S. Vasantha Kumar

Bio: S. Vasantha Kumar is an academic researcher from VIT University. The author has contributed to research in topics: Variance inflation factor & Regression analysis. The author has an hindex of 1, co-authored 4 publications receiving 7 citations.

Comparison of classification methods for urban green space extraction using very high resolution worldview-3 imagery

Abstract: Urban green space (UGS) plays a vital role in maintaining the ecological balance of a city and in ensuring healthy living of the city inhabitants. It is generally suggested that one-third of the ci...

Dealing with Multicollinearity Problem in Analysis of Side Friction Characteristics Under Urban Heterogeneous Traffic Conditions

Abstract: Disturbance to the moving traffic, such as on-street parking and bus stop on carriageway normally called as side friction in the literature, is one of the major problems that impact the vehicular speed on urban arterials in developing countries like India Regression model to study the mode-wise vehicular speeds using side friction and other influencing variables faces the problem of multicollinearity as there is a strong correlation between the independent variables which results in high R2 and high p values Instead of dropping the highly correlated variables which is the conventional practice to deal with multicollinearity, we proposed an approach of taking a linear combination and ratio of independent variables To test the applicability of the proposed approach, traffic data from five locations in Hyderabad and Vellore in India having a wide variety of geometric and traffic characteristics with one or more side frictions were collected and regression models were built It was found that the proposed approach of taking linear combination and ratio worked very well as the models exhibited a high R2, low p values and low variance inflation factor The regression models also forecast speeds with good and reasonable accuracy For better understanding of the impact of side friction, the present study proposed an index called side friction index and it was found that the number of side friction elements and their impact are directly proportional Also, the impact is not uniform across the different vehicle types Though the present study focussed on side friction problem, the approach that we proposed in this paper to deal with multicollinearity can be applied in other fields as well

Urban sprawl modelling and prediction using regression and Seasonal ARIMA: a case study for Vellore, India

Abstract: Analysing the changes in various landuses during the past, identifying the direction and type of urban sprawl, modelling its causative factors and predicting the future sprawl are some of the key areas which urban planners are generally interested in as they help them to properly plan cities in a more efficient way. In the present study, an attempt has been made to address these key areas using open-source geospatial data for the city of Vellore in India which has experienced significant urban growth in recent decades. Landuse maps were prepared using aerial photos, topographic maps and satellite images for the period 1967–2019 and using that a comprehensive urban sprawl analysis was carried out. The results indicated that the major highways that pass-through Vellore have strong influence on urban development as ribbon type of sprawl was witnessed along the principal transportation corridors. This has been confirmed while modelling of causative factors that when distance from highway increases, built-up density decreases. Similarly, the location of central business district (CBD) found to play a crucial role in urban growth as built-up density decreases when distance from CBD increases. Regression models were built to calculate the built-up density at any distance from city centre or major roads in space–time domain. The present study also developed models based on Seasonal ARIMA to forecast the future built-up density for the year 2045 using the built-up densities of 1967, 1991 and 2019. The results showed that more development is expected on suburban areas as those are the places where one can expect development to take place when compared to the interior zones which were already developed much during the past years. The methodology reported in this paper is well applicable for any city or town to analyse the landuse changes, model the causative factors and predict its future sprawl using completely the open-source data.

Measurement of Mode-Wise Individual Delay Components and Prediction of Queue Length at a Signalized Intersection Under Heterogeneous Traffic Conditions

Abstract: Accurate measurement of control delay and queue length is important for effective traffic management at signalized intersections and to assess its performance. The analytical methods which are commonly used to estimate delays are primarily developed for homogeneous lane-based traffic conditions and require lot of inputs for delay estimation. If cycle lengths are not the same for a signalized intersection operated by traffic police, then use of analytical methods is restricted. The present study tries to overcome these limitations by proposing an approach in which the individual delay components were directly measured using travel times from video as input and electric poles on median as reference points. Traffic captured through videographic survey at a busy intersection in Vellore, India, was used to illustrate the proposed approach. Instead of conventional stopped delay concept, a new delay called ‘Stop & Go’ delay which is more appropriate for heavily congested intersections has been proposed and used. This ‘Stop & Go’ delay concept captures the time lost due to movement of queue in addition to frequent stoppings which is rather not possible with stopped delay definition. It was found that the control delays experienced by different vehicle types are not the same and four-wheeler exhibits longer delays than two- and three-wheelers. One of the main advantages of the proposed approach is it does not require many parameters to calculate control delay and only travel times of sample vehicles are sufficient. Another advantage with the proposed approach is it can be applied to all types of signalized intersections irrespective of whether it has a fixed time signal or manually operated one. With the proposed approach, one can get vehicle class-wise individual delay components which may not be possible with the existing methods available in the literature. In addition to delay measurement, the present study also developed polynomial regression models to predict queue length at intersections using control delay as independent variable. The results are promising and can be used to predict the queue length at signalized intersections in India.

Remote Sensing And Image Interpretation

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Mitigating the Multicollinearity Problem and Its Machine Learning Approach: A Review

Abstract: Technologies have driven big data collection across many fields, such as genomics and business intelligence. This results in a significant increase in variables and data points (observations) collected and stored. Although this presents opportunities to better model the relationship between predictors and the response variables, this also causes serious problems during data analysis, one of which is the multicollinearity problem. The two main approaches used to mitigate multicollinearity are variable selection methods and modified estimator methods. However, variable selection methods may negate efforts to collect more data as new data may eventually be dropped from modeling, while recent studies suggest that optimization approaches via machine learning handle data with multicollinearity better than statistical estimators. Therefore, this study details the chronological developments to mitigate the effects of multicollinearity and up-to-date recommendations to better mitigate multicollinearity.

An Analysis of Factors Affecting the Severity of Cycling Crashes Using Binary Regression Model

Abstract: The increasing use of bicycles rises the interest in investigating the safety aspects of daily commuting. In this investigation, more than 14,000 cyclists’ injuries were analyzed to determine the relationship between severity, road infrastructure characteristics, and surface conditions using binary regression. Minor and major severity categories were distinguished. A binary equation consists of 28 factors is extracted. It has been found that each factor related to roadway characteristics has its negative and positive impacts on cyclist severity such as traffic control, location type, topography, and roadway divisions. Regarding the road surface components, good, paved, and marked roads are associated with a higher probability of major injuries due to the expected greater frequencies of cyclists on roads with good conditions. In conclusion, probabilities of major injuries are higher in urban areas, higher speed limits, signalized intersections, inclined topographies, one-way roads, and during the daytime which require more attention and better considerations.