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Collective Causal Networks in Transport and Public Infrastructure

Transport systems are inherently multi-organizational, with private railway operators, infrastructure maintenance companies, and public agencies like the Swedish Transport Agency each controlling different pieces of the puzzle. When these organizations lack shared understanding of how their decisions impact the entire system, coordination breaks down and interventions often miss their mark.

We help transport and public infrastructure organizations build collective causal networks that reveal how decisions ripple through the entire ecosystem. By mapping these relationships collaboratively, all stakeholders can see the same reality and coordinate interventions that actually work.

Delivered as a mix of advisory services, applied data science, and our software Skala.

Reality represented

Transport networks as interconnected systems: how a maintenance decision by one private operator affects capacity for others, how infrastructure investments by the Swedish Transport Agency influence service quality across multiple operators, and how emergency responses require coordinated action across organizational boundaries. We make these interdependencies visible so stakeholders can understand their role in the larger system and coordinate interventions for maximum impact.

Situation​

Transport causal network example

Transport systems involve multiple organizations working within complex ecosystems where their actions and decisions create ripple effects across the entire network. Without shared understanding of these causal relationships, stakeholders operate with incomplete mental models, leading to misaligned priorities and ineffective interventions.

Collaborative causal modeling is particularly effective in transport contexts because it creates a shared understanding of how different factors influence outcomes across organizational boundaries. By mapping both observed variables (those with measurable data) and unobserved variables (hypothesized influences), stakeholders can surface assumptions, test hypotheses, and build consensus on what drives results in their shared transport ecosystem.

Client​

We have developed collective causal networks across multiple different transport and public infrastructure engagements, working with a diverse mix of clients including public sector authorities, railway operators, maintenance companies like Infranord, and other infrastructure entrepreneurs. Our engagements span various applications:

  • Swedish Transport Agency coordination: Mapping relationships between regulatory decisions, infrastructure investments, and service quality across private operators and maintenance companies
  • Railway operator coordination: Understanding how decisions by different railway operators affect capacity, punctuality, and maintenance schedules across the network
  • Public transport coordination: Understanding how service frequency, reliability, and passenger satisfaction interact across different transport modes and agencies
  • Infrastructure planning: Connecting infrastructure investments to service quality, ridership, and economic development outcomes
  • Cross-border transport: Coordinating operations and maintenance across different countries and regulatory frameworks

These implementations demonstrate the versatility of collaborative causal modeling across different scales and contexts in transport and public infrastructure.

Challenge​

Different organizations in the transport ecosystem had conflicting views of what drives outcomes in their shared system. Each stakeholder saw only their piece of the puzzle, leading to misaligned priorities, wasted resources on ineffective interventions, and slow decision-making due to lack of shared understanding of causal relationships.

Objectives​

  • Create shared mental models of causal relationships across transport organizations
  • Enable coordinated interventions based on systemic understanding
  • Reduce resource waste through better alignment of efforts
  • Accelerate decision-making through shared situational awareness

What we did​

We facilitated a collaborative modeling process using Skala to build comprehensive causal networks:

  • Stakeholder mapping: Identified key decision-makers and influencers across all participating transport organizations
  • Individual interviews: Captured each organization's perspective on causal relationships and key drivers
  • Collaborative workshops: Brought stakeholders together to build shared models using Skala's real-time collaboration features, distinguishing between observed variables (with data) and unobserved variables (hypothesized)
  • Evidence validation: Tested causal relationships against available data and historical outcomes
  • Scenario modeling: Used the collective model to simulate different intervention strategies

The work combined subject-matter expertise from each organization with collaborative modeling tools to create a shared understanding of their transport ecosystem's causal dynamics.

Results​

The collaborative causal modeling process delivered clear value across the transport ecosystem:

  • Aligned priorities across all participating organizations based on shared understanding of leverage points
  • Coordinated interventions that addressed root causes rather than symptoms
  • Reduced duplication of efforts through better visibility of each organization's role
  • Faster decision-making through shared situational awareness and common mental models
  • Stronger relationships between organizations built on mutual understanding

Expected benefits for the transport ecosystem include:

  • More effective resource allocation based on systemic insights
  • Improved risk management through comprehensive mapping of interdependencies
  • Enhanced strategic planning with full context of cross-organizational impacts
  • Better crisis response through pre-established shared understanding
  • Accelerated innovation through coordinated R&D efforts

How it works​

  • Multi-stakeholder collaboration: Real-time editing and commenting in shared Skala workspaces
  • Causal relationship mapping: Visual representation of how different factors create outcomes across organizational boundaries
  • Evidence-based validation: Link each causal relationship to supporting data and documentation
  • Scenario simulation: Test different intervention strategies to predict outcomes before implementation
  • Continuous refinement: Update models as understanding evolves and new evidence emerges
  • Shared vocabulary: Common terminology and concepts across all participating organizations
  • System-wide perspective: Reveal hidden connections and interdependencies
  • Leverage point identification: Focus interventions where they will have maximum impact

Practical application: Intervention planning​

Once the causal network is established, organizations can use it to plan targeted interventions. For example, if the goal is to improve punctuality, the network reveals which activities and variables directly influence on-time performance:

  • Direct interventions: Activities that immediately impact punctuality (e.g., improving maintenance scheduling, optimizing timetables)
  • Indirect interventions: Activities that influence variables connected to punctuality (e.g., staff training programs that improve operational efficiency, which reduces delays)
  • System-wide interventions: Changes that affect multiple variables simultaneously (e.g., implementing new signaling systems that impact both capacity and reliability)

This enables organizations to choose the most effective intervention points based on their resources, timeline, and desired impact, rather than guessing which activities will produce the desired outcomes.