arXiv subjects

Xiyuan Ren

Publications and source records attributed to Xiyuan Ren.

8 recordsLinked to original sources

arXiv2026

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

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 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, introduce a nested choice structure that accounts for mode transfers, and calibrate hub-specific parameters using on-site survey data and ground truth trip counts. A sensitivity analysis demonstrates that the calibration remains robust when the share of hub trips is relatively low and travel demand spans multiple OD pairs. We apply this approach to a case study in Capital District, NY, using data from a survey conducted by the Capital District Transportation Authority (CDTA) and a mode choice model estimated with Replica Inc.'s synthetic data. A bootstrap procedure quantifies uncertainty in hub usage and all downstream impact estimates. The two implemented hubs, near UAlbany Downtown Campus and in Downtown Cohoes, are projected to generate 9.89 (95% CI: [3.20, 29.04]) and 6.98 ([3.86, 12.67]) multimodal trips per day, reduce daily vehicle-miles-traveled (VMT) by 43.29 ([11.69, 142.81]) and 32.12 ([1.04, 60.27]) miles, and increase daily consumer surplus by $3,870 ([2,020, 5,276]) and $1,790 ([1,202, 2,068]), respectively. A regional evaluation of 1,100 candidate locations highlights that optimal hub siting varies by planning objective, with hubs along intercity corridors and urban peripheries yielding the largest behavioral impacts.

econ.GN↗

arXiv2026

Deep and diverse population synthesis for multi-person households using generative models with conditional inputs

Traditional methods of population synthesis produce stable and interpretable populations but cannot capture the interrelationships between household- and individual-level attributes. Recent deep learning methods offer this flexibility, yet can overfit high-dimensional attribute relationships without structural guidance and deviate from known structures. We develop a household level synthetic population generation framework that adapts the existing conditional input directed acyclic tabular generative adversarial network, or ciDATGAN, to multi person households. The framework combines household size specific data construction, directed acyclic graphs (DAG) informed dependency regularization, and conditional population inputs as deterministic anchoring to preserve intrahousehold associations. We apply the model to generate an open access synthetic population for New York State. The synthetic population includes nearly 20 million individuals and 7.5 million households in 2021. Validation against withheld benchmarks shows close agreement: joint distribution matching of Public Use Microdata Areas (PUMA), age, race, and disability against the full public use microdata sample (PUMS) yields an R-squared of 0.602 and a Jensen-Shannon distance of 0.280, pairwise Cramer's V differences between generated and benchmark records average below 0.007 at the person level, and classifier two-sample tests on complete household records yield AUC values of 0.527-0.549, close to chance level. The generated households reproduce cross member associations while increasing diversity by 10-17% over the PUMS sample and 13.2% over PopGen alone.

cs.CY↗

arXiv2026

Using large scale GPS data to reveal EV driver activity patterns beyond charging sessions

Accurate insights into electric vehicle (EV) driver behavior are essential for long-term infrastructure planning, grid management, and understanding downstream economic impacts, yet individual level data on EV mobility remains limited. Here, we develop a scalable framework to infer EV ownership and charging behavior from passively collected, high-resolution mobility traces covering over 760,000 drivers across four major U.S. metropolitan areas. We identify likely EV drivers based on distinctive visitation patterns to charging stations and gas stations, frequency of visits, and daily travel behavior, and calibrate cohort size using aggregate EV registration statistics. The resulting EV cohort closely matches official registration data at the zip code level and exhibits charging patterns consistent with independent, charger level benchmark datasets, providing external validation of the inferred population. Leveraging this inferred cohort, we reconstruct charging events and associated activity patterns to examine how EV drivers interact with surrounding urban amenities. Compared to non-EV drivers, EV drivers exhibit systematically higher visitation rates to nearby cafes and restaurants during charging sessions, revealing significant economic spillover effects. Furthermore, we find EV drivers exhibit trip bundling behavior, visiting more POIs over less time and distance on days where they charge versus all other days. These patterns are not observable in conventional charging session data, which lack behavioral context beyond the charging event itself. Our results demonstrate the potential of using mobility data to enable a richer, behaviorally grounded understanding of the off-plug needs of EV drivers, providing a foundation for optimizing charging infrastructure deployment and co-locating complementary urban amenities in an increasingly electrified transportation landscape.

physics.soc-ph↗

arXiv2026

Distributional welfare impacts and compensatory transit strategies under NYC congestion pricing

Early evaluations of NYC's congestion pricing program indicate overall improvements in vehicle speed and transit ridership. However, its distributional impacts remain understudied, as does the design of compensatory transit strategies needed to mitigate potential welfare losses. This study identifies population segments and regions most affected by congestion pricing, and evaluates how those welfare losses can be compensated through transit improvements funded by the toll revenues. We estimate joint mode and destination models using aggregated synthetic trips in the NY-NJ-CT-PA Combined Statistical Area (CSA) and calibrate toll-related parameters using post-toll changes reported by MTA. Compensatory transit strategies are evaluated by quantifying the reductions in transit wait time and fare discounts required to offset the CS losses. The results show that the program leads to an accessibility-related CS loss of $397.23 million per year, while generating net passenger toll revenue of $523.44 million per year estimated based on the MTA's report--indicating a net welfare gain. However, these gains in benefits conceal significant disparities. Achieving a general compensation requires modest investment--a 0.63-minute (13%) reduction in wait time or $165.15 million in annual fare subsidies for NYC residents, and a 2.12-minute (28%) reduction or $171.42 million for New Jersey residents. However, ensuring that no population group and county unit is made worse off is substantially more costly and infeasible through transit improvements alone. These findings underscore the need for differentiated compensation strategies: uniform fare discounts lead to overcompensation for some groups, whereas segment-specific discounts, origin-based fare reductions, or commuter pass bundles can achieve equitable accessibility restoration at lower fiscal cost.

econ.GN↗

arXiv2026

Microtransit revenue management informed by citywide travel demand and joint subscription-mode choice modeling

As an IT-enabled multi-passenger mobility service, microtransit can improve accessibility, reduce congestion, and promote sustainability. However, realizing its business potential requires a deeper understanding of traveler preferences, highlighting the need for more effective tools for demand forecasting and revenue management, especially when actual usage data are limited. We propose an innovative modeling approach that integrates travel behavioral insights into microtransit policymaking. The approach operates by (1) leveraging citywide synthetic data to achieve greater spatiotemporal granularity, (2) estimating a nonparametric nested model for joint travel mode and ride-pass subscription choices, and (3) employing a simulation-based method to calculate revenue and traveler benefits under various policy scenarios. We demonstrate the applicability of our approach through a case study in Arlington, TX, one of the largest deployments of microtransit (Via) in the U.S. Using the simulation-based workflow, we evaluate alternative policy scenarios, including ride-pass discounts, event-based subsidies, and place-based subsidies, to assess their impacts on microtransit ridership, system revenue, and traveler welfare. The results indicate that reducing the weekly pass price from $25 to $18.9 and the monthly pass price from $80 to $71.5 would increase total revenue by approximately $127 per day. A 100% trip fare discount could reduce 61 car trips to AT&T Stadium during a game event while generating an additional 82 microtransit trips per day to Medical City Arlington. However, achieving these mode shifts would require subsidies of approximately $533 per event and $483 per day, respectively.

econ.EM↗

arXiv2026

Welfare, sustainability, and equity evaluation of the New York City Interborough Express using spatially heterogeneous mode choice models

The Metropolitan Transit Authority (MTA) proposed building a new light rail route called the Interborough Express (IBX) to provide a direct, fast transit linkage between Queens and Brooklyn. An open-access synthetic citywide trip agenda dataset and a block-group-level mode choice model are used to assess the potential impact IBX could bring to New York City (NYC). IBX could save 28.1 minutes to potential riders across the city. For travelers either going to or departing from areas close to IBX, the average time saving is projected to be 29.7 minutes. IBX is projected to have more than 272 thousand daily ridership after its completion (81% higher than reported in the official IBX proposal). Among those riders, more than 58 thousand people (21.4%) would come from low-income households while 185 thousand people (68.2%) would start or end along the IBX corridor. The addition of IBX would attract more than 40 thousand additional daily trips to transit mode, among which more than 16 thousand would be switched from using private vehicles, reducing potential greenhouse gas (GHG) emissions by 30.63 metric tons per day. IBX can also bring significant consumer surplus benefits to the communities, which are estimated to be $0.89 USD per trip. However, the service does not appear to significantly reduce the proportion of travelers whose consumer surpluses fall below 10% of the population average (already quite low).

cs.CY↗

arXiv2025

Planning for Rhythmized Urban Parks: Temporal Park Classification and Modes of Action

Problem, research strategy, and findings: Urban parks offer residents significant physiological and mental health benefits, improving their quality of life. However, traditional park planning often treats parks as static spatial resources, neglecting their temporal dimension of visitation rhythms. This study proposes a paradigm shift in classifying, programming, and designing parks. Utilizing 1.5 million mobile phone records, we classified 254 urban parks in Tokyo based on their visitation patterns across different times of the day, week, and year. Our results showed that parks are rhythmized by seasonal events and daily activities, exhibiting complex visiting patterns shaped by the combined effects of preference variation and accessibility barriers. We concluded by discussing modes of action for temporal park planning practice. Takeaway for practice: Park planners, designers, and policymakers should seek to incorporate temporality in activity-based park design and programming. This can be fulfilled in four ways by (1) tracing year-round, citywide park activities through data-driven methods, (2) implementing temporary and tactical designs for dynamic park demands, (3) establishing an inclusive park system that helps improve spatiotemporal equity, and (4) encouraging public engagement that cultivates the sense of time and identity.

physics.soc-ph↗

arXiv2025

Nonparametric mixed logit model with market-level parameters estimated from market share data

We propose a nonparametric mixed logit model that is estimated using market-level choice share data. The model treats each market as an agent and represents taste heterogeneity through market-specific parameters by solving a multiagent inverse utility maximization problem, addressing the limitations of existing market-level choice models with parametric estimation. A simulation study is conducted to evaluate the performance of our model in terms of estimation time, estimation accuracy, and out-of-sample predictive accuracy. In a real data application, we estimate the travel mode choice of 53.55 million trips made by 19.53 million residents in New York State. These trips are aggregated based on population segments and census block group-level origin-destination (OD) pairs, resulting in 120,740 markets. We benchmark our model against multinomial logit (MNL), nested logit (NL), inverse product differentiation logit (IPDL), and the BLP models. The results show that the proposed model improves the out-of-sample accuracy from 65.30% to 81.78%, with a computation time less than one-tenth of that taken to estimate the BLP model. The price elasticities and diversion ratios retrieved from our model and benchmark models exhibit similar substitution patterns. Moreover, the market-level parameters estimated by our model provide additional insights and facilitate their seamless integration into supply-side optimization models for transportation design. By measuring the compensating variation for the driving mode, we found that a $9 congestion toll would impact roughly 60 % of the total travelers. As an application of supply-demand integration, we showed that a 50% discount of transit fare could bring a maximum ridership increase of 9402 trips per day under a budget of $50,000 per day.

econ.EM↗