Large-scale simulation of traffic flow using Markov model

Journal article

Modeling and simulating movement of vehicles in established transportation infrastructures, especially in large urban road networks is an important task. It helps with understanding and handling traffic problems, optimizing traffic regulations and adapting the traffic management in real time for unexpected disaster events. A mathematically rigorous stochastic model that can be used for traffic analysis was proposed earlier by other researchers which is based on an interplay between graph and Markov chain theories. This model provides a transition probability matrix which describes the traffic's dynamic with its unique stationary distribution of the vehicles on the road network. In this paper, a new parametrization is presented for this model by introducing the concept of two-dimensional stationary distribution which can handle the traffic's dynamic together with the vehicles' distribution. In addition, the weighted least squares estimation method is applied for estimating this new parameter matrix using trajectory data. In a case study, we apply our method on the Taxi Trajectory Prediction dataset and road network data from the OpenStreetMap project, both available publicly. To test our approach, we have implemented the proposed model in software. We have run simulations in medium and large scales and both the model and estimation procedure, based on artificial and real datasets, have been proved satisfactory. In a real application, we have unfolded a stationary distribution on the map graph of Porto, based on the dataset. The approach described here combines techniques whose use together to analyze traffic on large road networks has not previously been reported

Authors: Besenczi, R; Bátfai, N; Jeszenszky, P; Major, R; Monori, F

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Public Library of Science
• Journal: PLoS ONE
• Volume: 16
• Issue: 2
• Pages: e0246062-e0246062
• Publication date: 2021-02-09
• DOI: 10.1371/journal.pone.0246062
• EISSN: 1932-6203
• ISSN: 1932-6203
• Language: English
• Keywords: Traffic simulation; Microsimulation; Stochastic matrix; Markov chain; Traffic generation model; Graph; Data mining; Trajectory; Traffic flow (computer networking); Real-time computing; Engineering; Machine learning; Theoretical computer science; Transport engineering; Computer science