City-Wide Traffic Flow Estimation From a Limited Number of Low-Quality Cameras

Citations Over TimeTop 10% of 2016 papers

Abstract

We present a new approach to lightweight intelligent transportation systems. Our approach does not rely on traditional expensive infrastructures, but rather on advanced machine learning algorithms. It takes images from traffic cameras at a limited number of locations and estimates the traffic over the entire road network. Our approach features two main algorithms. The first is a probabilistic vehicle counting algorithm from low-quality images that falls into the category of unsupervised learning. The other is a network inference algorithm based on an inverse Markov chain formulation that infers the traffic at arbitrary links from a limited number of observations. We evaluated our approach on two different traffic data sets, one acquired in Nairobi, Kenya, and the other in Kyoto, Japan.

Related Papers

• → A cellular automaton traffic flow model for online simulation of traffic(2001)103 cited
• → Intelligent traffic management at intersections: Legacy mode for vehicles not equipped with V2V and V2I communications(2013)45 cited
• → Application of Internet of Things Compressed Sensing and Information Interaction Technology in Intelligent Transportation Layout(2021)5 cited
• → Design of BP neural network urban short-term traffic flow prediction software based on improved particle swarm optimization(2019)3 cited
• → Smart city: road traffic evaluation based on traffic flow model(2022)1 cited