MaaS, Mobility as a Service uncovered

1. MaaS in Vlaanderen
360°

2. Journey
INTERMODAL ROUTE PLANNER
SMP
SMART MOBILITY PLANNER
BELLOT
TICKETING
PAYMENT
BLOCKCHAIN
ARTIFICIALINTELLIGENCE
TSP
API
INTERFACE
MaaS
MOBILITY
TMaaS
Customer’s Experience
Customer’s CONCENTRIC service
weather
Settlement
LESS-MOBILE
Customers
GTFS-RT
REAL TIME
BLINDness
Hard-hearing
elderl
y
RITUALSIST
EXPLORER
NEURAL
NETWORK
TIME TABLE
INTERUPTION
(BIG)DATA
BGTS
Nettex
AI-assisted
Service
interuptions
infrastructure
Law, mobility
budget
…table of
content…

3. MaaS
operator
Journey
Smart
Mobility
Planner
Journey, eco system providers
Steppy
Scotty
Lime
Uber
taxi
TSP/PTO

4. Klant_ID1
Klant_ID1
Klant_ID1
Customer_ID1
Klant_ID1 Klant_ID1
Klant_ID1
Klant_ID1
Klant_ID1
Customer Centric privacy management
The MaaS User manages and controls the access to personal
data (ID, travel preferences and ‘history’, payment data, and
other). TSP will only have access to Customer’s data:
i) When granted by Customer, and ii) having serviced the
Customer
initiating
billing
payment
MaaS
operator
Journey
NMBS MIVB DE LIJN
CAMBIO MOBIT
BLUE
BIKE
Logging
activities
settlement
1 2
3
TSP#X TSP#Y TSP#Z
Note: “Settlement” : blockchain (private, consortium)
TSP/P
TO

5. MaaS - 360° - Customer’s centric services
• Basic functionalities
• Onboarding : profile, payment preferences (card-on-file, third-party payment scheme), [“know your Customer”]
• Route planner (incl. requirements of less, or disabled people) : taking into account season pass, profile settings,…
• Ticketing : single, multi ticket, season pass, bundle(-s)
• Payment : cash, vouchers, credit, debit cards, Direct Carrier Billing, invoice
• Journey : location guiding to –next- stop, TSP, alternative, re-routing,
• {data, big data lake}
• For less- mobile , disabled persons
• Customised transport
• Deaf, hard-of-hearing, blind persons, elderly persons
• Help-desk assistance, enhanced – personalised – assistance
• Special features
• Indoor (building, public areas) gps’-function
• Occupation rates of TSP (public transport), impact on your journey
• AI-assisted operations enhancement (neural network)
• Prediction of your next journey stage
• Swift intervention in case of abnormalities, or service interruption
• Management
• MaaS
• Billing, settlement
• Integration van TSP’s
• TMaas, management of supply & demand of Customers and transport provider/service

6. predictive (neural network)
behaviour of the Customer
MaaS Big_Data
TSP capacity management
demand--side
supply-demand
Customer's concentric
profile
behaviour
weather
incidents
(traffic, mass events,
road incidents, fall-out ofMaaS Operations
management
Customer’s
Experience
Next slide

7. Customer's concentric
profile
behaviour
individual
family
groupe(-s)
‘business-travel’
• profile
• Behaviour
• Specific requirements
• profile
• Composition, behaviour
• Specific requirements
• profile
• Composition, behaviour
• Specific requirements
• Profile
• Specific requirements
Profile: age, own car or /and bike, weather-influenced travel behaviour, preference transport modi, regular festival, sport events,…
Specific requirement: handicap, less-mobility, blind, hard of hearing, wheel chair, buggy (children),…

8. other
OV
othercar
bike
MaaS

9. MaaS in Vlaanderen
Technology assisted

10. …..
• Block(-chain), python coded
• Certificate (signing)
• Timestamp
• Hashing
• Trans actioning
• Real time data (alle TSP en OV, indien beschikbaar)
• Weersimpact op transport in brede vorm (fiets, te voet, publiek transport,
alternatieve middelen (shared services), auto,…) [ 13-16% modal shift, afhankelijk
van ‘severity’]
• Impact door ‘events’ en ‘incidents’(welke oorzaak dan ook)
• Artificial intelligence & neural network voor optimalisatie van de ultieme Customer’s
Experience!
• Inclusief de bijzondere behoeften van ‘minder-validen’, die eveneens van een
‘ultieme Customer’s Experience’ kunnen genieten.

11. Success keys for MaaS
• qualitative and widespread 4G coverage, preferably 5G (roll out in preparation)
• widespread smartphone usage;
• cashless payment processors;
• contactless interchange of data;
• data (preferably real time data);
• legislation (i.e. mobility budget);
• need to change mobility of the Users (roadworks, eg. Antwerp );
• separate driving lanes for both tram, and buses;
• {popping-up idea to limit, or restrict the use of widespread ‘fuel cards’, attached to
the ‘salary cars’}
will need to form the base of any comprehensive MaaS system, alongside the ability of passengers to interchange seamlessly between
transport nodes, with this vision requiring the collaboration of diverse stakeholders, which may need support of Authorities to
establish.
Note:
seamlessly interchange: Leuven initiates clusters of mobility ‘centrals’ on selected areas across the town. On these ‘centrals’, a variety
of mobility services are centralised (coming together);

12. Infrastructure, and technology assistance
• MaaS will be successful if several conditions are met. Most of these conditions are without any doubt in the hands of Governments, Federal, or local.
• Buses are as fast as the surrounding traffic.
• Separate bus lanes are imperative. Also buses and trams must be enabled with features to influence the traffic lights when nearing a cross
• Government is taking some interesting initiatives to enable the shift:
• mobility budget. [potentially, Blue Ocean’s progressing to Red Ocean]
• Questioning ‘fuel card’ available for some Employees (part of salary car package)
• If / when Passengers will be confronted with the real transport cost for the society, they will be more tended to other mobility modes, than only
usage of a ‘individual car’-solution.
• Stepping on a public transport vehicle (bus, and tram), induces transactions.
• This can be i) ticket, ii) ticket type selection, and iii) ticket payment (selection of payment method). This causes time delays for the ‘next
‘passengers, and later departure of the public transport vehicle, causing delays in the line.
• Facilitating this process, would enable a faster throughput of people and transport vehicles.
• [there are several cases where payment is done ‘on the vehicle’, when seated, with i.e. QR enabling apps]
• Correct and accurate passengers ‘counting’, generates data for the Operator to manage additional vehicles on the line (loaded tram, or bus, on rush
hours, or rather unexpected moments in time). [ akas: occupation rate]
• Information sharing could facilitate the Operator to offer more transport vehicles, at a certain moment of time (i.e. Event organisation indicates end
of an event (with many people), so the Operator foresees additional vehicles)

13. predictive (neural network) behaviour of the Customer
MaaS Big_Data
TSP capacity management
demand--side
supply-demand
Customer's centric
profile
behaviour
weather
incidents
(traffic, mass events,
road incidents, fall-out ofMaaS Operations
management
Customer's Experience

14. Kohonen
In concept verwerkt, of beschouwd:

15. https://www.mckinsey.com/featured-insights/artificial-intelligence/notes-from-the-ai-frontier-applications-and-value-of-deep-learning?cid=other-eml-ttn-mip-mck&hlkid=4ae869ea19e04091b1e3e02de73db47e&hctky=9583324&hdpid=3b57a116-183f-412e-a787-
f8e957290e64#part1
In concept verwerkt, of beschouwd :

16. Settlement layer
journey
Mobit:
Bus:
Train:
Blue bike:
QR M-ticket;
mobib #
Ticket;
mobib#
code;
mobib#
timegeo
TSA
Mobit De Lijn NMBS-SNCB Blue Bike
Other TSP_y
CRM
MaaS
Start
journey
End
journey
Other TSP_x
Smart_contract_ID#
User_ID#
Smartphone_ID#;emei
Name_user
Surname_user
(birthdate)
Timestamp_in;timestamp_out
TSP_ID#
Mobib_ID#;m-ticket_#; validity_identificatory;
Traject_coordinates_in;traject_coordinates_out
Payment_amount_1
Consent&privacy
Policy, signing policy
-> encryption, hashing
Recorded data:
NFC(+Bluetooth) is
recommended
VPN,SSL, PKI, server certificate, CA, RA, policy
Blockchain approach
User_ID#

17. Blockchain approach
Sign-on MaaS Route planning delivering service payment CRM
[Smart_Contract] [Smart_Contract]
Collect ‘big
data’ :
management
steer
Pro-active &
predictive
Customer
behaviour
marketing
settlement
User ID# (certificate) User ID# (certificate) User ID# (certificate) User ID# (certificate)
(smarphone ID#) (smarphone ID#) (smarphone ID#) (smarphone ID#)
name User name User name User Name,voornaam User
voornaam User voornaam User voornaam User (birthdate)
(birthdate) (birthdate) (birthdate) timestamp
(time_indication, login/access) (time_indicatie, login/access) timestamp (tsp_ID)
Consent&privacy (tsp_ID) (tsp_ID) ticket ID#
Consent&privacy ticket ID# Validity identificator
Travel/stage coordinates Travel/stage coordinates
Consent&privacy payment_amount_1
Consent&privacy
Tariff policy, policy
Hashing & signing
hash algorithme hash algorithme hash algorithme hash algorithme
################# ################# ################# #################
TSP_ID_1 TSP_ID_1 TSP_ID_1
MaaS DB – back office
hash algorithme hash algorithme hash algorithme hash algorithme
################# ################# ################# #################
TSP_ID_1 TSP_ID_1 TSP_ID_1
(GATEWAY)-TSP
"+key "+key "+key "+key
hash algorithme hash algorithme hash algorithme hash algorithme
################# ################# ################# #################
TSP_ID_1 TSP_ID_1 TSP_ID_1
Both will be the same
‘device’
geo time

18. User ID# (certificate)
(smarphone ID#)
Name,voornaam User
(birthdate)
timestamp
(tsp_ID)
ticket ID#
Validity identificator
traject coördinaten
payment_amount_1
Consent&privacy
Tariff policy, policy
CA42750ABCBE20F46DB22C690D4B89CC52BCC88AA4C72C56DE7950E399BC9C6E

19. MaaS
Wat is belangrijk

20. home (startpoint)
stop
_15
stop
_14 stop
_6
stop
_7
stop
_5
stop
_16
stop
_17
4 min+2min
6 min
6 min
+1 min
5 min
6
min
1 min
8
min
2
min
3 min
time table
+1 min
line TSP
starting
time
walk to
stop
(elapsed
time)
hour
tabel
(elapsed
time)
impact of RT
(elapsed
time)
arrival at
station
(S time)
walk to
platform
(elapsed
time)
at
platform
(total
travel
time)
assessment
line blue 11:00 0:01 0:10 0:03 0:14 0:03 0:17 late
line green 11:00 0:08 0:05 0:00 0:13 0:02 0:15 timely

22. time

23. NMBS- station(exit_1)
exact(bus) stoplocation
example: Mechelen railroad station
P_10
NMBS- station(entry)

24. 7 overarching categories in users’ satisfaction survey:
1. onboard experience:
1. Cleanliness,
2. comfort,
3. seating capacity,
4. onboard information,
5. crowding,
6. quality of vehicles,
7. safety,
8. Illumination,
9. temperature and
10. accessibility (physical).
2. customer service:
1. Driver and personnel’s attitudes,
2. personnel skills and
3. complaint dealing
3. service delivery:
1. Reliability,
2. on-time performance/ punctuality,
3. frequency,
4. travel time,
5. access time,
6. network coverage,
7. number of transfers,
8. service provision hours,
9. convenience,
10. stop location,
11. station parking and
12. waiting time

25. 7 overarching categories in users’ satisfaction survey (2)
1. waiting conditions:
1. Waiting conditions,
2. information at stops and
3. safety at stops
2. Costs:
1. Value,
2. types of tickets and
3. passes and
4. ticket selling network
3. quality of transfers:
1. transfer time and
2. ease of transfer
4. Image
1. image and
2. environmentally friendly

26. Klant_ID1
Klant_ID1
Klant_ID1
Klant_ID1
Klant_ID1 Klant_ID1
Klant_ID1
Klant_ID1
Klant_ID1
Customer Centric privacy management
De MaaS User beheert zelf de access tot zijn/haar/het
persoonsgebonden gegevens (id, reisgedrag,
betaalgegevens,…)
initiating
billing
payment
MaaS
operator
Journey
NMBS
MIVB
DE LIJN
CAMBIO
MOBIT
BLUE
BIKE
Logging
activities
1
2
Journey, eco system providers
TSP/PTO
Steppy
Scotty
Lime
Uber
taxi