using swarm intelligence to prepare for the next carmageddon

© 2016 Ness SES. All Rights Reserved1

Background Image: This image was captured by Kuruvilla Mathew for this paper around Los Angeles and Corona, California, 2016

Using Swarm Intelligence to Prepare for the Next Carmageddon


@kmathew | @ness_tech

Kuruvilla MathewChief Innovation OfficerNess Software Engineering Serviceswww.ness-ses.com



In the next 30-40 minutes …

© 2016 Ness SES. All Rights Reserved

• Carmageddon Story• The Bigger Problem• Swarm Intelligence • Particle Swarm Optimization• Analyzing Data Flow patterns• Actionable Insights• Queries and discussion




Carmageddon

Southern California recently experienced a 55-hour closure of the 91 Freeway, resulting in a 6-mile stretch that intersected State Route 71 and Interstate 15. The closure was called the Coronageddon.

Just a few years ago, a big closure, dubbed Carmageddon, of California Highway 405 resulted in a traffic jam that reached immense proportions.


@kmathew | @ness_tech3



The Bigger ProblemThe extreme instances of massive traffic congestion are becoming increasingly common resulting in daily traffic jams that are created by early morning traffic as people get to work, school traffic, the lunch rush hour, and the all-too-familiar and stressful evening traffic.



Traffic flow patterns are studied by cities, but most use a low tech approach. They assign people to count vehicles as they pass through intersections at peak hours.



Swarm Intelligence



“A single ant or bee isn't smart, but their colonies are. The study of swarm intelligence is providing insights that can help humans manage complex systems…”Source: Swarm Theory, By Peter Miller, National Geographic Staff | http://ngm.nationalgeographic.com/2007/07/swarms/miller-text

Source: Wikipedia: https://en.wikipedia.org/wiki/Swarm_intelligence

Simply put, “Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial.”



Opportunities for Smart Cities



Implement higher-tech methods, such as swarm intelligence, to form a more accurate and complete picture of traffic flows, so cities understand where the real problems are.

Apply this analysis to optimize traffic flow and continually monitor, so adjustments can be made more quickly to avoid the next Carmageddon.



Understanding Traffic Flow PatternsUsing Particle Swarm Optimization

@kmathew | @ness_tech© 2016 Ness SES. All Rights Reserved

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1 2 3 4 5

2 – dimensional space



Scene of the Carmageddon

Source: Google Maps 2016, Google Map of Corona, California



In spite of the couple-day closure, work continues on freeways and adjacent roads and has been going on for a number of months. This aggravates commuters and contributes to a fair share of road rage.

Using PSO and the Bees algorithm, it is possible to understand and predict the behavior of the commuters at different times of the day. The changes of traffic patterns during the weekdays and weekends provide insights that can help city planners plan for future street and freeway closures.



Understanding Road Closures




We can begin to better understand the traffic flow by tagging the beacon from the vehicle and/or the driver and passenger in the vehicle. The effect of road closures that include streets and ramps can be understood by analyzing the vehicle/commuter between 2 points on the street.

Installation of scanners along the streets can capture Bluetooth and WiFi beacons of commuters’ smart devices, as well as the Bluetooth beacons from vehicles.



Applying the Bees Algorithm




Applying the Bees algorithm to school traffic is an effective method to understand the traffic flow that is a combination of foot traffic and vehicular traffic.

Traffic as a result of the start of a school day and dismissal will be an interesting pattern to observe.

For example, at this school, flow around certain intersection points had delays, but one intersection point was free flowing without delays.



Capturing Vehicle Data Using Scanners


As described earlier, Scanners can be installed on streets, typically on street light poles, to capture the Bluetooth and Wi-Fi beacons that are coming from the vehicle and commuter smart phones respectively.

The number of Scanners will vary, as they need to be placed in a manner that increases the chance of detection.

Having multiple Scanners also helps determine the vector of vehicle/commuter movement.

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Computing the Data


height

distance

RSSI

12

direction

The Scanner will provide a fair amount of data that needs to be computed and aggregated before it is usable. Some of the key points are:

• The Machine Address (MAC) will help uniquely define the commuter/vehicle• Received Signal Strength Indicator (RSSI) is used to calculate the distance • The scan from 2 Scanners on a given MAC will determine the direction vector• The Scanner mounted height is fixed and can be calibrated• The distance from the sensor and the direction of travel could be determined

based on the position of the vehicle• The vehicle MAC, commuter(s) MAC could be correlated to determine driving

behavior during rush hour, weekends and other commute times• The date and time should be synchronized to UTC to collate into a time series

database for correlation analysis

MAC



Analyzing the Bluetooth Data


The captured data looks as indicated from the Bluetooth data logs. This reflects the Received Signal Strength Indicator (RSSI), including time stamp, vendor and a service tag identifier (ID).

A RSSI closer to 0 means that the vehicle is closer, and a higher value means the vehicle is farther away.

Using the class of device (cod) filter, you can isolate the captured frames that are most likely from vehicles.



WiFi Scans


In addition to Bluetooth data, another beacon captured is the WiFi beacons sent from Smart Phones and devices. While the RSSI plays a key role in determining the distance for a given commuters smart phone (MAC), it required some fuzzy logic to extract out the kind of smart device it is from the vendor data.



Insights

RSSI (db)

In the visualization what is interesting to observe is the behavior of the swarm.

One can see the changing behavior with time progressing. This can help determine a tipping point, whether it is start of rush hour or the end of one.

These insights are valuable in understanding commuter behavior with real data that can help city planners.

Note: This data is from one Scanner.

Early morning

rush hour traffic

appears to show

erratic behavior

Things appear to be calm during the week.



In Conclusion


By using Swarm Intelligence (SI) algorithms, such as Particle Swarm Optimization (PSO), city planners can create simulations to understand potential congestion challenges based on how vehicles and pedestrians navigate public spaces.

PSO is a good algorithm to apply to large businesses in a city, as it helps them understand the behavior of each employee or a group of employees (beginning/ending of shifts) navigating out of facilities and getting on streets by walking, in vehicles, using public transport, etc.

Simulations using real data collected through this mechanism can help city planners determine potential traffic challenges at a highly-granular level—by street, intersection, freeway ramp, school area, etc. — to significantly improve the quality of empirical commuter data used in street flow planning and addressing existing congestion problems.



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Further Reading

• The Evolution of the Connected Home (DATAQUEST)• Improving Predictive Maintenance with IoT (IoT Central)• Edge Analytics an Antidote to IoT Data Deluge (InformationWeek)• Healthcare Things Are Getting Better at Supporting Wellness (IoT Global Network)• Improve Loss Prevention in Retail Stores by Applying Swarm Intelligence (Indian Retailer)• Contextualising Data Will Help Monetize The Internet of Things (InformationAge)• Inside the Connected Car’s Ego Network (Auto Tech Review)• Ness Whitepaper: Capitalizing on the Business Value of the Internet of Things• Ness Blog: Predictions 2016 – IoT, Payments & Loyalty Programs, APIs• Ness Blog: Does Every’Thing’ Matter in the Internet of Things• Ness Blog: Internet of Things and Industrial Analytics• Ness Blog: When Every Car Becomes a “Smart” Car

And a number of related readings on:• Ness Insights : http://www.ness-ses.com/insights/resource-library/ • Ness Blog: http://www.ness-ses.com/category/blog/

Here is a compilation of articles, whitepapers, and blog posts on IoT. They have been presented and published on various channels.

http://www.dqindia.com/the-evolution-of-the-connected-home/

http://www.ness-ses.com/improving-predictive-maintenance-with-industrial-iot/

http://www.informationweek.com/iot/edge-analytics-an-antidote-to-iot-data-deluge-/d/d-id/1323942



http://www.ness-ses.com/healthcare-things-are-getting-better-at-supporting-wellness/

http://www.ness-ses.com/improve-loss-prevention-in-retail-stores-by-applying-swarm-intelligence/

http://www.ness-ses.com/contextualising-data-will-help-monetize-the-internet-of-things/




http://autotechreview.com/latest-issue/item/2858-a-connected-cars-ego-network.html#.VlgnolIle4k.twitter

http://cdn2.hubspot.net/hubfs/431504/Resource_center/whitepaper/Ness_SES_-Whitepaper_-_Internet_of_Things.pdf?__hssc=&__hstc=&__hsfp=3483798360&hsCtaTracking=38414076-33b3-4445-8cf6-5d656326b7c1%7C18787a0e-f57a-4469-b8b7-c8b4526ac48a

http://www.ness-ses.com/predictions-2016-iot-payment-loyalty-programs-apis/

http://www.ness-ses.com/does-everything-matter-in-the-internet-of-things-iot/



http://www.ness-ses.com/internet-of-things-and-industrial-analytics/

http://www.ness-ses.com/when-every-car-becomes-a-smart-car/

http://www.ness-ses.com/insights/resource-library/

http://www.ness-ses.com/category/blog/



Kuruvilla Mathew • Chief Innovation Officer and SVP, Office of CTONess Software Engineering Services2001 Gateway Place, Suite 480W, San Jose, CA 95110 USAMobile: +1 949 678 9364 [email protected] | www.ness-ses.com@kmathew | @ness_techhttp://www.ness-ses.com/category/blog/