Gitakrishnan Ramadurai

A robust transportation signal control problem accounting for traffic dynamics

Transportation system analysis must rely on predictions of the future that, by their very nature, contain substantial uncertainty. Future demand, demographics, and network capacities are only a few of the parameters that must be accounted for in both the planning and every day operations of transportation networks. While many repercussions of uncertainty exist, a primary concern in traffic operations is to develop efficient traffic signal designs that satisfy certain measures of short term future system performance while accounting for the different possible realizations of traffic state. As a result,uncertainty has to be incorporated in the design of traffic signal systems. Current dynamic traffic equilibrium models accounting for signal design, however, are not suitable for quantifying network performance over the range of possible scenarios and in analyzing the robust performance of the system. The purpose of this paper is to propose a new approach-robust system optimal signal control model; a supply-side within day operational transportation model where future transportation demand is assumed to be uncertain. A robust dynamic system optimal model with an embedded cell transmission model is formulated. Numerical analysis are performed on a test network to illustrate the benefits of accounting for uncertainty and robustness.

Determinants of Changes in Mobility and Travel Patterns in Developing Countries: Case Study of Chennai, India

This study analyzes changes in sociodemographic, activity, land use, and mobility patterns and their effects on travel dimensions in the context of a developing country. More specifically, increase in vehicle ownership (both two-wheelers and cars) and changes in mode choice over time are observed and analyzed with the use of household data from Chennai, India. Three sources of dynamics are analyzed: exogenous variable dynamics, sensitivity changes over time, and the influence of lagged and persistent effects. The key drivers of growth in travel demand include the increase in vehicle ownership, the number of workers, and the increase in female drivers. The influence of social and technological factors on vehicle ownership and mode choice such as peer pressure and mobile phone ownership are also significant. In addition, the effect of land use, accessibility, and activity has been investigated. Results show significant evidence of differences in travel decisions across different user segments (on the basis of driving knowledge and vehicle–worker ratio) and over time. The proposed disaggregate models provide a reasonably good description (goodness of fit is 47% to 64%) of the observed changes in travel patterns. The findings and results assume importance in the context of increasing congestion, declining public transportation share, and the imminent need for enhancing urban transportation system capacity in cities of developing countries.

Data Fusion-Based Traffic Density Estimation and Prediction

Traffic congestion has become a major challenge in recent years in many countries of the world. One way to alleviate congestion is to manage the traffic efficiently by applying intelligent transportation systems (ITS). One set of ITS technologies helps in diverting vehicles from congested parts of the network to alternate routes having less congestion. Congestion is often measured by traffic density, which is the number of vehicles per unit stretch of the roadway. Density, being a spatial characteristic, is difficult to measure in the field. Also, the general approach of estimating density from location-based measures may not capture the spatial variation in density. To capture the spatial variation better, density can be estimated using both location-based and spatial data sources using a data fusion approach. The present study uses a Kalman filter to fuse spatial and location-based data for the estimation of traffic density. Subsequently, the estimated data are utilized for predicting density to future time intervals using a time-series regression model. The models were estimated and validated using both field and simulated data. Both estimation and prediction models performed well, despite the challenges arising from heterogeneous traffic flow conditions prevalent in India.

Dynamic Traffic Equilibrium: Theoretical and Experimental Network Game Results in Single-Bottleneck Model

The existence of equilibrium solutions in single-bottleneck models with homogeneous travelers having the same preferred arrival times is explored from both theoretical and experimental frameworks. First, an equal payoff solution obtained in past studies is proved not to be in dynamic user equilibrium (DUE). Necessary conditions for DUE are presented, and the nonexistence of DUE in a single-bottleneck model with homogeneous commuters is proved. Second, a multiplayer online experimental network game that emulates the single-bottleneck model is discussed. The game is played online by real players and is repeated 21 times, each under no-information and online-information settings. The experimental setup and the results are described with insights from graphical plots and analysis. Specific emphasis is placed on the systems dynamic evolution in each game and the pattern of a users decision making. It is anticipated that these results will provide important insight into the underlying behavioral decision making of travelers and will thereby facilitate the development of better dynamic traffic assignment models.

B‐Dynamic: An Efficient Algorithm for Dynamic User Equilibrium Assignment in Activity‐Travel Networks

Multi-dimensional choice in dynamic traffic assignment (DTA)—for example, a combined model of activity location, time of participation, duration, and route choice decisions—results in exponentially increasing choice alternatives. Any efficient algorithm for solving the multi-dimensional DTA problem must avoid enumeration of alternatives. In this article an algorithm that does not enumerate paths is presented. The algorithm is a novel extension of Algorithm B (Dial, 2006) to dynamic networks and hence referred to as Algorithm B-Dynamic. The DTA model proposed here uses a point queue model for traffic propagation that reduces computational complexity. The activity participation decision dimensions are incorporated through utility functions, which are a linear function of duration and schedule delay (early or late arrival penalty). Numerical examples are then presented to illustrate both the steps of the algorithm and its capabilities. Overall, the algorithm performed well for up to medium-sized networks. Further, the algorithm scales fairly well with increasing demand levels.

Statistical Analysis of Bus Networks in India

In this paper, we model the bus networks of six major Indian cities as graphs in L-space, and evaluate their various statistical properties. While airline and railway networks have been extensively studied, a comprehensive study on the structure and growth of bus networks is lacking. In India, where bus transport plays an important role in day-to-day commutation, it is of significant interest to analyze its topological structure and answer basic questions on its evolution, growth, robustness and resiliency. Although the common feature of small-world property is observed, our analysis reveals a wide spectrum of network topologies arising due to significant variation in the degree-distribution patterns in the networks. We also observe that these networks although, robust and resilient to random attacks are particularly degree-sensitive. Unlike real-world networks, such as Internet, WWW and airline, that are virtual, bus networks are physically constrained. Our findings therefore, throw light on the evolution of such geographically and constrained networks that will help us in designing more efficient bus networks in the future.

Contagion processes on urban bus networks in Indian cities

Bus transportation is the most convenient and cheapest way of public transportation in Indian cities. Due to cost-effectiveness and wide reachability, buses bring people to their destinations every day. Although the bus transportation has numerous advantages over other ways of public transportation, this mode of transportation also poses a serious threat of spreading contagious diseases throughout the city. It is extremely difficult to predict the extent and spread of such an epidemic. Earlier studies have focused on the contagion processes on scale-free network topologies; whereas, real-world networks such as bus networks exhibit a wide-spectrum of network topology. Therefore, we aim in this study to understand this complex dynamical process of epidemic outbreak and information diffusion on the bus networks for six different Indian cities using SI and SIR models. We identify epidemic thresholds for these networks which help us in controlling outbreaks by developing node-based immunization techniques.

Simulation of truck congestion in Chennai port

The primary focus of this study is to understand the current port operating condition and recommend short term measures to improve traffic condition in the port of Chennai. The cause of congestion is identified based on the data collected and observation made at port gates as well as at terminal gates in Chennai port. A simulation model for the existing road layout is developed in micro-simulation software VISSIM and is calibrated to reflect the prevailing condition inside the port. The data such as truck origin/destination, hourly inflow and outflow of trucks, speed, and stopping time at checking booths are used as input. Routing data is used to direct traffic to specific terminal or dock within the port. Several alternative scenarios are developed and simulated to get results of the key performance indicators. A comparative and detailed analysis of these indicators is used to evaluate recommendations to reduce congestion inside the port.

Grid-based real-time image processing (GRIP) algorithm for heterogeneous traffic

The paper presents a fast algorithm for real-time image processing for counting and classification of vehicles in heterogeneous traffic recorded using a single stationary camera. The proposed method uses a single feature as the base parameter which is given by the user to classify the vehicles into four different classes. The algorithm has an error of 6.1% on an average for the total count when studied under varying illumination and weather conditions.

Strategies for traffic signal control in Indian cities

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.