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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-step approach for integrating data collection, processing, and predictive modeling in travel behavior research using conversational surveys, machine learning, and large language models (LLMs). The approach was tested on student commuters in different weather scenarios. Results showed that random forest achieved the highest accuracy of 69.6% in five-class prediction, while the best text-only zero-shot LLM reached 69.9%. Habitual travel information, Expert framing, and persona information were found to be beneficial for improving predictions.

What the Document Says

  • The research combines digital data collection with predictive modeling for travel behavior studies.
  • A three-agent workflow is proposed that integrates conversational data collection, structured data processing, and behavioral prediction.
  • A chatbot-administered survey was conducted on student commuters in five weather scenarios to collect mode choices.
  • The study analyzed weather-related associations using a multinomial logit model and compared the results with logistic regression and random forest.
  • Nine large language models (LLMs) were evaluated for their performance across different prompt conditions and configurations.
  • Random forest achieved 69.6% five-class accuracy in predictions.
  • The best text-only zero-shot LLM reached 69.9% without task-specific fitting.
  • Habitual travel information, Expert framing, persona information were found to be beneficial for improving predictions.
  • Few-shot prompting improved prediction for several models.
  • Using the same weather images shown to respondents, the best vision-based configuration reached 71.5% five-class accuracy.

Key Concepts Mentioned

  • travel behavior research
  • digital data collection
  • predictive modeling
  • chatbot
  • stated-preference survey
  • weather scenarios
  • multinomial logit model
  • logistic regression
  • random forest
  • large language models (LLMs)
  • zero-shot prompt-and-context conditions
  • persona information
  • few-shot prompting
  • vision-based configuration

Claims Made by the Document

  • The proposed approach combines data collection, processing, and predictive modeling.
  • Random forest achieved the highest accuracy in five-class prediction.
  • Text-only zero-shot LLM reached 69.9% without task-specific fitting.
  • Habitual travel information, Expert framing, persona information were beneficial for improving predictions.

Evidence Used

  • The study proposes a three-agent workflow
  • The results of the study are presented
  • The accuracy rates are stated in the text

Methods / Approach

  • Chatbot-administered stated-preference survey
  • Multinomial logit model
  • Logistic regression
  • Random forest
  • Large language models (LLMs) evaluation

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

The provided text lacks information about potential limitations of the methods used in the study.

Open Questions

  • What were the specific weather scenarios?
  • Were there any other factors considered in the travel behavior prediction?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • It is unclear what specific weather scenarios were considered in the study.
  • The text does not specify if there were any other factors considered in the travel behavior prediction.

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.