About

I study large language models in the pipelines that produce official travel statistics: the data behind mode-share figures, activity-based demand models, and infrastructure investment cases. My research documents a failure mode with real policy stakes. LLMs will "repair" data to satisfy whatever an automated verifier asserts, even when the verifier itself is wrong, and the result carries a verified label it did not earn.

The constructive half of the work builds automated checks that flag only what the data can actually prove, not artifacts of how it was measured, so repair and generation loops are never asked to fix what the data cannot show is broken. Five connected studies carry this arc from measurement to policy use, and my dissertation extends it from repairing observed travel diaries to generating certified daily activity schedules, the core representation behind activity-based travel demand models.

  • 5 papersone connected arc: measure → diagnose → construct → apply → stress-test
  • 961 → 42studies screened into an evidence-scored map of AI travel models
  • 10,592person-day activity chains in the NHTS 2022 corpus prepared for the dissertation's scheduler
Smooth kernel-density curves of activity start times by trip purpose for Daejeon 2021, with home, work, and school peaks
When a city travels: activity start times by purpose (Daejeon 2021).
PTV Visum view of Daejeon's zone system with assigned demand shown as red flow bands over the road network
Where it travels: Daejeon zones and demand in PTV Visum.

News

  • A third manuscript, on automated quality control in household travel surveys, is being prepared for journal submission. Title and venue will appear here after the journal's decision.
  • Two manuscripts on verification soundness for LLM data repair entered double-blind peer review. Details will appear here after notification.
  • Revised manuscript of the AI-ABM trilemma review resubmitted to the International Journal of Urban Sciences.

Research

Ternary plot mapping AI travel models across interpretability, fidelity, and efficiency, colored by model family
The interpretability, fidelity, and efficiency map: 42 AI-enhanced activity-based models scored on three axes (from the review below). Most cluster toward fidelity.

Measure

An Evidence-Based Review of AI-Enhanced Activity-Based Models: Trade-offs Between Interpretability, Fidelity, and Efficiency

Kervin Joshua Lucas, Inhi Kim

Int. J. of Urban Sciences · revision under review

42 scored studies, one map: AI travel models pile up on predictive fidelity, while behavioral interpretability and computational efficiency go chronically unmeasured.

Abstract & key numbers

Progress in AI-enhanced activity-based travel models is constrained by an under-articulated three-way trade-off among behavioral interpretability, predictive fidelity, and computational efficiency. From a structured review (961 records screened to 42 core studies), every study is scored on falsifiable indicators and placed on a constant-sum trilemma map (I + F + E = 20), a visualization device whose headline pattern holds under three alternative normalizations. Quality is use-case-dependent: progress reads as movement within target regions, not toward a universal optimum.

  • 961 → 42screening funnel to core scored studies
  • I + F + E = 20constant-sum trilemma map; pattern robust to three alternative normalizations

Diagnose · Construct · Apply

Three manuscripts, details withheld

Titles, venues, and results withheld until each venue reaches a decision

Two under anonymized review, one in preparation

The diagnostic, constructive, and applied core of the arc: one paper shows that verifier-grounded LLM repair complies with flawed automated checks; one builds checks that flag only what the data can prove; one asks whether an automated edit that satisfies a data checker has actually corrected the record. Each is listed in full once its venue reaches a decision, the first two expected late 2026.

Stress-test

Can LLM Activity Schedulers Be Trusted for Transport Policy Counterfactuals?

Kervin Joshua Lucas, Inhi Kim

Transportation Research Part A: Policy and Practice · special issue on generative AI · planned

Can a generated schedule carry a policy conclusion? Whether LLM activity schedulers are trustworthy enough to answer counterfactual policy questions, building on the verifier-soundness line in the work above.

Scope

Extends the verified-LLM thread of the papers above from repairing observed travel records to generating the schedules a policy question would be asked of. Planned for the special issue on generative AI in transport policy and applications, and currently at the scoping stage.

Dissertation

LLM-Powered Activity Scheduling with Sound Verifier Guarantees (working title). Thesis: LLM-in-the-loop pipelines can generate and repair daily activity schedules that are simultaneously interpretable, distributionally faithful, and efficient, provided they are grounded by verifiers that are sound under measurement uncertainty.

Ch.StageFocusStatus
2MeasureThe interpretability, fidelity, and efficiency trilemmaIJUS, R1 under review
3DiagnoseHow unsound verifiers corrupt LLM repairmanuscript, in review
4ConstructProvably sound verificationmanuscript, in review
5ApplyAutomated editing of survey travel diariesmanuscript, in preparation
6GenerateVerifier-certified activity scheduler (NHTS 2022)new core work, designed
Chapter 6 design

Chapter 6 generalizes the repair loop into a generation loop: an LLM proposes a full-day activity schedule for a persona, a sound scheduler-verifier audits it, provable violations return as structured critiques, and the loop accepts only at zero provable violations, with every accepted schedule carrying a constraint certificate. Built on the NHTS 2022 NextGen survey (10,592 person-day chains, 31,074 trips), evaluated on the Chapter 2 trilemma axes against transformer, Markov, and rule-based baselines.

Publications

Peer-reviewed journal and conference work. Full list and live citation counts on Google Scholar.

Journal articles
  • Smart region mobility framework
    R.K.C. Billones, M.A. Guillermo, K.C. Lucas, M.D. Era, E.P. Dadios, A.M. Fillone · Sustainability 13(11), 6366 · 2021 · 43 citations
  • Strategic transit route recommendation considering multi-trip feature desirability using a logit model with optimal travel-time analysis
    M.A. Guillermo, M.C. Rivera, K.J.C. Lucas, R.S. Concepcion II, A.A. Bandala, et al. · Journal of Advanced Computational Intelligence and Intelligent Informatics · 2022 · 7 citations
Korean conferences
  • Recursive neurosymbolic optimization for the semantic repair of structurally inconsistent household travel surveys
    K.J. Lucas, J. Lee, I. Kim · Korea ITS Society Conference (한국 ITS 학회 학술대회) · 2026
  • Prediction models for travel mode and trip purpose using operator networks to advance activity-based models
    K.J. Lucas, I. Kim · Korean Society of Transportation Conference (대한교통학회 학술대회) · 2025
  • 여행 연쇄 예측에서 시공간적 영향 평가 (Evaluating spatiotemporal influence on trip-chain prediction)
    K.J. Lucas, J. Lee, S. Paek, I. Kim · Korea ITS Society 2025 Spring Conference (한국 ITS 학회 2025 춘계학술대회) · 2025
  • Developing a framework for a complete transport forecasting system: a literature review
    K.J. Lucas, I. Kim · Korea ITS Society Conference (한국 ITS 학회 학술대회) · 2024
International conferences
  • Transportation Infrastructure Impacts Calculator (TIIC): an infrastructure assessment tool
    A.E.C. Ruiz, N.R. Roxas Jr, K.I.D.Z. Roquel, K.D.S. Yu, A.M. Fillone, K.J.C. Lucas · Transportation Research Procedia 82 · 2025
  • Trip generation in the Philippines: analysis of traditional, geospatial, and AI-based models
    K.J. Lucas, A. Fillone, I. Kim · Suwon ITS Asia-Pacific Forum · 2025
  • Evaluating spatiotemporal influence on trip-chain prediction
    K.J. Lucas, J. Lee, S. Paek, I. Kim · Suwon ITS Asia-Pacific Forum · 2025
  • What if Metro Manila developed a comprehensive rail transit network?
    J.R.F. Regidor, D.S. Aloc, A.M. Fillone, K.J.C. Lucas · Proceedings of the Eastern Asia Society for Transportation Studies (EASTS), vol. 11 · 2017 · 9 citations
Philippine conferences
  • Metro Manila transportation network: big data analytics and applications
    A. Fillone, E. Dadios, N. Roxas, M. Era, K. Lucas, R. Abad, K. Roquel · Sustainable Mobility Research Unit · 2020 · 2 citations
  • GIS for better public transportation and transit
    M.C. Paringit, K.J. Lucas, M. Cutora · De La Salle University Research Congress, Manila · 2019 · 1 citation

Other work

Earlier applied studies that shaped this direction, including trip generation under spatial heterogeneity in Aklan, Philippines, comparing classical, geospatial (GWR), and operator-learning (DeepONet) models.

Education

  • PhD, MobilitySince 2024
    Cho Chun Shik Graduate School of Mobility, KAIST · Daejeon, South Korea
    Third-year candidate, expected 2028. Travel demand modeling and trustworthy AI for activity-based models.
  • MSc, Civil Engineering2017–2020
    De La Salle University · Manila, Philippines
    Thesis: effects of traffic congestion pricing schemes on travel behavior in Bonifacio Global City, Taguig.
  • BSc, Civil Engineering2009–2014
    De La Salle University · Manila, Philippines

Experience

  • Assistant Professorial Lecturer2021–2023
    De La Salle University · Part-time
    Taught transportation engineering and transport-modeling software courses.
  • Science Research Specialist2015–2022
    Civil Engineering Department, De La Salle University · Full-time
    Transportation-planning data collection and analysis; modeled Metro Manila road and traffic networks for transport studies; supported teaching and departmental research.