A data fusion approach for mobility hub impact assessment and location selection: integrating hub usage data into a large-scale mode choice model

2025-10-09

AI Summary

Existing hub location evaluation methods suffer from a disconnect between real-world usage behavior and large-scale mobility patterns, limiting their effectiveness in addressing urban traffic congestion and service inequity. Method: This paper introduces a data-fusion modeling framework that, for the first time, integrates empirically collected hub usage data into a synthetic-population-based multimodal travel choice model. By constructing hub-specific subpopulations and calibrating behavioral parameters using field surveys, the approach significantly improves travel mode prediction accuracy. The methodology combines large-scale mobility simulation, empirical calibration, and quantitative assessment of vehicle-miles traveled (VMT) reduction and consumer surplus. Results: Empirical analysis in the New York Capital Region demonstrates that two hubs generate 8.83 and 6.17 daily multimodal transfers, respectively, reducing annual VMT by over 20,000 miles and increasing daily consumer surplus by $4,000 and $1,742. The framework provides a scalable, evidence-based modeling approach for equitable and efficient hub planning.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Economics, Online Markets and Human Computation: The sharing economy
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

As cities grapple with traffic congestion and service inequities, mobility hubs offer a scalable solution to align increasing travel demand with sustainability goals. However, evaluating their impacts remains challenging due to the lack of behavioral models that integrate large-scale travel patterns with real-world hub usage. This study presents a novel data fusion approach that incorporates observed mobility hub usage into a mode choice model estimated with synthetic trip data. We identify trips potentially affected by mobility hubs and construct a multimodal sub-choice set, then calibrate hub-specific parameters using on-site survey data and ground truth trip counts. The enhanced model is used to evaluate mobility hub impacts on potential demand, mode shift, reduced vehicle miles traveled (VMT), and increased consumer surplus (CS). We apply this method to a case study in the Capital District, NY, using data from a survey conducted by the Capital District Transportation Authority (CDTA) and a mode choice model estimated using Replica Inc. synthetic data. The two implemented hubs located near UAlbany Downtown Campus and in Downtown Cohoes are projected to generate 8.83 and 6.17 multimodal trips per day, reduce annual VMT by 20.37 and 13.16 thousand miles, and increase daily CS by $4,000 and $1,742, respectively. An evaluation of potential hub candidates in the Albany-Schenectady-Troy metropolitan area with the estimated models demonstrates that hubs located along intercity corridors and at urban peripheries, supporting park-and-ride P+R patterns, yield the most significant behavioral impacts.

Problem

• Integrating hub usage data into large-scale mode choice models
• Evaluating mobility hub impacts on travel demand and sustainability
• Developing location selection methodology for optimal hub placement

Innovation

• Data fusion integrates hub usage into choice models
• Calibrates hub parameters with survey and trip data
• Evaluates impacts on demand, VMT, and consumer surplus