How can we make traffic flow better so fewer of us are sitting in traffic jams for shorter periods of time – if at all? Researcher Lina Kattan looks at Intelligent Traffic Systems that optimize the operation, safety and costs of a city’s transportation network through sustainable traffic control and transportation management strategies. These systems are designed to manage traffic congestion, signal controls and prediction of bus and LRT arrivals. Read on to learn about solutions that are working and how new developments will change the traffic jigsaw in the not-to-distant future. You can also see the full webinar recording at: http://www.ucalgary.ca/explore/can-we-make-traffic-jams-obsolete

1. Can we make traffic jams obsolete?
Lina Kattan
Associate Professor in Civil Engineering
Schulich School of Engineering
December 20, 2016

2. Lina Kattan
 Associate professor of civil
engineering
 Urban Alliance professor in
transportation systems
optimization engineering
 PhD from the University of Toronto
 Research focused on the
application of emerging technology
to improve the transportation
system
 Published in a variety of leading
transportation journals and
national and international
conferences proceedings 2

3. Presentation outline
3
Transportation systems and their functions
Interaction between the demand for travel and
the transportation system
What causes congestion?
Solution strategies – long term to shorter term
solutions
Variable speed limits under connected/
autonomous vehicle environments

4. Transportation systems and their
functions
 Transportation systems are a major
component of the society and
economy
 Transportation systems move us
from where we are to where we are
going; connecting us to our
institutions, our families, our lives
 Transportation systems consist of
complex interdependent
systems/subsystems (road, rail, bus,
freight, bike networks, etc.)
4

5. Today’s challenges
 Today more than 54% of the world’s
population lives in cities
 Canada’s population is largely urban -
more than 80% of Canadians live in
cities
 Increased urbanization results in
increased pressure on our
transportation systems:
• growing challenges of providing
safe and efficient access to goods,
services and opportunities 5

6. Traffic congestion

7. Travel Demand
Location choice
Mode of transportation choice
Time of departure choice
Auto ownership
Habits
Household characteristics, etc.
Transportation Supply (capacity)
Road networks and their characteristics (traffic
lights, tolling, etc.)
Transit system (frequency, crowding, station
location, reliability, rail, buses, etc.)
Bike and pedestrian networks
Why congestion?
7

8. Why congestion?
Time of Day
Traffic
Transport
Demand
Transport
Capacity
Morning
RushHours
Afternoon
RushHours
8

9. Solutions to traffic congestion:
expanding the physical infrastructure
Not a sustainable long term solution to traffic congestion!
Traffic
congestion
Building
more roads
Reduced
Travel time
More trips
are attracted
9

10. Long- and short-term solutions

11. Source: http://www.busandcoach.travel
Congestion mainly caused by
commuters in single-occupant vehicles
11

12. Expanding the
infrastructure for
sustainable travel modes
(e.g. investment in transit, pedestrian and
cycling related infrastructure)
Land use
improvement
(e.g. smart growth and compact
communities and transit and pedestrian
oriented development)
Solutions to traffic congestion:
land use and transit investment
Long-term solutions
12

13. Shorter-term solution
Demand management
(incentives/disincentives)
Solutions to traffic congestion
13

14. Demand management
• Congestion is caused
mainly by commuters
travelling in single-
occupant vehicles during
the peak period
• The key is managing the
demand and distributing it
over space (different
routes), time and across
modes (car pooling,
transit, biking, walking etc.)
Time of Day
Traffic
Transport
Demand
Transport
Capacity
Morning
RushHours
Afternoon
RushHours
14

15. Shorter-term solution
Traffic operation and
Intelligent Transportation Systems (ITS)
(e.g. traveler information and traffic control, autonomous and connected vehicle/transit)
Solutions to traffic congestion
15

16. Causes of traffic congestion
Source: Federal Highway Administration (http://www.fhwa.dot.gov/)
16

17. What is an Intelligent
Transportation System (ITS)?
 comprehensive sensor and
communication systems
 Intelligent control:
• Real-time collection and
analysis
• Automated deployment of
actions
• E.g.: adaptive signal control,
transit priority, freeway control,
multimodal real time travel
information and guidance,
incident and emergency
management
More efficient use of existing capacity through Information and
Communication Technologies:
17

18. Emerging vehicle technologies:
autonomous/connected vehicle/transit
Source: US DOT 18

19. The faster you get in the
slower you get out!
19
 Video
source: Doug MacDonald - Rice and Traffic Congestion

20. 0
1000
2000
3000
4000
5000
6000
7000
8000
9000
0 50 100 150 200 250 300
Flow(veh/hr/direction)
Density (veh/km/direction)
Capacity can drop between 10% and 30%
Seiran Heshami , Lina Kattan and Zhengyi Gong (2016). Macroscopic Traffic Flow Model Calibration and Stochastic Capacity Analysis Based on Weather Condition. To
be presented in the Annual Transportation Research Board Meeting, Washington DC, January 2017.
Traffic
Breakdown
Uncongested
capacity
Congested
capacity
Concept of capacity drop
20

21. Phantom jam
21
 Video
Source: PTV Vissim: Motorway Shockwave

22. Phantom jam
22
 Video
Source: Andrew Marr / BBC / January 2011: The Phantom Traffic Jam.

23. Freeway control and management
• When everyone strives to get ahead of everyone
else, we all fall behind!
• The price of anarchy: penalty we pay for not coordinating
our action
• How to coordinate our actions using emerging
technologies?
23

24. Variable speed limits under
connected/autonomous vehicles

25. What are variable speed limits?
 Freeway speed limits are lowered
when
• traffic congestion increases at
peak times
• an accident occurs
• adverse weather conditions
 To ensure the freeway continues
to move as smoothly as possible
and ensure safety of travellers
Source: http://www.itv.com/news/central/topic/m42-motorway/
25

26. Advantages of variable speed
limits
1. Serving more vehicles
2. Less disturbance – smoother flow
We are going to show that through computer
simulation!
26

27. Evaluation tools:
Microsimulation modelling
To create a realistic virtual replica of the network to be studied
27

28. Initialization
(sets of dynamic
ODs and supply
parameters)
Simulator
(PARAMICS)
Evaluation (Z)
Network
(synthetic,
large-scale)
GA Operators
(simple/
advanced)
Observed
Data
(count,
speed)
Meet
Criteria?
STOP (Collect
calibrated OD flows
and driver behaviour
parameters)
Yes
Create NEW
generation to an
input form
No
R. Omrani and Lina Kattan: Simultaneous Calibration of Microscopic Traffic Simulation Model and Estimation of Origin/Destination (OD) Flows based on
Genetic Algorithms in a High-Performance Computer. Proceedings of the 16th International IEEE Annual Conference on Intelligent Transportation Systems
(ITSC 2013), The Hague, The Netherlands, October 6-9, 2013
Calibration framework
28
 Calibrate driver behaviour:
• Vehicle follows another car
• Aggressiveness
• Merging and response time
• Route choice
• Response to information
 Demand profile and
loading levels
 Route capacities and
capacity drops

29. Microsimulation calibration
R. Omrani and Lina Kattan: Simultaneous Calibration of Microscopic Traffic Simulation Model and Estimation of Origin/Destination (OD) Flows based on
Genetic Algorithms in a High-Performance Computer. Proceedings of the 16th International IEEE Annual Conference on Intelligent Transportation Systems
(ITSC 2013), The Hague, The Netherlands, October 6-9, 2013
29
After calibration

30. Study area in Calgary
30

31. Freeway control using variable speed limit
distrurbancedemand
Control input:
Speed Limits from
early time step t+1
are implemented
System Wide Optimization at each time step
t (Rolling Horizon)
-Optimization is conducted for the short-term
Rolling Horizon (t+T)
- Rolling horizon is shifted after each time step
Input Parameters:
- Speed data from vehicle probes
- Length of vehicle queue on-ramp
Traffic State Estimation/Prediction on the
freeway:
- Estimation of densities and flows
- Short term travel time prediction over Horizon
t+Tat each
time step t
31
Source: http://www.continental-corporation.com/
Source : Lina Kattan, Bidoura Khondakera, Olesya Derushkinaa, and Eswar Poosarlab (2014): Probe-Based Variable Speed Limit System
- A Sensitivity Analysis. Journal of Intelligent Transportation Systems Technology, Planning, and Operations, Vol. 19, – Iss 4.

32. Traffic flow distribution
AM traffic heading south from Deerfoot
Current fixed speed limit system Variable Speed limit system
Source : Lina Kattan, Bidoura Khondakera, Olesya Derushkinaa, and Eswar Poosarlab (2014): Probe-Based Variable Speed
Limit System - A Sensitivity Analysis. Journal of Intelligent Transportation Systems Technology, Planning, and Operations, Volume
19, 2015 – Issue 4.
32

33. Anticipatory lane changing
8 km
1 km 1 km 1 km 1 km 1 km 1 km 1 km 1 km
Default Speed Limit = 100 km/hr
VSL1 VSL2 VSL4VSL3 VSL5 VSL6
Location of incident
Bidoura Khondaker and Kattan (2015). Variable Speed Limit: A Microscopic Analysis in a Connected Vehicle Environment. in Transportation Research- Part –C: Emerging Technologies,
Volume 58, Part A, Pages 146–159
33

34. Connected vehicle performance results
Bidoura Khondaker and Kattan (2015). Variable Speed Limit: A Microscopic Analysis in a Connected Vehicle Environment. in Transportation Research- Part –C: Emerging Technologies,
Volume 58, Part A, Pages 146–159
34
No variable speed
Variable speed

35. Impact of congestion and %
penetration of a connected vehicle
Bidoura Khondaker and Kattan (2015). Variable Speed Limit: A Microscopic Analysis in a Connected Vehicle Environment. in Transportation Research- Part –C: Emerging Technologies,
Volume 58, Part A, Pages 146–159
35
Current fixed speed limit system Variable Speed limit system
120
90
50
25
Speed in Km/hr
120
90
50
25
Speed in Km/hr

36. Impact of % market penetration
-30%
0%
30%
36

37. Key findings
• Variable speed limits can provide safer and more
efficient commute
• If we coordinate the way we drive (e.g. slow down,
change lanes less) we reach our destination faster!
• Safety and travel time improvements are expected at
higher % market penetration rates of
connected/autonomous vehicles
37

38. Summary
 More roads do not necessarily mean better traffic
 Long-term solutions
• Compact and diverse communities with more
transportation choices
 Solutions that focus on the demand
 We are all traffic
 We all need to cooperate to make traffic congestion
obsolete
38

39. Summary
 Municipalities and government
 Demand management strategies – tolling, parking
management, car pooling/sharing, etc.
 Companies in downtown
• Offer employees transit passes instead of parking passes
• More flexible work schedule and the option of working
from home
 Commuters
• Take transit, bike, walk or carpool and avoid rush hour
traffic as much as possible
39

40. Thank you
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on the scholar’s field of study

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