Our hack stemmed from our dissatisfaction with the way traffic is handled currently in cities such as Toronto. Many traffic lights cycle between on and off using a constant timer throughout the entire day, even though the flow of traffic changes! In addition, left turn advances are often not catered to the specific intersection. They are on for too long or too short leaving many drivers unsatisfied.
In Toronto, congestion results in a loss of billions of dollars per year. In 2009, there was a loss of $3.3 billion; this number has increased significantly with our population increase. In 2031 it is projected to cost $7.8 billion to our economy. Around 71% of commuters utilize their vehicles, so traffic inefficiencies are a problem that affect many people. The pollution caused by traffic also costs billions that are spread throughout lost productivity, health care costs, pain and suffering, and premature loss of life. Our group feels that designing a hack which allows city planners to get updated and relevant data on traffic would be helpful in designing modern roads that lessen the congestion and pollution on roads.
TrafAnalysis uses video information to identify cars at the intersection. Currently, it is able to detect the number of cars travelling in each direction. This information is uploaded to a server where the data is processed and put into visual organizers for the user.
Further extensions of our hack would be to use the video data to calculate the speeds of traffic, differentiate the different types of vehicles, find the occupancy of a location, and to calculate the concentration of cars per kilometre. The information obtained from TrafAnalysis can be used in a variety of ways to aid in combatting traffic congestion and inefficiencies. The data can be used to detect motion patterns and abnormalities, predict non-recurring short-term motion patterns, evaluate traffic simulation and create models based on real data, create traffic studies, and to evaluate road, pedestrian, and cyclist safety.
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