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Source note3 min read

An Agentic Approach for Active Data Collection, Travel Behavior Modeling, and Weather-Sensitive Demand Prediction

Source

arXiv

arxiv.org/abs/2608.20320v1

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Source

  • Source name: arXiv
  • Source kind: paper
  • Authors / organization: Narges Ahmadi, Yubo Jiao, Jônatas Augusto Manzolli, Jiangbo Yu, Luis Miranda-Moreno
  • Venue: arXiv: cs.AI
  • Published: 2026-08-20
  • Collected: 2026-08-21
  • URL: http://arxiv.org/abs/2608.20320v1
  • PDF: https://arxiv.org/pdf/2608.20320v1
  • DOI: UNKNOWN
  • Registry group: academic_apis_and_preprints
  • Evidence role: canonical_academic_index
  • Language: en
  • Country / region: UNKNOWN

Plain-Language Brief

The study presents a three-stage approach for combining digital data collection, predictive modeling, and weather-sensitive demand forecasting. It uses a chatbot to collect travel mode choices from student commuters based on five predefined weather scenarios. The authors analyze the collected data using various methods, including machine learning, and compare the performance of large language models in predicting travel behavior.

What the Document Says

  • The study proposes a three-stage workflow that integrates conversational data collection, structured data processing, and behavioral prediction.
  • A chatbot-administered survey collected travel mode choices from student commuters in five weather scenarios, yielding 454 observations.
  • Weather-related associations were analyzed using a multinomial logit model and compared with logistic regression and random forest.
  • Nine large language models (LLMs) were evaluated for their performance in predicting travel behavior across four conditions.
  • Random forest achieved 69.6% accuracy, while the best text-only LLM reached 69.9% without task-specific fitting.
  • Habitual travel information, Expert framing, and persona information showed varying degrees of improvement in prediction.
  • Few-shot prompting improved prediction for several models, with gains stabilizing after a small number of examples.
  • Using the same weather images shown to respondents, the best vision-based configuration reached 71.5% accuracy.
  • The study concludes by demonstrating how various methods can be coordinated within an auditable multi-agent workflow.

Key Concepts Mentioned

  • travel behavior
  • digital data collection
  • predictive modeling
  • weather-sensitive demand prediction
  • chatbot
  • multinomial logit model
  • large language models
  • machine learning

Claims Made by the Document

  • The proposed three-stage workflow integrates conversational data collection, structured data processing, and behavioral prediction effectively.
  • The best text-only LLM can reach 69.9% accuracy in predicting travel behavior without task-specific fitting.

Evidence Used

  • 454 respondent-scenario observations from a chatbot-administered survey
  • Results of analyses using multinomial logit model, logistic regression, and random forest
  • Performance comparison of nine LLMs across four conditions

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Travel behavior research increasingly combines digital data collection with predictive modeling, yet these stages are often developed and evaluated separately. This study proposes a three-agent workflow integrating conversational data collection, structured data processing, and behavioral prediction. A chatbot-administered, image-augmented stated-preference survey collected mode choices from student commuters across five predefined weather scenarios, yielding 454 respondent-scenario observations. Weather-related associations were analyzed using a multinomial logit model, while logistic regression and random forest provided machine-learning benchmarks. Nine locally deployed large language models (LLMs), ranging from 2 to 35 billion parameters, were evaluated across four zero-shot prompt-and-context conditions and extended through persona, few-shot, and vision-based configurations. Random forest achieved 69.6% five-class accuracy, while the best text-only zero-shot LLM reached 69.9% without task-specific fitting. Habitual travel information produced the most consistent gains, Expert framing generally outperformed Role-Play, and persona information was most useful when habitual travel information was unavailable. Few-shot prompting improved prediction for several models, with gains stabilizing after a small number of examples. Using the same weather images shown to respondents, the best vision-based configuration reached 71.5% five-class accuracy, indicating that visual context may provide additional predictive information for selected models. Overall, the study shows how conversational surveys, structured data processing, conventional behavioral modeling, machine learning, and multimodal LLM prediction can be coordinated within an auditable multi-agent workflow.