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Considering the influence of slope sections and random degradation of the communication function of cooperative adaptive cruise control vehicles and the effect of the cooperative adaptive cruise control vehicle group driving, a linear stability criterion for heterogeneous traffic flow composed of cooperative adaptive cruise control vehicles and humanly-driven vehicles on slopes is derived, and a parameter sensitivity analysis is carried out. On this foundation, the safety of traffic flow on two-lane roads up and down hills is simulated while considering the lane-changing behavior of automobiles. The stability study shows increased cooperative adaptive cruise control vehicle penetration and uphill gradient will help improve traffic flow stability. In contrast, the increase in the downhill slope will intensify the transmission amplitude of the disturbance. When vehicle starting sensitivity is 2.5, and the gradient is 60, the minimum cooperative adaptive cruise control vehicle penetration required for mixed traffic flow to maintain stability on a downhill at any initial equilibrium speed is 85%, and the minimum penetration required on an uphill is 73%, and this proportion decreases with the increase in the uphill gradient and increases with the rise in the downhill slope. According to the safety study, increasing cooperative adaptive cruise control vehicle penetration and implementing lane-changing rules will improve traffic flow safety. Compared with the straight section, under the same conditions, the traffic flow safety of the uphill section is the best, and the safety of the downhill section is poor, which is consistent with the stability analysis results. This study is of great significance for future traffic control strategies for mountain roads. [ABSTRACT FROM AUTHOR]

Realistic microscopic traffic simulation is essential for prospective evaluation of the potential impacts of new traffic control strategies. Freeway corridors with interacting bottlenecks and dedicated lanes generate complex traffic flow phenomena and congestion patterns, which are difficult to reproduce with existing microscopic simulation models. This paper discusses two alternative driving behavior models that are capable of modeling freeways with multiple bottlenecks and dedicated lanes over an extended period with varying demand levels. The models have been calibrated using archived data from a complicated 13-mile long section of the northbound SR99 freeway near Sacramento, California, for an 8-hour time period in which the traffic fluctuated from free-flow to congested conditions. The corridor includes multiple bottlenecks, multiple entry and exit ramps, and an HOV lane. Calibration results show extremely good agreement between field data and model predictions. The models have been cross-validated and produced similar macroscopic traffic performance. The main behavior that should be captured for successful modeling of such a complex corridor includes the anticipative and cooperative driver behavior near merges, lane preference in presence of dedicated lanes, and variations in desired headway along the corridor. [ABSTRACT FROM AUTHOR]

Highlights • Trajectories play a critical role in CF model calibration and validation. • Developed a pattern recognition algorithm to differentiate driving regimes. • Developed a method to distinguish between free flow and CF sections. • Applied the algorithm to US-101 and reconstructed I-80 data. • Most trajectories in these datasets belong to acceleration, deceleration, and following. Abstract Vehicular trajectories are widely used for car-following (CF) model calibration and validation, as they embody characteristics of individual driving behaviour (each trajectory reflects an individual driver). Previous studies have highlighted that the trajectories should contain all the major vehicular interactions (driving regimes) between the leader and the follower for reliable CF model calibration and validation. Based on Dynamic Time Warping and Bottom-Up algorithms, this paper develops a pattern recognition algorithm for vehicle trajectories (PRAVT) to objectively, accurately, and automatically differentiate different driving regimes in a trajectory and then select the most complete trajectories (i.e. trajectories containing a maximum number of regimes). PRAVT is rigorously tested using synthetic data and then applied to the NGSIM data. We have observed that the NGSIM data are dominated by the trajectories which contain only three regimes, namely acceleration, deceleration, and following, 77% of the trajectories lack the standstill regime, and no trajectory in the NGSIM data is complete. These findings' impact on how to properly utilize NGSIM data can be profound. Given the extensive use of the NGSIM data in the traffic flow community, this paper also provides insights about the types of regimes contained in each trajectory of the NGSIM data. [ABSTRACT FROM AUTHOR]

The objective of this paper is to investigate about the driving style in different types of roads, with the final aim to establish some domains of road safety based on speeds and accelerations. We identify a tool that can provide an objective indicator of driving behaviour, represented by the percentage of points outside the domain: the more the percentage of points outside the domain is, the more the driving risk is. The proposed work is supported by an experimental survey consisting in the monitoring of real paths ran by drivers. For each path, the instantaneous speed and acceleration along the pattern were registered by means of GPS-enabled smartphones. Three different roads placed in the South of Italy were analysed, and specifically, a minor rural arterial road, a principal rural arterial road, and a freeway. The registered parameters were used for defining a safety domain which establishes if the driver had a safe behaviour or an unsafe one. The proposed methodology starts from the dynamic equilibrium of the vehicle, and arrives to establish a curve expressing the relationship between the speed and the acceleration vectors, which represent the domain within which the driver behaviour can be considered as safe. Having different formulations concerning the friction factor for the three different kinds of road, we obtained three different curves, or domains. Interesting findings were obtained by comparing the results from the proposed methodology for the different types of roads. As expected, the limits of the domain are quite different among the roads, because each road has its own characteristics. [ABSTRACT FROM AUTHOR]

When driving a vehicle, the driver must allocate adequate attention to the demands of driving in order to be safe. Based on analyses of vehicle data, driving environment data, and videos of the road ahead, a driving task demand prediction model based on real-time road traffic data was established in this study. Assessments of driving task demands allowed for the validation of this model. In addition, intervention strategies were proposed for distractions from in-vehicle secondary tasks at different levels of demand. The analysis showed that at a high driving task demand, the control of all in-vehicle information systems (IVIS), except for the playing of radio or CD, should be warned against or forbidden. Meanwhile, distractions from secondary tasks originated from 91% of the in-vehicle foreign objects, and 67% of the in-vehicle facilities can be avoided by formulating precaution strategies. This study provides methods and technical support for the management of driver distraction precaution. [ABSTRACT FROM AUTHOR]

To connect microscopic driving behaviors with the macro-correspondence (i.e., the fundamental diagram), this study proposes a flexible traffic stream model, which is derived from a novel car-following model under steady-state conditions. Its four driving behavior-related parameters, i.e., reaction time, calmness parameter, speed- and spacing-related sensitivities, have an apparent effect in shaping the fundamental diagram. Its boundary conditions and homogenous case are also analyzed in detail and compared with other two models (i.e., Longitudinal Control Model and Intelligent Driver Model). Especially, these model formulations and properties under Lagrangian coordinates provide a new perspective to revisit the traffic flow and complement with those under Eulerian coordinate. One calibration methodology that incorporates the monkey algorithm with dynamic adaptation is employed to calibrate this model, based on real-field data from a wide range of locations. Results show that this model exhibits the well flexibility to fit these traffic data and performs better than other nine models. Finally, a concrete example of transportation application is designed, in which the impact of three critical parameters on vehicle trajectories and shock waves with three representations (i.e., respectively defined in x - t , n - t and x - n coordinates) is tested, and macro- and micro-solutions on shock waves well agree with each other. In summary, this traffic stream model with the advantages of flexibility and efficiency has the good potential in level of service analysis and transportation planning. [ABSTRACT FROM AUTHOR]

This paper firstly show that 2 Dimensional Intelligent Driver Model (Jiang et al., 2014) is not able to replicate the synchronized traffic flow. Then we propose an improved model by considering the difference between the driving behaviors at high speeds and that at low speeds, which is in the framework of three-phase traffic theory. Simulations show that the improved model can reproduce the phase transition from synchronized flow to wide moving jams, the spatiotemporal patterns of traffic flow induced by traffic bottleneck, and the concave growth pattern of traffic oscillations (i.e. the standard deviation of the velocities of vehicles increases in a concave/linear way along the platoon). Validating results show that the empirical time series of traffic speed obtained from Floating Car Data can be well simulated as well. [ABSTRACT FROM AUTHOR]

Car following modeling framework seeks for a more realistic representation of car following behavior in complex driving situations to improve traffic safety and to better understand several puzzling traffic flow phenomena, such as stop-and-go oscillations. Calibration and validation techniques pave the way towards the descriptive power of car-following models and their applicability for analyzing traffic flow. However, calibrating these models is never a trivial task. This is caused by the fact that some parameters, such as reaction time, are generally not directly observable from traffic data. On the other hand, traffic data might be subject to various errors and noises. This contribution puts forward a Cross-Entropy Method (CEM) based approach to identify parameters of deterministic car-following models under noisy data by formulating it as a stochastic optimization problem. This approach allows for statistical analysis of the parameter estimations. Another challenge arising in the calibration of car following models concerns the selection of the most important parameters. This paper introduces a relative entropy based Probabilistic Sensitivity Analysis (PSA) algorithm to identify the important parameters so as to reduce the complexity, data requirement and computational effort of the calibration process. Since the CEM and the PSA are based on the Kullback–Leibler ( K – L ) distance, they can be simultaneously integrated into a unified framework to further reduce the computational burden. The proposed framework is applied to calibrate the intelligent driving model using vehicle trajectories data from the NGSIM project. Results confirm the great potential of this approach. [ABSTRACT FROM AUTHOR]

We have carried out car-following experiments with a 25-car-platoon on an open road section to study the relation between a car’s speed and its spacing under various traffic conditions, in the hope to resolve a controversy surrounding this fundamental relation of vehicular traffic. In this paper we extend our previous analysis of these experiments, and report new experimental findings. In particular, we reveal that the platoon length (hence the average spacing within a platoon) might be significantly different even if the average velocity of the platoon is essentially the same. The findings further demonstrate that the traffic states span a 2D region in the speed-spacing (or density) plane. The common practice of using a single speed-spacing curve to model vehicular traffic ignores the variability and imprecision of human driving and is therefore inadequate. We have proposed a car-following model based on a mechanism that in certain ranges of speed and spacing, drivers are insensitive to the changes in spacing when the velocity differences between cars are small. It was shown that the model can reproduce the experimental results well. [ABSTRACT FROM AUTHOR]

Automated driving on public roads is affected by many foreseeable and unforeseeable driving situations. Depending on the driving task, the environmental and road conditions, and the behavior of other drivers, different actions have to be taken. This paper provides a high-level overview of the development of highly automated driving systems and illustrates challenging situations and use cases. We outlined the impact of these use cases on system design, key technologies, and their technical realization for a highly automated driving system. Furthermore, the paper demonstrates how certain aspects of the system design as well as their implementation are country specific and how continuous testing is required for robust implementation of the functionalities. [ABSTRACT FROM AUTHOR]

Over the past decades there has been a considerable development in the modeling of car-following (CF) behavior as a result of research undertaken by both traffic engineers and traffic psychologists. While traffic engineers seek to understand the behavior of a traffic stream, traffic psychologists seek to describe the human abilities and errors involved in the driving process. This paper provides a comprehensive review of these two research streams. It is necessary to consider human-factors in CF modeling for a more realistic representation of CF behavior in complex driving situations (for example, in traffic breakdowns, crash-prone situations, and adverse weather conditions) to improve traffic safety and to better understand widely-reported puzzling traffic flow phenomena, such as capacity drop, stop-and-go oscillations, and traffic hysteresis. While there are some excellent reviews of CF models available in the literature, none of these specifically focuses on the human factors in these models. This paper addresses this gap by reviewing the available literature with a specific focus on the latest advances in car-following models from both the engineering and human behavior points of view. In so doing, it analyses the benefits and limitations of various models and highlights future research needs in the area. [ABSTRACT FROM AUTHOR]

Passing on rural two-lane highways is one of the most important driving tasks. More than 28000 vehicles were observed and the passing maneuvers were recorded of 10 two-lane highways in northern Iraq during June and July months of 2005. It was found that the number of passing maneuver increase as the flow rate for both directions increase up to 1500 veh/hr flow rate. The passing maneuvers decreases with the increase in flow rate. The increase in flow rate causes an increase in demand of passing and decrease in passing supply. Volume of around 212 veh/hr gives a balance between demand of passing and passing supply. [ABSTRACT FROM AUTHOR]

Highlights: [•] Systematic manipulation of dynamic traffic complexity in a driving simulator study. [•] Workload fluctuations were assessed using subjective and performance measures. [•] Driver workload varies linearly with traffic flow up to moderate levels. [•] Presence of lane changes in surrounding influence workload and driving performance. [Copyright &y& Elsevier]

The stability of the heterogeneous traffic flow mixed by two driving styles was studied. The corresponding car-following models of the two driving styles, the optimal velocity car-following model and the intelligent driver car-following model, were calibrated first using the real traffic data. The stability of the heterogeneous traffic flow was derived, based which the stability functions of the traffic flow and the driving style were defined. The simulations were conducted to analyze the stability condition. The results show that the stability of the heterogeneous traffic flow is impacted by the two factors: the proportion of the driving style and the stability function of the driving style. The simulation results showed that the intelligent driver driving style always has better stability effect than the optimal velocity driving style. In addition, the neutral stability lines with respect to the proportions of the two driving styles are obtained under the rage of the whole feasible velocity. The results provide the theoretical supports for the control of the heterogeneous traffic flow stability and the traffic congestion. [ABSTRACT FROM AUTHOR]

Abstract: Safely crossing an intersection requires that drivers actively control their approach to the intersection with respect to characteristics of the flow of incoming traffic. To further our understanding of the perceptual-motor processes involved in this demanding manoeuvre, we designed a driving simulator experiment in which 13 participants actively negotiated intersections by passing through a gap in the train of incoming traffic. Task constraints were manipulated by varying the size of the traffic gap and the initial conditions with respect to the time of arrival of the traffic gap at the intersection. Environment constraints were manipulated by varying the intersection geometry through changes in the angle formed by the crossroads. The results revealed that the task constraints systematically gave rise to continuous and gradual adjustments in approach velocity, initiated well before arriving at the intersection. These functionally appropriate adjustments allowed the drivers to safely cross the intersection, generally just slightly ahead of the center of the traffic gap. Notwithstanding the fact that the geometry of the intersection did not affect the spatiotemporal constraints of the crossing task, approach behavior varied systematically over geometries, suggesting that drivers rely on the traffic gap''s bearing angle. Overall, the pattern of results is indicative of a continuous coupling between perception and action, analogous to that observed in locomotor interception tasks. [Copyright &y& Elsevier]

In this study, we evaluated a driving support system by using a microscopic traffic simulation which was modified by adding the function of perception-response process. In this approach, a dangerous situation is assessed by detecting conflict through simulation. For this assessment, a safety indicator named TIDSS (Time Integral of Difference of Space distance and Stopping distance) was defined from the time integral of the relative distance between the leading and following vehicle when the leading vehicle brakes suddenly. This indicator is superior to existing indicators because it can evaluate any type of situation and consider the degree and duration of danger for each vehicle. The proposed approach was applied to evaluate the adaptive cruise control system (ACC) and automated platoon system (APS) for heavy trucks on the Urban expressway. To evaluate the impact of ACC and APS on traffic flow, many cases with different installation rates of ACC and APS were simulated using the proposed approach, and the relation between installation rate, safety indicators, and traffic flow rate was analyzed. Thus, we found that the introduction of ACC might mitigate dangerous situations on the expressway by decreasing the traffic flow rate. In contrast, introduction of APS might mitigate dangerous situations and increase the traffic flow rate. [ABSTRACT FROM AUTHOR]

The first-order continuum traffic model has been extensively studied in the state-of-the-art of traffic flow theory due to its simplicity and capability to represent many real traffic problems, such as a shock-wave formation. Furthermore, recent extension of the first-order model to multiclass traffic dynamics has revealed some interesting non-linear traffic phenomena such as hysteresis and capacity drop. However, most of the existing first-order continuum models do not display the widely scattered flow-density relationship. We argue in this article that the widely scattered flow-density relationship might be caused by the random variations in driving behaviour. It is shown that both of the hysteresis transitions and the wide scattering can be reproduced by a multiclass first-order model with a stochastic setting in the model parameters. The simulation results support our findings and are in good agreement with the real data. [ABSTRACT FROM AUTHOR]

The continuity equation involving traffic flow expresses the relationship between density, flow, and speed. Density equals the flow divided by space mean speed. The formulation of this equation in the year 1952 has two important assumptions. One assumption is that spacing and speed are constant, i.e., uncongested conditions with moderate to slightly high volumes. The other assumption is that homogeneous traffic prevails, vehicle composition is uniform and vehicles behave within strict lane discipline rules. To determine if the continuity equation is valid under nonhomogeneous traffic conditions, one performs an experiment involving data collection of density, flow, and speed at three midblock sites in India. Data collection occurred when uncongested conditions prevailed with moderate to slightly high volumes. Comparing the average density derived from observed densities in the field to the density derived from the continuity equation reveals whether or not the continuity equation accurately predicts average density under nonhomogeneous traffic conditions. Similar traffic operating characteristics served as the basis for grouping vehicles into five traffic entity types. The association between average density based on observed densities of nonhomogeneous traffic and density derived from the continuity equation had a correlation coefficient of +0.88 for light, four-wheeler type, +0.85 for heavy vehicle type, +0.90 for motorized, three-wheeler type, +0.83 for motorized, two-wheeler type, and +0.50 for nonmotorized, two- and three-wheeler type. Additionally, a nonparametric test, i.e., Wilcoxon signed-rank test, compared observed and derived densities. At a 95% confidence level, no significant difference existed between observed and derived densities of light four-wheeler type, heavy vehicle type, motorized two-wheeler type, nonmotorized two- and three-wheeler type, and between cumulative observed and cumulative derived density. Only in the case of the motorized three-wheeler type, the observed, and derived densities are significantly different. These moderate to strong correlations coupled with the results of the nonparametric test validate the application of the continuity equation when traffic is nonhomogeneous. [ABSTRACT FROM AUTHOR]

Abstract: The growth in motorised traffic on rural lanes in the UK has increased the dangers of, and dissuades people from, walking, cycling or horse riding on roads in the countryside. A UK Government initiative, “Quiet Lanes”, aims to address this contra-sustainability development and make rural lanes safe and attractive for non-motorised users. Although traffic calming measures have been employed in urban areas, their translation into more environmentally sensitive rural areas has been problematic, largely on aesthetic grounds as they often have an urban appearance. Innovative solutions are necessary to reduce traffic speed but it would be prudent to assess experimentally the likely effectiveness and acceptability of any new measures before they are built. This paper discusses the use of simulated environments by means of manipulated colour photographs to predict changes in driving behaviour associated with changing road environments. It was found that respondents were able to differentiate between the different simulated engineering solutions and their suggested driving behaviour accurately reflected that associated with road use under similar conditions elsewhere. [Copyright &y& Elsevier]

In this paper a comprehensive microscopic model of driver behavior in uninterrupted traffic flow is developed. The model aims to explain or predict the actions of a driver in a variety of driving scenarios ranging from free flow conditions on wide roads to forced flow conditions on narrow two-way roads. The salient features of the proposed theory are (1) a single model (or framework) is used to describe driver behavior in different driving scenarios and (2) the response of drivers through both steering control and speed control are modeled. Driver behavior is simulated in 19 different driving scenarios. Results from these simulations show the efficacy of the proposed model in explaining/predicting driver behavior. [ABSTRACT FROM AUTHOR]

: Anticipation in traffic means that drivers estimate their leaders' velocities for future timesteps. In the article a specific stochastic car-following model with non-unique flow-density relation is investigated with respect to anticipatory driving. It is realized by next-nearest-neighbour interaction which leads to large flows and short temporal headways. The underlying mechanism that causes these effects is explained by the headways of the cars which organize in an alternating structure with a short headway following a long one, thereby producing a strong anti-correlation in the gaps or in the headways of subsequent cars. For the investigated model the corresponding time headway distributions display the short headways observed in reality. Even though these effects are discussed for a specific model, the mechanism described is in general present in any traffic flow models that work with anticipation. [ABSTRACT FROM AUTHOR]

According to the intra-vehicle interaction, a traffic flow can generally be divided into three homogeneous states (1) that of free driving, (2) that of bunched driving, and (3) that of standing. The parameter describing the state of free driving is the desired speed, for the state of bunching it is the intra-vehicle gaps (time headway) within the convoy and the mean speed of the convoy, and for the state of standing it is the maximum jam density. These are the most essential parameters which do not depend on the actual traffic situation.This paper introduces a new model which considers the Fundamental Diagram (equilibrium speed–flow–density relationship) as a function of the homogeneous states. All traffic situations in reality can be considered as combinations of the homogeneous states and therefore can be described by the essential parameters mentioned above. The non-congested (fluid) traffic is a combination (superposition) of the states of free driving and bunched driving, the congested (jam, stop, and go) traffic is a combination of the states of bunched driving (go) and standing (stop). The contribution of the traffic states within the differently congested traffic situations can then be easily obtained from the queuing and probability theory. As a result, Fundamental Diagram in all equilibrium traffic situations is derived as simple functions of the essential parameters.According to the new model the capacity of freeways and rural highways can be determined by measuring the essential parameters. This is much easier than measuring the capacity directly.Furthermore, the probabilities of the various traffic states can be obtained from the new model. This leads to new possibilities in real-time controlling and telematics.The new model is verified by comprehensive measurements carried out on freeways and rural highways in Germany. [Copyright &y& Elsevier]

When a slow-moving vehicle occupies one of the lanes of a multilane highway, it often causes queueing behind it, not unlike one caused by an actual stoppage on that lane. This happens when the traffic flow rate upstream from the slow vehicle exceeds a certain critical value. We define this situation as the case of the Moving Bottleneck. There are other situations when people caught in a traffic jam slowly advance to the point of a suspected breakdown or other obstruction, only to find out that no such obstruction exists but suddenly the flow of traffic is free and rapid. We define this situation as that of the Phantom Bottleneck. We present a model describing these two situations on the basis of reasonable assumptions about the behavior of drivers whose movement is obstructed by the slow vehicle. Using a phenomenological relationship between flow and concentration, we derive formulas describing the behavior of the moving queue caused by the obstruction. Finally, we suggest procedures for validating the model and using it for a rational approach to the design of allowable minimum speeds on highways. [ABSTRACT FROM AUTHOR]