Faryal Ali

A new traffic model is developed to evaluate the impact of driver heterogeneity, including driver behavior and braking distance. This is achieved by incorporating the effect of different drivers and changes in braking distance. Thus, real world traffic conditions are considered. It is shown that the proposed model is hyperbolic and stable, so it is well posed and admissible. The performance of this model is compared with the widely used Payne–Witham (PW) model over a 3000 m circular road in MATLAB using the FORCE scheme. This represents a worst-case traffic scenario. The results obtained indicate that the velocity and driver anticipation evolve realistically with the proposed model, so it can effectively characterize driver heterogeneity. Conversely, the PW model produces unreasonable results.

Driver behavior significantly affects vehicle energy consumption and emissions which are critical global concerns. This paper proposes a microscopic traffic model to characterize energy consumption considering the physiological and psychological behavior of a driver. Physiological behavior reflects driver response to traffic conditions while psychological behavior includes driver sensitivity and awareness. Thus, the proposed model encompasses driver response, sensitivity, and awareness. Data was obtained using a sensor node installed at the roadside. Traffic was monitored for six days and the data was modeled using regression analysis. The models and vehicle fuel consumption rate are integrated into the proposed model. The Intelligent Driver (ID) model is based on a constant exponent so driver behavior does not vary with traffic conditions. Thus, fuel consumption is ignored. It is shown that the proposed model results in stable traffic, i.e. traffic returns to a steady state following a disturbance, whereas the ID model produces unstable traffic behavior. Further, it results in smaller fluctuations in speed, flow, and density. Thus, the proposed model can be used to lower fuel consumption and emissions, thereby reducing air pollution and contributing to climate change mitigation.

Cybersecurity has increased in importance due to advances in connected vehicle technology. To evaluate the impact of cyberattacks in mixed and fully connected vehicle environments, a novel microscopic traffic model is given that incorporates the connected autonomous vehicle (CAV) penetration rate. The intelligent driver (ID) model assumes uniform driver behavior based on a constant which is unsuitable for this environment. Thus, a variable exponent based on the cyberattack intensity is proposed that integrates the CAV penetration rate. The proposed model is evaluated over a 1000 m circular for 500 s with a platoon of 28 vehicles with 60% of vehicles affected by an attack. The results obtained indicate that cyberattacks reduce traffic stability, particularly at low CAV penetration rates. At high penetration rates, these attacks have less of an impact due to faster reaction time and coordination of unaffected CAVs. Furthermore, the results demonstrate that the proposed model can effectively characterize traffic behavior under cyberattacks, and so can be used to alleviate congestion in the presence of cybersecurity threats.

Road traffic significantly impacts global energy consumption and emissions, both of which contribute to climate change. Thus, energy conservation and emission reduction in road transportation are critical concerns, and traffic flow modeling is key to evaluating and improving these metrics. Therefore, this paper develops a microscopic traffic model to characterize energy consumption reflecting driver energy saving awareness. The well-known Intelligent Driver (ID) model cannot predict traffic dynamics within an energy saving driving environment because it is based on a fixed acceleration exponent. Simulation results are presented which demonstrate that the energy consumption in the proposed model decreases as driver energy saving awareness increases. Furthermore, traffic, in the proposed model, experiences smaller variations in flow, speed, acceleration, and density, higher speeds, and less congestion compared to the ID model. Thus, the proposed model can be employed to conserve energy and reduce emissions, thereby decreasing the overall carbon footprint of traffic and contributing to a more sustainable environment.

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.

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.