Macroscopic Traffic-Flow Modelling Based on Gap-Filling Behavior of Heterogeneous Traffic

Traffic-flow modelling has been of prime interest to traffic engineers and planners since the mid-20th century. Most traffic-flow models were developed for the purpose of characterizing homogeneous traffic flow. Some of these models are extended to characterize the complex interactions involved in heterogeneous traffic flow. Existing heterogeneous traffic-flow models do not characterize the driver behavior leading to gap filling in heterogeneous traffic conditions. This study aimed at explaining the gap-filling behavior in heterogeneous traffic flow by using the effusion model of gas particles. The driver’s behavior leading to gap filling in heterogeneous traffic was characterized through developing analogies between the traffic flow and the Maxwell–Boltzmann equation for effusion of gases. This model was subsequently incorporated into the Payne–Whitham (PW) model by replacing the constant anticipation term. The proposed model was numerically approximated by using Roe’s scheme, and numerical simulation of the proposed model was then carried out by using MATLAB. The results of the proposed and PW models were therefore compared. It is concluded that the new model proposed in this study not only produces better results compared to the PW model, but also better captures the expected reality. The main difference between the behavior of the two models is that the effect of bottleneck in the density of traffic is propagated in the form of a shockwave travelling backwards in time in the new model, while the PW model does not exhibit this effect.

A cellular automaton-based model of ship traffic flow in busy waterways

In busy waterways, spatial-temporal discretisation, safe distance and collision avoidance timing are three of the core components of ship traffic flow modelling based on cellular automata. However, these components are difficult to determine in ship traffic simulations because the size, operation and manoeuvrability vary between ships. To solve these problems, a novel traffic flow model is proposed. Firstly, a spatial-temporal discretisation method based on the concept of a standard ship is presented. Secondly, the update rules for ships’ motion are built by considering safe distance and collision avoidance timing, in which ship operation and manoeuvrability are thoroughly considered. We demonstrate the effectiveness of our model, which is implemented through simulating ship traffic flow in a waterway of the Yangtze River, China. By comparing the results with actual observed ship traffic data, our model shows that the behaviours and the characteristics of ships’ motions can be represented very well, which also can be further used to reveal the mechanism that affects the efficiency and safety of ship traffic.

Towards Traffic Flow Modelling Approaches Updating

The paper describes an analysis of the traffic flow characteristics and individual vehicle characteristics used for design decisions. Traffic flow statistic obtained with the help of Weigh in Motion System is presented. The brief analysis of the main traffic flow characteristics is performed, the need for traffic flow modelling approaches updating is outlined.

Modelling Traffic on Road Junctions on Parallel Computing Systems Using Cellular Automata Approach

<p class="0abstract">The paper deals with traffic flow modelling and simulation on supercomputer systems. The created mathematical model is based on cellular automata (CA) approach and allows to depict multilane traffic on various complex road configurations. The numerical algorithms for the model are designed for supercomputer calculations.</p>The main focus of this research is the validation of the created models and the comparison with results obtained by other researchers, as well as with experimental data. Several test problems were solved to carry out the comparison. The results obtained show that the model represents traffic flow characteristics efficiently and correctly and can be used for real-life traffic predictions.