CrowdNavi: Demystifying Last Mile Navigation With Crowdsourced Driving Information

With detailed digital map of the transport network and even real-time traffic, today's navigation services provide good quality routes in the major route level. Once entering the last mile near the destination, they unfortunately can be ineffective and, instead, local drivers often have a better understanding of the routes there. With the deep penetration of 3G/4G mobile networks, drivers today are well connected anytime and anywhere; they can readily access information from the Internet and share information to the driver's community. This motivates our design of CrowdNavi, a complementary service to existing navigation systems, seeking to combat the last mile puzzle. CrowdNavi collects the crowdsourced driving information from users to identify their local driving patterns, and recommend the best local routes for users to reach their destinations. In this paper, we present the architectural design of CrowdNavi and identifies the unique challenges therein, particularly on identifying the last segment in a route from the crowdsourced driving information and navigate drivers through the last segment. We offer a complete set of algorithms to identify the last segment from the drivers' trajectories, scoring the landmark, and locating best routes with user preferences. We then present effective navigation algorithm to locate the best route along the landmarks for the last segment. We further realize the potential risks of attacks in crowdsourced systems and develop a multisensor cross-validation method against them. We have implemented the CrowdNavi app on Android mobile OS, and have examined its performance under various circumstances. The experimental results demonstrate its superiority in navigating drivers in the last segment toward the destination.