Newell's Car-following Model Research Articles

Modeling the impact of lane-changing’s anticipation on car-following behavior

Lane-changing (LC) in congested traffic has been identified as a trigger for the sudden deceleration behavior of the new follower in the target lane, leading to severe traffic disturbances. Thus, investigating the response of the new follower to an LC maneuver is an important research topic in the literature. To date, numerous efforts have been devoted to understanding the impact of the lane changer on the new follower after the insertion, while less attention has been given to this influence during the pre-insertion stage (anticipation). Therefore, this paper aims to establish a new car-following (CF) model to capture the new follower’s driving behavior during anticipation. Specifically, we introduce an attention mechanism deviating from Newell’s CF rules to quantify the impact of anticipation. Then, we apply a neural network with an attention layer to estimate the attention mechanism and incorporate it into the Newell CF model, which yields a new CF model, denoted as CF_Attention. Using real-world trajectory data, we design three experiments and select three representative CF models to validate the CF_Attention. The results indicate that the CF_Attention outperforms the other models in predicting the new follower’s trajectory, which is not affected by the heterogeneous behavior of the new follower and the anticipation duration. Additionally, the CF_Attention is proven effective in capturing the speed-space relationship and the formation of oscillation. Finally, our transferability test suggests that the CF_Attention is promising for different locations and times without requiring retraining. The results of this study could advance the integration of the LC impact and CF behavior, and could be implemented into commercial traffic simulation programs to describe vehicle movements in traffic flow more accurately.

Modification of Newell’s car-following model incorporating multidimensional stochastic parameters for emission estimation

Existing studies have indicated that the vehicle trajectories derived from Newell's car-following model (NCM) fail to capture driving behavior heterogeneity, resulting in considerable emission estimation errors. This study investigated the situation-dependent heterogeneity of car-following behavior, based on field vehicle trajectories in Beijing, and proposed a multidimensional stochastic Newell car-following model (MSNCM) incorporating three stochastic parameters: random response time, speed-dependent critical jam spacing, and speed difference- and spacing-dependent acceleration. The comparison between the field data and numerical simulations of the NCM and MSNCM shown that the MSNCM performed well in generating realistic vehicle trajectories for emission estimation. The relative errors of the emission factors derived from the field and the MSNCM simulated trajectories were 0.26%, 0.91%, 1.37%, and 0.25% for CO2, CO, HC, and NOx, respectively, which represented reductions of approximately 15%-46% compared with the traditional NCM.

Traffic flow with multiple quenched disorders.

We study heterogeneous traffic dynamics by introducing quenched disorders in all the parameters of Newell's car-following model. Specifically, we consider randomness in the free-flow speed, the jam density, and the backward wave speed. The quenched disorders are modeled using beta distributions. It is observed that, at low densities, the average platoon size and the average speed of vehicles evolve as power laws in time as derived by Ben-Naim, Krapivsky, and Redner. No power-law behavior has been observed in the time evolution of the second moment of density and density distribution function, indicating no equivalence between the present system and the sticky gas. As opposed to a totally asymmetric simple exclusion process, we found no power-law behavior in the stationary gap distribution and the transition from the platoon forming phase to the laminar phase coincides with the free flow to congestion transition and is always of first order, independent of the quenched disorder in the free-flow speed. Using mean-field theory, we derived the gap distribution of vehicles and showed that the phase transition is always of first order, independent of the quenched disorder in the free-flow speed corroborating the simulation results. We also showed that the transition density is the reciprocal of the average gap of vehicles in the platoon in the thermodynamic limit.

Is more always better? The impact of vehicular trajectory completeness on car-following model calibration and validation

This paper investigates the impact of vehicular trajectory completeness on car-following (CF) model calibration and validation. Synthetic data with different levels of trajectory completeness, i.e., different number of driving regimes, generated from carefully designed numerical experiments are mainly used to calibrate and validate the Intelligent Driver Model (IDM) and the Newell's CF model. Model calibration results suggest that some driving regimes in a trajectory impact calibration errors and the particular regime and its exact impact are model-specific, e.g., the presence of the standstill and the absence of the cruising regimes impacts IDM and Newell's CF model calibration errors, respectively. However, level of trajectory completeness has no impact. The acceleration behaviour of IDM drivers in different driving regimes is determined by more than one parameter, i.e., a one-to-one mapping between the parameters and the driving regimes do not exist. On the contrary, for Newell's CF model, there exists a one-to-one mapping between the cruising regime and the desired speed. Furthermore, level of trajectory completeness impacts IDM and Newell's CF model validation. More specifically, the average calibrated parameters obtained from more complete trajectories performs better in validation and leads to smaller validation errors. These findings can have a profound impact on how future research on CF model calibration and validation using trajectories should be planned and implemented.

Follower behavioural change based on natural patterns in traffic oscillation

An understanding of the change in follower behaviour as a driver characteristic in traffic simulations is necessary because these behavioural changes are frequently observed in traffic oscillations. The change in follower behaviour due to traffic acceleration and deceleration waves can be classified according to different behavioural patterns resulting from different driver reactions. Combinations of existing behavioural patterns (under-reaction–timid, over-reaction–timid and constant over-reaction–timid patterns) were thus analysed. The method of analysis was based on an artificial neural network model at the microscopic level in order to simulate the extent to which follower behaviour deviates from Newell's car-following model. The most influential parameters of all the behavioural patterns were determined. Based on the under-reaction–timid pattern, decreasing the reaction speed resulted in a change in behaviour due to the development of safe spacing at the reaction point. In addition, follower spacing was the most influential in the over-reaction–timid pattern, resulting in increasing and then decreasing behavioural change. In the constant over-reaction–timid pattern, the follower's speed resulted in increasing and then decreasing the change in behaviour. The application of behavioural change to an intelligent transportation system would improve safety.

Integrated optimization of traffic signals and vehicle trajectories at isolated urban intersections

Existing traffic signal control systems only allocate green time to different phases to avoid conflicting vehicle movements. With advances in connected and automated vehicle (CAV) technologies, CAV trajectories not only provide more information than existing infrastructure-based detection systems, but also can be controlled to further improve mobility and sustainability. This paper presents a mixed integer linear programming (MILP) model to optimize vehicle trajectories and traffic signals in a unified framework at isolated signalized intersections in a CAV environment. A new planning horizon strategy is applied to conduct the optimization. All vehicle movements such as left-turning, right-turning and through are considered. Phase sequences, green start and duration of each phase, and cycle lengths are optimized together with vehicle lane-changing behaviors and vehicle arrival times for delay minimization. Vehicles are split into platoons and are guaranteed to pass through the intersection at desired speeds and avoid stops at stop bars. Exact vehicle trajectories are determined based on optimized vehicle arrival times. For the trajectory planning of platoon leading vehicles, an optimal control model is implemented to minimize fuel consumption/emission. For following vehicles in a platoon, Newell's car-following model is applied. Simulation results validate the advantages of the proposed control method over vehicle-actuated control in terms of intersection capacity, vehicle delays, and CO2 emissions. A sensitivity analysis is conducted to show the potential benefits of a short minimum green duration as well as the impacts of no-changing zones on the optimality of the proposed model.

A car-following model with the consideration of vehicle-to-vehicle communication technology

Recently, the research on traffic flow system based on some classical models, such as cellular automata and car-following models, has attracted much attention. Some meaningful achievements have been obtained in the past few years by scholars from various fields. This paper starts with literature review on traffic flow theory studies. Car-following models, including the initial model proposed by Newell in 1961 (Newell G F 1961 Oper. Res. 9 209) and some later modified ones (e.g. full velocity difference model, or FVD model for short) have been deeply investigated. Based on Newell's car-following model, an extension of car-following model with consideration of vehicle-to-vehicle (V2V) communication is then developed. The vehicle-to-vehicle communication technology, which was proposed in the early 2000s, enable vehicles to collect traffic condition information from other vehicles (e.g. speed, headway, position, acceleration, etc.) and provide them for drivers in almost real time. Compared with those without V2V devices, drivers with information from V2V devices can react to traffic flow fluctuation timelier and more precisely. To represent the pre-reaction of drivers to traffic flow information provided by V2V devices, a parameter, , is newly introduced into Newell's car-following model. Then by second-order Taylor series expansion, a new car-following model with the influence of V2V (called V2V model) is proposed. Neutral stability condition of V2V model as well as phase diagram is derived theoretically with linear analysis method. The phase diagram of linear stability condition is divided into stable and unstable regions. By analyzing stability performance of the proposed model, it is evident that V2V communication technology can improve the stability of traffic flow system. Numerical simulation is demonstrated to study the influence of V2V devices on traffic flow on the one hand, and to acquire density waves as well as hysteresis loops under different values of parameter on the other hand. The sensitive analysis method are adopted as well.The numerical simulation results indicate that: 1) when compared with FVD model, V2V model can make vehicles react to traffic flow fluctuation earlier and reduce the speed changes under start-up, brake and incident conditions; this indicates that the consideration of V2V devices can improve the safety and ride comfort of traffic flow system; 2) the V2V model is sensitive to the value changes of parameter and T; the stability of traffic flow can be improved if the value of parameter increases, or parameter T decreases; this outcome precisely agrees with the above theoretical analysis; 3) the characteristics of traffic flow can influence the performance of V2V technology: compared with under low density condition, V2V communication technology can significantly increase the average speed of traffic flow under high density condition.

Calibration of Nonlinear Car-Following Laws for Traffic Oscillation Prediction

Frequency-domain analysis has been successfully used to (i) predict the amplification of traffic oscillations along a platoon of vehicles with nonlinear car-following laws and (ii) measure traffic oscillation properties (e.g., periodicity, magnitude) from field data. This paper proposes a new method to calibrate nonlinear car-following laws based on real-world vehicle trajectories, such that oscillation prediction (based on the calibrated car-following laws) and measurement from the same data can be compared and validated. This calibration method, for the first time, takes into account not only the driver's car-following behavior but also the vehicle trajectory's time-domain (e.g., location, speed) and frequency-domain properties (e.g., peak oscillation amplitude). We use Newell's car-following model (1961) as an example and calibrate its parameters based on a penalty-based maximum likelihood estimation procedure. A series of experiments using Next Generation Simulation (NGSIM) data are conducted to illustrate the applicability and performance of the proposed approach. Results show that the calibrated car-following models are able to simultaneously reproduce observed driver behavior, time-domain trajectories, and oscillation propagation along the platoon with reasonable accuracy.

Estimating Individual Speed-Spacing Relationship and Assessing Ability of Newell's Car-Following Model to Reproduce Trajectories

Capturing variability within flow is an important task for traffic flow models. The linearity of the congested part of the fundamental diagram induces a linear speed-spacing relationship at an individual level, characterized by two parameters. This study assumes that most intervehicle variability can be accounted for by estimating these two parameters for each vehicle. Two methods are presented to quantify individual linear speed-spacing relationships. The first method is based on data: it estimates the speed-spacing relationship by fitting the experimental speed-spacing scatter plot with a straight line. The second method is based on simulation: it computes the optimum parameters so that the simulated trajectories obtained by Newell's car-following algorithm reproduce as closely as possible the experimental vehicle's trajectories. Both proposed methods are implemented on the Next Generation Simulation trajectory data set recorded on I-80. The individual parameters for the speed-spacing relationship are quantified, and their distributions are specified. The need to distinguish driver behavior on a lane-by-lane basis is discussed. The results tend to prove that taking into account individual variability between drivers can improve the accuracy of simulated trajectories.