Smart  Mobility:
Optimization  and  Behavioral  Modeling
Moshe  Ben-­Akiva
ITS  Lab

Smart  Mobility:  Introduction  • Mobile  technology• Real-­time  /  on-­demand• Personalized• Shared2

Smart  Mobility:  Mobile  Technology  AutonomousApp-­‐based

Connectivity

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Smart  Mobility:  On-­DemandUber,	  Lyft…

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Smart  Mobility:  Personalized
Flexible  Mobility   On-­Demand  (FMOD)
Traveler

Fleet
REQUEST
OFFER

FMOD
server

ALLOCATE

CHOOSE

Maximizing
Profit/Welfare

Mobility   as  a  Service  (MaaS)

Taxi
Shared  taxi
Mini-­bus

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Smart  Mobility:  Shared
Car	  sharing,	  carpooling	  lanes,	  ride	  sharing,	  bike	  sharing….

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By  The  New  Yorker,  MAY  16,  20167

Research  Agenda
Behavioral  Data

Behavioral  Models/Optimization

SolutionsDesigning  Effective  Smart  Mobility  Solutions• Efficiency

optimization• Personalization

behavioral  modeling• Real-­time                                              app-­based  platform  (FMS)
http://its.mit.edu/future-­mobility-­sensing• Testing                                                      SimMobility
http://its.mit.edu/research/simmobility

9Research  Projects:  Solutions• Real-­time  Toll  Optimization  based  on  Prediction• Flexible  Mobility  on  Demand  (FMOD)• Autonomous  Mobility  on  Demand  (AMOD)• Mobility  Electronic  Market  for  Optimized  Travel  (MeMOT)

10Real-­time  Toll  Optimization  based  on  Prediction

Real-­time  Toll  Optimization  based  on  Prediction
Users
Guidance

Data  fusion
Incidents
Self-­calibration

Data

Behavioral  models  (e.g.,  route  choice,  departure  time  choice)Prediction
ControlOptimization
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Rolling  Horizon13

DynaMIT 2.0:  System  Architecture
Historical  Database

State  Estimation
Scenario  Analyzer
Real  Time  Data
DemandSupply

State  Prediction
NetworkDemandSupply

Strategy  Optimization

Guidance  and  control
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Case  studies• Area-­wide  tolling  in  Singapore• Managed  lanes  in  Texas

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Case  Study:  Area-­wide  dynamic  tolling••••

Minimize  total  travel  time  in  the  network  (fixed  total  demand)
Historical  dataset  on  incidents/road  works  (Sept.  15th,  2011)
Simulation  period:  7:30  AM  ~  2:30  PM
13  toll  gantries- Toll  rates  changing  at  5  min  interval  16

Three  Scenarios

Base  caseNo  guidance

Guidance

Predictive  guidance  with  DynaMIT

Guidance  and  toll  optimization

DynaMIT guidance  and  optimized  tolls

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Total  Travel  Time  (veh.  hrs)

Reduction  in  Network  Delay
-­25%

-­33%

Scenario

Travel Time of  affected*drivers  (veh.  hrs)

Total  Travel  Time(veh. hrs)

Base

2,184

87,645

Guidance

1,648  (-­25%)

81,626  (-­7%)

Guidance &  toll  optimization

1,473  (-­33%)

79,141  (-­10%)*Affected  vehicles  are  defined  as  vehicles  passing  incident  locations

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Flexible  Mobility  on  Demand  (FMOD)

Flexible  Mobility  on  Demand  (FMOD)
FMOD  provides  a  personalized and optimized  menu  of  travel  options  in  real-­time.
Traveler

Fleet
REQUEST
OFFER

FMOD
server

ALLOCATE

CHOOSE

Maximizing
Profit/Welfare

Taxi
Shared  taxi
Mini-­bus

Dynamic  allocation  of  vehicles  to  services
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FMOD  Services
Flexibility to  choose  from  different  levels  of  services• Taxi:  door-­to-­door,  private• Shared-­taxi:  door-­to-­door,  shared• Mini-­bus: fixed  stops,  shared

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FMOD User  Experience
Request:Origin: A, Destination: B
Preferred Departure Time: 8:00 – 8:30
/ Preferred Arrival Time: 8:45 – 9:00

Traveler

REQUEST
OFFER
Offer:taxi:
DT: 8:25/AT: 8:45, $20
shared-taxi: DT: 8:27/AT: 8:57, $10
as the 4th passenger
mini-bus: DT: 8:14/AT: 8:59, $5
as the 6th passenger

CHOOSE
Choice:service: shared-taxi
DT: 8:27/AT: 8:57, $10

Supply Demand
FMOD
ServerOptimization
andPreferencesMaximizing
profit/welfare

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Menu  optimization
Phase1.  Feasible  product  set  generation• Existing  commitments• Capacity  constraints• Scheduling  constraints

Phase  2.  Assortment  optimization
Menu  offered  to  the  traveler  from  the  feasible  set
• Maximize  profit/welfare  based  on  a  behavioral  model  (mode  choice)23

Simulation  Experiments  in  Singapore
1.  Base  Case
Taxi,    Private  vehicle,  MRT,  Bus

Extended  CBD  area••

2.  Scenario  with  FMOD
Taxi,  Private  vehicle,MRT,  Bus,  FMODNetwork configuration:- 2706  links  -­ 1294  intersections- More  than  2000  loop  sensors- 46  MRT  stations
Simulation setting- 6:00  – 7:00  AM- Calibrated  demand  (08/2013)- 10%  of  all  road  users  have  access  to  FMOD
- 500  FMOD  vehicles
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Results:  Offer  and  Choice
T:  taxi,  S:  shared-­taxi,  M:  minibus•••

Large  share  of  taxis  with  ‘Profit  maximization’Large  share  of  shared-­taxi  with  ‘Consumer  surplus’Lower  reject  rate  with  ‘Consumer  surplus’25

Results:  Operator  and  User  Benefit
Comparison  of    different  strategies  (PM  and  CS)

Percentage  difference=(PM  -­ CS)  /  PM  *100•

‘Profit  Maximization’  (PM)−−•More  revenue  for  the  operator
Less  waiting  time  for  the  user‘Consumer  Surplus  Maximization’  (CS)−More  consumer  surplus    26Results:  Network  Performance

CDF

Comparison  of  FMOD  (Max.  ‘Consumer  surplus’  )  and  Base  Case  with  same  demand-­-­‐FMODBase	  CaseV/C  ratio• Significantly  lower  V/C  ratio  in  FMOD  w/o  increasing  travel-­time  - 10~20%  decrease  in  average  V/C  ratio- Similar  travel-­time  (avg.  difference  <  10sec)
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Autonomous  Mobility  on  Demand  (AMOD)

Autonomous  Mobility  on  Demand  (AMOD)●●Bus●MRT●Taxi●Private vehicles

Bus ● MRT ● Taxi ● Private Vehicles ● Autonomous MOD29

Mode  choice
Results:  User  Choices
75%

Before

50%

After

25%
0%

Public  transport
Public	  Transport

BeforePrivate  carPrivate	  Transport

AMODAMOD

Taxi
Taxi

After30

90  perct.

Results:  Fleet  Performance31Effect  on  through  traffic
Results:  Network  Performance
BeforeAfter

PathLengthTravel	  TimeSharePathLength

Travel	  TimeShare

110.6	  km17	  min13.0%

1

18.7	  km32	  min

10.1%

2

10.1	  km14	  min32.4%

2

15.4	  km29	  min25.3%
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Mobility  Electronic  Market  for  Optimized  Travel  (MEMOT)MeMOT:  Concept  • Optimized  and  personalized
menu with  information  and  incentives in  a  trip  planner  app
• Incentives  based  on  real-­time  system  optimization  predicting  network  conditions  and  energy  savings

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MeMOT:  Framework
Users

Travel  Choices

MeMOT

Personalized  Menu Token  Value  &  Trip  Attributes
User’s  preferences
UserOptimization

Transportation  Network

Experienced
NetworkConditions

SystemOptimization

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MeMOT:    2-­Level  Optimization  1. A  simulation-­based   system  optimization  framework  that  predicts  traffic,  energy  consumption  and  energy  efficiency  in  real-­time.
2. A personalized menu  optimization with  information  and  incentives integrated into  an  app-­based  travel  diary36

MeMOT:    2-­Level  Optimization  Framework  Trip  Information&Token  Value

OptimalRewards

Personalized
Menu

User
Request

Choices

User  Optimization

Optimal  TokenAllocation

Real-­time  Data

EnergyPrediction

Users’  Preferences
Simulation  &  State  Prediction

System  Optimization
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Smart  Mobility:
Optimization  and  Behavioral  Modeling
Behavioral  Data

Behavioral  Models/Optimization

Solutions38

References  (1)  • Atasoy,  B.,  Ikeda,  T.  and  Ben-­Akiva,  M.  (2015),  "Optimizing  a  Flexible   Mobility  on  Demand  System”, Transportation  Research  Record  (TRR) ,  Vol.  2536,  pp.  76-­85.
• Atasoy,  B.,  Ikeda,  T.,  Song,  X.  and  Ben-­Akiva,  M.  (2015),  “The  Concept  and  Impact  Analysis  of  a  Flexible   Mobility  on  Demand  System”, Transportation  Research  Part C:  Emerging  Technologies, Vol.  56,  pp.  373-­392.
• Ben-­Akiva,  M.,  McFadden,  D.,  and  Train,  K.  (2015),  Foundations  of  stated  preference  elicitation,  consumer  choice  behavior  and  choice-­based  conjoint  analysis.

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References  (2)  • Kamargianni,  M.,  Matyas,  M.,  Li,  W.,  and  Schafer,  A.  (2015).  
Feasibility   Study  for  “Mobility  as  a  Service”  concept  for  London.  
Report  prepared  for  the  UK  Department  for  Transport.  Available   at:  https://www.bartlett.ucl.ac.uk/energy/docs/fs-­maas-­compress-­final• Lu,  L.,  Yan,  X.,  Antoniou,  C.  and  Ben-­Akiva,  M.  (2015),  “W-­SPSA:  An  Enhanced  SPSA  Algorithm  for  the  Calibration   of  Dynamic  Traffic  Assignment  Models”,  Transportation  Research  Part  C,  Vol.  51,  pp.  
149-­166• Lu,  Y.,  Pereira,  F.  C.,  Seshadi R.,  O'Sullivan  A.,  Antoniou,  C.,  and  Ben-­Akiva,  M.  (2015),  “DynaMIT2.0:  Architecture  Design  and  Preliminary  Results  on  Real-­time  Data  Fusion  for  Traffic  Prediction  and  Crisis  Management”,  IEEE  18th  International  Conference  on  Intelligent  Transportation  Systems• Pereira,  F.  C.,  Rodrigues,  F.,  and  Ben-­Akiva,  M.  (2013),  “Text  analysis  in  incident  duration  prediction”,  Transportation  Research  Part  C:  Emerging  Technologies,  Vol.  37,  pp.  177–192
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APPENDIXMEMOTControl  Architecture  Framework:  The  Timeline  MeMOT:  2-­Level  Optimization  User  Optimization(user  1,  user  2,  …)Pre-­trip  requestEn-­route  request

timeroll  periodroll  period

Disruption

System  Optimization

System  Optimization  Framework
Goal:  Generate  reference  token  value  and  trip  System  Optimization attributes  for  system-­wide  optimized  scenario
Network,  Historical  demand/supply  parameters  Real Time Data

Historical Database

State Estimation
SupplySupply
Trajectorie Energy Estimation
SimulatorsDemandDemandSimulator
Predicted  network  state  and  optimized  token  energy  value  from  previous  roll  period

Trip  requests
Menu

Simulated
User Optimization

Average  energy  savings  for  the  rest  of  the  daynetwork  stateState Prediction
SupplySimulator
DemandSimulator

SupplyTrajectories

Energy Estimation

DemandTrip  requests
Menu

Simulated
User Optimization

expected  token  usage
expecteSystemd  Optimizer
energy  savings
token  energy  valueOverview:  Architecture
User  Optimization
Goal:  Generate  a  personalized  menu  with  trip  attributes  and  token  rewards

System Optimization

System-­optimized  token  value

Database  on  User  Preferences

Predicted  trip  attributes

User’s  preferences

Back-­EndFMS-­Advisor  App

UserOptimization

Info  o f  validated  choice&  awarded  tokens

ChoiceOptimized  choice  setChoiceTrip  Menu

User  Behavior  Monitor
Info  o f  a warded  tokens  &   validated   Interface
trip

Trip  tracking

Navigation

Dashboard

Marketplace

User4

Overview:  Trip  Menu
Trip  Menu

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Future  Mobility  Sensing  (FMS)
FMS  Platform
SMARTPHONE  APP/  TRACKING  DEVICES

PROCESSED  DATARAW  DATAMACHINE  LEARNING  BACKEND

MOBILE/WEB  INTERFACEVERIFIED  DATA

Sensing  Technologies
GPSWiFi

GSM
BluetoothAccelerometer

Context  Info••••••Transit  Network
Points  of  Interest
Land  UseEventsUser  Info…

Activity  Diary
Stops
Modes
Activities
Other  info47