Masterslides Trafiklabmeetup 6 dec

Dagens fullspäckade agenda
16:30 - Intro
16:40 - Telia
16:50 – Samtrafiken – Projekt öppna data
17:05 – SL
17:10 - Linked open data, A look ahead in 15’
17:25 – Let’s talk about open data - Swiftly
17:40 - Prisutdelning Årets trafiklabhjälte
17:50 - 5 min paus
17:55 – Deep learning and Savantic in transport
18:10 – Platform thinking
18:20 – Trafikverkets öppna API
18:35 – Trainhack 2018
18:45 – Mingel och wraps

SAVEA Ticket API
Very quick rundown of the API
BROUGHT TO YOU BY SAVEA

How does it work?
Step One – Search
Which company, where from, where to, when, and how many?
(Following Samtrafiken’s BoB standard read more via labs.savea.se)
Step Two – Request payment link
Request using product id
(again, following Samtrafiken’s standard)
Step Three – Send user to payment link
User pays for trip on our site, user gets ticket

9
Projekt Öppna Data
Projekt Öppna Data Etapp1 - 2018 (2019)

10
Samtrafiken inför CCO
• Ny licens (CCO) på Trafiklab fr o m 2018-12-06 för GTFS Regional, GTFS Sverige 2 och
ResRobot-APIerna
• Minskade krav på licenstagaren jämfört med dagens licens
• Lättnader i villkor (ex. inga krav på hänvisningar till källa)

11
Harmonisering
• Utredning genomförd för hållplatser och överlappande data
• Rapport ute på remiss
• Förslag innehåller gemensamma ID
• Idag många ID som inte går att matcha

12
9021001005310000
740001617
741617
9001
SCI
Stockholm City

Sa
13
Trafikföretag 2018 Hållplatser/
Stationer
Rutter/
Linjer
Realtid
Planerad
trafik
Realtid
Störningar
Positions-
data
Andel av
totala
resor i %
Östgötatrafiken Q1 x x x x x 1,8
UL Q1 x x x x 2,7
SLL Q2 x x x x 52
Skånetrafiken Q2 x x x x x 10,1
Västtrafik Q3 x x 18
JLT Q3-19 1,4
X-Trafik Q4 x x x x x 1
Kalmar Q3 x x x x x 0,6
VTAB Q4 x x x x x 0,8
Dalatrafik Q4 x x x x x 0,6
89,0Produktionssättning beräknad till början av Q2

•Point-of-interest och cyklar
finns i data.
•Nya versioner av api:er med
dokumentation februari 2019.
•Förbättrad prestanda
• E-post: andreas.stromberg@sll.se
Trafiklab

Stockholm has a population of 231 persons

Linked Open Data
A look ahead in 15’
Bert Marcelis
Developer at
Samtrafiken

Contents
State of technology
Linked Open Data
Linked Connections
Possibilities
19Bert Marcelis

Current state of technology
APIs - Question & Answer
GTFS - Data Dump
20Bert Marcelis
State of technology – Linked Open Data – Linked Connections – Demo

9021001005310000
740001617
741617
9001
SCI
Stockholm City

http://se.lc.bertmarcelis.be/stops/740001617
22Bert Marcelis

23Bert Marcelis

http://se.lc.bertmarcelis.be/stops/740001617
URIs are the key of Linked Data
24Bert Marcelis

Leaving our walled gardens …
25Bert Marcelis
SL
GTFS Sverige
2
Resrobot
GTFS
Regional
Realtime
GTFS
Regional
Static
Stopconversion.txt
GTFS Sverige
2 Historical
Pendelkoll
Trafikverket

…using Linked Open Data
26Bert Marcelis
SL
GTFS Sverige
2 + historic
Resrobot Other APIs
GTFS
Regional
Static +
Realtime
Rikshållplatse
r
Occupancy
data
WikiData
GeoNames
Danish PTAs Spanish PTAs
Belgian PTAs
Finnish PTAs
Wikipedia
Linked
Connections
+ historical Agencies
Trips, routes,
…
Reliability
data
UK PTAs
German PTAs
Norwegian
PTAs
Air pollution
data
Swedish
datasets

What is Linked Open Data
Open data: accessible, reusable (e.g. CC0)
Linked data: datasets are interoperable, work together
All data is machine readable
Standard formats: JSON-LD, RDF, …
27Bert Marcelis

4 Rules of open data
Use URIs as names for things
Use HTTP URIs so that people can look up those names
On URI lookup, useful information according to standards
Include links to other URIs
28Bert Marcelis

5 Stars of Linked Open Data
Trafiklab
now
Trafiklab
2020?
29Bert Marcelis

What is Linked Connections
Public transport schedules as LOD
30Bert Marcelis
10:0
0
10:0
8
10:1
2
10:1
9
Kungsträdgården T-Centralen Rådshuset Fridhemsplan
Connection 1 Connection 2 Connection 3 Connection 4
http://.../stop/1 http://.../stop/695 http://.../stop/87 http://.../stop/48

31Bert Marcelis
10:0
0
10:1
0
10:2
0
’Hydra:next’ = ’http://...’
Bus 1
Departure 1
Bus 2
Departure 2
Bus 2
Departure 1

Public transport schedules as LOD
Chronological list of all departures: Dumb server, smart clients

Demo time
33Bert Marcelis

Demo time
34Bert Marcelis

Demo time
35Bert Marcelis

Demo time
Temporary Proof-of-Concept
Not an official project (yet)
https://se.lc.bertmarcelis.be/demo
https://se.lc.bertmarcelis.be/heat/isochrone-sl.html
https://se.lc.bertmarcelis.be/sl/connections
36Bert Marcelis

Summary
Linked Open Data opens new possibilities
Linked Connections opens new possibilities
Lets take Swedish (transport) data to the next level
37Bert Marcelis

Tack!
Ask me anything!
Share your ideas!
Bert.marcelis@samtrafiken.se
Github: @Bertware

SWIFTLY & TRAFIKLAB
Let’s talk about
Open Data

introductions
MARTA ZAMANILLO
Customer Success
Manager

What is
Swiftly?
The first big data platform
for public transit
// Highly accurate real-time
passenger information
// Understand and analyze network
in real time and historically
// Quickly visualize data to build consensus
past, present, and future
Trusted by over 50 transit
agencies

Swiftly
products
Swiftly Transitime
Swiftly Insights

swiftly transitime
1 // Real-time arrival information for passengers
2 // Prediction engine is up to 30% more accurate
3 // Third-party data access: GTFS-rt, JSON, XML APIs
4 // Flexible data can be sent anywhere
Mobile Apps
SMS & Text
Responsive Web
Electronic Displays

swiftly insights
1 // Compress multi-month planning studies
down to days
2 // Discover where and when issues occur
3 // Increase service efficiency and reliability
RUN-TIMES
Analysis of actual run-times vs. scheduled
run-times
ON-TIME PERFORMANCE
Analysis of all vehicles by early, late, and on
time
SPEED MAP
Route map by segment speeds
GPS PLAYBACK
On-the-fly retrospective analysis of
individual vehicles

seamless
integration
Swiftly integrates with your
current & future infrastructure
Only two data feeds are required
GTFS
AVL / GPS

SWIFTLY
Collects and analyzes billions of data points per month

on time performance
Quickly determine where and when issues are occurring
STOP
LEVEL
TIME OF DAY
SEVERITY (HISTOGRAM)
TRIP &
STOP

running time by trip
Quickly determine running times for any route, trip, or even between stops
Rapidly detect and solve scheduling or performance issues

running time by stop
Quickly determine running times for any route, trip, or even between stops
Rapidly detect and solve scheduling or performance issues

run-times suggested schedule by timeband
Quickly find and prioritize the highest-impact OTP improvements within their schedules at the route, time
point, and stop level
Quickly determine running times for any route, trip, or even between stops.
Rapidly

run-times cumulative by stop
Quickly compare, in one view, the run-times of a route over two time ranges

travel speeds & delays
Visualize the impact down to the intersection

GTFS WIFI
TransitimeInsights
let’s grow it further together
CAD/AVL ETC

GTFS WIFI
TransitimeInsights
let’s grow it further together
CAD/AVL ?
?

thank you
MARTA ZAMANILLO
CUSTOMER SUCCESS MANAGER
e marta@goswift.ly p +34 653 83 95 27
a 1 Sutter Street, Suite 500 | San Francisco, CA 94104

Prisutdelning: Årets trafiklabhjälte

Utmärkelsen ska gå till en person som
utmärkt sig på ett eller flera sätt:
• Bidragit med ideellt engagemang och drivkraft
• Skapat ett eller flera viktiga verktyg i vardagen för många resenärer
• Utmanat traditionella kollektivtrafikaktörer
• Skapat en eller flera tjänster med en etablerad användarbas eller potential till
att skapa en etablerad användarbas
• Skapat tjänster som ökar resandet eller gjort det enklare att resa med
kollektivtrafiken
• Skapat en eller flera tjänster som bygger på öppna data från Trafiklab

Historiska vinnare...
2015: Johan Nilsson
2016: Mattias Jäderskog
2017: Tomas Tydal
2018: ???

…och vinnaren är…
Martin Harari Thuresson

About Savantic…
➢ Founded 1999
➢ 20 researchers specialized in Machine Learning
➢ We make advanced Machine Learning available
to our customers
Business analysis
Data strategy
Sensors
Data acquisition
Model design
and training
Data & Image analysis
Validation
SAVANTIC

The Deep Learning Revolution
Top 5 classification errors (%)
Hand-crafted, feature-based
designs
Deep CNN-based designs
Deep CNN = large error rate reduction
SAVANTIC

Before Deep Learning
x1
x2
x3
xn
w1
w2
w3
wn
෍ 𝑥𝑖 𝑤𝑖 + 𝑏ⅈ𝑎𝑠
activation
y
SAVANTIC

Convolutional layer
෍
𝑘=−1
1
෍
𝑗=−1
1
ℎ 𝑗, 𝑘 𝑓 𝑥 − 𝑗, 𝑦 − 𝑘
activation
𝑔 𝑥, 𝑦
SAVANTIC

Convolutional Neural Network
Calculate
features
Classifier
Dog
Horse
Cat
SAVANTIC

Our models outperform the experts! Accuracy
Experts 90%
Deep Learning 97%

The model teaches the expert SAVANTIC

Not only image classification
➢Natural Language Processing (e.g., sentiment analysis)
➢Medical application
➢Tracking objects in videos
➢…
SAVANTIC

…and in transport
➢ CNN to extract spatial-temporal
features from images of traffic
speed
➢ Prediction of traffic speed up to 20
minutes into the future
SAVANTIC
➢ Detect and classify persons, vehicles, traffic signs,…(autonomous vehicles)
➢ Classify and count persons on-board of vehicles or on platforms (e.g., to calculate
the grade of service)
➢ To predict traffic flow

SAVANTIC
➢ Prediction up to 1 hour
…and in transport

Savantic in Mistra-SAMS
Prototype of a digital platform:
➢ Combined mobility services on a digital
platform
➢ Dynamic incentives based on road traffic
conditions, available parking slots,…
SAVANTIC
Consideration on the open data:
➢ Prediction on traffic available from private
company (e.g., Google).
➢ Parking spots: no information, no booking.
➢ No information on available bikes from bike
sharing services

All over the world, the challenges of traffic grow in both
growing cities and sparsely populated areas. Urbanization
causes congestion, health and climate problems and
inefficient, costly transport.
Through the depopulation, rural areas and small urban areas
are affected by declining grounds and poor service.
Today's infrastructure is inefficiently used, accessibility is
deteriorating, business development is hampered and public
finances are affected.
Expanded capacity with more roads attracts more and more
traffic and does not solve the problems. New players are
challenging digital solutions, but without coordination, they
also lead to more traffic and increased problems.
PROBLEM

Savantic in Predictive Movement
➢ Look at open data and need of new
➢ Investigate how mobility data (phone-to-mast) can be used in the project
➢ Lab platform to test ideas with users
➢ Collect data from Norbotten (e.g., public transport, sick-trips, goods transport carried out by
Region Norbotten)
Lessons learnt:
➢ Aggregation/anonymization of phone-to-mast data does not allow for fine grain analysis (e.g.,
gain of drive-sharing)
➢ No response regarding data from Norbotten except sick transport data
➢ Sick transport data were anonymized: only ’from’ and ’to’ no O-D pair.
SAVANTIC

Requirements on data (machine learning perspective)
➢ Open/Missing (e.g., parking slots)
➢ Standardized (e.g., bike sharing)
➢ Clear description (i.e., what each field represents)
➢ Data cleaning (e.g., remove incorrect data)
➢ Data reduction (e.g., aggregation). Selecting important attributes and method of aggregation
is dependent on application. Solution: to extract data from open database depending on the
application?
SAVANTIC
Common to other applications

THE RISE OF PLATFORM
ECOSYSTEMS
UMEÅ UNIVERSITY

• Platform businesses≠ Stand alone Products/Service business
o Market potential
o Structural difference
▪ Traditional industries
• Exchanges follow a linear path as vendors purchase
inputs, transform them, and sell output.
▪ Platform-mediated networks exchanges have a triangular
structure
UMEÅ UNIVERSITY
PLATFORM THINKING

• Platform businesses≠ Stand alone Products/Service business
o Management style
▪ Traditional industries
• Attracting customers
• Planned innovation
• Command and control structures
▪ Platform-mediated networks
• Attract users on sides of the platform
• Innovation is emergent
• orchestration
UMEÅ UNIVERSITY
PLATFORM THINKING

What is a Platform: “an extensible software product or service that serves as a
foundation on which independent outside parties can build complementary products or
services (application) that interoperate through the platform’s interfaces” (Tiwana, 2014)
ELEMENTS OF A PLATFORM
ECOSYSTEM
Source: Tiwana, (2013, p. 6)
UMEÅ UNIVERSITY

Third-party applications and external actors participating in the innovation
process to create value based on the platform’s interface (Tiwana et al., 2014)
DIGITAL ECOSYSTEM
UMEÅ UNIVERSITY

• Deepening specialization
• Packetization
• Software embedding
• The internet of things
o Endless amount of data generated by inter connected devices
o Innovation based on data pulled across different firms and users
o Increased context awareness
• Ubiquity
UMEÅ UNIVERSITY
DRIVERS OF THE MIGRATION
TOWARDS PLATFORMS

• Platform businesses≠ Stand alone Products or Service business
o Supply chains not considered as platform in this context
o Platform need to link/interact with two or more distinct groups of
users
MULTI-SIDEDNESS
Source: Eisenmann et al., (2008).
UMEÅ UNIVERSITY

NETWORK EFFECT
• Platform are characterized by network effect
• The main thrust of the network effect builds on the rationale that users derive less value from
certain products when consumed in isolation compared to when other users consume similar
products(Katz and Shapiro 1985, 1994).
• Critical Mass
UMEÅ UNIVERSITY

• The value of a platform is often tightly connected to the user base and interactions in the
ecosystem
o Platform owners
o App developers
o End-users
THE VALUE PROPOSITION OF
PLATFORMS
Tiwana, 2013
UMEÅ UNIVERSITY

VALUE PROPOSITION FOR PLATFORM OWNERS
• Platform owners
o Massively Distributed innovation
o Risk transfer
o Competitive sustainability
VALUE PROPOSITION FOR APP DEVELOPERS
• APP Developers
o Technology foundation
o Market Access
VALUE PROPOSITION FOR END-USERS
• End-Users
o Mix-and-Match Customization
o Faster innovation and network benefits
o Lower search and transaction cost
UMEÅ UNIVERSITY

• Most research focuses at mature phases of a platform and
dominant platforms.
o Little focuses on the start-up phases when platforms orchestrators
may not have the power to negotiate with key actors
o What are the key challenges different actors face when seeking to
generate innovation from platform?
o What are are the essential roles that platforms can provide to
stimulate innovation and collaboration?
o How can organizations transition from internal development to
external development of services with third-parties through
platforms
UMEÅ UNIVERSITY
RESEARCH INTERESTS

THANKS
UMEÅ UNIVERSITY
QUESTIONS?

TMALL0141Presentationv1.0
Lars-Olof Hjärp
Tommy Frisk

98
Trafikverkets öppna
API
ver 2

99
Trafikverkets öppna API
• Funktionalitet
– ”Push” (Lab)
ver 2
Pull med ”continous delivery”
Ge mig det du har nu och fortsätt leverera nyheter så länge vi har kontakt

100
• Funktionalitet
– Function (Lab)
ver 2
Funktioner kopplade till vissa datamängder
Tex snap koordinat till närmaste väg

101
• Funktionalitet
– Function (Lab)
• Nya datamängder
– Passagedata (Traffic flow)
– Beläggningsdata (PMS)
– Site E18
ver 2

102
• Funktionalitet
– Function (Lab)
– Site E18
ver 2

103
• Funktionalitet
– Function (Lab)
– Site E18
• Metadataportal
ver 2

104
• Funktionalitet
– Function (Lab)
– Site E18
• Metadataportal
ver 2
NÄR?
Utvärderas nu
Om allt faller väl ut
lansering under våren
Utvecklas nu
Om allt går bra
lansering under våren
Utvecklas nu
Succesiv lansering under våren

105
• Hack for Sweden
Årligt event -> 365
Partner
• NÅP
Samtrafikens projekt för öppen data inom kollektivtrafiken
• ODIN
Nordiskt samarbete om kollektivtrafikdata
Några andra initiativ där vi är inblandade

Trainhack 2018
kollektivtrafik
60% tillsammans
40% trafik

Personliga favoriter
RunningLate
Coffeeing
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Masterslides Trafiklabmeetup 6 dec

Masterslides Trafiklabmeetup 6 dec

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