https://etd.iisc.ac.in/handle/2005/31 2026-04-27T12:12:23Z https://etd.iisc.ac.in/handle/2005/5660 Activity-travel behavior modeling of pilgrims in mass religious gatherings Khandelwal, Tarun The number of participants in mass gatherings like Kumbh Mela is ever increasing. Simulations for pre-event crowd modeling, risk assessment, and control planning can help set up robust crowd management and control mechanisms. However, it is necessary to understand better the processes and crowd movement patterns in mass gatherings to model and simulate crowds in such contexts. To this end, the activity-based modeling approach helps analyze the factors that influence different aspects of activity participation and time allocation for pilgrims. These aspects include the type of activities performed, the location and timing preferences for performing these activities, the time spent in different activity locations, etc. A good understanding of these factors can help in better modeling and simulating the spatio-temporal evolution of population density at the location of a mass gathering. This thesis analyzes the factors influencing activity participation and duration for pilgrim groups and presents corresponding behavioral interpretations and policy implications. The groups mainly comprise non-resident individuals who are not necessarily from the same household. We use the activity diary and demographic data collected in the Kumbh Mela held in Ujjain in 2016 for our analyses. We contribute in the following ways towards the literature through our approach to analyze group behavior in mass religious gatherings: first, we abstract the activities into religious activities at ghat, temple, and camp locations, and identify the group demographic attributes that can influence the activity participation and duration. We then develop empirical models to analyze group activity participation and duration using binary logit, linear regression, and multiple discrete-continuous extreme value (MDCEV) models. The behavioral interpretations, backed up by findings from prior studies, and policy implications, appear consistent across our models. Our MDCEV models may be useful for aggregate activity time allocation along with models for location-specific allocations. https://etd.iisc.ac.in/handle/2005/6795 Adaptive and Optimal Control based Artificial Pancreas for Type-1 Diabetes Mellitus Patients Chandrasekhar, Abishek This research focuses on the development of adaptive and optimal control algorithms and their implementation to develop Artificial Pancreas (AP) systems for glucose regulation in Type-1 Diabetes Mellitus (T1DM) patients. Artificial Pancreas systems have two modes of operation: (i) Bolus mode, the insulin required to account for glucose rise due to food intake, and (ii) basal mode, the insulin required to regulate glucose levels throughout the day. In order to develop an AP system, a robust algorithm is required for both basal and bolus control to ensure glucose levels are within a safe range at all times. The first portion of the thesis focuses on the development of an autonomous bolus control algorithm. A minimal mathematical model that captures the glucose-insulin interaction in T1DM patients during the meal cycle was selected after extensive literature studies. Next, the Mixed Meal Tolerance Tests (MMTT) were conducted on Indian T1DM patients in order to collect time-tagged blood samples from patients. The parameters were identified for T1DM patients through the blood glucose and blood insulin concentrations that were estimated from the blood samples. The parameters of the Indian patients were compared with the Caucasian population, which showed that Indian T1DM patients have lower insulin sensitivity. An implication of this variation is that Indian patients would require more insulin to lower glucose levels when compared to the Caucasian population. Therefore, this served as sufficient motivation to develop an Indian population-specific AP system. A model predictive control (MPC) algorithm is proposed to regulate glucose levels within normal ranges during the meal cycle. The proposed MPC strategy exploits the minimal models and is customized to individual patients by fitting the model to a patients blood glucose and blood insulin data from the MMTT. In order to account for day-to-day intra-patient variability, the MPC scheme is augmented with neuro-adaptive learning in the glucose state dynamics. However, the adaptive learning scheme cannot be used to estimate the parameter uncertainties in the other state dynamic equations. An unscented transform-based “Least Risk” MPC is proposed to handle the uncertainties in the parameters of the other state dynamics. A model-based algorithm such as MPC requires state-feedback information, for which an unscented Kalman filter (UKF) is synthesized to estimate the states. Note that the continuous glucose monitor (CGM) readings are delayed representations of blood glucose levels and are prone to errors. This measurement is corrected for errors first before being utilized in the UKF for estimating the states. Next, the proposed closed-loop bolus control algorithm was implemented in an Android smartphone-based artificial pancreas system. The proposed AP system is realized using commercially available insulin pumps, CGMs, and additional communication devices. The proposed algorithms were integrated into an Android smartphone app. The algorithms were implemented through two software architectures that are proposed in this thesis: (i) A dual app architecture with a Java-based front-end App and a MATLAB/Simulink-based back-end App, and (ii) a single app architecture with a Java-based front-end and a Python-based back-end. The proposed android AP systems and the algorithms were tested on actual T1DM patients. The patients underwent the standard MMTT in order to identify the model parameters after obtaining their consent. The algorithm was customized to the individual patient parameters and tested for the morning bolus cycle in a clinical environment under medical supervision. The proposed AP system was shown to successfully control the glucose values autonomously in several clinical trials, thereby proving the efficacy and robustness of the AP system with the proposed bolus control algorithm. Hence, to continue developing a complete AP system further, the basal model and control algorithm were developed using the T1DM simulator, which was approved by the Food and Drug Administration (FDA) of the United States of America. The second part of the thesis was only tested through in-silico simulations due to a lack of funds to conduct clinical trials. The basal modes are categorized into different sub-modes due to the variation of the glucose-insulin interaction throughout the day. As there are various modes of operation in an AP system, and the transition between various modes cannot be explicitly defined, an Interactive Multiple Model Filter is also proposed to address this issue. These models were utilized for the formulation of an MPC scheme to control the glucose levels throughout the day. The proposed algorithm was tested vigorously through in-silico tests to showcase its efficacy and robustness. In order to account for uncertainties and user errors, such as failing to declare a meal, a meal detection, estimation and meal compensation strategy is also proposed in this thesis. The proposed basal and bolus algorithm, with the other associated algorithms, was evaluated through vigorous robustness tests using the FDA-approved T1DM simulator. Therefore, the research proposed in this thesis can lead towards the development of a verifiable AP system that can benefit millions of T1DM patients. A brief note on current and future research activities is given in the Concluding chapter of this thesis. https://etd.iisc.ac.in/handle/2005/6990 Barrier Coverage using UAVs and Camera Sensors Kumar, Amit With advancements in camera sensor technology, camera sensor networks are increasingly utilized for border surveillance. UAVs equipped with downward-facing cameras also serve as effective sensors, and their features, such as rapid deployment and adjustable field of view, make them particularly suitable for border surveillance, often termed as barrier coverage in the literature. This thesis first addresses a barrier coverage problem using UAVs equipped with downward-facing cameras. It proposes both deterministic and optimization-based deployment strategies to ensure barrier coverage. In deterministic deployment, UAVs are initially aligned to a barrier line to maximize coverage based on the number of sensors and height constraints. For optimization-based deployment, a resolution cost of the belt is introduced to enhance the quality of existing barrier coverage. An optimization problem is also proposed to achieve barrier coverage with an overlapping constraint for UAVs placed arbitrarily within the belt. The approach is further demonstrated to be applicable to borders of any shape by considering a multi-belt problem. Additionally, a local fault-tolerance model is proposed to ensure continuous coverage if some UAVs become faulty. Also, the barrier coverage of a belt with varying resolution requirements is investigated. For a barrier covered network with certain regions within the belt that need higher resolutions, an optimization problem is formulated to determine the final placement of UAVs ensuring barrier coverage with the requirements. When handling vision-based sensors for tasks such as intruder detection, it is essential to account for occlusion caused by objects, as it can create a false sense of coverage. This thesis addresses the impact of occluders on barrier coverage networks involving UAVs. The study assesses whether a given UAV network, along with permeable and impermeable occluders in the belt, achieves barrier coverage. For permeable occluders, a dual graph approach, one along the length and one along the width of the belt is introduced to evaluate barrier coverage. Further, coverage metrics to distinguish any two barrier-covered networks are proposed. Overall, this thesis provides a comprehensive study of barrier coverage problems with UAVs, considering both the presence and absence of occluders. The thesis also tackles the barrier coverage problem for terrain-like borders using UAVs where achieving optimal UAV placement is challenging due to factors such as resolution, overlap constraints, and varying altitudes. We first simplify the 3D problem into an equivalent 2D model and introduce a resolution cost to assess terrain coverage quality. Additionally, we define the overlapping length and formulate an optimization problem to secure barrier coverage. In the second part of this thesis, the barrier coverage problem with camera sensor networks is investigated. For a network identified as barrier-uncovered, an optimization problem is formulated to determine the optimal positions and orientations of each sensor to ensure barrier coverage. An attraction force-based motion strategy is then used to relocate the sensors to the desired positions and orientations. Similar to UAVs, the impact of occluders on camera sensor networks is also considered. The limitations of conventional methods are highlighted, and a coverage model is proposed that introduces a novel sector division approach, utilizing the interaction between occluders and sensor regions. Additionally, the existing graph-based method is modified to assess the barrier coverage of a sensor network in the presence of occluders. Additionally, deployment strategies for ensuring barrier coverage when it is initially lacking are discussed. First, a deterministic approach using a barrier curve derived from a weighted graph is presented. Finally, an optimization-based deployment strategy, utilizing the sector division method for barrier coverage constraints, is proposed to ensure coverage in the presence of occluders. https://etd.iisc.ac.in/handle/2005/6701 Collaboration Models for Ride-hailing and Transit Service Providers to Facilitate First- and Last-mile Connectivity Kushwaha, Vishal Mobility-as-a-Service (MaaS) has gained significant popularity in the past few years. Various ride-hailing service providers (RSPs, e.g., Uber, Lyft, Didi, Ola, etc.) have entered the transportation market to provide this service. They offer door-to-door connectivity, online booking and payment facility, customizable rides, and other such features to the travelers. All these attractive features have caused a substantial increase in the mode shares of the RSPs. However, this rising popularity has raised a concern among the city transportation planners. Quite often, the RSPs operate their vehicles at an occupancy which is much less than the full capacity. As a result, more vehicles are required to serve the traveler demand which has the potential to cause traffic congestion on roads. This leads to wastage of non-renewable fuel as well as increased travel times and carbon dioxide emissions due to the vehicles. On the other hand, public transit is a potentially cheaper and sustainable mode option. However, in many cases, transit stops are located far from the travelers' residential and/or activity locations. This hinders the extensive usage of public transit as travelers may have to walk for long distances or arrange an intermediate mode of transport to/from transit stops. Therefore, the travelers shift to other modes, such as personal vehicle, ride-hailing service, etc., which are more comfortable options. Due to these issues, there is an interest in exploring the collaboration between the RSPs and the transit agencies. In such a collaborative mobility service, the RSPs will provide connectivity between the travelers' residential/activity locations and the transit stops (first- and last-mile connectivity) and the transit agency will facilitate travel on the long-haul part of the journey. Such a collaborative service can potentially bring together the best of both operators' services by providing seamless door-to-door travel while keeping the overall travel prices low and reducing traffic congestion. However, in many cities, barring a few exceptions, neither the RSPs nor the transit agencies appear eager to co-operate with each other. This is because the scientific literature lacks evidence and methods to formulate, evaluate, and facilitate discussions for such a collaboration. Therefore, the objectives of this thesis are to formulate models for evaluating collaboration between the RSPs and the transit agencies in the context of first- and last-mile services. In particular, we focus on optimal pricing models to assist the RSPs and the transit agencies in pricing the collaborative mobility service to enhance the mode shares and profits of both operators. The thesis is divided into three parts. In the first part, we propose a game theory and discrete choice theory based tri-level collaboration model. The proposed model is a Stackelberg game with the bus agency as the leader and the RSPs as followers. It is applicable to a stylized four-node network with one origin-destination (O-D) pair. All the players in the proposed game have the objective of profit maximization as a function of their price in the collaborative service. Needless to say, the optimal price setting should consider the travel mode choice preferences of the travelers. We apply the proposed model to two major travel corridors of Bengaluru, a major metropolitan city in India. The simulation results indicate promising results in the form of increased profits and mode shares of the collaborating agencies and improved travel times due to a reduction in congestion. The travelers also benefit in the form of a better expected maximum value of utility in the context of mode choice. The new collaborative mode turns out to be a well-balanced alternative in terms of travel price and travel time (lower travel time than the walk and bus mode combination and lower travel cost than the direct RSP mode between travel origin and destination). In the second part of the thesis, we extend the model formulated in the first part for one O-D pair to a city-scale network with multiple O-D pairs. This model is also a tri-level model, formulated using the concepts from game theory, random utility maximization based discrete choice theory, and traffic network equilibrium theory. It is applicable to a network with one bus route. In this model, the bus agency and the RSPs play a Stackelberg game to set the price for their part in the collaborative service. We apply the city-scale model to the Sioux Falls test network to test the robustness of the model. Also, we apply the model to a sub-network in the Bengaluru city. The simulation results for both the networks corroborate the findings in the previous part of the thesis. The RSPs and the transit agencies observe a higher profit and effective person trips due to collaboration. There is also a slight improvement in the average speeds and a small reduction in the carbon dioxide emissions due to the vehicles. In the third part of the thesis, we further extend the city-scale model to include an auction component. This helps the bus agency to optimally select RSPs for collaboration. In this model, the bus agency conducts an auction inviting price bids from the RSPs which are defined as the amount per traveler corresponding to the collaborative service that the RSPs are willing to pay/receive from the bus agency for collaborating. Based on the submitted bids, the bus agency announces the winners of the auction. Later, the winning RSPs and the bus agency play a Stackelberg game to set prices for their part in the collaborative service. We apply the model to the Sioux Falls test network and compare the optimal pricing model with- and without-auction. For the setting under consideration, we do not observe a significant difference between the two cases. However, the auction is able to increase the competition on the supply-side (between RSPs) which can potentially result in better services for the travelers.