arXiv subjects

Tarun Rambha

Publications and source records attributed to Tarun Rambha.

10 recordsLinked to original sources

arXiv2026

A Branch-and-Cut Algorithm for the Optimal Design of Parking Lots with One-way and Two-way Lanes

We address the problem of maximizing the number of stalls in parking lots where vehicles park perpendicular to the driveways. Building on recent research on two-way driving lanes, we first formulate a mixed integer program to maximize the number of parking stalls using a flow-based approach. Parking lots are rasterized into a grid, and the proposed MIP model optimizes them in a generic manner, adapting to the grid resolution and stall size without requiring custom formulations. The constraints ensure the connectivity of parking stalls and driveways to the entrance/exit. This formulation is then extended to the case of one-way driving lanes. We then propose valid inequalities and a reformulation that can be solved using a branch-and-cut algorithm. This approach eliminates flow variables and big-M-type constraints, and improves solution times for medium-sized instances. The effectiveness of the suggested models is showcased on 325 parking lots from New York City. For instances where the flow version could be solved in 15 minutes, the branch-and-cut algorithm improved the median runtimes by 87.43% for the one-way case and by 79.36% for the two-way case, and achieved better optimality gaps than the baseline flow-based formulation for the other instances. Similar advantages were observed when run with a time budget of two hours. One-way configurations accommodated, on average, 18.63% more vehicles on average than their two-way counterparts across all instances. Modifications to the proposed formulations that account for vehicle turning characteristics and the presence of multiple entrances and exits are also examined.

math.OC↗

arXiv2026

Charge Schedule Optimization and Infrastructure Planning for Solar-Integrated Electric Bus Transit Systems

As urban transit systems transition towards electrification, using renewable energy sources (RES), such as solar, is essential to make them efficient and sustainable. However, the intermittent nature of renewables poses a challenge in deciding the solar panel requirements and battery energy storage system (BESS) capacity at charging locations. To address these challenges, we propose a two-stage multi-scenario model that considers seasonality in solar energy generation while incorporating temperature-based variations in bus energy consumption and dynamic time-of-use electricity prices. Specifically, we formulate the problem as a multi-scenario linear program (LP) where the first-stage long-term variables determine the charging station power capacity, BESS capacity, and the solar panel area at each charging location. The second-stage scenario-specific variables prescribe the energy transferred to buses directly from the grid or the BESS during layovers. We demonstrate the effectiveness of this framework using data from Durham Transit Network (Ontario) and Action Buses (Canberra), where bus schedules and charging locations are determined using a concurrent scheduler-based heuristic. Solar energy data is collected from the National Renewable Energy Laboratory (NREL) database. We solve the multi-scenario LP using Benders' decomposition, which performs better than the dual simplex method, especially when the number of scenarios is high. With solar energy production at select terminals, our model estimated a cost savings of 9.72% and 23.79% for the Durham and Canberra networks, respectively. Our results also show that the scenario-based schedule adapts better to seasonal variations than a schedule estimated from average input parameters.

math.OC↗

arXiv2026

Dynamic capacity allocation of hybrid transportation units for cargo-hitching in urban public transportation systems

To improve the utilization of public transportation systems (PTSs) during off-peak hours, we present an algorithmic framework that designs PTSs with hybrid transportation units (HTUs), which can transport passengers or freight by leveraging a flexible interior. Against this background, we study a capacitated network design problem to enable cargo-hitching in existing PTSs. Specifically, we study a setting with fixed vehicle routes and timetables in which vehicles can be equipped with HTUs to enable cargo-hitching. We optimize the network design from a total cost perspective to account for normalized network design costs tied to the investment in HTUs and freight routing costs. We present an algorithmic framework that encodes some of the problem's constraints in a spatially and temporally expanded, layered graph, and solves the resulting network design problem with a price-and-branch algorithm. We apply this framework to a case study based on the subway network in the city of Munich. Our algorithm outscales commercial solvers by a factor of six and yields integer feasible solutions with a median integrality gap of less than 1.56% for all instances. We show that cargo-hitching with HTUs increases the utilization of PTSs, especially during off-peak hours, without cannibalizing passenger service level and quality. We quantify the value of hybrid transportation units (HTUs) at up to 3.2% of the total cost. Moreover, we present a sensitivity analysis that indicates that cargo-hitching is worthwhile if truck-based transport occurs at an externality cost of more than 1.5 EUR per vehicle and kilometer and loading and unloading costs of less than 2.0 EUR per passenger equivalent.

math.OC↗

arXiv2025

The Urban Vision Hackathon Dataset and Models: Towards Image Annotations and Accurate Vision Models for Indian Traffic

This report describes the UVH-26 dataset, the first public release by AIM@IISc of a large-scale dataset of annotated traffic-camera images from India. The dataset comprises 26,646 high-resolution (1080p) images sampled from 2800 Bengaluru's Safe-City CCTV cameras over a 4-week period, and subsequently annotated through a crowdsourced hackathon involving 565 college students from across India. In total, 1.8 million bounding boxes were labeled across 14 vehicle classes specific to India: Cycle, 2-Wheeler (Motorcycle), 3-Wheeler (Auto-rickshaw), LCV (Light Commercial Vehicles), Van, Tempo-traveller, Hatchback, Sedan, SUV, MUV, Mini-bus, Bus, Truck and Other. Of these, 283k-316k consensus ground truth bounding boxes and labels were derived for distinct objects in the 26k images using Majority Voting and STAPLE algorithms. Further, we train multiple contemporary detectors, including YOLO11-S/X, RT-DETR-S/X, and DAMO-YOLO-T/L using these datasets, and report accuracy based on mAP50, mAP75 and mAP50:95. Models trained on UVH-26 achieve 8.4-31.5% improvements in mAP50:95 over equivalent baseline models trained on COCO dataset, with RT-DETR-X showing the best performance at 0.67 (mAP50:95) as compared to 0.40 for COCO-trained weights for common classes (Car, Bus, and Truck). This demonstrates the benefits of domain-specific training data for Indian traffic scenarios. The release package provides the 26k images with consensus annotations based on Majority Voting (UVH-26-MV) and STAPLE (UVH-26-ST) and the 6 fine-tuned YOLO and DETR models on each of these datasets. By capturing the heterogeneity of Indian urban mobility directly from operational traffic-camera streams, UVH-26 addresses a critical gap in existing global benchmarks, and offers a foundation for advancing detection, classification, and deployment of intelligent transportation systems in emerging nations with complex traffic conditions.

cs.CV↗

arXiv2024

A Bi-criterion Steiner Traveling Salesperson Problem with Time Windows for Last-Mile Electric Vehicle Logistics

This paper addresses the problem of energy-efficient and safe routing of last-mile electric freight vehicles. With the rising environmental footprint of the transportation sector and the growing popularity of E-Commerce, freight companies are likely to benefit from optimal time-window-feasible tours that minimize energy usage while reducing traffic conflicts at intersections and thereby improving safety. We formulate this problem as a Bi-criterion Steiner Traveling Salesperson Problem with Time Windows (BSTSPTW) with energy consumed and the number of left turns at intersections as the two objectives while also considering regenerative braking capabilities. We first discuss an exact mixed-integer programming model with scalarization to enumerate points on the efficiency frontier for small instances. For larger networks, we develop an efficient local search-based heuristic, which uses several operators to intensify and diversify the search process. We demonstrate the utility of the proposed methods using benchmark data and real-world instances from Amazon delivery routes in Austin, US. Comparisons with state-of-the-art solvers shows that our heuristics can generate near-optimal solutions within reasonable time budgets, effectively balancing energy efficiency and safety under practical delivery constraints.

math.OC↗

arXiv2024

On the Impact of Co-Optimizing Station Locations, Trip Assignment, and Charging Schedules for Electric Buses

As many public transportation systems around the world transition to electric buses, the planning and operation of fleets can be improved via tailored decision-support tools. In this work, we study the impact of jointly locating charging facilities, assigning electric buses to trips, and determining when and where to charge the buses. We propose a mixed integer linear program that co-optimizes planning and operational decisions jointly and an iterated local search heuristic to solve large-scale instances. Herein, we use a concurrent scheduler algorithm to generate an initial feasible solution, which serves as a starting point for our iterated local search algorithm. In the sequential case, we first optimize trip assignments and charging locations. Charging schedules are then determined after fixing the optimal decisions from the first level. The joint model, on the other hand, integrates charge scheduling within the local search procedure. The solution quality of the joint and sequential iterated local search models are compared for multiple real-world bus transit networks. Our results demonstrate that joint models can help further improve operating costs by 14.1% and lower total costs by about 4.1% on average compared with sequential models. In addition, energy consumption costs and contracted power capacity costs have been reduced significantly due to our integrated planning approach.

math.OC↗

arXiv2022

An Agent-Based Fleet Management Model for First- and Last-Mile Services

With the growth of cars and car-sharing applications, commuters in many cities, particularly developing countries, are shifting away from public transport. These shifts have affected two key stakeholders: transit operators and first- and last-mile (FLM) services. Although most cities continue to invest heavily in bus and metro projects to make public transit attractive, ridership in these systems has often failed to reach targeted levels. FLM service providers also experience lower demand and revenues in the wake of shifts to other means of transport. Effective FLM options are required to prevent this phenomenon and make public transport attractive for commuters. One possible solution is to forge partnerships between public transport and FLM providers that offer competitive joint mobility options. Such solutions require prudent allocation of supply and optimised strategies for FLM operations and ride-sharing. To this end, we build an agent- and event-based simulation model which captures interactions between passengers and FLM services using statecharts, vehicle routing models, and other trip matching rules. An optimisation model for allocating FLM vehicles at different transit stations is proposed to reduce unserved requests. Using real-world metro transit demand data from Bengaluru, India, the effectiveness of our approach in improving FLM connectivity and quantifying the benefits of sharing trips is demonstrated.

cs.MA↗

arXiv2022

Scalable Algorithms for Bicriterion Trip-Based Transit Routing

This paper proposes multiple extensions to the popular bicriterion transit routing approach -- Trip-Based Transit Routing (TBTR). Specifically, building on the premise of the HypRAPTOR algorithm, we first extend TBTR to its partitioning variant -- HypTBTR. However, the improvement in query times of HyTBTR over TBTR comes at the cost of increased preprocessing. To counter this issue, two new techniques are proposed -- a One-To-Many variant of TBTR and multilevel partitioning. Our One-To-Many algorithm can rapidly solve profile queries, which not only reduces the preprocessing time for HypTBTR, but can also aid other popular approaches such as HypRAPTOR. Next, we integrate a multilevel graph partitioning paradigm in HypTBTR and HypRAPTOR to reduce the fill-in computations. The efficacy of the proposed algorithms is extensively tested on real-world large-scale datasets. Additional analysis studying the effect of hypergraph partitioning tools (hMETIS, KaHyPar, and an integer program) along with different weighting schemes is also presented.

cs.DS↗

arXiv2020

GoCoronaGo: Privacy Respecting Contact Tracing for COVID-19 Management

The COVID-19 pandemic is imposing enormous global challenges in managing the spread of the virus. A key pillar to mitigation is contact tracing, which complements testing and isolation. Digital apps for contact tracing using Bluetooth technology available in smartphones have gained prevalence globally. In this article, we discuss various capabilities of such digital contact tracing, and its implication on community safety and individual privacy, among others. We further describe the GoCoronaGo institutional contact tracing app that we have developed, and the conscious and sometimes contrarian design choices we have made. We offer a detailed overview of the app, backend platform and analytics, and our early experiences with deploying the app to over 1000 users within the Indian Institute of Science campus in Bangalore. We also highlight research opportunities and open challenges for digital contact tracing and analytics over temporal networks constructed from them.

cs.CY↗

arXiv2017

Traffic Optimization For a Mixture of Self-interested and Compliant Agents

This paper focuses on two commonly used path assignment policies for agents traversing a congested network: self-interested routing, and system-optimum routing. In the self-interested routing policy each agent selects a path that optimizes its own utility, while the system-optimum routing agents are assigned paths with the goal of maximizing system performance. This paper considers a scenario where a centralized network manager wishes to optimize utilities over all agents, i.e., implement a system-optimum routing policy. In many real-life scenarios, however, the system manager is unable to influence the route assignment of all agents due to limited influence on route choice decisions. Motivated by such scenarios, a computationally tractable method is presented that computes the minimal amount of agents that the system manager needs to influence (compliant agents) in order to achieve system optimal performance. Moreover, this methodology can also determine whether a given set of compliant agents is sufficient to achieve system optimum and compute the optimal route assignment for the compliant agents to do so. Experimental results are presented showing that in several large-scale, realistic traffic networks optimal flow can be achieved with as low as 13% of the agent being compliant and up to 54%.

cs.MA↗