Simulation is known to be an effective technique to understand
and manage traffic in cities of developed countries. However, in developing countries, traffic management is lacking due to a wide diversity of vehicles on the road, their chaotic movement, little instrumentation to sense traffic state and limited funds to create IT and physical infrastructure to ameliorate the situation. Under these conditions, in this paper, we present our approach of using the Megaffic traffic simulator as a service to gain actionable insights for two use-cases and cities in India, a first. Our approach is general to be readily used in other use cases and cities; and our results give new insights: (a) using demographics data, traffic demand can be reduced if timings of government offices are altered in Delhi, (b) using a mobile company’s Call
Data Record (CDR) data to mine trajectories anonymously,
one can take effective traffic actions while organizing events
in Mumbai at local scale.
Case Studies in Managing Traffic in a Developing Country with Privacy-Preserving Simulation as a Service
1. CASE STUDIES IN MANAGING TRAFFIC
IN A DEVELOPING COUNTRY WITH
PRIVACY-PRESERVING SIMULATION AS
A SERVICE
Biplav Srivastava, Madhavan Pallan,Mukundan Madhavan,Ravindranath Kokku
IBM Research
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Acknowledgements: Seema Nagar for development; Takashi Imamichi, Hideyuki Mizuta,
Sachiko Y. for help with Megaffic simulator, and Karthik Visweswariah for guidance.
2. Outline
■ Traffic problem and role of simulation
■ Role of traffic simulation and considerations for running as a service
■ Case studies
– Government office timing with Open Data at New Delhi
– Event management with Telco’s Anonymized CDR data at Mumbai
■ Conclusion
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4. Congestion is the daily pain of cities
■ The costs of traffic congestion are enormous.
■ The choices that drivers make affect roadway
congestion and air quality at the neighborhood, city, and
metropolitan levels.
■ Vehicle speed and pollution
– Very
low
and
very
high
traffic
speeds
have
higher
emissions
– Moderate
speed
has
low
emissions
– Vehicles idling in traffic causesubstantially more air pollution than if they
were moving at optimal speeds.
■ Drivers of change: Exploding populations, urbanization,
globalization and technology are driving change.
■ This creates unique challenges and opportunities for
transportation providers.
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Source: Traffic Congestion and Greenhouse Gases, by Matthew Barth and
Kanok Boriboonsomsin. From:
http://www.uctc.net/access/35/access35_Traffic_Congestion_and_Grenh
ouse_Gases.shtml
5. What needs to be done to learn about
traffic of a place* ?
■ Create Origin-Destination (O-D) information for a region on a periodic (e.g., daily) basis.
■ Leverage simulation technology to define the overall view of traffic
■ Allow stakeholders to assess traffic impact of their decisions quickly (i.e., minutes).
Practical considerations
■ Maintaining data privacy
■ Controlling setup and operational cost
*simplistic picture, but sufficient for talk
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6. Examples: Who get benefited from
traffic data?
■ Cities
– Congestion reduction initiatives
– Design of policies to boost business growth
– Improving city services like police and fire brigade’s response to events
■ Private Companies
– Demand Prediction for services companies like Taxi
– Route prediction for ambulance of private hospitals
■ Citizens
– Visibility of traffic to plan their day better / efficient
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7. 7
Inductive Loop
Technology
Video Image
Processor
Floating Car
data
Mobile Traffic
Probe ( eg. CDR )
An electro-mechanical device under road to measure presence of
vehicles based on weight. Mature technology for small types of
vehicles; Needs up-front cost to setup, point-by-point
deployment
Use analytics over video feeds of roads to measure presence of
vehicles. Mature technology applicable for most weather
conditions; Needs up-front cost to setup, point-by-point
deployment
Collect data from a sample of GPS-enabled vehicles; Limited by
sample size and expensive to cover large road networks
continuously.
Family of technologies that analyzes over cross-purposed telecom
data. Can be obtained leveraging CDR data and location update data
today. Provides large spatial and temporal coverage at sufficient
granularity.
Illustration of options available with stake-
holders for transportation data
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9. Traffic Simulation With Open Data
• Traffic simulation is a promising tool to do what-if analysis impacting
traffic demand, supply or every-day business decisions
• What is the congestion if everyone takes out their vehicles?
• What is the impact if failure rate of buses (public transportation) doubles?
• What happens if visitors constituting 20% of city traffic come for an event?
• However, simulators need to be setup with realistic road network,
traffic patterns and decision choices
• Open data is an important source for
• Road network (e.g., Open Street Maps)
• Creating pattern (e.g., vehicle Origin-Destination pairs, accidents)
• Framing and interpreting decision choices
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10. Megaffic Simulation System View
Inputs
(1) Traffic demand (given or learnt)
– Origin and destination information
(2) Road network data contains
– legal speed
– traffic signal parameters (offset, cycle length, split)
– the latitude and longitude of cross points
– the number of lanes
– traffic regulation information (one-way traffic etc.)
(3) Driving behavior model
– Velocity determination model - calculated by using the legal
speed, the vehicular gap and the sign of traffic signals
– Route selection model
– Fixed routing (fixed route bus)
– Route selection (passenger car)
– Stochastic Utility Maximization
– Utility Maximization
Outputs
(1) Trip travel time
(2) Link travel time
(3) Amount of vehicle CO2 emission
(4) Traffic volume for each link.
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11. Why traffic simulation as a service?
Service orientation
a) allows sharing of Information Technology (IT) costs,
b) allows sharing of simulation and traffic skills,
c) gives confidence to government and businesses to share data, and
d) makes benchmarking of traffic improvement systematic.
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12. Preserved privacy in our approach by
■ Managing anonymity of source traffic pattern data, AND
– Used open data about traffic characteristic (New Delhi)
– Used anonymized CDR for finding traffic patterns (Mumbai)
■ Using Megaffic feature of generating new traffic Origin-Destination patterns given an
input traffic distribution
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14. New Delhi Area Selection
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Area selected from openstreetmap.org with (top) (bottom)
(left)(right) co-ordinates as(28.6022)(28.5707)
(77.1990)(77.2522) for our experiment.
15. Office Timing Change Decision Choices
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Last second of morning commute by different strategies
16. Discussion
■ Changing office timings is a promising use-case for both government and private
offices, and enabling the right strategy using a simulator as a service makes it
widely accessible.
■ There are anecdotal accounts of private organizations changing office timing for
traffic reasons. Companies in Gurgaon, India have preponed office timings by an
hour (to 8:30am-4:30pm) to beat traffic. But systematic analysis is missing [2].
■ Government office timings also vary from region to region[3] in an ad-hoc basis.
They all can benefit from simulation-based setting of timing for traffic convenience.
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17. Event management with Telco’s Anonymized CDR
data at Mumbai
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18. Traffic
Demand
Feed
Data
Decision
Strategies
(Baseline,
Modifications)
Generate baseline trips Collect statistics,
generate output
Ingestion Processing
Traffic
Impact
Simulation
using
CDR-‐Derived
Aggregate
Trips
(Origin
Destination
Insight)
Road
Network
(OSM)
Driving
Behavior
(Inbuilt) Maps,
Graphs
Google Earth,
Web app (Dojo)
(1 server)
Megaffic Instance on Softlayer
Run simulation
Generate comparative
statistics, Visualizations
Generate modified trips
Visualization
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19. 19
CDR DATA MINE DATA
SIIMULATE AND
VISULIZE
MEET BUSINES
DEMAND
1. Capture CDR data ( or any network associationdata in the future)
2. Create Origin-Destination matrices for a city on a periodic(e.g., daily) basis.
3. Mine data to gather and create insights of customers
4. Use simulator(Megaffic) to simulate and visualize strategies
5. Enable what-if scenarios to answer business questions
Smarter cities – Transportation Pilot Approach
20. Validating the Known and Learning
New Insights
■ Known
• Traffic has peaks and off-peaks
• Mumbai traffic is high
throughout the day
• No current data and at scale
■ New results
• Cansimulate for significant
part of Mumbai in one go
• Simulationclose to ground
truth (known alternative –
Google map/ traffic)
20
Source: Traffic variation (total in PCUs), Mumbai Metropolitan
Region Development Authority (MMRDA), 2008.
21. Total Mumbai Area Simulated = 3264 Sq KM
68.441 km
!
47.569 km
Reference of tool used: http://www.gpsvisualizer.com/calculators
22. Comparing Simulation with Ground
Truth / Other Alternatives
Take away:
Highfidelity simulationof Mumbai traffic. First time demonstrated in India.
23. Ground Truth Source: Google Directions (Map @ 9:20am on 25 Feb 2015)
120 245670576 1879817829 31 19.1029616 72.8885178 19.1301018 72.8768824 543 1.17 535 -‐1.473296501
Trip
ID Start
CP End
CP #Hops Start
Lat Start
Long End
Lat End
Long Trip
time CO2
Goog Trip
time
Difference
(%)
Complexity
captured:
Passing of vehicle
through 31
intermediate
intersections
Distance: 4.5 km
CO2 emission:
1.17 Kg
Difference:
Simulator slightly
slower (~ -2%)
Note: Evaluation
is time dependent
24. Ground Truth Source: Google Directions (Map @ 10pm on 25 Feb 2015)
6 861128029 2250278516 276 18.9604197 72.8367367 19.1126175 73.1162452 3378 7.6 4022 19.06453523
Trip
ID Start
CP End
CP #Hops Start
Lat Start
Long End
Lat End
Long Trip
time CO2
Goog Trip
time
Difference
(%)
Complexity
captured:
Passing of vehicle
through 276
intermediate
intersections
Distance: 52.2 km
CO2 emission: 7.6
Kg
Difference:
Simulator faster
(~ 19%)
Note: Evaluation
is time dependent
28. Number of vehicles that passed the roads in the last interval
(12-14: morning off-peak)
Note:
• Usefulness example: Helps
understand choke points if
doing a road work
• Color by reverse intuition
(red means more passage)
29. Average speed for the last interval within the entire area
(12-14: morning off-peak)
Note:
• Usefulness example: Helps
understand driving and
road level issues. E.g,
where to have speed
checks, where to change
road direction
30. Comparing day and night for
the same region
(Average speed for the last interval withinthe
entire area)
Notes:
• Speed sensitivity varies.
• Some roads are
unaffected.
(12-14: morning off-peak)
(22-8: evening off-peak)
31. (8-12: morning peak)
(14-22: evening peak)
Business Problems
that can be
tackled:
• Should I hold an event in
morning or evening?
• Should I hold it at venue A
or B if I must have it in
evenings?
• Should I hold it at venue A
or B if in mornings?
• Where should I have parking
(e.g. B) and walk facility if
holding event in evening at
A?
A
B
A
32. Scenario
Name
Number
of
trips
(1
hr
simulation)
08
To
12 9951
12
To
14 12123
14
To
22 11999
22
To
8 1463
Simulation of 1 hour for Each Traffic
Interval Using Trajectory Distribution in
CDR Data
Simulating at Mumbai Scale
~ 3264 Sq KM
Take away:
First time full day view demonstrated in India for a city.
33. 8-12 traffic pattern 12-14 traffic pattern
14-22 traffic pattern22-8 traffic pattern
Mumbai
in
a
day:
Number
of
vehicles
per
one
meter
on
each
road
at
the
last
second
of
simulation
One can conceivably simulate for specific days to compare traffic patterns and identify best time and regions to introduce new services ; e.g., during Ganesh festival
34. 34
Scenario
Name
number
of
cars
CO2
emission
(t)
jam
length
(km)
(avg.
speed
<=
5.00
km/h)
jam
length
(km)
(avg.
speed
<=
10.00
km/h)
jam
length
(km)
(avg.
speed
<=
15.00
km/h)
jam
length
(km)
(avg.
speed
<=
20.00
km/h)
jam
length
(km)
(avg.
speed
<=
1000.00
km/h)
08
To
12 3894 23.06 0.33 0.37 0.44 1.15 3749.7
12
To
14 4950 27.98 3.32 3.4 3.44 4.12 3884.14
14
To
22 4745 27.54 1.68 2.02 2.05 2.77 3911.46
22
To
8 548 3.56 0 0 0 0.35 1777.41
Scenario
Name
number
of
trips
(1
hr
simulation)
08
To
12 9951
12
To
14 12123
14
To
22 11999
22
To
8 1463
KPIs
of
Traffic
at
Last
Second
of
Simulation
35. Discussion
■ We explored organizing events and how traffic data could be useful. We showed that
simulation is consistent with known traffic results but offers new and timely insights
at unprecedented scale
■ Here, we repurposed existing data with telecommunication companies, CDRs, and
showed how they can be used to extract trajectories and eventually, traffic volumes
preserving mobile user’s privacy.
■ Although promising, there are policy and business considerations that need to be
sorted out in many countries before such an approach will be considered
mainstream
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36. Conclusion
■ Traffic simulation as a service is a promising direction to understand and tackle traffic in
developing countries despite there being a lack of good traffic data
■ We demonstrated two use-cases (government office timing and event management) for
two large cities in India using open data and CDR data, respectively
■ Maintaining privacy needed attention to data and also simulator feature of generating
new data from given input traffic distribution
■ In future, one can
– Take benefits to more usecases
– Do simulation for more cities
– Improve accuracy with existing and new data data from more time periods and establish
a continuous process to augment learnt trajectories
36
37. Traffic References
■ Tutorial on AI-Driven Analytics In Traffic Management, in conjunction with International Joint Conference on Artificial
Intelligence (IJCAI-13), Biplav Srivastava, Akshat Kumar, at Beijing, China, Aug 3-5, 2013 (tutorial-slides).
■ Tutorial on Traffic Management and AI, in conjunction with 26th Conference of Association for Advancement of Artificial
Intelligence (AAAI-12), Biplav Srivastava, Anand Ranganathan, at Toronto, Canada, July 22-26, 2012 (tutorial-slides).
■ Making Public Transportation Schedule Information Consumable for Improved Decision Making, Raj Gupta, Biplav
Srivastava, Srikanth Tamilselvam, In 15th International IEEE Annual Conference on Intelligent Transportation Systems
(ITSC 2012), Anchorage, USA, Sep 16-19, 2012.
■ Mythologies, Metros & Future Urban Transport , by Prof. Dinesh Mohan, TRIPP, 2008
■ A new look at the traffic management problem and where to start, by Biplav Srivastava, In 18th ITS Congress, Orlando,
USA, Oct 16-20, 2011.
■ Arnott, Richard and K.A. Small, 1994, “The Economics of Traffic Congestion,” American Scientist, Vol. 82, No. 5, pp. 446-
455.
■ Chengri Ding and Shunfeng Song , Paradoxes of Traffic Flow and Congestion Pricing, 2008
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