International Conference · 2026

A Transformer-based Approach for Attribute-Aware Route Optimization Guided by ILP Solutions

Riku Nakao , Akihito Hiromori , Hamada Rizk , Hirozumi Yamaguchi

ICDCN 2026 Doctoral Symposium

DOI: 10.1145/3737611.3776947

Abstract

This paper formulates the Nursing Care Taxi Dispatch (NCTD) and proposes a novel approach that combines Transformer with a heuristic algorithm. Conventional methods struggle with NCTD due to its complex constraints and the frequent risk of violation. To improve the quality, we first obtain relatively high-quality solutions based on Integer Linear Programming (ILP), and use them as training data to learn a Transformer-based architecture. By incorporating user attributes strongly related to constraint satisfaction, the model can well capture the path features of ILP solutions without constraint violation. It also uses the masking mechanism and a heuristic algorithm to address all constraints. We evaluate our approach on datasets created from real-world operational data in two Japanese cities and compare it with existing machine learning-based methods in terms of quality, execution speed, and violation rate. Results show that our method can reduce the mean travel time by 1-2 minutes for problems with fewer than 30 nodes, and provides a good balance between quality, execution time, and violation rate.

A Platform for Digitalizing Knowledge of Regional Communities

A Platform for Digitalizing Knowledge of Regional Communities

Japan Science and Technology Agency (JST) CREST
「基礎理論とシステム基盤技術の融合によるSociety 5.0のための基盤ソフトウェアの創出」領域

Environment-Aware Distributed Scheduling for Emergency LoRa Networks

Yuto Inaba, Tatsuya Amano, Akihito Hiromori, Hirozumi Yamaguchi

2026 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), SPT-IoT 2026, pp. 1366–1371

DOI 10.1109/PerComWorkshops68308.2026.11585469

Disaster CommunicationLoRa +4

A Lightweight Vision-Language Model for Disaster Image Summarization

Hibiki Yoshizaki, Akira Uchiyama, Akihito Hiromori, Mineo Takai, Hirozumi Yamaguchi

2026 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), PerconAI 2026, pp. 1203–1208

DOI 10.1109/PerComWorkshops68308.2026.11585419

Semantic CommunicationDisaster Response +4

Physics-Integrated Deep Learning for Urban Landslide Prediction

Ren Ozeki, Hamada Rizk, Hirozumi Yamaguchi

2026 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), URBSENSE 2026, pp. 1094–1099

DOI 10.1109/PerComWorkshops68308.2026.11585337

Landslide PredictionPhysics-Integrated Learning +3

A Simulation Framework for Precision Formation Flying of Massive Satellite Swarms

Tatsuya Amano, Akihito Hiromori, Hirozumi Yamaguchi, Sumio Morioka

2026 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), PerVehicle , pp. 230–235

DOI 10.1109/PerComWorkshops68308.2026.11585321

Satellite Formation FlyingDistributed Simulation +4

Ray-Tracing-Driven Pattern-Based Vehicle Recognition in ISAC Radar

Heetae Jin, Akira Uchiyama

2026 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), PerRad 2026, pp. 328–333

DOI 10.1109/PerComWorkshops68308.2026.11585327

ISACBeyond 5G +4

A Questionnaire-Only Counterfactual Machine Learning Approach to Assess the Spatial Impact of Green Mobility Vehicles in Urban Parks

Rami Naeem, Srikant Manas, Tatsuya Amano, Hirozumi Yamaguchi

ICDCN 2026 Workshop: IWNDSC2026

DOI 10.1145/3737611.3776620