Case Studies in Managing Traffic in a Developing Country with Privacy-Preserving Simulation as a...

CASE STUDIES IN MANAGING TRAFFIC IN A DEVELOPING COUNTRY WITHPRIVACY-PRESERVING SIMULATION AS A SERVICE

Biplav Srivastava, Madhavan Pallan, Mukundan Madhavan, Ravindranath KokkuIBM Research

SCC 2016, San Francisco, June

Acknowledgements: Seema Nagar for development; Takashi Imamichi, Hideyuki Mizuta, Sachiko Y. for help with Megaffic simulator, and Karthik Visweswariah for guidance.

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

SCC 2016, San Francisco, USA2

Traffic problem

SCC 2016, San Francisco, USA 3

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 cause substantially 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.


Source: Traffic Congestion and Greenhouse Gases, by Matthew Barth and Kanok Boriboonsomsin. From: http://www.uctc.net/access/35/access35_Traffic_Congestion_and_Grenhouse_Gases.shtml

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


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


7

Inductive LoopTechnology

Video ImageProcessor

Floating Cardata

Mobile TrafficProbe ( 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

SCC 2016, San Francisco, USA

Simulation as a service


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


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.


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.


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


Government office timing with Open Data at New Delhi


New Delhi Area Selection


Area selected from openstreetmap.org with (top) (bottom) (left)(right) co-ordinates as(28.6022)(28.5707) (77.1990)(77.2522) for our experiment.

Office Timing Change Decision Choices


Last second of morning commute by different strategies

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.


Event management with Telco’s Anonymized CDR data at Mumbai


Traffic Demand

Feed Data

DecisionStrategies (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,

GraphsGoogle Earth,

Web app (Dojo)(1 server)Megaffic Instance on Softlayer

Run simulation

Generate comparative statistics, Visualizations

Generate modified trips

Visualization


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CDR DATA MINE DATA SIIMULATE AND VISULIZE

MEET BUSINES DEMAND

1. Capture CDR data ( or any network association data 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

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

• Can simulate for significant part of Mumbai in one go

• Simulation close to ground truth (known alternative –Google map/ traffic)

20

Source: Traffic variation (total in PCUs), Mumbai Metropolitan

Region Development Authority (MMRDA), 2008.

Total Mumbai Area Simulated = 3264 Sq KM

68.441 km

!47.569 km

Reference of tool used: http://www.gpsvisualizer.com/calculators

Comparing Simulation with Ground Truth / Other Alternatives

Take away: High fidelity simulation of Mumbai traffic. First time demonstrated in India.

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 CO2Goog 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

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 CO2Goog 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

Case Study

Take away: Detailed decision support for business cases possible. First time demonstrated in India.

Area of Interest Note: Could have been any area

The view simulator sees

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)

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

Comparing day and night forthe same region(Average speed for the last interval within the entire area)

Notes: • Speed sensitivity varies.• Some roads are

unaffected.

(12-14: morning off-peak)

(22-8: evening off-peak)

(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

ScenarioName

Number of trips (1 hrsimulation)

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.

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

ScenarioName

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 <= 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

ScenarioName

number of trips (1 hrsimulation)

08 To 12 9951

12 To 14 12123

14 To 22 11999

22 To 8 1463

KPIs of Traffic at Last Second of Simulation

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


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

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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Case Studies in Managing Traffic in a Developing Country with Privacy-Preserving Simulation as a...

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