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 (it ran through the heart of Corona). Just a few years before, a big closure of Highway 405, dubbed Carmageddon, resulted in a traffic jam that reached immense proportions.
These are extreme instances of massive traffic congestion, but more commonly, we all deal with daily traffic jams 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. This data is collected over a period of time, and then cities make decisions on whether to expand a road or add a traffic light or stop sign.
High-tech techniques do exist that can be applied to better plan and reduce traffic congestion. For example, already existing technologies can detect smart phone Bluetooth radios (for short range) and WiFi radios (for longer ranges) from vehicles as they pass through points where sensor detectors record the car’s presence. By placing sensor detectors at key locations along roadways, one can detect the general path of the vehicle as it passes through these points. Now think of the possibilities of understanding common traffic patterns of thousands of vehicles in a crowded city. Having much greater transparency into traffic flow and congestion points could help city planners identify opportunities to smooth traffic patterns and more accurately plan infrastructure to support their cities’ growing needs.
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 the schools, as it helps schools understand the behavior of each student (particle) or a group of students (swarm) navigating out of the schools and getting on streets by walking, on bikes, being picked up 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 reduce error rates in planning and address current congestion problems more quickly.
Chief Innovation Officer at Ness Software Engineering Services
Kuruvilla Mathew is Chief Innovation Officer at Ness Software Engineering Services. He is responsible for expanding the growing portfolio of digital transformation services at Ness by cultivating innovative technologies, solutions and methods. He works closely with Ness clients to provide direction and guidance on keeping pace with innovations that can help them leap frog into the future.
Mathew is an Enterprise Architect with a deep working knowledge of architectures and technologies across various stacks and platforms. He also works with Ness’s clients on Microservices ecosystems, Mobility architectures and several bleeding-edge technology stacks. He leads a group of technology experts at Ness, who engage with clients and execution teams to build digital solutions.
Mathew has over 20 years of experience in product and services development in various industries working with Fortune 500 clients worldwide. He has led several enterprise initiatives as Chief Architect for clients in leading-edge technologies through thought leadership and innovation. He was the CTO of a startup developing solutions for social media marketing and monitoring in Retail and Hospitality, and he led an innovation technology center where he designed and built a showcase lab. Mathew works on a number IoT projects and has showcased several of them at different forums.
Government, Enterprise, OEM,
Swarm Intelligence, Analytics, Infrastructure, Cities,
CxO, VP / Director, Technical, Operations,
Government / Public Sector, Automotive
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