Road surface wear leads to the formation of cracks and holes known as potholes. Potholes disrupt the smooth flow of traffic and can lead to accidents. The Intelligent Driver (ID) model is commonly employed but it assumes uniform traffic behavior for all conditions. This oversimplified approach is unrealistic as it does not consider the impact of real-world factors such as potholes on traffic patterns. This paper proposes a microscopic traffic model to address the impact of these road surface irregularities on traffic. The effect of small, medium, and large conical potholes is investigated using fundamental diagrams for traffic flow and velocity. The results obtained indicate that the proposed model outperforms the ID model as it can more accurately characterize how potholes and driver sensitivity affect vehicle behavior.

The impact of fog on visibility is a major factor affecting traffic congestion and safety. This paper proposes a microscopic traffic model that captures the features of traffic in foggy weather and characterizes it based on visibility. The intelligent driver (ID) model is based on a constant acceleration exponent and produces similar traffic behaviour for all conditions, which is unrealistic. The performance of the ID and proposed models is evaluated on a 2.2 km ring road for 250 s with a platoon of 51 vehicles. Results are presented which show that the proposed model characterizes traffic realistically with lower acceleration and deceleration compared to the ID model. Further, it does not create stop‐and‐go waves and is stable even during foggy weather. The proposed model can be used to reduce fuel consumption and pollution resulting from traffic congestion.

A new microscopic traffic model is proposed that characterizes driver response according to reaction and sensitivity. Driver response in the intelligent driver (ID) model is based on a fixed acceleration exponent and so does not follow traffic physics. This inadequate characterization results in unrealistic traffic behavior. With the proposed model, drivers can be aggressive, sluggish, or typical. It is shown to be string stable, and for appropriate distance headway and velocity (speed), the traffic flow is smooth. Furthermore, the proposed model has better stability than the ID model because it is based on driver reaction and sensitivity, while the ID model is based on a fixed exponent. The ID and proposed models are evaluated on a circular road of length 1200 m with a platoon of 21 vehicles for 150 s. The results obtained show that the proposed model characterizes traffic more realistically than the ID model.

Road surfaces are affected by rain, snow, and ice, which influence traffic flow. In this paper, a microscopic traffic flow model based on weather conditions is proposed. This model characterizes traffic based on the weather severity index. The Intelligent Driver (ID) model characterizes traffic behavior based on a constant acceleration exponent resulting in similar traffic behavior regardless of the conditions, which is unrealistic. The ID and proposed models are evaluated over a circular road of length $800$ m. The results obtained indicate that the proposed model characterizes the velocity and density better than the ID model. Further, variations in the traffic flow with the proposed model are smaller during adverse weather, as expected. It is also shown that traffic is stable with the proposed model, even during adverse weather.

The intelligent driver (ID) model characterizes traffic behavior with a constant acceleration exponent and does not follow traffic physics. This results in unrealistic traffic behavior. In this paper, a new microscopic heterogeneous traffic flow model is proposed which improves the performance of the ID model. The forward and lateral distance headways are used to characterize traffic behavior. The stability of the ID and proposed models is examined over a 1000 m circular road with a traffic disturbance after 30 s. The results obtained show that the proposed model is more stable than the ID model. The performance of the proposed and ID models is evaluated over an 1800 m circular road for 150 s with a platoon of 51 vehicles. Results are presented which indicate that traffic evolves realistically with the proposed model. This is because it is based on the lateral distance headway.

A microscopic traffic model is presented which employs differences in velocity to characterize driver behavior. The Intelligent Driver (ID) model is based on an acceleration constant which cannot capture different traffic conditions. Further, it is not based on traffic physics and so can produce inaccurate results. The proposed model is an improved ID model and both are evaluated on a 2000 m circular road. The results obtained show that the proposed model can appropriately characterize traffic flow and density. Further, the variations in flow and velocity are smoother than with the ID model. This is because the proposed model is based on actual traffic parameters rather than an unrealistic traffic exponent.