Overview

Project occurred while Andy was seconded into the Model Development Team (TfNSW).

Background

TfNSW's Enhanced Train Crowding Model (ETCM) provides a detailed analysis of rail demand, delivering precise forecasts that include train line loadings, the balance of seating versus standing durations, levels of in-train crowding, the dynamics of passenger boarding and alighting, movements on platforms, and the use and accessibility of stations within key areas of the metropolitan rail network. Updates to the model were necessitated by recent advancements in the Public Transport Project Model (PTPM), as well as shifts in foundational demand patterns attributed to the pandemic's impact.

Objectives

Andy led an Advanced Analytics & Insights (AAI) team in conducting comprehensive assurance reviews of model updates completed by external consultants, to guarantee their suitability for use in critical projects, including Sydney Metro West.

Role

Andy was tasked with spearheading and managing the assurance activities for the Advanced Analytics & Insights team (AAI) at Transport for NSW (TfNSW).

Tasks and stages

This involved the following assurance activities:

1. Verifying the 2021 demand matrix, developed to reflect conditions free from pandemic influences, to ensure it serves as a reliable foundation for forecasting future rail demand.
2. Ensuring that the rail services data for the base year 2021 used in ETCM4 aligns with PTPM6.31, utilizing SEN v1.23 data derived from GTFS.
3. Validating the ETCM4 assignment process by comparing station entry and exit figures from the base year AM 1-hour model assignment outputs with the initial demand inputs.
4. Assessing the ETCM4 assignment process through comparisons of screenline volume data from the base year AM 1-hour model outputs against ROAM data.
5. Confirming the integration of the demand distribution module updated with TZP22 in developing the ETCM4 AM demand matrix.
6. Verifying the pivoting method used in ETCM4 for consistency with methods employed in other AAI models, including STM and PTPM.
7. Evaluating ETCM4's AM model forecasting capabilities through analysis of its forecasting results.
   

Analysis, Interpretation, and Judgement

• EMME (PTPM): Conducting thorough investigations to align the Enhanced Train Crowding Model (ETCM) with the Public Transport Project Model (PTPM). This included extracting and analyzing PTPM results, and creating detailed plots to visualize differences.
• CUBE (ETCM): Operating the ETCM, running simulations, and extracting data for further examination.
• Excel: Performing independent analysis on both outputs and inputs to validate findings and assumptions.
• Rail Service Timetable Interchange Format Tool (Marco TT): Utilizing specialized software for detailed timetable analysis, ensuring that rail service schedules were accurately represented and integrated into the models.

Stakeholder Engagement and Endorsement

• Supervision and Coordination: Managing project tasks effectively using Microsoft Teams' Kanban board to track progress and ensure timely completion of all stages.
• Report Writing: Compiling comprehensive reports that detailed findings, methodologies, and conclusions, and also involved reviewing team contributions to maintain quality and coherence.
• Liaising with Consultants and Teams: Acting as the key point of contact between the transport modelling consultants developing ETCM4 and the Sydney Metro West Modelling team, facilitating smooth communication and collaboration.
  
  

Outcomes

The meticulous execution of these tasks and the rigorous analysis conducted by Andy contributed to:

• The successful assurance of ETCM4's alignment with current data standards and modeling practices, ensuring the model's readiness for use in significant transport projects like the Sydney Metro West.
• Enhanced model accuracy and reliability, bolstered by the integration of updated data sources and validation of modeling methodologies, thus providing a solid foundation for strategic transport planning and decision-making processes.
• Strengthened stakeholder confidence in the model's capabilities, underpinned by transparent communication, detailed reporting, and collaborative problem-solving efforts.