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Microscopic modeling of driving behavior is the basis for traffic design and traffic simulation studies and can be applied to automated driving systems to provide human-like decision making. Previous modeling methods can be mainly divided into scenario-based modeling methods and field theory-based modeling methods. Scenario-based models are based on behavior theories that can explain behavioral mechanisms and field theory-based models are convenient for application to different scenarios. Combining two behavior theories and field theory, this paper aims to present a novel method to uniformly model the driving behavior in different scenarios. Risk homeostasis theory and preview-follower theory are used as the theoretical foundation, and field theory is utilized to connect the two behavior theories. A new risk field model is constructed for better coupling these behavior theories. Integrating these theories, this study then develops a subjectively perceived risk quantification method and a trajectory and motion planning model, which are validated using naturalistic data in car-following scenarios. Results show the effectiveness of this method and this model with reference to an effective risk quantification index (safety margin) and in comparison with the classical models (desired safety margin model and intelligent driver model) using naturalistic data in car-following scenarios. [ABSTRACT FROM AUTHOR]

In recent years, the adaptive cruise control (ACC) system has become widely adopted. The vehicle-positioning system plays an important role in automotive platoon driving and can provide vehicle-position information through vehicle-to-vehicle communication. However, the acquired vehicle-position information often presents uncertainties given the existence of variations in the traffic environment and of noise in the vehicle-positioning system. These uncertainties, in turn, influence the car-following performance of a vehicle platoon. In this study, we employ the desired safety margin (DSM) model, an ACC control strategy, to investigate the influence of the uncertainty level of vehicle-position information (ULVPI) on string stability and car-following safety. The stability criterion of the DSM model with ULVPI is derived through linear stability theory. Theoretical analysis results are verified by numerical simulations. Analytical results indicate that a negative ULVPI value can expand the stable region and improve string stability and that a positive ULVPI value can reduce delay time. Moreover, a negative ULVPI value can improve car-following safety during the stopping and evolution processes, whereas a positive ULVPI values can increase the safety margins of vehicles during the starting process. In the car-following process, a negative ULVPI value can improve car-following safety and reduce rear-end collision risk. The variation in ULVPI values can improve string stability and reduce the risk of rear-end collisions when mean and standard deviation are reasonable. Therefore, the improvement of a vehicle’s dynamic performance, string stability, and safety relies on the effect of ULVPI in different traffic scenarios. These results are useful in designing different control strategies that stabilize traffic flow and improve traffic safety for vehicles with ACC systems. [ABSTRACT FROM AUTHOR]

The application of short-term traffic forecasting can guide the operation of traffic networks efficiently and reduce the traffic cost for travelers. On the basis of radial basis function (RBF) neural network, this paper introduces a tunable and transferable RBF (TT-RBF) model to conduct on-line forecasting and transfer forecasting. Considering the spatiotemporal correlation of traffic flows in a road network, a spatiotemporal state matrix formed by the detrended cross-correlation analysis is used for the model input. With the on-line forecasting process, an improved on-line structure and parameter adjustment are proposed to enhance the existing model. Thus, the TT-RBF model can be adaptive to time-varying traffic states, especially to deal with the difference between non-peak and peak hours. Moreover, the proposed model can be transferred from one road segment to act on other road segments. By this way, the traffic states of numerous road segments can be forecasted conveniently without complex model training processes. The floating car data of a typical road network in Beijing are used for the performance verification of the TT-RBF model, and some frequently used forecasting models are selected for comparisons. The numerical experiments show that the TT-RBF model can get more accurate results than those in single-step forecasting, multi-step forecasting, and transfer forecasting. [ABSTRACT FROM AUTHOR]

A growing body of speed control methods for signalized intersections have been developed using V2I and V2V technology. However, limited efforts are made to reduce the vehicle fuel consumption and emissions during the driving process at successive signalized intersections. To fill the gaps, this paper proposed a novel speed control method for the successive signalized intersections under connected vehicles environment. Using the upcoming traffic signal phasing, timing information and the vehicle queues at the signalized intersection, vehicle speed was optimized to reduce the vehicle fuel consumption and emissions. To validate its effectiveness, a real-time simulation framework embodied the characteristics of the connected vehicles environment is conducted using the multi-agent technology. In different traffic flows, the fuel consumption, CO2 emissions and travel time based on the speed control method are compared with that of without speed control. The effectiveness of the proposed speed control method in different signal timing plan was also tested. Simulation results show that the proposed speed control method could reduce more than 18% fuel consumption and CO2 emissions and 9% travel time in the medium density of traffic flow, which is the best control effect. [ABSTRACT FROM AUTHOR]

Car following is the most common phenomenon in single-lane traffic. However, the propagation of the small perturbation of the velocity of the leading car will affect traffic flow. Driving behaviors play an important role in the determination of the qualitative dynamics of vehicles in the car-following process. Under different traffic environments, driving behaviors depend on the level of perceived risk, acceleration and deceleration habits, and reaction characteristics of the driver. The desired safety margin (DSM) model can directly describe the driving behaviors in the car-following process by using the parameters of the risk perception of drivers, sensitivity coefficient of acceleration and deceleration, and response time. In this paper, we investigate the influence of the accepted risk level, response time, and sensitivity factor on the traffic flow via the DSM model. The stability criterion of the simplified DSM model is derived via linear stability theory. Analytical results indicate that a backward propagating of perturbation would enlarge or shrink with the change of three driving behavior parameters of accepted risk level, sensitivity coefficient of acceleration or deceleration, and response time. Results show that careful driving can improve the stability of traffic flow and that system stability can be maintained by adjusting the acceleration and deceleration control parameters, increasing DSM, or decreasing response time for the adaptive cruise control or vehicular platoon control system. The results can provide reasonable values of driving behavior parameters for the stability of the primitive DSM model using the simplified DSM model. Furthermore, we analyze the influence of interval DSMs, and the acceleration and deceleration sensitivity of the primitive DSM model on the stability of traffic flow through numerical simulations. Results imply that the lower limit of the DSM influences traffic flow more significantly than the upper limit of the DSM. Moreover, the increase in deceleration sensitivity has a more important influence on the stability of traffic flow than the increase in acceleration sensitivity. The numerical simulation results are in good agreement with the analytical results and the relevant experimental results of previous studies. [ABSTRACT FROM AUTHOR]

Presents information pertaining to the rapid thermal chemical vapor deposition (RTCVD) of polysilicon. Information on the capacity of the simulator; What role the computer simulation plays; Basis of the mathematical models for equipment and materials processes.

Studies contamination control for gas delivery from a liquid source in semiconductor manufacturing. Experimental procedure used; Results of the study.

Presents information on optimization as it relates to the semiconductor manufacturing industry. Investigation into rapid thermal chemical vapor deposition (RTCVD); Details on the requirements of environmentally-conscious manufacturing (ECM); Indepth look at the dynamic simulator description.