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The Role of Electrification of Transport in Mitigating
Climate Change, use of Big Data to Manage Traffic &
Congestion
Marta C. Gonzalez, City and Regional Planning, Civil and Environmental Engineering
Motivation
20+ M
FACTS:
5+ M 41% penetration
Daily movements
From Data to Information
From Information to Action:
Better informed policies for
the environment, public health
and inclusive well being
• Data Driven Transportation
• Taking into account sparse and
incomplete data allows for
Demand Management
Strategies
Intervention, research design
Travel demand prediction during Rio Olympics
(a)Tourist travel mode split
(a)Total travel demand during Olympics: 1.8 million people and
0.5 million vehicles in peak hour.
Traffic Model
Rio CDR + Waze Data
Smart-app (routing)
Modifications on the level of altruism:
User Equilibrium
component
Social Optimun
component
l=[0..1]
SORoute
λ=1
25 mins
UERoute
λ=0
20 mins
Optional Route
λ=0.2
22 mins
OriginOrigin
Destination
-5 0 5 10 15
102
103
104
105 λ =1.0
λ =0.1
-5 0 5 10 15 -5 0 5 10 15 -5
-20 -10 0 10 20
0.2
0.4
0.6
0.8
1
-20 -10 0 10 20 -10 0 10 -30
λ = 1.0
λ = 0.1
λ = 1.0
λ = 0.1
net benefit in commuting travel time, min
% decrease in congestion
socialgoodweight,λ#ofvehicletrips
rio bay bos(b)
(c)
(a)
Smart-app (routing)
Recommendations of Car reduction per Origin and Destination
Vehicle demand decrease: ~1.3%
Total travel time decrease: ~10.5%
Coupling Electric and Mobility Demand
A: Mobility with TimeGeo1
model
B: Charging sessions data
C: EV mobility
D: Peak power load mitigation
PEV charging session profiles
Arrival Time and Departure Time
The Challenge: Peak Demand
Peak load optimization Benefits to
incentivize the recommendations
Discussion, policy implications
• ICT data can be used to generate urban scale mobility models
• With infrastructure demand we can plan traffic, introduction of new
technologies and energy savings
• Knowledge of individual mobility increase the feasibility of travel
management strategy
• Proper mechanisms to convince PEV owners is necessary to accurately
grasp the future energy demand as well as pressure of the power grid.

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Pathways to Decarbonization & Digital Innovation in Transport: The Role of Electrification of Transport in Mitigating Climate Change and Use of Big Data to Manage Traffic & Congestion

  • 1. The Role of Electrification of Transport in Mitigating Climate Change, use of Big Data to Manage Traffic & Congestion Marta C. Gonzalez, City and Regional Planning, Civil and Environmental Engineering
  • 2. Motivation 20+ M FACTS: 5+ M 41% penetration Daily movements From Data to Information From Information to Action: Better informed policies for the environment, public health and inclusive well being
  • 3. • Data Driven Transportation • Taking into account sparse and incomplete data allows for Demand Management Strategies Intervention, research design
  • 4. Travel demand prediction during Rio Olympics (a)Tourist travel mode split (a)Total travel demand during Olympics: 1.8 million people and 0.5 million vehicles in peak hour.
  • 5. Traffic Model Rio CDR + Waze Data
  • 6. Smart-app (routing) Modifications on the level of altruism: User Equilibrium component Social Optimun component l=[0..1]
  • 7. SORoute λ=1 25 mins UERoute λ=0 20 mins Optional Route λ=0.2 22 mins OriginOrigin Destination -5 0 5 10 15 102 103 104 105 λ =1.0 λ =0.1 -5 0 5 10 15 -5 0 5 10 15 -5 -20 -10 0 10 20 0.2 0.4 0.6 0.8 1 -20 -10 0 10 20 -10 0 10 -30 λ = 1.0 λ = 0.1 λ = 1.0 λ = 0.1 net benefit in commuting travel time, min % decrease in congestion socialgoodweight,λ#ofvehicletrips rio bay bos(b) (c) (a) Smart-app (routing)
  • 8. Recommendations of Car reduction per Origin and Destination Vehicle demand decrease: ~1.3% Total travel time decrease: ~10.5%
  • 9. Coupling Electric and Mobility Demand A: Mobility with TimeGeo1 model B: Charging sessions data C: EV mobility D: Peak power load mitigation
  • 11. Arrival Time and Departure Time
  • 13. Peak load optimization Benefits to incentivize the recommendations
  • 14. Discussion, policy implications • ICT data can be used to generate urban scale mobility models • With infrastructure demand we can plan traffic, introduction of new technologies and energy savings • Knowledge of individual mobility increase the feasibility of travel management strategy • Proper mechanisms to convince PEV owners is necessary to accurately grasp the future energy demand as well as pressure of the power grid.