arXiv subjects

Tejas Santanam

Publications and source records attributed to Tejas Santanam.

10 recordsLinked to original sources

arXiv2026

The Impact of Shared Autonomous Vehicles in Microtransit Systems: A Case Study in Atlanta

Microtransit systems represent an enhancement to solve the first- and last-mile problem, integrating traditional rail and bus networks with on-demand shuttles into a flexible, integrated system. This type of demand responsive transport provides greater accessibility and higher quality levels of service compared to conventional fixed-route transit services. Advances in technology offer further opportunities to enhance microtransit performance. In particular, shared autonomous vehicles (SAVs) have the potential to transform the mobility landscape by enabling more sustainable operations, enhanced user convenience, and greater system reliability. This paper investigates the integration of SAVs in microtransit systems, advancing the technological capabilities of on-demand shuttles. A shuttle dispatching optimization model is enhanced to accommodate for driver behavior and SAV functionalities. A model predictive control approach is proposed that dynamically rebalances on-demand shuttles towards areas of higher demand without relying on vast historical data. Scenario-driven experiments are conducted using data from the MARTA Reach microtransit pilot. The results demonstrate that SAVs can elevate both service quality and user experience compared to traditional on-demand shuttles in microtransit systems.

eess.SY↗

arXiv2026

GeoRC: A Benchmark for Geolocation Reasoning Chains

Vision Language Models (VLMs) are good at recognizing the global location of a photograph -- their geolocation prediction accuracy rivals the best human experts. But many VLMs are startlingly bad at \textit{explaining} which image evidence led to their prediction, even when their location prediction is correct. In this paper, we introduce GeoRC, the first benchmark for geolocation reasoning chains sourced directly from Champion-tier GeoGuessr experts, including the reigning world champion. This benchmark consists of 800 ``ground truth'' reasoning chains across 500 query scenes from GeoGuessr maps, with expert chains addressing hundreds of different discriminative attributes, such as soil properties, architecture, and license plate shapes. We evaluate LLM-as-a-judge and VLM-as-a-judge strategies for scoring VLM-generated reasoning chains against our expert reasoning chains and find that Qwen 3 LLM-as-a-judge correlates best with human-expert scoring. Our benchmark reveals that while large, closed-source VLMs such as Gemini and GPT 5 rival human experts at predicting locations, they still lag behind human experts when it comes to producing auditable reasoning chains. Small open-weight VLMs such as Llama and Qwen catastrophically fail on our benchmark -- they perform only slightly better than a baseline in which an LLM hallucinates a reasoning chain with oracle knowledge of the photo location but \textit{no visual information at all}. We believe the gap between human experts and VLMs on this task points to VLM limitations at extracting fine-grained visual attributes from high resolution images. We open source our benchmark for the community to use.

cs.CV↗

arXiv2025

Ease and Equity of Point of Interest Accessibility via Public Transit in the U.S

Equitable access to essential Points of Interest (POIs) such as healthcare facilities and grocery stores via public transit is a critical urban planning challenge. This paper introduces an interactive tool that analyzes the ease and equity of such access in major U.S. cities. Using a 2-step floating catchment area (2SFCA) approach, we calculate granular accessibility indices and present them on an interactive, web-based platform. A key contribution of this work is a scenario analysis feature that allows users to simulate the impact of adding or removing POIs and visualize the resulting changes in accessibility equity. Our analysis reveals significant disparities across demographic groups, and we further utilize machine learning models to demonstrate a strong, quantifiable link between accessibility scores and neighborhood racial composition, highlighting systemic inequities. The tool provides a practical framework for policymakers to make more informed and equitable infrastructure decisions.

stat.AP↗

arXiv2024

Survey of Methods, Resources, and Formats for Teaching Constraint Programming

This paper provides an overview of the state of teaching for Constraint Programming, based on a survey of the community for the 2023 Workshop on Teaching Constraint Programming at the CP 2023 conference in Toronto. The paper presents the results of the survey, as well as lists of books, video courses and other tutorial materials for teaching Constraint Programming. The paper serves as a single location for current and public information on course resources, topics, formats, and methods.

cs.CY↗

arXiv2023

MARTA Reach: Piloting an On-Demand Multimodal Transit System in Atlanta

This paper reports on the results of the six-month pilot MARTA Reach, which aimed to demonstrate the potential value of On-Demand Multimodal Transit Systems (ODMTS) in the city of Atlanta, Georgia. ODMTS take a transit-centric view by integrating on-demand services and traditional fixed routes in order to address the first/last mile problem. ODMTS combine fixed routes and on-demand shuttle services by design (not as an after-thought) into a transit system that offers a door-to-door multimodal service with fully integrated operations and fare structure. The paper fills a knowledge gap, i.e., the understanding of the impact, benefits, and challenges of deploying ODMTS in a city as complex as Atlanta, Georgia. The pilot was deployed in four different zones with limited transit options, and used on-demand shuttles integrated with the overall transit system to address the first/last mile problem. The paper describes the design and operations of the pilot, and presents the results in terms of ridership, quality of service, trip purposes, alternative modes of transportation, multimodal nature of trips, challenges encountered, and cost estimates. The main findings of the pilot are that Reach offered a highly valued service that performed a large number of trips that would have otherwise been served by ride-hailing companies, taxis, or personal cars. Moreover, the wide majority of Reach trips were multimodal, with connections to rail being most prominent.

cs.CY↗

arXiv2023

Modern Constraint Programming Education: Lessons for the Future

This paper details an outlook on modern constraint programming (CP) education through the lens of a CP instructor. A general overview of current CP courses and instructional methods is presented, with a focus on online and virtually-delivered courses. This is followed by a discussion of the novel approach taken to introductory CP education for engineering students at large scale at the Georgia Institute of Technology (Georgia Tech) in Atlanta, GA, USA. The paper summarizes important takeaways from the Georgia Tech CP course and ends with a discussion on the future of CP education. Some ideas for instructional methods, promotional methods, and organizational changes are proposed to aid in the long-term growth of CP education.

cs.CY↗

arXiv2023

Changes in Commuter Behavior from COVID-19 Lockdowns in the Atlanta Metropolitan Area

This paper analyzes the impact of COVID-19 related lockdowns in the Atlanta, Georgia metropolitan area by examining commuter patterns in three periods: prior to, during, and after the pandemic lockdown. A cellular phone location dataset is utilized in a novel pipeline to infer the home and work locations of thousands of users from the Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. The coordinates derived from the clustering are put through a reverse geocoding process from which word embeddings are extracted in order to categorize the industry of each work place based on the workplace name and Point of Interest (POI) mapping. Frequencies of commute from home locations to work locations are analyzed in and across all three time periods. Public health and economic factors are discussed to explain potential reasons for the observed changes in commuter patterns.

cs.LG↗

arXiv2022

Deep Image Style Transfer from Freeform Text

This paper creates a novel method of deep neural style transfer by generating style images from freeform user text input. The language model and style transfer model form a seamless pipeline that can create output images with similar losses and improved quality when compared to baseline style transfer methods. The language model returns a closely matching image given a style text and description input, which is then passed to the style transfer model with an input content image to create a final output. A proof-of-concept tool is also developed to integrate the models and demonstrate the effectiveness of deep image style transfer from freeform text.

cs.CV↗

arXiv2021

Resiliency of On-Demand Multimodal Transit Systems During a Pandemic

During the COVID-19 pandemic, the collapse of the public transit ridership led to significant budget deficits due to dramatic decreases in fare revenues. Additionally, public transit agencies are facing challenges of reduced vehicle capacity due to social distancing requirements, additional costs of cleaning and protective equipment, and increased downtime for vehicle cleaning. Due to these constraints on resources and budgets, many transit agencies have adopted essential service plans with reduced service hours, number of routes, or frequencies. This paper studies the resiliency during a pandemic of On-Demand Multimodal Transit Systems (ODMTS), a new generation of transit systems that combine a network of high-frequency trains and buses with on-demand shuttles to serve the first and last miles and act as feeders to the fixed network. It presents a case study for the city of Atlanta and evaluates ODMTS for multiple scenarios of depressed demand and social distancing representing various stages of the pandemic. The case study relies on an optimization pipeline that provides an end-to-end ODMTS solution by bringing together methods for demand estimation, network design, fleet sizing, and real-time dispatching. These methods are adapted to work in a multimodal setting and to satisfy practical constraints. In particular, a limit is imposed on the number of passenger transfers, and a new network design model is introduced to avoid the computational burden stemming from this constraint. Real data from the Metropolitan Atlanta Rapid Transit Authority (MARTA) is used to conduct the case study, and the results are evaluated with a high-fidelity simulation. The case study demonstrates how ODMTS provide a resilient solution in terms of cost, convenience, and accessibility for this wide range of scenarios.

math.OC↗

arXiv2021

Public Transit for Special Events: Ridership Prediction and Train Optimization

Many special events, including sport games and concerts, often cause surges in demand and congestion for transit systems. Therefore, it is important for transit providers to understand their impact on disruptions, delays, and fare revenues. This paper proposes a suite of data-driven techniques that exploit Automated Fare Collection (AFC) data for evaluating, anticipating, and managing the performance of transit systems during recurring congestion peaks due to special events. This includes an extensive analysis of ridership of the two major stadiums in downtown Atlanta using rail data from the Metropolitan Atlanta Rapid Transit Authority (MARTA). The paper first highlights the ridership predictability at the aggregate level for each station on both event and non-event days. It then presents an unsupervised machine-learning model to cluster passengers and identify which train they are boarding. The model makes it possible to evaluate system performance in terms of fundamental metrics such as the passenger load per train and the wait times of riders. The paper also presents linear regression and random forest models for predicting ridership that are used in combination with historical throughput analysis to forecast demand. Finally, simulations are performed that showcase the potential improvements to wait times and demand matching by leveraging proposed techniques to optimize train frequencies based on forecasted demand.

math.OC↗