arXiv subjects

Omer Verbas

Publications and source records attributed to Omer Verbas.

13 recordsLinked to original sources

arXiv2026

Modeling of Mobility and Energy Policies in an Agent-Based Framework: Case Studies for Chicago Region in 2050

Metropolitan regions are simultaneously pursuing several interventions to improve mobility, accessibility, and energy efficiency, necessitating integrated tools to understand how these policies interact to affect travel behavior, energy use, and infrastructure needs. This paper evaluates the combined impacts of electrification, freight demand management, road pricing, parking reform, and transit expansion on the Chicago metropolitan transportation system in 2050, using a business-as-usual (BAU) scenario as the baseline. We employ POLARIS, a large-scale agent-based modeling framework calibrated to 2019 conditions, to simulate nine policy scenarios for the seven-county northeastern Illinois region. The framework co-simulates activity-based passenger demand, endogenous freight generation, multimodal traffic assignment, and transit operations, with charging infrastructure and freight operations optimized for each case. Our findings reveal that under the high electrification scenario, total fuel mass declines by 68% while total charging energy increases by approximately 4-8x from BAU, resulting in a peak power demand near 4 GW concentrated in the urban core. Furthermore, freight management policies reduce freight VMT by increasing trip frequency but shortening distances, smart road pricing most effectively reduces auto VMT, and transit expansion boosts ridership by 18% relative to BAU. By presenting the first integrated, agent-based scenario framework for Chicago that jointly evaluates these interventions, this study provides actionable insights for regional transportation planning, grid infrastructure investment, and emissions reduction, highlighting the value of targeted charger upgrades and coordinated policy bundles.

physics.soc-ph↗

arXiv2026

Bus Fleet Electrification Under Capital Cost and Scheduling Constraints: A Five-Agency Case Study

As transit agencies consider bus fleet electrification, understanding the efficiency and cost of replacing diesel buses (DBs) with battery electric buses (BEBs) is critical. To evaluate this, this study applies a mixed-fleet optimization model, integrating scheduling, charging, and fleet composition decisions, across five agencies: Santa Monica's Big Blue Bus (BBB), the Chicago Transit Authority (CTA), Knoxville Area Transit (KAT), the Metropolitan Atlanta Rapid Transit Authority (MARTA), and Manhattan's Metropolitan Transportation Authority (MTA) bus service. By calculating electric fleet share, the BEB/DB replacement ratio, transit-link density, and vehicle activity-time allocation, the study finds that while optimized fleets remain majority-electric, vehicle substitution is rarely one-to-one. Average replacement ratios range from 1.101 for CTA to 1.245 for KAT, with higher transit-link density networks like CTA and MTA requiring fewer replacement buses per diesel bus displaced than lower-density networks like MARTA and KAT. While these relationships are descriptive rather than causal, non-revenue vehicle activity may help explain the differences. By shifting the focus from simple electric fleet share to diesel replacement efficiency, this multi-agency comparison demonstrates that transit agencies should use the replacement ratio to accurately forecast additional fleet capacity requirements and avoid the costly assumption of strict one-to-one vehicle substitution.

math.OC↗

arXiv2026

Integrated Optimization of Scheduling and Flexible Charging in Mixed Electric-Diesel Urban Transit Bus Systems

The transition of transit fleets to alternative powertrains offers a potential pathway to reducing the cost of mobility. However, the limited range and long charging durations of battery electric buses (BEBs) introduce significant operational complexities, necessitating innovative scheduling and charging strategies. This study proposes an integrated mixed-integer linear programming model to optimize vehicle scheduling and charging strategies for mixed fleets of BEBs and diesel buses. Unlike existing models, which often assume a fixed BEB fleet size or restrict charging to a single charger type, our approach simultaneously determines the optimal fleet composition, scheduling, and flexible partial charging strategy incorporating both slow and fast chargers at garages and terminal stations. The model minimizes combined fleet purchase and operational costs. A queuing strategy is introduced, departing from traditional first-come, first-served methods by dynamically allocating waiting and charging times based on operational priorities and resource availability, improving overall scheduling efficiency. To overcome computational complexities arising from numerous variables, a column generation framework is developed, facilitating scalable solutions for large-scale transit networks. Numerical experiments using real-world transit data from the Chicago Transit Authority and the Pace suburban bus systems demonstrate the model's effectiveness. Results indicate that while a full transition to alternative powertrains results in a modest cost increase, optimal mixed-fleet configurations can actually reduce total system costs. Furthermore, sensitivity analyses reveal that restricting charging to garages significantly increases fleet size and operational costs, underscoring the potential of distributed opportunistic charging.

math.OC↗

arXiv2025

Extreme-Scale EV Charging Infrastructure Planning for Last-Mile Delivery Using High-Performance Parallel Computing

This paper addresses stochastic charger location and allocation problems under queue congestion for last-mile delivery using electric vehicles (EVs). The objective is to decide where to open charging stations and how many chargers of each type to install, subject to budgetary and waiting-time constraints. We formulate the problem as a mixed-integer non-linear program, where each station-charger pair is modeled as a multiserver queue with stochastic arrivals and service times to capture the notion of waiting in fleet operations. The model is extremely large, with billions of variables and constraints for a typical metropolitan area; even loading the model in solver memory is difficult, let alone solving it. To address this challenge, we develop a Lagrangian-based dual decomposition framework that decomposes the problem by station and leverages parallelization on high-performance computing systems, where the subproblems are solved by using a cutting plane method and their solutions are collected at the master level. We also develop a three-step rounding heuristic to transform the fractional subproblem solutions into feasible integral solutions. Computational experiments on data from the Chicago metropolitan area with hundreds of thousands of households and thousands of candidate stations show that our approach produces high-quality solutions in cases where existing exact methods cannot even load the model in memory. We also analyze various policy scenarios, demonstrating that combining existing depots with newly built stations under multiagency collaboration substantially reduces costs and congestion. These findings offer a scalable and efficient framework for developing sustainable large-scale EV charging networks.

math.OC↗

arXiv2025

Equity Impacts of Public Transit Network Redesign with Shared Autonomous Mobility Services

This study examines the equity impacts of integrating shared autonomous mobility services (SAMS) into transit system redesign. Using the Greater Chicago area as a case study, we compare two optimization objectives in multimodal transit network redesign: minimizing total generalized costs (equity-agnostic) versus prioritizing service in low-income areas (equity-focused). We evaluate the achieved accessibility of clustered zones with redesigned transit networks under two objectives, compared to driving and the existing transit network. The transit access gaps across zones and between transit and driving are found to be generally reduced with the introduction of SAMS, but less so with the subsequent improved infrastructure under budget. Differential improvement in equity is seen across suburbs and areas of the city, reflecting the disparity in current transit access and improvement potential. In particular, SAMS bridges the transit access gaps in suburban and city areas currently underserved by transit. The City of Chicago, which is also disproportionately home to vulnerable populations, offers an avenue to improve vertical equity. These findings demonstrate that SAMS can enhance both horizontal and vertical equity in transit systems, particularly when equity is explicitly incorporated into the design objective.

eess.SY↗

arXiv2024

Joint Optimization of Multimodal Transit Frequency and Shared Autonomous Vehicle Fleet Size with Hybrid Metaheuristic and Nonlinear Programming

Shared autonomous vehicles (SAVs) bring competition to traditional transit services but redesigning multimodal transit network can utilize SAVs as feeders to enhance service efficiency and coverage. This paper presents an optimization framework for the joint multimodal transit frequency and SAV fleet size problem, a variant of the transit network frequency setting problem. The objective is to maximize total transit ridership (including SAV-fed trips and subtracting boarding rejections) across multiple time periods under budget constraints, considering endogenous mode choice (transit, point-to-point SAVs, driving) and route selection, while allowing for strategic route removal by setting frequencies to zero. Due to the problem's non-linear, non-convex nature and the computational challenges of large-scale networks, we develop a hybrid solution approach that combines a metaheuristic approach (particle swarm optimization) with nonlinear programming for local solution refinement. To ensure computational tractability, the framework integrates analytical approximation models for SAV waiting times based on fleet utilization, multimodal network assignment for route choice, and multinomial logit mode choice behavior, bypassing the need for computationally intensive simulations within the main optimization loop. Applied to the Chicago metropolitan area's multimodal network, our method illustrates a 33.3% increase in transit ridership through optimized transit route frequencies and SAV integration, particularly enhancing off-peak service accessibility and strategically reallocating resources.

eess.SY↗

arXiv2024

Joint Optimization of Pattern, Headway, and Fleet Size of Multiple Urban Transit Lines with Perceived Headway Consideration and Passenger Flow Allocation

This study addresses the urban transit pattern design problem, optimizing stop sequences, headways, and fleet sizes across multiple routes and periods simultaneously to minimize user costs (composed of riding, waiting, and transfer times) under operational constraints (e.g., vehicle capacity and fleet size). A destination-labeled multi-commodity network flow (MCNF) formulation is developed to solve the problem at a large scale more efficiently compared to the previous literature. The model allows for flexible pattern options without relying on pre-defined candidate sets and simultaneously considers multiple operational strategies such as express/local services, short-turning, and deadheading. It evaluates perceived headways of joint patterns for passengers, assigns passenger flows to each pattern accordingly, and allows transfers across patterns in different directions. The mixed-integer linear programming (MILP) model is demonstrated with a city-sized network of metro lines in Chicago, IL, USA, achieving near-optimal solutions in hours. The total weighted journey times are reduced by 0.61% and 5.76% under single-route and multi-period multi-route scenarios respectively. The model provides transit agencies with an efficient tool for comprehensive service design and resource allocation, improving service quality and resource utilization without additional operational costs.

eess.SY↗

arXiv2024

Impact of Transit on Mobility, Equity, and Economy in the Chicago Metropolitan Region

Transit is essential for urban transportation and achieving net-zero targets. In urban areas like the Chicago Metropolitan Region, transit enhances mobility and connects people, fostering a dynamic economy. To quantify the mobility and selected economic impacts of transit, we use a novel agent-based simulation model POLARIS to compare baseline service against a scenario in which transit is completely removed. The transit-removal scenario assumes higher car ownership and results in higher traffic congestion, numerous activity cancellations, and economic decline. In this scenario, average travel times increase by 14.2% regionally and 34.7% within the City of Chicago. The resulting congestion causes significant activity cancellations despite increased car ownership: 11.8% of non-work and 2.8% of work/school activities regionally, totaling an 8.6% overall cancellation rate. In the city, non-work cancellations would reach 26.9%, and work/school cancellations 7.3%, leading to a 19.9% overall cancellation rate. The impact varies between groups. Women and lower-income individuals are more likely to cancel activities than men and higher-income groups. Women account for 53.7% of non-work and 53.0% of total cancellations. The lowest 40% income group experiences 50.2% of non-work and 48.0% of overall cancellations. Combined, activity cancellations, travel time losses, and increased car ownership cost the region $35.4 billion. With annual public transit funding at $2.7 billion, the ratio is 13 to 1, underscoring transit's critical role in mobility, equity, and economic health.

math.OC↗

arXiv2024

Modeling Transit in a Fully Integrated Agent-Based Framework: Methodology and Large-Scale Application

This study presents a transit routing, assignment, and simulation framework which is fully embedded in a multimodal, multi-agent transportation demand and supply modeling platform. POLARIS, a high-performance agent-based simulation platform, efficiently integrates advanced travel and freight demand modeling, dynamic traffic and transit assignment, and multimodal transportation simulation within a unified framework. We focus on POLARIS's transit routing, assignment, and simulation components, detailing its structural design and essential terminologies. We demonstrate how the model integrates upstream decision-making processes - activity generation, location and timing choices, and mode selection, particularly for transit-inclusive trips - followed by routing, assignment decisions, and the movement of travelers and vehicles within a multimodal network. This integration enables modeling of interactions among all agents, including travelers, vehicles, and transportation service providers. The study reviews literature on transportation system modeling tools, describes the transit modeling framework within POLARIS, and presents findings from large-scale analyses of various policy interventions. Results from numerical experiments reveal that measures such as congestion pricing, transit service improvements, first-mile-last-mile subsidies, increased e-commerce deliveries, and vehicle electrification significantly impact transit ridership, with some interactions between these levers exhibiting synergistic or canceling effects. The case study underscores the necessity of integrating transit modeling within a broader multimodal network simulation and decision-making context.

eess.SY↗

arXiv2024

Problem of Locating and Allocating Charging Equipment for Battery Electric Buses under Stochastic Charging Demand

Bus electrification plays a crucial role in advancing urban transportation sustainability. Battery Electric Buses (BEBs), however, often need recharging, making the Problem of Locating and Allocating Charging Equipment for BEBs (PLACE-BEB) essential for efficient operations. This study proposes an optimization framework to solve the PLACE-BEB by determining the optimal placement of charger types at potential locations under the stochastic charging demand. Leveraging the existing stochastic location literature, we develop a Mixed-Integer Non-Linear Program (MINLP) to model the problem. To solve this problem, we develop an exact solution method that minimizes the costs related to building charging stations, charger allocation, travel to stations, and average queueing and charging times. Queueing dynamics are modeled using an M/M/s queue, with the number of servers at each location treated as a decision variable. To improve scalability, we implement a Simulated Annealing (SA) and a Genetic Algorithm (GA) allowing for efficient solutions to large-scale problems. The computational performance of the methods was thoroughly evaluated, revealing that SA was effective for small-scale problems, while GA outperformed others for large-scale instances. A case study comparing garage-only, other-only, and mixed scenarios, along with joint deployment, highlighted the cost benefits of a collaborative and a comprehensive approach. Sensitivity analyses showed that the waiting time is a key factor to consider in the decision-making.

math.OC↗

arXiv2024

Large-Scale Evaluation of Mobility, Technology and Demand Scenarios in the Chicago Region Using POLARIS

Rapid technological progress and innovation in the areas of vehicle connectivity, automation and electrification, new modes of shared and alternative mobility, and advanced transportation system demand and supply management strategies, have motivated numerous questions and studies regarding the potential impact on key performance and equity metrics. Several of these areas of development may or may not have a synergistic outcome on the overall benefits such as reduction in congestion and travel times. In this study, the use of an end-to-end modeling workflow centered around an activity-based agent-based travel demand forecasting tool called POLARIS is explored to provide insights on the effects of several different technology deployments and operational policies in combination for the Chicago region. The objective of the research was to explore the direct impacts and observe any interactions between the various policy and technology scenarios to help better characterize and evaluate their potential future benefits. We analyze system outcome metrics on mobility, energy and emissions, equity and environmental justice and overall efficiency for a scenario design of experiments that looks at combinations of supply interventions (congestion pricing, transit expansion, tnc policy, off-hours freight policy, connected signal optimization) for different potential demand scenarios defined by e-commerce and on-demand delivery engagement, and market penetration of electric vehicles. We found different combinations of strategies that can reduce overall travel times up to 7% and increase system efficiency up to 53% depending on how various metrics are prioritized. The results demonstrate the importance of considering various interventions jointly.

cs.CY↗

arXiv2023

Heuristic Solutions to the Single Depot Electric Vehicle Scheduling Problem with Next Day Operability Constraints

This study focuses on the single depot electric vehicle scheduling problem (SDEVSP) within the broader context of the vehicle scheduling problem (VSP). By developing an effective scheduling model using mixed-integer linear programming, we generate bus blocks that accommodate electric vehicles (EVs), ensuring successful completion of each block while considering recharging requirements between blocks and during off-hours. Next day operability constraints are also incorporated, allowing for seamless repetition of blocks on subsequent days. The SDEVSP is known to be computationally complex, deriving optimal solutions unattainable for large-scale problems within reasonable timeframes. To address this, we propose a two-step solution approach: first solving the single depot VSP, and then addressing the block chaining problem (BCP) using the blocks generated in the first step. The BCP focuses on optimizing block combinations to facilitate recharging between consecutive blocks, considering operational constraints. A case study conducted reveals that nearly 100% electrification for Chicago, IL and Austin, TX transit buses is viable yet requires 1.6 EVs at 150-mile range per diesel vehicle.

math.OC↗

arXiv2021

A Deep Learning Approach for Macroscopic Energy Consumption Prediction with Microscopic Quality for Electric Vehicles

This paper presents a machine learning approach to model the electric consumption of electric vehicles at macroscopic level, i.e., in the absence of a speed profile, while preserving microscopic level accuracy. For this work, we leveraged a high-performance, agent-based transportation tool to model trips that occur in the Greater Chicago region under various scenario changes, along with physics-based modeling and simulation tools to provide high-fidelity energy consumption values. The generated results constitute a very large dataset of vehicle-route energy outcomes that capture variability in vehicle and routing setting, and in which high-fidelity time series of vehicle speed dynamics is masked. We show that although all internal dynamics that affect energy consumption are masked, it is possible to learn aggregate-level energy consumption values quite accurately with a deep learning approach. When large-scale data is available, and with carefully tailored feature engineering, a well-designed model can overcome and retrieve latent information. This model has been deployed and integrated within POLARIS Transportation System Simulation Tool to support real-time behavioral transportation models for individual charging decision-making, and rerouting of electric vehicles.

cs.LG↗