Abstract
With the explosive growth of vehicles on the road, traffic congestion has become an inevitable problem when applying guidance algorithms to transportation networks in a busy and crowded city. In our study, the authors proposed an advanced prediction and navigation models on a dynamic traffic network. In contrast to the traditional shortest path algorithms, focused on the static network, the first part of our guiding method considered the potential traffic jams and was developed to provide the optimal driving advice for the distinct periods of a day. Accordingly, by dividing the real-time Global Positioning System data of taxis in Shenzhen city into 50 regions, the equilibrium Markov chain model was designed for dispatching vehicles and applied to ease the city congestion. With the reveals of our field experiments, the traffic congestion of city traffic networks can be alleviated effectively and efficiently, the system performance also can be retained.
Highlights
Traffic congestion is a common phenomenon which may cause serious consequences, such as economic loss, additional delay, and air pollution
Many practical vehicle navigation algorithms are proposed to tackle routing problems in the traffic condition changing in real-time condition
The authors collected the taxi routing information in Shenzhen city, China, and proposed the newly real-time predication and navigation system on the dynamic city transportation network to solve the problem in Shenzhen city
Summary
Traffic congestion is a common phenomenon which may cause serious consequences, such as economic loss, additional delay, and air pollution. Many practical vehicle navigation algorithms are proposed to tackle routing problems in the traffic condition changing in real-time condition. The authors collected the taxi routing information in Shenzhen city, China, and proposed the newly real-time predication and navigation system on the dynamic city transportation network to solve the problem in Shenzhen city. With the derivation of the matrix, our system can predicate the traffic condition, and the equilibrium state can be computed with the progress of Markov chain. The authors proposed a method that can solve the time-dependent network routing problem, and the derived navigation path can be updated timely by referring the traveling time arrangement. The congestion alleviation strategy is designed to balance the equilibrium states via the computation of Markov transferring probability matrix.
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