Stochastic Traffic Flow Algorithm Based on a Macroscopic LWR Model for Real-Time Traffic Prediction

Mehboob Ali Jatoi, Shakeel Ahmed Kamboh

Abstract

Congestion into the traffic flow model is the main factor that affects the free flow of traffic due to the increasing number of vehicles on a daily basis, which is the main drawback of developed countries. There are many disadvantages of the congestion like the increase in accidents, time consumption, air pollution, and many others. The intelligent transportation system depends upon the well prediction of random traffic. As the average flow of traffic is failing due to increasing vehicles. To overcome the disadvantages of a transportation system, an algorithm is proposed to forecast the future state of traffic flow. The proposed method can be used to predict the traffic flow of any section of congested road to predict the traffic. A section of road is considered for real-time traffic data for a week from 7:00 to 19:00 with one hour of time interval. Shiparo-Wilk test with 95% confidence level is applied for the normality of the data which fails to be a normal. Then various statistical tests for the distribution are applied using the MATLAB distribution fit tool box, and different distributions like normal, lognormal, Weibull, exponential, and gamma distributions are applied for the best fit over the collected data, and a lognormal distribution with less standard error is the best fitted distribution over the collected data. For stochastic traffic flows, a forcing function with incorporation of initial density and flow using Brownian motion is studied for the proposed model, and a lognormal distribution is replaced by Brownian motion, which shows randomness in to the model. Godunov’s numerical method was used for the proposed model with discontinuity and was incorporated using the Riemann problem solver, and the algorithm with randomness followed the collected data, which showed that the model had good predicted performance.

 

Keywords: Macroscopic Lighthill, Whitham and Richards (LWR) model, randomness, Shiparo-Wilk test, distributions, Godunov’s method

 

https://doi.org/10.55463/issn.1674-2974.51.6.31


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