Mobile Crowd-Based Outsourcing for Intelligent Traffic Navigation
Location
Nessmith-Lane Atrium
Session Format
Poster Presentation
Research Area Topic:
Engineering and Material Sciences - Electrical
Abstract
Vehicle traffic is an ever important issue in today’s world. Efficient transportation systems is a tenet of stronger and healthier economy. Urban and rural areas each have their own unique set of challenges. However, the biggest cities are the ones that are most affected by alterations in traffic conditions. Knowing the real time traffic density allows for optimal traffic management. It is important to be able to quickly determine the present conditions of the road accurately and forecast future traffic conditions from the given data. The multiple sources for slowdowns and stoppages on people’s commutes including accidents, insufficient capacity, and delays at Red Lights. There is always a demand for more efficient traffic navigation techniques. Crowdsourcing is always a way to collect data from the field with minimal compliance by the users. By installing an application on the smart phones of people in the field, we can determine real-time conditions and send the data to all the users for optimally personalized traffic navigation. We then collect the data and use time series analysis and projection to help with the predictions. Our goal is to accurately predict future conditions and divert drivers to the proper traffic channel before slowdowns occur. To test out our algorithms, we use simulations to see how accurate our predictions are. From there we can apply a probability percentage to forecast the chance for a condition, similar weather predictions. To further apply this application, we have the data grouped to make heat maps of driver and road densities. The hazardous areas are shown on a map so it can be viewed and avoided by the users in the system. As the number of users increase, we will be able to have more accurate traffic information and statuses. We show the configuration of the application and the resulting data from all the mobile crowd-based outsourcing.
Presentation Type and Release Option
Presentation (Open Access)
Start Date
4-16-2016 2:45 PM
End Date
4-16-2016 4:00 PM
Recommended Citation
Ames, David, "Mobile Crowd-Based Outsourcing for Intelligent Traffic Navigation" (2016). GS4 Georgia Southern Student Scholars Symposium. 59.
https://digitalcommons.georgiasouthern.edu/research_symposium/2016/2016/59
Mobile Crowd-Based Outsourcing for Intelligent Traffic Navigation
Nessmith-Lane Atrium
Vehicle traffic is an ever important issue in today’s world. Efficient transportation systems is a tenet of stronger and healthier economy. Urban and rural areas each have their own unique set of challenges. However, the biggest cities are the ones that are most affected by alterations in traffic conditions. Knowing the real time traffic density allows for optimal traffic management. It is important to be able to quickly determine the present conditions of the road accurately and forecast future traffic conditions from the given data. The multiple sources for slowdowns and stoppages on people’s commutes including accidents, insufficient capacity, and delays at Red Lights. There is always a demand for more efficient traffic navigation techniques. Crowdsourcing is always a way to collect data from the field with minimal compliance by the users. By installing an application on the smart phones of people in the field, we can determine real-time conditions and send the data to all the users for optimally personalized traffic navigation. We then collect the data and use time series analysis and projection to help with the predictions. Our goal is to accurately predict future conditions and divert drivers to the proper traffic channel before slowdowns occur. To test out our algorithms, we use simulations to see how accurate our predictions are. From there we can apply a probability percentage to forecast the chance for a condition, similar weather predictions. To further apply this application, we have the data grouped to make heat maps of driver and road densities. The hazardous areas are shown on a map so it can be viewed and avoided by the users in the system. As the number of users increase, we will be able to have more accurate traffic information and statuses. We show the configuration of the application and the resulting data from all the mobile crowd-based outsourcing.
