TRCLC 15-6
Integrated Crowdsourcing Platform to Investigate Non-Motorized Behavior and Risk Factors on Walking, Running and Cycling Routes
PIs: Ala Al-Fuqaha, Jun-Seok Oh and Valerian Kwigizile (蜜桃社区 Michigan University)
In 2013, there were 1,902 crashes involving bicyclists and 29 were fatal crashes among them throughout the state of Michigan. While the number of bicycle crashes decreased by 15.3 percent from 2004, the number of fatal bicycle crashes increased by 38.1 percent from 2004 in Michigan [1]. Informing the public about the potential risk factors on walking, running, and cycling routes plays a critical role in saving lives. Information Technology (IT) plays an important role to keep the public and relevant city/county offices informed about risk factors on walking, running, and cycling routes in their areas of interest. The deployment of intelligent systems that help the public identify, track, and monitor risk factors in their routes of interest will be of vital interest to the local communities, city/county departments, and the local economy.
A major goal of this research is to work with the Kalamazoo Bicycle Club, the Kalamazoo Area Runners Club, and other stakeholders and the local city/county authorities to build and experiment with an intelligent software system that enables citizens to utilize a mobile app to inform local authorities of risk factors on local walking, running, and cycling routes. Our proposed system will enable local authorities to operate more efficiently to handle the feedback provided by the citizens. Also, the local government will be able to provide statistical reports that provide estimates of the traffic on the different routes throughout the local community.
At the core of the proposed route traffic analysis system, we will perform statistical analysis of data collected by the general public. The results of this analysis will be summarized and made available to the public through asynchronous alerts sent to their mobile devices using a color coded system to help the public interpret the results easily. We intend our novel approach to be scalable to handle a large number of public subscribers.
Extensive and systematic experiments will be conducted to demonstrate the capabilities of the proposed system. These experiments will be conducted by teams from 蜜桃社区 Michigan University and local authorities in Kalamazoo in collaboration with volunteers from the local bicycle and runner clubs. This enables us to perform rigorous tests of the proposed platform in real-life scenarios before large-scale deployment of the platform for use by local authorities and the general public.
This platform will help local authorities in south western Michigan to timely inform the public about risk factors on local routes and manage their resources to prioritize the removal of these risk factors. Thus, saving lives and reducing liability issues. Furthermore, the deployment of the proposed software in Kalamazoo will serve as a model to encourage nearby communities, cities, and hopefully the rest of the nation to deploy similar systems.