Teresa Tapia

Teresa.Tapia@streetlightdata.com

Customer Success Manager

StreetLight Data

Big Data Analytics for Active Transportation

and Multimodal Planning

-- Proprietary and Confidential -- 2

Overview

I. Who We Are

II. Big Data for Active Transportation

I. Our M2 Initiative

II. How We Capture Active Modes

III. Pilot Project Results

-- Proprietary and Confidential -- 3

Zone Activity

Volume

Origin

Destination

Matrices

StreetLight InSight®

We Provide the Best Big Data Resources and

Processing Software for Transportation

AADT/AAHT

Trip Length

Trip Circuity

Congestion

(Free Flow

Factor)

Aggregate

Home/Work

Locations

Select Link

Trip Duration

Demographics

Trip Speed

Big Data +

Contextual Data

-- Proprietary and Confidential -- 4

What Big Data Are We Working With?Mobile device data from ~23% of US and Canadian adults and ~12% of commercial truck trips

Video shows a subset from Oct 8th, 2017 in San Bernardino, California

-- Proprietary and Confidential -- 6

Our On-Demand Platform Delivers Real-World Transportation

Analytics for Data-Driven Policy and Infrastructure Planning

StreetLight InSight®: The Only On-Demand Platform For

Running Actionable Transportation Analytics

1300+Analyses

Supported

Each Month

-- Proprietary and Confidential -- 7

Why We Launched Our Multimodal Measurement

Initiative: All Modes Count – But Not All Are Counted

-- Proprietary and Confidential -- 8

We Are Currently Engaged in Pilot Projects for M2

Working Group Partners

Spring 2018 Summer 2018 Fall 2018 Winter 2018

V1 Active Mode + Gig Driving

Algorithms Finalized

Bike/Ped Pilot Selection and Deliveries

(Selecting working group members with

calibration data and curiosity!)

Launch of full Active Mode

Metrics in StreetLight InSight

Algorithm and product tweaks, based on partner feedback

Gig Driving Pilot Deliveries for Working Group

Big Data Resources for

Active Transportation

-- Proprietary and Confidential -- 10

What’s Out There?

Cell Tower Vehicle / Truck –

Navigation-GPS

Multi-App

Location-Based

Services Data

Mode-

Specific App

Data

Inroad Sensors Video Readers

What

Modes?

Air, some car Car, truck Air, car, truck, bike,

pedestrian, air,

TNC, boat, train,

bus, etc.

Mode that app

is “for”

Bike, car Car, bike, ped

What

Patterns

?

OD, ~count OD, route, speed OD, route, speed,

demographics,

~count, tours

OD, route,

~tours

Count, RT

Presence

Count, RT

Presence

-- Proprietary and Confidential -- 11

Multimodal Planning Still Needs Car Data!

-- Proprietary and Confidential -- 12

But Let’s Talk About the Harder Stuff

-- Proprietary and Confidential -- 13

Active Mode Data Options: Which to Choose?

Multi-App Locational Data Mode-Specific App

Data

Inroad Sensors Video Readers

Pros? “Always all” (all trip types

and modes), large

reprsentative sample size,

OD/route, demographics,

trip purpose, frequency

Mode certainty,

OD/route, good

sample size

Complete count (in

theory), operational

applications

Complete count (in

theory), operational

applications, view many

types of events

Cons? Mode probabalistally inferred User must turn on,

skewed user group

Expense + maintenance,

extendibility, limited

metrics, bikes/peds roam

Expense, extendibility,

limited metrics, occlusion

-- Proprietary and Confidential -- 14

Active Mode Data Options: Which to Choose?

Multi-App Locational Data Mode-Specific App

Data

Inroad Sensors Video Readers

Pros? “Always all” (all trip types

and modes), large

reprsentative sample size,

OD/route, demographics,

trip purpose, frequency

Mode certainty,

OD/route, good

sample size

Complete count (in

theory), operational

applications

Complete count (in

theory), operational

applications, view many

types of events

Cons? Mode probabalistally

inferred

User must turn on,

skewed user group

Expense + maintenance,

extendibility, limited

metrics

Expense, extendibility,

limited metrics, occlusion

We Choose: All of the Above

-- Proprietary and Confidential -- 15

We’re Developing Partnerships with Mode-

Specific Smartphone Apps

-- Proprietary and Confidential -- 16

Machine Learning: Speed is Not Adequate!

-- Proprietary and Confidential -- 17

How We’re Putting Everything Together

+B

A• 40 on Road Z • 60 on Road Z

1000 Bike Trip Counts

• 100 on Road Z

TOTAL

Pilot Project Results

-- Proprietary and Confidential -- 19

Completed Pilot Project – Active O-D to Transit

Stations in Sacramento

Implication for policy

makers and planners:

A large portion of people

walk and bike to and from

areas across the freeway.

Prioritize connectivity

across the freeway for

pedestrians / bike bridges.

bit.ly/ConnectingSacramentoOrigins and destinations of active trips to and from Zinfandel Station and

Cordova Town Center

Zinfandel Station

Cordova Town Center Station

-- Proprietary and Confidential -- 20

Completed Pilot Project – Campus Circulation

Study for University of Miami in Ohio

Two main arterials, US 27

and SR 73, bring regional

traffic through campus

Implication for University’s

Transportation Planning:

Shows where additional

cross walks and improved

signal timing are most

needed to improve flow of

vehicle traffic during class

exchange times.

Active transportation (bike + ped

combined) data showed 46% of

active trips cross US 73 into Zone

1

Teresa Tapia

Teresa.Tapia@streetlightdata.com

Customer Success Manager

StreetLight Data

Thank You