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A microscopic traffic flow model is developed that incorporates vehicle vibrations due to pavement condition. The Intelligent Driver (ID) model employs a fixed exponent so traffic behavior is the same regardless of the road condition. Thus, it ignores the underlying physics. To address this limitation, the proposed model employs the Pavement Condition Index (PCI) in describing traffic behavior. The performance of both models is evaluated on a 3000 m circular road using the Euler numerical discretization technique. The results show that the performance of the proposed model varies with the pavement condition (PCI), as expected. Furthermore, the traffic flow increases with vehicle speed. The oscillations in speed and density with the proposed model decrease as the PCI increases, and are larger when the speed is higher. Consequently, the results with the proposed model align more closely with reality as they are based on the PCI, and so are a more accurate representation of traffic behavior. [ABSTRACT FROM AUTHOR]

Radius of road curvature and the information of preceding vehicles have a significant influence on traffic flow in the vehicle-infrastructure cooperation environment. However, the existing car-following model is subjective in considering the type of preceding vehicles' information, and does not comprehensively consider the synergistic effect of road curvature radius and preceding vehicles' information. Therefore, in this study, an intelligent driver model for curved roads considering multi-preceding vehicles' information is established in the vehicle-infrastructure cooperation environment. Then, using the Lyapunov method to analyze the stability of the extended model, the linear stability condition is obtained, and the influence law of radius of road curvature and weight coefficient of the preceding vehicle on traffic flow is mastered. Finally, the theoretical analysis of the model is verified by numerical simulation experiments. The simulation results are consistent with the theoretical analysis, and both show that the stability of traffic flow increases with the increase of radius of road curvature and weight coefficient of the preceding vehicle. This shows that the extended model can not only accurately describe the car-following behavior of vehicles on a curved road in the vehicleinfrastructure cooperation environment but also stabilize the traffic flow and suppress traffic jams. [ABSTRACT FROM AUTHOR]

Traditional car-following models usually prioritize minimizing inter-vehicle distance error when tracking the preceding vehicle, often neglecting crucial factors like driving economy and passenger ride comfort. To address this limitation, this paper integrates the concept of eco-driving and formulates a multi-objective function that encompasses economy, comfort, and safety. A novel eco-driving car-following strategy based on the deep deterministic policy gradient (DDPG) is proposed, employing the vehicle's state, including data from the preceding vehicle and the ego vehicle, as the state space, and the desired time headway from the intelligent driver model (IDM) as the action space. The DDPG agent is trained to dynamically adjust the following vehicle's speed in real-time, striking a balance between driving economy, comfort, and safety. The results reveal that the proposed DDPG-based IDM model significantly enhances comfort, safety, and economy when compared to the fixed-time headway IDM model, achieving an economy improvement of 2.66% along with enhanced comfort. Moreover, the proposed approach maintains a relatively stable following distance under medium-speed conditions, ensuring driving safety. Additionally, the comprehensive performance of the proposed method is analyzed under three typical scenarios, confirming its generalization capability. The DDPG-enhanced IDM car-following model aligns with eco-driving principles, offering novel insights for advancing IDM-based car-following models. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Determining the trajectories of all vehicles on freeways is a challenging yet critical topic as trajectories reflect the characteristics of traffic flow and serve as a good basis for traffic management and control. With the advances of mobile sensing technology, connected and automated vehicles (CAVs) as a new source of probe car can provide high-resolution sampled trajectory data. Furthermore, as CAVs sense the surrounding traffic situation, they can offer information to understand the vehicle motions around them. Utilizing the data from CAVs thus supports the trajectory reconstruction of fully-sampled traffic flow and enables sophisticated evaluation of traffic states. This study develops a CAV detection data-based trajectory reconstruction method for freeway traffic. First, the intelligent driver model (IDM) is used to judge the motion of undetected human-driven vehicles (HV) between trajectories. The undetected vehicles will be inserted in traffic flow with the position and speed estimated by a modified IDM model. Subsequently, the complete trajectories of the inserted HVs will be reconstructed by IDM. Last, the validity of the method is verified by both simulation and empirical experiments. The results demonstrate the proposed method enables sufficient reconstruction of vehicle trajectories under different traffic densities and penetration rates of CAVs. [ABSTRACT FROM AUTHOR]

Random traffic flow load (TFL) simulation is an important analysis method for bridge design and safety assessment, and accurate TFL modelling is a prerequisite for high-quality simulation. The existing TFL modelling methods almost all rely on the load data monitored by the weigh-in-motion system (WIM system). However, the WIM system has natural defects such as unsatisfactory measurement accuracy at low speed and the inability to measure vehicle lengths and transverse positions in the lane, limiting the improvement of TFL simulation accuracy. Regarding this, a TFL monitoring system that integrates the functions of machine vision and WIM system is developed in this paper. In this system, a deep learning method is applied, for the accurate detection of vehicles and wheels in the video, and the extraction of key parameters for TFL modelling based on detection results. According to the long-term monitoring value, statistical distributions of key parameters are determined, and then an intelligent TFL model is derived from the Intelligent Driver Model (IDM), considering the car-following behavior of vehicles. Correspondingly, this paper further suggests a TFL simulation method and achieves an accurate TFL simulation. A cable-stayed bridge is taken as an example to verify the feasibility of the method. The results show that, compared to the modelling and simulation methods that only rely on the WIM system, the proposed method not only reduces the measurement error of vehicle dimensions by nearly 4 times, but also performs higher resolution in time measurement. The proposed method effectively overcomes the shortcomings of existing schemes and has good application potential in engineering. [ABSTRACT FROM AUTHOR]

This contribution analyzes the widely used and well-known "intelligent driver model" (briefly IDM), which is a second-order car-following model governed by a system of ordinary differential equations. Although this model was intensively studied in recent years for properly capturing traffic phenomena and driver braking behavior, a rigorous study of the well-posedness has, to our knowledge, never been performed. First, it is shown that, for a specific class of initial data, the vehicles' velocities become negative or even diverge to -∞ in finite time, both undesirable properties for a car-following model. Various modifications of the IDM are then proposed in order to avoid such ill-posedness. The theoretical remediation of the model, rather than post facto by ad hoc modification of code implementations, allows a more sound numerical implementation and preservation of the model features. Indeed, to avoid inconsistencies and ensure dynamics close to the one of the original model, one may need to inspect and clean large input data, which may result in practically impossible scenarios for large-scale simulations. Although well-posedness issues might only occur for specific initial data, this may happen frequently when different traffic scenarios are analyzed and especially in the presence of lane changing, on-ramps, and other network components, as it is the case for most commonly used microsimulators. On the other side, it is shown that well-posedness can be guaranteed by straight-forward improvements, such as those obtained by slightly changing the acceleration to prevent the velocity from becoming negative. [ABSTRACT FROM AUTHOR]

In this paper, a new microscopic traffic model based on forward and rearward driver response is proposed. Driver response is characterized using the distance and time headways. Existing models such as the Intelligent Driver (ID) model characterize traffic flow based on a constant acceleration exponent. This exponent reflects uniform driver behaviour during different conditions which is unrealistic. Driver response is slow with a large distance headway and quick with a short time headway. Conversely, it is quick with a small distance headway and slow with a long time headway. Thus, a new microscopic traffic model is proposed which incorporates driver response. Results are given that show the proposed model provides better traffic stability than the ID model as this stability is based on traffic physics. Further, for effective utilization of road infrastructure, shorter time and longer distance headways are preferred. The performance of the ID and proposed models was evaluated over an 800 m circular road with a string of 15 vehicles for 120 s. These models are numerically discretized using the Euler scheme. The results obtained show that traffic queue dissemination with the proposed model is more realistic than with the ID model and the changes in density with the proposed model are smaller. This is because traffic dissemination with the proposed model is based on traffic parameters rather than a constant exponent. [ABSTRACT FROM AUTHOR]

Car-following models describe the driver behaviour along the longitudinal direction in a traffic stream at a microscopic level. Therefore, the associated vehicle dynamics (i.e., velocity, acceleration) for different vehicles moving in pairs and platoons under mixed traffic situations using the car-following theory needs further exploration. Moreover, the response of a subject vehicle in mixed traffic stream depends upon the vehicle type and the manoeuvring capability. In this paper, we aim to evaluate some existing car-following models such as Gipp's car-following Model and the Intelligent Driver Model (IDM) for mixed traffic situations. Field trajectory data of mixed traffic flow on Kuala Lumpur urban highway were utilised in this study. From the field data, the leader-follower pairs are identified. Various parameters such as reaction time, speed and vehicle types affecting the vehicle-following behaviour for different leader-follower pairs are examined. Calibration and validation of these models are performed for different vehicle following pairs. The results show that the parameters used in the model vary based on the subject vehicle type and vehicle following pairs. It is concluded that Gipps model produces better results for a mixed platoon of vehicles by including vehicle-dependent parameter, whereas IDM model produces better and more stable results as compared to the Gipps model under the homogeneous car-following regime. This work has a significant influence in the development of a hybrid model for a realistic description of mixed traffic conditions. [ABSTRACT FROM AUTHOR]

This paper presents an uncontrolled intersection-passing algorithm with an integrated approach of stochastic model-predictive control and prediction uncertainty estimation for autonomous vehicles. The proposed algorithm is designed to utilize information from sensors mounted on the autonomous vehicle and high-definition intersection maps. The proposed algorithm is composed of two modules, namely target state prediction and a motion planner. The target state prediction module has predicted the future behavior of intersection-approaching vehicles based on human driving data. The recursive covariance estimator has been utilized to estimate the prediction uncertainty for each approaching vehicle. The desired driving mode has been determined based on the uncontrolled intersection theory. The estimated prediction uncertainty has been used to define the probability distribution of the stochastic model-predictive controller to cope with time-varying uncertainty characteristics of the perception algorithm. The constrained stochastic model-predictive controller based on safety indexes has determined the desired longitudinal acceleration. The proposed robust intersection-passing algorithm has been evaluated via computer simulation based on Monte Carlo simulation with a sensor model. The simulation results showed that the proposed algorithm guarantees the minimum safety constraints and improves the ride comfort at uncontrolled intersections by estimating the uncertainty of sensors and prediction. [ABSTRACT FROM AUTHOR]

The lane-changing behavior of different types of vehicles at the merging section of an urban road is assessed by using a new lanechanging model proposed in the current work. The lane-changing model known as MOBIL (minimizing overall braking induced by lane changes) is modified and combined with the intelligent driver model (IDM) to implement the lane-changing rules for different vehicle classes by applying the politeness and vehicle-type factors. It is noted that the heterogeneity of surrounding vehicles during lane-changing, which is common in developing countries, is not considered in most of the existing lane-changing models. The preceding problem is addressed by incorporating the vehicle-type dependent factor in the proposed lane-changing model. This new model considers the effect of motorcycle movement on the lane-changing decision and evaluates the aggressiveness of motorcyclists during lane-changing. The merging maneuver data from video recording is utilized to calibrate and validate the models. The lane changing rate is the highest for motorcycles and the least for trucks (at a given politeness factor). Motorcycles exhibit lower lane changing durations for politeness factors p = 0 and p = 1, showing integrated movements due to their pushy or erratic maneuverability. [ABSTRACT FROM AUTHOR]

This paper presents a motion-planning framework for urban autonomous driving at uncontrolled intersections. The intention and future state of the target vehicles are predicted using information obtained from the environment sensors. The target state prediction module employs an Interacting Multiple Model (IMM) filter to infer the intention of targets. The prediction results of each model of the IMM filter are fused to predict the future state of targets. The proposed predictor uses the intelligent driver model based-driver behavior model to construct the local filter of IMM. The driving mode decision is realized as a state machine consisting of two phases, ‘Approach’ and ‘Risk Management’. The risk management phase is composed of two sub-modes, ‘Cross’ and ‘Yield’. The state transition conditions between phases and modes are defined by introducing the concepts of ‘Critical gap’ and ‘Follow-up gap’. Based on the determined driving mode, the motion planning module consists of two sub-modules for each phase. The required deceleration determination for the approach phase is proposed to consider the occluded region in order to prevent inevitable collisions caused by fast approaches. The model predictive controller for the risk management phase is designed to determine the desired acceleration to guarantee safety and prevent unnecessary deceleration simultaneously. Both computer simulation studies and vehicle tests are conducted to evaluate the proposed framework. The results indicate that the proposed framework ensures the safety at uncontrolled intersections with a driving pattern similar to that of a driver. [ABSTRACT FROM AUTHOR]

This study presents a three‐lane highway overtaking strategy for an automated vehicle, which is based on a heuristic planning reinforcement learning algorithm. The proposed decision‐making controller focuses on keeping the autonomous vehicle operating safely and efficiently. First, the modelling of the overtaking driving scenario is introduced and the reference approaches named intelligent driver model and minimise overall braking induced by lane changes are formulated. Second, the Dyna‐H algorithm, which combines the modified Q‐learning algorithm with a heuristic planning policy, is utilised for highway overtaking decision‐making. Three different heuristic strategies are formulated to improve learning efficiency and compare performance. This algorithm is applied to determine the lane change and speed selection for an ego vehicle in the environment with uncertainties. Finally, the performance of Dyna‐H is estimated in the autonomous overtaking scenario by comparing it with the reference and traditional learning methods. Furthermore, the Dyna‐H‐enabled decision‐making strategies are validated and analysed in an open‐sourcing driving dataset. Results prove that the proposed decision‐making strategy could produce superior performance in convergence rate and control. [ABSTRACT FROM AUTHOR]

Connected and autonomous vehicle (CAV) technologies are expected to change driving/vehicle behavior on freeways. This study investigates the impact of CAVs on freeway capacity using a microsimulation tool. A four-lane basic freeway segment is selected as the case study through the Caltrans Performance Measurement System (PeMS). To obtain valid results, various driving behavior parameters are calibrated to the real traffic conditions for human-driven vehicles. In particular, the calibration is conducted using genetic algorithm. A revised Intelligent Driver Model (IDM) is developed and used as the car-following model for CAVs. The simulation is conducted on the basic freeway segment under different penetration rates of CAVs and different freeway speed limits. The results show that with an increase in the market penetration rate, freeway capacity increases, and will increase significantly as the speed limit increases. [ABSTRACT FROM AUTHOR]

Microscopic car following models describe the motion of individual vehicles in different flow conditions and numerical integration methods are usually used to model vehicle behavior in computer simulation. In this paper following behavior of two vehicles were simulated in spreadsheets using iterative computation and dynamic charts graphically, showing clear representation of parameter changes, formulas and numerical computation process. Euler method and Ballistic update rules were used as numerical integration schemes in simulation of Intelligent Driver Model and Optimal Velocity Model. Application of numerical integration methods in traffic simulation were discussed comparing simulation results in smooth and non-smooth situations. The method of spreadsheet simulation does not need computer programming skills, would be a reference to model simulation for other kind of car following models in research, experiment and education practices. [ABSTRACT FROM AUTHOR]

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This study aims to develop car following models which simulate various adaptive cruise control (ACC) behaviors for a microscopic traffic simulator. There is a need for a microscopic traffic simulator to evaluate the impact of ACC penetration on highway traffic conditions. If the method of modeling of the simulator follows ACC technology as it is in the real world, the update frequency will be in millisecond order. This may result in an unexpected increase in the calculation time and often spoil the practical use of the simulator. To avoid this situation, it is necessary to develop a car following model which can simulate ACC with sufficient accuracy for impact assessment and which works in sub-second frequency, which is common for many microscopic traffic simulators. This paper outlines the Intelligent Driver Model and its derivations, which have many preferable features. Those model equations are modified to simulate three types of ACC behaviors which retain distance gap, time gap and time headway, and we verified their behaviors through computational platoon experiments with four cars. [ABSTRACT FROM AUTHOR]

This paper is concerned with designing and numerically implementing a V2X (Vehicle-to-Vehicle and Vehicle-to-Infrastructure) control system architecture for a platoon of autonomous vehicles. The V2X control architecture integrates the well-known Intelligent Driver Model (IDM) for a platoon of Autonomous Driving Vehicles (ADVs) with Vehicle-to-Infrastructure (V2I) Communication. The main aim is to address practical implementation issues of such a system as well as the safety and security concerns for traffic environments. To this end, we first investigated a channel estimation model for V2I communication. We employed the IEEE 802.11p vehicular standard and calculated path loss, Packet Error Rate (PER), Signal-to-Noise Ratio (SNR), and throughput between transmitter and receiver end. Next, we carried out several case studies to evaluate the performance of the proposed control system with respect to its response to: (i) the communication infrastructure; (ii) its sensitivity to an emergency, inter-vehicular gap, and significant perturbation; and (iii) its performance under the loss of communication and changing driving environment. Simulation results show the effectiveness of the proposed control model. The model is collision-free for an infinite length of platoon string on a single lane road-driving environment. It also shows that it can work during a lack of communication, where the platoon vehicles can make their decision with the help of their own sensors. V2X Enabled Intelligent Driver Model (VX-IDM) performance is assessed and compared with the state-of-the-art models considering standard parameter settings and metrics. [ABSTRACT FROM AUTHOR]