Lecip Awarded Project for MLIT's 'COMmmmONS' Regional Transport DX Initiative
Lecip Corporation has been awarded a contract under the Ministry of Land, Infrastructure, Transport and Tourism's (MLIT) 'COMmmmONS' regional transport DX project to conduct a pilot study on automating bus scheduling using AI. By utilizing proprietary mathematical algorithms, the company aims to support bus operators facing labor shortages and operational silos, ultimately driving efficiency and improving working conditions.
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- 📰 Published: May 21, 2026 at 19:00
- 🔍 Collected: May 21, 2026 at 10:31
- 🤖 AI Analyzed: May 21, 2026 at 21:28 (10h 56m after Collected)
Lecip Corporation, a consolidated subsidiary of Lecip Holdings Corporation, has been selected for the Ministry of Land, Infrastructure, Transport and Tourism's (MLIT) 'COMmmmONS' regional transport digital transformation (DX) project. The contract covers a pilot study on the 'automation of vehicle scheduling using AI technology to promote regional transport DX.'
This initiative aims to support the creation of practical and optimal bus operation plans using proprietary algorithms.
■ Background and Current Challenges
Bus operators nationwide are currently facing a severe management environment due to critical labor shortages. Creating necessary operational schedules—including bus timetables, duty assignments (shigyo), and individual shifts—requires strict adherence to laws such as the Labor Standards Act and the Industrial Safety and Health Act, demanding significant time and labor. Additionally, these tasks have become 'person-dependent,' relying on the experience and intuition of specific staff, making standardization and knowledge transfer difficult.
■ Details of the Pilot Study
The study involves developing and verifying a system that automatically calculates optimal operation plans by analyzing complex business constraints and applying proprietary mathematical algorithms. It lowers the barrier to entry by enabling standard API integration with existing systems. Furthermore, it implements a 'residual shift optimization function,' which allows for the coexistence of manual scheduling by experts and AI-generated automatic assignments.
■ Objectives
We aim to achieve the following:
- Reducing the burden on scheduling personnel: Cutting down man-hours for duty assignment and shift creation through proprietary algorithms.
- Smoother operation management: Reducing the workload for operation managers in shift management.
- Improving driver working conditions: Providing fair shifts that comply with rest intervals and holiday regulations.
By providing versatile, standardized solutions, we aim to enhance the safety and reliability of public transport infrastructure and contribute to the realization of sustainable regional public transport.
This initiative aims to support the creation of practical and optimal bus operation plans using proprietary algorithms.
■ Background and Current Challenges
Bus operators nationwide are currently facing a severe management environment due to critical labor shortages. Creating necessary operational schedules—including bus timetables, duty assignments (shigyo), and individual shifts—requires strict adherence to laws such as the Labor Standards Act and the Industrial Safety and Health Act, demanding significant time and labor. Additionally, these tasks have become 'person-dependent,' relying on the experience and intuition of specific staff, making standardization and knowledge transfer difficult.
■ Details of the Pilot Study
The study involves developing and verifying a system that automatically calculates optimal operation plans by analyzing complex business constraints and applying proprietary mathematical algorithms. It lowers the barrier to entry by enabling standard API integration with existing systems. Furthermore, it implements a 'residual shift optimization function,' which allows for the coexistence of manual scheduling by experts and AI-generated automatic assignments.
■ Objectives
We aim to achieve the following:
- Reducing the burden on scheduling personnel: Cutting down man-hours for duty assignment and shift creation through proprietary algorithms.
- Smoother operation management: Reducing the workload for operation managers in shift management.
- Improving driver working conditions: Providing fair shifts that comply with rest intervals and holiday regulations.
By providing versatile, standardized solutions, we aim to enhance the safety and reliability of public transport infrastructure and contribute to the realization of sustainable regional public transport.
FAQ
レシップ株式会社が受託したプロジェクトの内容は?
国土交通省の「COMmmmONS」プロジェクトにおいて、「地域交通DXの推進に向けたAI技術を活用した仕業編成作業の自動化技術の実証調査業務」を受託しました。
なぜバス業界で仕業編成の自動化が必要なのですか?
深刻な人手不足に加え、運行ダイヤや勤務シフト作成が特定の担当者の経験に依存する「属人化」状態にあり、業務標準化や次世代への引き継ぎが困難であるためです。
「残ダイヤ最適化機能」とは何ですか?
手作業で運行ダイヤや勤務シフトの一部を割り当てた状態から、残りの未割当部分をAIが自動生成する機能のことです。
この実証調査が目指す効果は?
編成担当者の負担軽減(工数削減)、運行管理の効率化、および勤務時間規制遵守による乗務員の働き方改善を目指します。
本システムの現場への導入の利点は?
データレイクの構築といった大規模なシステム改修をせずとも、既存システムと標準的なAPI連携を行うことで導入ハードルを下げています。