Shankar C. Subramanian
Bio: Shankar C. Subramanian is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Brake & Air brake. The author has an hindex of 17, co-authored 137 publications receiving 1243 citations. Previous affiliations of Shankar C. Subramanian include Indian Institutes of Technology & Texas A&M University.
Papers published on a yearly basis
TL;DR: One of the first attempts at real-time short-term prediction of travel time for ITS applications in Indian traffic conditions is presented, using global positioning system data collected from public transportation buses plying on urban roadways in the city of Chennai, India.
Abstract: Travel time information is a vital component of many intelligent transportation systems (ITS) applications. In recent years, the number of vehicles in India has increased tremendously, leading to severe traffic congestion and pollution in urban areas, particularly during peak periods. A desirable strategy to deal with such issues is to shift more people from personal vehicles to public transport by providing better service (comfort, convenience and so on). In this context, advanced public transportation systems (APTS) are one of the most important ITS applications, which can significantly improve the traffic situation in India. One such application will be to provide accurate information about bus arrivals to passengers, leading to reduced waiting times at bus stops. This needs a real-time data collection technique, a quick and reliable prediction technique to calculate the expected travel time based on real-time data and informing the passengers regarding the same. The scope of this study is to use global positioning system data collected from public transportation buses plying on urban roadways in the city of Chennai, India, to predict travel times under heterogeneous traffic conditions using an algorithm based on the Kalman filtering technique. The performance of the proposed algorithm is found to be promising and expected to be valuable in the development of APTS in India. The work presented here is one of the first attempts at real-time short-term prediction of travel time for ITS applications in Indian traffic conditions.
TL;DR: A detailed description of the development of the pneumatic subsystem of an air brake system that is used in commercial vehicles and of the experimental setup used to corroborate this model for various realistic test runs is presented.
Abstract: This paper deals with the development of a fault-free model of the pneumatic subsystem of an air brake system that is used in commercial vehicles. Our objective is to use this model in brake control and diagnostic applications. The development of a diagnostic system would be useful in automating enforcement inspections and also in monitoring the condition of the brake system in real-time. This paper presents a detailed description of the development of this model and of the experimental setup used to corroborate this model for various realistic test runs.
TL;DR: A model-based diagnostic system based on a nonlinear model for predicting the pressure transients in the brake chamber that correlates the brake Chamber pressure to the treadle valve (brake application valve) plunger displacement and the pressure of the air supplied to the brake system is presented.
Abstract: The safe operation of vehicles on roads depends, among other things, on a properly functioning brake system. Air brake systems are widely used in commercial vehicles such as trucks, tractor-trailers, and buses. In these brake systems, compressed air is used as the energy transmitting medium to actuate the foundation brakes mounted on the axles. In this paper, a model-based diagnostic system for air brakes is presented. This diagnostic system is based on a nonlinear model for predicting the pressure transients in the brake chamber that correlates the brake chamber pressure to the treadle valve (brake application valve) plunger displacement and the pressure of the air supplied to the brake system. Leaks and "out-of-adjustment" of push rods are two prominent defects that affect the performance of the air brake system. Diagnostic schemes that will monitor the brake system for these defects will be presented and corroborated with experimental data obtained from the brake testing facility
TL;DR: This study presents a model-based algorithm that uses real-time data from field and takes delays automatically into account for an accurate prediction of bus arrival time and shows a clear improvement in the prediction accuracy.
Abstract: The accuracy of Bus Traveler Information Systems (BTIS) depends on several factors such as accuracy of the input data, speed of data transfer, data quality control and performance of the prediction scheme. A majority of the existing BTIS in India does not take into account the real-time data and the quality control of data. Also, there is a scope for improving the performance of the underlying prediction schemes. There are several studies on real-time bus arrival time prediction under homogeneous traffic conditions. However, the traffic condition in India is different and direct implementation of those studies may not yield the best results. One of the main components of bus travel time is the delay time at bus stops, in addition to the other common delays. These delays need to be incorporated in the prediction scheme for better accuracy, which is not the case currently in most studies. Also, there is a need to develop an accurate automated bus arrival time prediction system using real-time data under heterogeneous traffic conditions. This study presents a model-based algorithm that uses real-time data from field and takes delays automatically into account for an accurate prediction of bus arrival time. The results obtained are compared with the currently adopted field method and show a clear improvement in the prediction accuracy.
TL;DR: The proposed approach based on using vehicle tracking data is good enough for the considered application of bus travel time prediction and was able to perform better than historical average, regression, and ANN methods and the methods that considered either temporal or spatial variations alone.
Abstract: The accuracy of travel time information given to passengers plays a key role in the success of any Advanced Public Transportation Systems (APTS) application. In order to improve the accuracy of such applications, one should carefully develop a prediction method. A majority of the available prediction methods considered the variation in travel time either spatially or temporally. The present study developed a prediction method that considers both temporal and spatial variations in travel time. The conservation of vehicles equation in terms of flow and density was first re-written in terms of speed in the form of a partial differential equation using traffic stream models. Then, the developed speed based equation was discretized using the Godunov scheme and used in the prediction scheme that was based on the Kalman filter. From the results, it was found that the proposed method was able to perform better than historical average, regression, and ANN methods and the methods that considered either temporal or spatial variations alone. Finally, a formulation was developed to check the effect of side roads on prediction accuracy and it was found that the additional requirement in terms of location based data did not result in an appreciable change in the prediction accuracy. This clearly demonstrated that the proposed approach based on using vehicle tracking data is good enough for the considered application of bus travel time prediction.
01 Jan 2016
TL;DR: The stochastic processes and filtering theory is universally compatible with any devices to read and will help you to get the most less latency time to download any of the authors' books like this one.
Abstract: Thank you for reading stochastic processes and filtering theory. Maybe you have knowledge that, people have look numerous times for their favorite novels like this stochastic processes and filtering theory, but end up in harmful downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they are facing with some infectious bugs inside their computer. stochastic processes and filtering theory is available in our digital library an online access to it is set as public so you can download it instantly. Our digital library saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the stochastic processes and filtering theory is universally compatible with any devices to read.
TL;DR: In this paper, the authors present an analysis of the behavior of composite materials and their properties, such as bending, buckling, and vibration of Laminated Plates, as well as the maximum and minima of functions of a single variable.
Abstract: 1.Introduction to Composite Materials 2. Macrochemical Behavior of a Lamina 3.Micromechanical Behavior of a Lamina 4.Macromechanical Behavior of a Laminate 5.Bending, Buckling, and Vibration of Laminated Plates 6.Other Analysis and Behavior Topics 7.Introduction to Design of Composite Structures Appendix A.Matrices and Tensors Appendix B.Maxima and Minima of Functions of a Single Variable Appendix C.Typical Stress-Strain Curves Appendix D.Governing Equations for Beam Equilibrium and Plate Equilibrium, Buckling, and Vibration Index
01 Jan 1999
TL;DR: 1. Control Methodology 2. Dynamical Systems 3. Applications to Social and Environmental Problems 4.
Abstract: 1. Control Methodology 2. Dynamical Systems 3. Applications to Social and Environmental Problems
01 Jan 2007
TL;DR: In this article, the authors consider a robot with two drive wheels, of radius r on an axle of length d, rotating at different velocities: the right wheel at a velocity of φRt and the left at a speed of ΆLt.
Abstract: where xt+1 is the position and orientation of the robot (with respect to a reference frame) at time t + 1, with (ξ, η) giving the x and y coordinates and θ the angle (with respect to the x-axis) that the robot is facing. The robot has two drive wheels, of radius r on an axle of length d. During time period t the right wheel is believed to rotate at a velocity of φRt and the left at a velocity of φLt. In this example, these velocities are fixed with φRt = 0.4 and φLt = 0.1. The state update function, F , calculates where the robot should be at each time point, given its previous position. However, in reality, there is some random fluctuation in the velocity of the wheels, for example, due to slippage. Therefore the actual position of the robot and the position given by equation F will differ. The magnitude of these random fluctuations is included in the model through Σx.
TL;DR: The new concept of consensual 3D speed maps allows the essence out of large amounts of link speed observations and reveals a global and previously mostly hidden picture of traffic dynamics at the whole city scale, which may be more regular and predictable than expected.
Abstract: In this paper, we investigate the day-to-day regularity of urban congestion patterns. We first partition link speed data every 10 min into 3D clusters that propose a parsimonious sketch of the congestion pulse. We then gather days with similar patterns and use consensus clustering methods to produce a unique global pattern that fits multiple days, uncovering the day-to-day regularity. We show that the network of Amsterdam over 35 days can be synthesized into only 4 consensual 3D speed maps with 9 clusters. This paves the way for a cutting-edge systematic method for travel time predictions in cities. By matching the current observation to historical consensual 3D speed maps, we design an efficient real-time method that successfully predicts 84% trips travel times with an error margin below 25%. The new concept of consensual 3D speed maps allows us to extract the essence out of large amounts of link speed observations and as a result reveals a global and previously mostly hidden picture of traffic dynamics at the whole city scale, which may be more regular and predictable than expected.