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Multi-Modal Transport Data Sharing

We worked with transport industry partners across different regions to explore how data sharing across organizational boundaries can address operational challenges in multi-modal transport systems. Through collaborative data sharing between multiple organizations across different transport modes and regions, we identified opportunities for improved efficiency, better decision-making, and complete journey optimization.

This project helped shift industry attitudes toward data sharing, moving from initial skepticism to increased engagement. The initiative shows how cross-organizational and cross-regional collaboration across transport modes can create value that individual organizations cannot achieve alone, while building trust in sharing operational data for full journey planning and optimization.

Reality represented

Cross-organizational data sharing in multi-modal transport: multiple organizations across different transport modes (rail, road, air, sea) and regions sharing different types of data that can be analyzed and visualized in various ways. This creates a shared understanding of complete journey patterns and enables collaborative problem-solving across traditional organizational, modal, and regional boundaries.

Situation​

Moving passengers or goods from point A to point B often involves multiple transport modes and regions, and depends on collaboration across organizational boundaries. To solve the challenges and complex problems that multi-modal transport faces, methodology and technology adapted for cross-boundary, cross-modal, and cross-regional problem-solving is needed.

Client​

Transport industry collaboration involving multiple organizations across different transport modes and regions including rail operators, road transport companies, infrastructure managers, and service providers. [Specific organizations and regions anonymized for confidentiality]

Challenge​

The partners were interested in exploring how to:

  • Connect data across different organizations, transport modes, and regions
  • Create shared understanding of complex operational challenges across multi-modal journeys and regions
  • Develop methodologies for cross-organizational, cross-modal, and cross-regional data sharing
  • Build trust and confidence in sharing sensitive operational data across regions
  • Demonstrate concrete value from data sharing initiatives for complete journey optimization across different regions

What we did​

Over 2022/23, we worked with transport industry partners to explore data sharing across organizational, modal, and regional boundaries:

Data exploration and analysis:

  • Explored data sharing possibilities between multiple organizations across different transport modes and regions
  • Combined data from multiple sources into unified models for comprehensive analysis
  • Developed methodologies for cross-organizational, cross-modal, and cross-regional data analysis
  • Created visualization tools and presentations for shared data insights across complete journeys and regions

Collaborative analysis:

  • Explored multiple areas where data sharing could benefit future multi-modal transport operations across regions
  • Conducted deep geographical analysis of operational patterns across different transport modes and regions
  • Identified opportunities for optimization and improvement in complete journey planning across different regions

Trust building:

  • Demonstrated concrete value through practical applications and presentations for multi-modal journeys across regions
  • Showed how data sharing could solve real operational challenges across transport modes and regions
  • Built confidence in sharing operational data for full journey optimization across different regions

Results​

The project demonstrated the value of cross-organizational data sharing:

Industry engagement:

  • Railway industry awareness of data sharing benefits increased
  • Organizations became more comfortable with sharing data
  • Industry began exploring data utilization approaches used in other sectors

Project outcomes:

  • Identified optimization opportunities through shared data analysis
  • Developed methodologies for cross-organizational problem-solving
  • Showed how data sharing investments can yield positive results

Key insights:

  • Data sharing requires careful attention to trust and security
  • Visualization and analysis tools help create shared understanding
  • Cross-organizational collaboration can create value that individual organizations cannot achieve alone
  • Railway industry has potential for data-driven optimization

Impact​

This project shows how data sharing, technology, and methodology can achieve positive results with relatively small investments. By enabling cross-organizational data sharing, we've created new value through collaborative capabilities that benefit the railway ecosystem.

Lessons learned:

  • Trust building is important for successful data sharing
  • Concrete demonstrations of value help with adoption
  • Cross-organizational collaboration requires dedicated methodology
  • Data sharing investments can yield positive returns