VTA HACKATHON Gather ideas for how to visualize and leverage - - PowerPoint PPT Presentation

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May 24, 2023 633 likes •916 views

VTA HACKATHON Gather ideas for how to visualize and leverage real-time data Swiftly received multiple awards VTA established partnership with Swiftly VTA feeds real time data to Swiftly VTA used Swiftly data in the VTA Trip

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Swiftly received multiple awards
VTA established partnership with Swiftly

– VTA feeds real time data to Swiftly – VTA used Swiftly data in the VTA Trip Planner – VTA began utilizing Swiftly reports and analytics tools

VTA HACKATHON

Gather ideas for how to visualize and leverage real-time data

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THE PROBLEM

Hard to make use of messy and large volumes of data

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FUTURE DATA FLOW

Centralized data collection, analysis, visualization, and dissemination hub

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1. 1. Ex Exte tern rnal use

1. Mobile apps

2. Web-based trip planners
3. Electronic stop signs
4. SMS & Voice

2. 2. In Inte tern rnal us use:

– Planning – Scheduling – Operations

DATA GOALS

Create high quality data for internal and external use

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WHY THIS IS IMPORTANT

Goal: A conceptual model for measuring transit induced stress at stops. Em Empa path thy Measure res:

Visib ible

– Amenities

Non-Vis Visib ible

– Schedule Adherence – Trip Frequency

Da Data Sources:

VTA Transit Passenger Environment Plan (TPEP)
Swiftly on-time performance

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External Use

Real-time Passenger Information

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PASSENGER EXPECTATIONS

More Riders 1.7% increase in NYC weekday ridership Happy Riders 92% of customers report greater satisfaction Time Savings Customers report an average of 2 minutes saved

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YOU MAY NOT KNOW

9% of riders say they reduced their transit use after receiving errors in real-time predictions.

Source: “Benefits of Real-Time Transit Information and Impacts of Data Accuracy on Rider Experience” by Aaron Gooze, Kari Edison Watkins, and Alan Borning

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OUR GOAL: DATA ACCURACY

LEGACY CAD/AVL

2 minute polling rate

WIFI

5 second polling rate

Mobile Apps Trip Planners Stop Signs Etc…

Combine feeds in real-time
Sophisticated prediction

algorithms

Up to 30% increase in RTPI

accuracy

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VTA TRIP PLANNER

VTA Real Time data processed by Swiftly used in Trip Planner

Real Time information at Stops Real Time information of Vehicles

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Internal Use

Analytics for Planning, Scheduling, and Operations

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PERFORMANCE MONITORING

Very difficult to get clean data
Only can use CAD/AVL data which has data accuracy challenges
Hard to analyze large datasets

Download Data Plot Data

Segments Join Data Export Data Import Data Re-Join Re-Export

Analyze Data Results PREVIOUS WORK FLOW Download Data Analyze Data NEW WORK FLOW Results

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OUR GOAL: DATA ACCURACY & RAPID ANALYSIS

LEGACY CAD/AVL

2 minute polling rate

WIFI

5 second polling rate

On-Time Performance Vehicle Speeds Dwell Times Etc…

Combine feeds in real-time
Rapid big data analysis

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Clean and high fidelity data
Analyze millions of data

points in seconds

Training staff

– Transit Planners – Service Planners – Reporting and Analysis

Providing feedback

THE PLATFORM

It’s all about computing and visualizing big data

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ON-TIME PERFORMANCE

Using Swiftly to discover performance issues

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DRILL INTO THE DETAILS

STOP LEVEL TIME OF DAY SEVERITY (HISTOGRAM) TRIP & STOP

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You Miss your Ride
Transit leaves Early
Transit arrives Late
Dark un-lit stop
Dirty stop
No ability to know when the next

vehicle will arrive Tr Tran ansi sit pl planners rs and ope pera rato tors rs must t de demonstrate Empathy.

CAUSES OF TRANSIT STRESS

How can we use data and analysis to prioritize transit environment improvements

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TRANSIT PLANNING: SPEEDS & DELAYS

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DWELL TIME

Stop dwell Stop dwell SANTA CLARA & 1ST 69.5 SANTA CLARA & 13TH 8.9 1ST & PASEO DE SAN ANTONIO 58.0 SAN CARLOS & DELMAS 8.7 VALLEY FAIR TRANSIT CENTER 55.2 STEVENS CREEK & LOPINA 8.7 STEVENS CREEK & KIELY 39.3 STEVENS CREEK & PORTAL 8.6 STEVENS CREEK & LOMA LINDA 37.6 ALUM ROCK & MCCREERY 8.4 SAN CARLOS & BIRD 36.5 STEVENS CREEK & ALBANY 8.1 ALUM ROCK & JACKSON 34.4 BELLEROSE & CLARMAR 7.7 SANTA CLARA & 3RD 29.3 STEVENS CREEK & MAPLEWOOD 7.6 STEVENS CREEK & MILLER 29.2 ALUM ROCK & 34TH 7.4 ALUM ROCK & KING 27.4 ALUM ROCK & MUIRFIELD 7.4 SAN CARLOS & BASCOM 26.0 STEVENS CREEK & RICHFIELD 7.4 SANTA CLARA & 17TH 25.8 SAN CARLOS & JOSEFA 7.0 SAN CARLOS & GRAND 25.6 STEVENS CREEK & CASA VIEW 6.7 SANTA CLARA & 7TH 22.2 ALUM ROCK & CHECKERS 5.9 STEVENS CREEK & STERN 21.7 STEVENS CREEK & HENRY 5.7

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DWELL TIME: DEEPER DIVE

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TRANSIT SIGNAL PRIORITY & DWELL TIME

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STOP LEVEL ANALYSIS

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TRANSIT NETWORK RE-DESIGN

Draft Plan Final Plan

Transit stress model identified areas that align with routes that

have been slated for removal in the Next Network

Two independent studies providing similar results.

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8/30/17
CTA, Miami-Dade, Honolulu, MBTA, VTA, RATP
Reviewed VTA analysis
Reviewed New Dashboard
Continued development of Swiftly for improved Transit Big Data

Analysis

SWIFTLY TECHNOLOGY ALLIANCE

A forum to share learnings

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Thank you to:

– Jason Kim, Senior Transportation Planner, VTA – Vivek Bansal, GIS Programmer, VTA – Mike Smith, CIO & Cofounder, Swiftly – Will Dayton, CTO & Cofounder, Swiftly