gradient boosting machine learning models(Springer, 2026-01) Singh, Shamsher Bahadur; Barai, Sudhir KumarThis study assesses the predictive performance of three gradient-boosting Machine Learning (ML) models, Gradient Boosting Machine (GBM), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), in axial load prediction of circular FRP-concrete-steel double-skin tubular columns (hybrid DSTCs). Data from 275 specimens were compiled from 22 publications in the literature to train and test ML models. Input variables consist of the height of column (), outer diameter of the FRP tube (), outer thickness of the FRP tube (), diameter of the inner steel tube (), thickness of the inner steel tube (), tensile strength of the outer FRP tube (), yield strength of the inner steel tube (), and compressive strength of concrete (), with the ultimate axial load () serving as the output variable. Performance of all three gradient ML models was evaluated using statistical measures including coefficient of determination (R2), root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) for training and testing datasets. Results indicate that the XGBoost model performed better than the other two gradient Models (GBM and LightGBM) with R2 values of 0.97 on the training data and 0.95 on the testing data. Further analysis of the XGBoost model assessed the relative importance of input features on the output feature.Item A simplified mix proportioning method for structural grade lightweight concrete using sintered flyash lightweight aggregate(Springer, 2026-01) Singh, Shamsher Bahadur; Barai, Sudhir KumarStructural concrete of a designed strength can be produced for specific requirements by using the accepted methods of mix design but the present Indian standards on mix design does not takes into account the procedure for mix design of sintered flyash lightweight coarse aggregate based concrete. The sintered flyash lightweight aggregate mainly produced from ash generated from coal based thermal power plants has higher water absorption and lower specific gravity. The mix proportioning of sintered flyash lightweight coarse aggregate based concrete is reported to be cumbersome and less accurate than normal weight concrete as the water absorption characteristics of these aggregates is a main concern. The current study has been done to develop a simplified mix design procedure for production of structural grade lightweight concrete using commercially available sintered flyash lightweight aggregate. The study also highlights the procedure for corrections need to be done with respect to higher water absorption characteristics of sintered flyash lightweight aggregate taking into account effect of cement paste during concrete mix proportioning. Based on the developed mix design method, a trend of 28-day cube compressive strength versus w/c for sintered flyash lightweight aggregate based concrete has been plotted for wide range of w/c from 0.3 to 0.7 and compared with curve given in IS: 10262-2019 for normal weight concrete.