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TriMet IMI Smart Mobility Platform

USER STORY WORKSHOP

December 11, 2020

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Today’s Agenda

Introduction & Background

IMI Grant Overview (Bibiana McHugh)
Review TriMet Resident Profiles (Fehr & Peers)
Relationship to Strategic Business Plan (Alan Lehto)
Review Mobility Performance Metrics and Current TriMet KPIs (Fehr & Peers)

Breakout Sessions

Identify problems / unknowns by business group
Hypotheses for the problems and potential solutions from a Smart Mobility Platform

Regroup & Discuss

Share breakout group takeaways
Next steps

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IMI Research Project

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One objective of Task 3 is to capture, monitor and analyze data to measure the goals and objectives across all three area.

For example, in Task 2 Guy and Frank are have set up a machine learning model using historical data to improve transit predictions or TransitTracker. As we feed the model with real-time features such as traffic, incidents and weather, we need to determine which features work and monitor the progress of the model as it progresses, or learns.

Another objective is related to Mobility. As TriMet evolves into the management of an integrated mobility ecosystem, we need to approach transit with a more holistic perspective. This means looking beyond traditional KPIs and NTD reporting.

For example, we need to identify how to move people faster, cheaper and safer door-to-door (which requires multiple modes or service providers), and we need to measure their effectiveness or ineffectiveness.

And further, how does moving people faster, cheaper and safer from Point A to B affect our existing KPIs, like ridership.

A third objective is to identify and gather the data that is required to measure new KPIs, such as SOV or emission reductions.

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NEXT GENERATION MOBILITY

Next Generation MaaS

personal travel assistant

Emerging Technologies

open

architecture

Data Revolution

personalized traveler information (incentives)
data and measurements beyond just ridership counts
more accessible and standardized data (environmental, health…)
historical, real-time, predicted with machine learning
analytical tools

Operations

how we operate and make decisions

Business

beyond the bottom line

Cultural

change

Policy

cross jurisdictions & providers

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Task 3 Objectives

Measure success and progress of IMI Grant Objectives

Example: Task 2 Machine Learning Model to Improve TransitTracker™
Identify new Mobility Performance Metrics

Mobility - Management of an integrated mobility ecosystem

Example: Identify ways to move people faster, cheaper and safer door-to-door (which requires multiple service providers) and measure effectiveness or ineffectiveness.
How does moving people faster, cheaper and safer from Point A to B affect our existing KPIs, like ridership.

Smart Mobility Data Platform

Example: Identify and acquire new data that is required to measure new KPIs, such as SOV or emission reductions.
Maintain open architecture to enable extensibility

One objective of Task 3 is to capture, monitor and analyze data to measure the goals and objectives across all three area.

For example, in Task 2 Guy and Frank are have set up a machine learning model using historical data to improve transit predictions or TransitTracker. As we feed the model with real-time features such as traffic, incidents and weather, we need to determine which features work and monitor the progress of the model as it progresses, or learns.

Another objective is related to Mobility. As TriMet evolves into the management of an integrated mobility ecosystem, we need to approach transit with a more holistic perspective. This means looking beyond traditional KPIs and NTD reporting.

For example, we need to identify how to move people faster, cheaper and safer door-to-door (which requires multiple modes or service providers), and we need to measure their effectiveness or ineffectiveness.

And further, how does moving people faster, cheaper and safer from Point A to B affect our existing KPIs, like ridership.

A third objective is to identify and gather the data that is required to measure new KPIs, such as SOV or emission reductions.

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TriMet�Rider�Profiles�

“What information would this traveler need to complete their trip successfully?”

“What protections would they need in terms of information?”

TriMet Attitude and Awareness Survey 2018

Customer Effort Score Survey 2020

Helps identify barriers and friction customers face when using a service

Source: de la Pena, Benjamin. Traveler personas and their information needs. https://medium.com/@benjiedlp/traveler-personas-and-their-information-needs-92a0131e063a

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TriMet Rider Profiles

Demographics

About half have a household income over $70k; 13% have a household income under $30k
23% people of color
7% English is not their first language
87% of riders have a smartphone
14% transit dependent

Travel Choices

20% use ridehailing at least several times a month
Most use transit primarily for recreation (41%), followed by work (26%)
54% of riders use TriMet less than once a month, 20% ride several times a week or more
39% of e-scooter users surveyed used it to access transit at least once*

Attitude and Awareness Survey (2018)

*PBOT E-scooter Pilot User Survey Results

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TriMet Rider Profiles

Travel Choices:

49% use a smartphone app (Google Maps etc) for step-by-step directions; 37% use TriMet trip planner

91% use physical or virtual Hop card (up from 22% in 2018 survey)

Customer Effort Score Survey 2020

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Strategic Business Plan

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Strategic Business Plan

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Strategic Business Plan

But what is the “Why?”

Five primary goals in the region:

Support our economy and provide opportunity
Ease congestion
Provide mobility for those with few options
Help shape the future of our region
Reduce emissions and support environmental sustainability

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MOBILITY PERFORMANCE METRICS

Customer Satisfaction (survey responses, return users)
Time Effectiveness (wait time, on-time performance)
Cost Effectiveness (cost or price per trip)
Reliability (accuracy of predictions)
Availability (number of trip options presented)
Safety (crash or incident rates, personal security)
Accessibility (number of jobs or other destinations within 30 minutes)
Demand for Integrated Mobility Systems (number of trips planned or completed)
Knowledge Transfer (types and accuracy of data generated)

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TriMet Performance Dashboard

Ridership
Efficiency
Budget
Safety
Data

https://trimet.org/about/dashboard/index.htm

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Hop Fast Pass KPIs

Taps
Hop Value and Products Loaded
Passenger Revenues

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User Story Breakout Sessions

Session 1

IT

Capital Projects

Session 2

Business Plan

Finance

Executives

Marketing & Communications

Session 3

Service Planning

Service Delivery

Paratransit