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Natalia Zuniga-Garcia

Publications and source records attributed to Natalia Zuniga-Garcia.

6 recordsLinked to original sources
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

Fleet-mix Electric Vehicle Routing Problem for the E-commerce Delivery with Limited Off-Hour Delivery Implementation

Freight truck electrification for last-mile delivery is one of the most important research topics to reduce the dependency on fossil fuel operations. Although a battery electric truck still has limitations on daily operations with lower driving ranges and higher purchasing cost than a conventional truck, operations with electrified trucks reduce total energy usage and driving noise on routes. In this paper, we propose a fleet-mix and multi-shift electric vehicle routing problem for joint implementation of fleet electrification and off-hour delivery in urban e-commerce delivery systems. Every electrified truck is assumed to have two shifts for both daytime and nighttime delivery operations while conventional trucks can operate during daytime only because of municipal restrictions on nighttime deliveries, which are related to engine noise. Also, every electrified truck must recharge between shifts at its depot. A fleet owner decides the best electrification ratio of the fleet and the proper number of chargers which gives the minimum total cost. The optimization problem is described as a mixed-integer linear programming model including common constraints for vehicle routing problem, recharging constraints, and two-shift operation of electrified trucks. A bi-level VNS-TS heuristic is also suggested for efficient solution search. The upper-level problem assigns trucks with engine type and brief route information using variable neighborhood search heuristic, and the lower-level problem finds the best route of each assigned truck using a tabu search heuristic. Scenarios with different EV driving ranges and nighttime operation availabilities are developed and evaluated with the POLARIS transportation simulation framework, and results are reported.

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↗
arXiv2021

How Likely are Ride-share Drivers to Earn a Living Wage? Large-scale Spatio-temporal Density Smoothing with the Graph-fused Elastic Net

Ride-sourcing or transportation network companies (TNCs) provide on-demand transportation service for compensation, connecting drivers of personal vehicles with passengers through smartphone applications. In this study, we consider the problem of estimating a spatiotemporally varying probability distribution for the productivity of a TNC driver, using data on more than 1.2 million TNC trips in Austin, Texas. We propose a graph-based smoothing approach that allows for distinct spatial and temporal dynamics, including different degrees of smoothness, spatio-temporal interactions, and interpolation in regions with little or no data. For such a goal, we introduce the Graph-fused Elastic Net (GFEN) and use it in combination with a dyadic tree decomposition for density estimation. In addition, we present an optimization-driven approach for fast point estimates scalable to massive graphs. Bayesian inference and uncertainty quantification with MCMC are also illustrated. The main results demonstrate that the optimization strategy is an effective exploration tool for selecting adequate regularization schemes using Bayesian optimization of the cross-validation loss. Two key empirical findings made possible by our method include: 1) the probability that a TNC driver can expect to earn a living wage in Austin exhibits high variability in space and time, from as low as 25% to as high as 85%; and 2) some drivers suffer considerable "tail risk", with the bottom 10% of the earnings distribution falling below $10 per hour -- grossly below a living wage in Austin for a single adult -- for specific times and locations. All code and data for the paper are publicly available, as a Shiny app for visualizing the results and a software package in Julia for implementing the GFEN.

stat.AP↗
arXiv2019

Evaluation of Ride-Sourcing Search Frictions and Driver Productivity: A Spatial Denoising Approach

This paper considers the problem of measuring spatial and temporal variation in driver productivity on ride-sourcing trips. This variation is especially important from a driver's perspective: if a platform's drivers experience systematic disparities in earnings because of variation in their riders' destinations, they may perceive the pricing model as inequitable. This perception can exacerbate search frictions if it leads drivers to avoid locations where they believe they may be assigned "unlucky" fares. To characterize any such systematic disparities in productivity, we develop an analytic framework with three key components. First, we propose a productivity metric that looks two consecutive trips ahead, thus capturing the effect on expected earnings of market conditions at drivers' drop-off locations. Second, we develop a natural experiment by analyzing trips with a common origin but varying destinations, thus isolating purely spatial effects on productivity. Third, we apply a spatial denoising method that allows us to work with raw spatial information exhibiting high levels of noise and sparsity, without having to aggregate data into large, low-resolution spatial zones. By applying our framework to data on more than 1.4 million rides in Austin, Texas, we find significant spatial variation in ride-sourcing driver productivity and search frictions. Drivers at the same location experienced disparities in productivity after being dispatched on trips with different destinations, with origin-based surge pricing increasing these earnings disparities. Our results show that trip distance is the dominant factor in driver productivity: short trips yielded lower productivity, even when ending in areas with high demand. These findings suggest that new pricing strategies are required to minimize random disparities in driver earnings.

stat.AP↗