Cloud-Assisted Mobile Crowd Sensing for Route and Congestion Monitoring

Abstract

Accurate and reliable real-time urban traffic management can benefit urban citizens’ daily life by reducing stress, travel time and carbon footprint. The provision of reliable and accurate traffic information has however proven to be a major challenge in intelligent transportation systems. Citizens carrying smartphones can be exploited as an important provider of traffic information and the mobile crowd sensing paradigm can be used as a solution to this challenge. In this paper, an urban traffic monitoring system which exploits the power of participatory sensing and cloud messaging is proposed. Crowd intelligence which is used to estimate traffic congestion levels, arrival times, while average road speed is harvested from crowd sensed data. Traffic congestion control at route level is implemented with a route guidance system. Proactive warnings or recommendations to drivers in the vicinity of, or on the route to, reported events are provided. The drivers can also report short-term traffic events and physical road conditions for road monitoring. Real-world experiments have been conducted with a prototype implementation and the results demonstrate both system feasibility and traffic estimation accuracy.

Keywords

• Crowd intelligence
• intelligent transportation system
• mobile crowd sensing
• real-time traffic management
• traffic congestion