# Optimizing Group Utility in Itinerary Planning: A Strategic and Crowd-Aware Approach

arXiv: https://arxiv.org/abs/2304.08495 · HTML: https://mlsanity.com/paper/2304.08495/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 78 references that are in the map.
- First posted on arXiv 4 Apr 2023.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [GETNext: Trajectory Flow Map Enhanced Transformer for Next POI Recommendation](https://mlsanity.com/paper/2303.04741/) (2023)
- [CrisisBERT: a Robust Transformer for Crisis Classification and Contextual Crisis Embedding](https://mlsanity.com/paper/2005.06627/) (2020)
- [A Transformer-based Framework for POI-level Social Post Geolocation](https://mlsanity.com/paper/2211.01336/) (2022)
- [A Reinforcement Learning Approach to the Orienteering Problem with Time Windows](https://mlsanity.com/paper/2011.03647/) (2020)
- [EPIC30M: An Epidemics Corpus Of Over 30 Million Relevant Tweets](https://mlsanity.com/paper/2006.08369/) (2020)
- [User Preferential Tour Recommendation Based on POI-Embedding Methods](https://mlsanity.com/paper/2103.02464/) (2021)
- [IPOD: An Industrial and Professional Occupations Dataset and its Applications to Occupational Data Mining and Analysis](https://mlsanity.com/paper/1910.10495/) (2019)
- [Photozilla: A Large-Scale Photography Dataset and Visual Embedding for 20 Photography Styles](https://mlsanity.com/paper/2106.11359/) (2021)

## Built on by (most cited first, 1 of 1)

- [GroupTravelBench: Benchmarking LLM Agents on Multi-Person Travel Planning](https://mlsanity.com/paper/2605.25200/) (2026)

## Abstract (arXiv)

Itinerary recommendation is a complex sequence prediction problem with numerous real-world applications. This task becomes even more challenging when considering the optimization of multiple user queuing times and crowd levels, as well as numerous involved parameters, such as attraction popularity, queuing time, walking time, and operating hours. Existing solutions typically focus on single-person perspectives and fail to address real-world issues resulting from natural crowd behavior, like the Selfish Routing problem. In this paper, we introduce the Strategic and Crowd-Aware Itinerary Recommendation (SCAIR) algorithm, which optimizes group utility in real-world settings. We model the route recommendation strategy as a Markov Decision Process and propose a State Encoding mechanism that enables real-time planning and allocation in linear time. We evaluate our algorithm against various competitive and realistic baselines using a theme park dataset, demonstrating that SCAIR outperforms these baselines in addressing the Selfish Routing problem across four theme parks.