Gitakrishnan Ramadurai

Bio: Gitakrishnan Ramadurai is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Traffic flow & Bottleneck. The author has an hindex of 17, co-authored 61 publications receiving 1108 citations. Previous affiliations of Gitakrishnan Ramadurai include Rensselaer Polytechnic Institute & Indian Institutes of Technology.

On the Existence of Pricing Strategies in the Heterogeneous Single Bottleneck Model

Abstract: Since the single bottleneck with homogeneous and heterogeneous commuters was formulated as a linear complementarity problem (LCP), it has enabled the research community to look at important extensions. Based on the LCP formulations, this paper will explore useful extensions to contribute to the single bottleneck literature. First, the congestion pricing for homogeneous and heterogeneous commuters is studied. This paper shows that the system optimal for single bottleneck model with heterogeneous commuters might be obtained by an appropriate pricing scheme. Second, the models with flexible arrival time for both homogeneous and heterogeneous models are formulated. It is shown that the system optimal for heterogeneous commuter model might be obtained with a proper toll scheme. Finally, the LCP formulation is extended to study the elastic capacity problem for the homogeneous case. The result shows that, in the particular condition, the existence and uniqueness for homogeneous model with elastic capacity can be guaranteed.

Detecting Vehicles on the Edge: Knowledge Distillation to Improve Performance in Heterogeneous Road Traffic

Abstract: The drastic growth in the number of vehicles in the last few decades has necessitated significantly better traffic management and planning. To manage the traffic efficiently, traffic volume is an essential parameter. Most methods solve the vehicle counting problem under the assumption of state-of-the-art computation power. With the recent growth in cost-effective Internet of Things (IoT) devices and edge computing, several machine learning models are being tailored for such devices. Solving the traffic count problem on these devices will enable us to create a real-time dashboard of network-wide live traffic analytics. This paper proposes a Detect-Track-Count (DTC) framework to count vehicles efficiently on edge devices. The proposed solution aims at improving the performance of tiny vehicle detection models using an ensemble knowledge distillation technique. Experimental results on multiple datasets show that the custom knowledge distillation setup helps generalize a tiny object detector better.

Strategies for traffic signal control in Indian cities

Abstract: Most Indian cities face oversaturated flow conditions during peak periods. In this paper we revisit the traffic signal control to improve urban network performance. Three novel strategies that address congested heterogeneous traffic flow are presented. The first two strategies - keeping intersection signal cycle times shorter and bottleneck metering - are derived from field observed data while the third - exclusive lanes and storage area near intersections for two wheelers - is supported based on a micro-simulation model. Potential improvements are presented based on the strategies. While most recent studies have focused on area wide / network signal control improvements our study shows potential for significant improvement even at the level of an isolated signal.

Submission to the DTA2012 Special Issue: A Case for Higher-Order Traffic Flow Models in DTA

Abstract: An accurate Dynamic Traffic Assignment (DTA) model should capture real world traffic flow dynamics and predict ‘dynamic’ travel times. Traditional DTA models used simple traffic flow functions such as exit flow functions, delay functions, point queues, and deterministic physical queue models. Recently, simulation based models apply well accepted traffic flow theoretic models to simulate traffic flow. However, a significant number of papers over the last decade have adopted an approximation of LWR traffic flow model, the cell transmission model, for simulating traffic flow in a DTA model. This paper compares three models, namely, LWR, Payne and Aw-Rascle, models, for their suitability to be embedded in a DTA model. Model calibration and flow simulation is performed separately using two different speed–density relationships. Results showed the importance of choice of speed-density relationship in traffic flow simulation. Models were used to simulate traffic state at different discretization levels and it was observed that as discretization becomes finer, the models' accuracy increases. Finally, the models were applied to a two node, two link network to analyze their performance in a DTA framework. The higher-order models captured congestion dissipation better than LWR model which consistently underestimates congestion and travel time.

A practical guide to the American Community Survey (3-year estimates)

Abstract: Historically, most demographic data for states and substate areas were collected from the long version of the decennial census questionnaire. A “snapshot” of the characteristics of the population on the April 1 census date was available once every 10 years. The long form of the decennial census has been replaced by the American Community Survey (ACS) that has been conducted on an ongoing basis for the entire country since 2005. Instead of a snapshot in which all of the data are gathered at one time, the ACS aggregates data collected over time, making the results more difficult to interpret. However, the ACS data are updated annually.

Transportation network analysis

Abstract: Transportation Networks. Optimality. Cost Functions. Deterministic User Equilibrium Assignment. Stochastic User Equilibrium Assignment. Trip Table Estimation. Network Reliability. Network Design. Conclusions. References. Index.

A linear programming formulation for autonomous intersection control within a dynamic traffic assignment and connected vehicle environment

Abstract: This paper develops a novel linear programming formulation for autonomous intersection control (LPAIC) accounting for traffic dynamics within a connected vehicle environment. Firstly, a lane based bi-level optimization model is introduced to propagate traffic flows in the network, accounting for dynamic departure time, dynamic route choice, and autonomous intersection control in the context of system optimum network model. Then the bi-level optimization model is transformed to the linear programming formulation by relaxing the nonlinear constraints with a set of linear inequalities. One special feature of the LPAIC formulation is that the entries of the constraint matrix has only {−1, 0, 1} values. Moreover, it is proved that the constraint matrix is totally unimodular, the optimal solution exists and contains only integer values. It is also shown that the traffic flows from different lanes pass through the conflict points of the intersection safely and there are no holding flows in the solution. Three numerical case studies are conducted to demonstrate the properties and effectiveness of the LPAIC formulation to solve autonomous intersection control.