Congested traffic states in empirical observations and microscopic simulations

Intelligent Driver Model (IDM) is a well-known microscopic model of traffic flow within the traffic engineering societies. While it is a powerful technique for modeling traffic flows, the Intelligent Driver Model lacks the potential of accommodating the notion of drivers’ heterogeneous behavior whenever they are on roads. Concerning the above mentioned, this paper takes the lane to recognize the heterogeneity in drivers’ behavior based on Heterogeneity Vector. Heterogeneity vector is an integral part of a new model that holds the potential to provide a method that in turn can accommodate the effect of the above mentioned differentiation in the traffic pattern. The Intelligent Driver Model in combination to Heterogeneous vector results to Intelligent Driver Model Heterogeneous Calibration (IDMHC) which in turn has the capability to improve the accuracy of IDM calibration, and as a result, enhances its performance under real conditions of traffic systems. Following the pre-stated, the study formulates that, the heterogeneity vector, as an output of the computation block, will apply in the simulation of the traffic of vehicles. To validate the performance of the IDMHC model, NGSIM project has been applied. As such, the most notable contributions of this study are, presenting a new method for calibration of microscopic flow model based on individual trajectory data, depicting the differences among drivers based on the newly defined heterogeneity measure, and illustrating the differences among drivers shape, traffic patterns that are causing different distributions of macroscopic variables such as travel time. Based on the study, the results obtained depicts that the difference between the presumed values regarding the IDM parameters has a great difference when compared with the calculated values for each vehicle based on a 50% variance. These results have the likelihood to significantly affect the mode in which microscopic models simulate and predict the traffic situation.

In case of the Intelligent Driver Model (IDM) the actual Velocity–Density law V(D) applied by this dynamic system is not defined, only the dynamic behaviour of the vehicles/drivers is determined. Therefore, the logical question is whether the related investigations enhance an existing and known law or reveal a new connection. Specifically, which function class/type is enhanced by the IDM? The publication presents a model analysis, the goal of which was the exploration of a feature of the IDM, which, as yet, ‘remained hidden’. The theoretical model results are useful, this analysis important in the practice in the field of hybrid control as well. The transfer of the IDM groups through large-scale networks has special practical significance. For example, in convoys, groups of special vehicle, safety measures with delegations. In this case, the large-scale network traffic characteristics and the IDM traffic characteristics should be taken into account simultaneously. Important characteristics are the speed–density laws. In case of effective modelling of large networks macroscopic models are used, however the IDMs are microscopic. With careful modelling, we cannot be in contradiction with the application of speed–density law, where there IDM convoy passes. Therefore, in terms of practical applications, it is important to recognize what kind of speed–density law is applied by the IDM convoys in traffic. Therefore, in our case the goal was not the validation of the model, but the exploration of a further feature of the validated model. The separate validation of the model was not necessary, since many validated applications for this model have been demonstrated in practice. In our calculations, also the applied model parameter values remained in the range of the model parameters used in the literature. This paper presents a new approach for Velocity–Density Model (VDM) synthesis. It consists in modelling separately each of the density and the velocity (macroscopic parameter). From this study, safety time headway (microscopic parameter) can be identified from macroscopic data by mean of interpolation method in the developed map of velocity–density. By combining the density and the velocity models, a generalized new VDM is developed. It is shown that from this one, some literature VDMs, as well as their properties, can be derived by fixing some of its parameters.

Long span bridges are critical parts of a nation’s infrastructure network and congested traffic loading is the governing form of traffic loading. Groups of trucks travelling in conveys are created when faster moving vehicles, such as cars, change lane. In this research the authors investigate how the control of these lane-changing events can help reduce the traffic load effects on long span bridges. Real traffic data is used to simulate a traffic stream on a virtual road and bridge using a microsimulation model. Various lane-changing restrictions are examined and compared to the typical case of free lane changing. It is shown that restriction of lane changing is an effective means of reducing long-span bridge traffic load effect. This result may assist bridge owners in implementing measures to prolong the life of existing infrastructure. mance of the vehicle, and the psychological nature of the driver (including aggressiveness, perception, and reaction times). Yousif & Hunt (1995) describe some of these factors, giving an intuitive explanation of the pressure to change lane, in terms of the fundamental diagram of traffic (see for example, Gartner et al. 2005), shown here as Figure 1. Figure 1. Relationship between the fundamental diagram of traffic and the pressure to change lane (adapted from Yousif & Hunt 1995). Complicating the problem of understanding lane changing further, is the difficulty in obtaining experimental data. This is because the phenomenon is spatially and temporally distributed. Attempts to monitor individual vehicle trajectories have been made (see for example, Xuan 2006, who uses Global Positioning Satellites), but suffer from the fact that the driver is aware of the monitoring. The difficulty in determining individual vehicle’s lane-changing characteristics can be somewhat overcome using empirical observations. Yousif & Hunt (1995), Sparmann (1979), Brackstone et al. (1998), Hidas (2005) describe lane change rates as a function of flow, and these observations can be used to calibrate a lane-change model. Further efforts use other approaches, such as kinematic wave theory (Laval et al. 2006) and a gas-kinetic (Boltzmann) model (Helbing & Greiner (1997)). Hidas (2002 and 2005) outlines some recent work in the development of microscopic models for lane changing. The lane changing model used in this work is the microscopic MOBIL model proposed by Kesting et al. (2007) and explained later. 2 MICROSIMULATION MODEL 2.1 The Intelligent Driver Model The Intelligent Driver Model (IDM) is a microscopic car-following model developed by Treiber and others (Treiber et al. (2000a), Treiber et al. (2000b)). Its equations describe the motion of an individual vehicle in response to its surroundings, given some physically-meaningful mechanical and driver performance parameters. In particular the IDM is based on the idea that a driver tries to minimize braking decelerations. The acceleration a vehicle undergoes is defined by:

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.

Traffic tools simulations are based on different models whose purpose is to describe the behavior and dynamics of traffic flow on network. There are three modelling approaches which describe the road traffic flow: macroscopic, mesoscopic and microscopic. The microscopic models are used to describe the interactions between vehicles and their environment. They model the individual behaviour of each vehicle considering, in particular, interactions between two successive vehicles. Microscopic models aim to reproduce the behaviour of each pair vehicle-driver. In this paper, two car following models have been implemented: linear model [1] [2] and intelligent driver model (IDM) [3]. However, their shortcomings emanate from the difficulty to appreciate the consistency of behaviour of all traffic flow. Emphasizing on microscopic simulation, this paper is a comparative analysis of these microscopic models by identification of properties and limitations of each model. In order to describe road traffic at a granular level, the present study focuses on the kinematic variables. The variables used are essentially: spacing headway, relative speed, position, velocity and acceleration. They are designed for each vehicle and are closely related to its dynamics. In fact, microscopic models should be able to reproduce a real traffic situation, that's why an efficient calibration on real data is necessary to clarify the relevance of results. Therefore, the calibration of these microscopic models would be on real data.

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.

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.

Bottlenecks reduce both traffic safety and efficiency, resulting in congestion and collisions. The introduction of connected autonomous vehicles (CAVs) has had a significant impact on road networks and can improve traffic efficiency at bottlenecks. This paper proposes a microscopic traffic model to investigate CAV behavior at bottlenecks and examine the effect of cyberattacks. The model is developed using data collected from a roadside sensor node. It is implemented in MATLAB using the Euler scheme to simulate a platoon of vehicles on a circular road of length 1 km. The performance is compared with the intelligent driver (ID) model. The results obtained indicate that the road capacity with the proposed model is 1.4 times higher than with the ID model. Further, the proposed model results in nearly constant speeds with small variations, which is realistic. Conversely, the ID model produces large speed variations that are unrealistic. In addition, the proposed model results in less acceleration and deceleration, which leads to lower vehicle emissions and pollution. The efficiency is better than with the ID model due to CAV communication and coordination, so queues dissipate faster. The traffic flow with the proposed model increases as the density decreases, which is consistent with traffic dynamics. It is also shown that the proposed model can characterize CAV behavior under cyberattacks that cause disruptions in the data. Thus, it can be employed for traffic control and forecasting when bottleneck conditions exist and there is malicious behavior.

Different micro-meteorological conditions can affect a driver's judgment of road conditions, leading to changes in following behavior. On rainy days, water films on the road reduce traction, increasing the likelihood of hydroplaning and traffic accidents. While there are existing following models under various weather conditions, research on the specific impact of micro-meteorological factors is insufficient. To achieve fine management in intelligent transportation and real-time monitoring of vehicle states, it's essential to study following behavior under different micro-meteorological conditions and establish corresponding models. This paper focuses on the Intelligent Driver Model (IDM) and the Wiedemann99 model, considering the impact of micro-meteorological conditions. By incorporating a driver's judgment factor, λ, the IDM and Wiedemann99 models are improved, leading to the development of new models: I-IDM and I-Wiedemann99. Simulation validation is used to choose speed and following distance as performance indicators for parameter calibration of the I-IDM and I-Wiedemann99 models, with the sum of Root Mean Square Percentage Error (RMSPE) as the goodness-of-fit function. Comparisons are made between the driving paths, speeds, and accelerations of following vehicles before and after calibration, verified through simulations. The conclusions are as follows: the average error and standard deviation of the improved I-IDM model are smaller than those of the I-Wiedemann99 model, with the maximum Root Mean Square Percentage Error (RMSPE) for I-IDM model parameter calibration being 0.4568 and the minimum being 0.1324. For the I-Wiedemann99 model, the maximum RMSPE is 0.4613 and the minimum is 0.1376. The parameter calibration results of the I-Wiedemann99 model are more dispersed compared to those of the I-IDM model, indicating that the I-IDM model simulates following behavior more effectively than the I-Wiedemann99 model. The findings of this study can provide a reference for further improving the theory of following behavior, and offer a theoretical basis and IoT technology support for refined traffic management under rainy conditions.

Modified Intelligent Driver Model for driver safety and traffic stability improvement

Microscopic traffic simulation models are being increasingly used to evaluate Intelligent Transportation Systems (ITS) strategies and to complement empirical data in developing new analytical procedures and methodologies. Lane changing rules are an essential element of any microscopic traffic simulation model. While most of these rules are based on theories and hypotheses, to date no attempt has been made to investigate the consistency of lane changing behaviour from microscopic simulation with empirical observations. The research presented in this paper examined this consistency at freeway weaving areas using empirical data. These data were collected in the late 1980s at several major freeway weaving sections in the State of California. The microscopic traffic simulation model INTEGRATION was used to perform simulation experiments in this research. Vehicle distributions, both total and by type of movement, were used as measures to investigate the lane changing activity that took place at these freeway areas. This examination revealed significant agreement between patterns of lane changing behaviour as observed in the field and as reproduced by microscopic simulation. Most quantitative discrepancies were shown to be a function of user-specified input data or due to some inherent limitations in the empirical data.Key words: simulation, lane changing, weaving, freeways.

The subject of traffic flow modeling began over fifty years ago when Lighthill and Whitham used flow continuity equation from fluid dynamics to describe traffic behavior. Since then, a multitude of models, broadly classified into macroscopic, mesoscopic, and microscopic models, has been developed. Macroscopic models describe the space-time evolution of aggregate quantities such as traffic flow density whereas microscopic models describe behavior of individual drivers/vehicles in the presence of other vehicles. In this paper, we consider tracking of vehicles using a specific microscopic model known as the intelligent driver model (IDM). As in other microscopic models, the IDM equations of motion of a vehicle are nonlinearly coupled to those of neighboring vehicles, with the magnitudes of coupling terms becoming larger as vehicles get closer and smaller as vehicles get farther apart. In our approach, the state of weakly coupled groups of vehicles is represented by separated probability distributions. When the vehicles move closer to each other, the state is represented by a joint probability distribution that takes into account the interaction among vehicles. We use a sum of Gaussians approach to represent the underlying interaction structure for state estimation and reduce computational complexity. In this paper we describe our approach and illustrate the approach with simulated examples.

Car-following models are the core component of microscopic traffic simulation. Most of the deterministic models take fixed parameter values for different drivers. However, considerable behavioral differences exist between individual drivers. Simulating car-following behaviors of different drivers thus poses a challenge for microscopic traffic simulation. In this study, three approaches to calibrating car-following models for a group of heterogeneous drivers (calibrating an `average' driver, calibrating at an individual-driver level, calibrating based on clustered drivers' data) were tested with real-world driving data. Specifically, twenty randomly selected drivers' car-following trajectories extracted from the Safety Pilot Model Deployment (SPMD) project were used to calibrate the intelligent driver model (IDM) with the abovementioned three calibration approaches. The errors of replicating drivers' behavior in the validation datasets were used to evaluate the performances of the three calibration approaches.Results show that 1) calibrating at the individual level (i.e., each driver has its own model parameters) has the best performance in replicating a group of drivers' car-following behavior; 2) calibrating an `average' driver based on all drivers' data performs worst; 3) calibrating at the cluster level achieves intermediate performance; and 4) simply averaging calibrated individual drivers' parameters is not a good way to simulate a group of heterogeneous drivers' car-following behavior. The results suggest that inter-driver heterogeneity in car-following should not be neglected in microscopic traffic simulation, and also that there is a need to develop archetypes of a variety of drivers to build a traffic mix in simulation.

While macroscopic traffic flow models adopt a fluid dynamic description of traffic, microscopic traffic flow models describe the dynamics of individual vehicles. Capturing macroscopic traffic phenomena accurately remains a challenge for microscopic models, especially in complex road sections. Based on a macroscopic network flow model calibrated to real traffic data and new rules for the acceleration and merging behavior on the on-ramp, we propose a microscopic model for on-ramps. To evaluate the performance of the new flow-based model, we conduct traffic simulations assessing speeds, accelerations, lane change positions, and risky behavior. Our results show that, although the proposed model exhibits some limitations, its performance is superior to the Intelligent Driver Model in the evaluated aspects. While the Intelligent Driver Model simulations are almost free of conflicts, the proposed model evokes a realistic amount and severity of conflicts and therefore can be considered for safety analysis.

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.