This paper describes a vehicle speed & vehicle-to-vehicle distance control algorithm for vehicle stop-and-go cruise control. So first, a complete dynamic model of car has been simulated that consists of an SI engine, automatic transmission. The vehicle longitudinal control scheme consists of a speed control algorithm and a distance control algorithm and throttle-brake control law. A desired acceleration for the vehicle has been designed using linear quadratic optimal control theory. It has been shown that the proposed control law provides good performance.

1. Introduction: Analyzing stop and go traffic that observes unexpected reasons on freeways is important for modeling generation and growth oscillation and estimate congestion effects on traffic flow. Numerous theories on traffic have been developed as traffic congestion gains to model congestion traffic, many traffic theorists have adopted theories from other fields such as fluid mechanics and thermodynamics. However, these theories cannot explain the complicated driving behavior patterns from the fluid mechanics’ perspective. …

Research on vehicle longitudinal control with a stop and go system is presently one of the most important topics in the field of intelligent transportation systems. The purpose of stop and go systems is to assist drivers for repeatedly accelerate and stop their vehicles in traffic jams. This system can improve the driving comfort, safety and reduce the danger of collisions and fuel consumption. Although there have been many attempts to model stop and go maneuver via traffic models, but predicting the future vehicle's acceleration in steps ahead has not been studied much in this models. The main contribution of this paper is in designing integrated genetic algorithm-artificial neural network (GA-ANN) which is a soft computing method to simulate and predict the future acceleration of the stop and go maneuver for different steps ahead based on US federal highway administration’s NGSIM dataset in real traffic flow. The results of this study are compared with two methods, back propagation based artificial neural network model (BP-ANN) and standard time series forecasting approach called ARX model. The mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination or R-squared (R2) are utilized as three criteria for evaluating predictions accuracy. The results showed the effectiveness of the proposed approach for prediction of driving acceleration time series. The proposed model can be employed in intelligent transportation systems (ITS), collision prevention systems (CPS) and driver assistant systems (DAS) such as adaptive cruise control (ACC) and etc. The outcomes of this study can be used for the automotive industries who have been seeking accurate and inexpensive tools capable of predicting vehicle speeds up to a given point ahead of time, known as prediction horizon, which can be used for designing efficient predictive controllers based on human behaviors.

Background: Musculoskeletal problems among drivers sourced from severe traffic congestion have become a substantial public health issue. Prominent driving risk exposures were deemed to inflict symptoms such as discomfort and ache among drivers that subsequently contribute to fatigue. This study aimed to investigate the relationship between frequency and period of getting stuck during driving through stop-go motion towards the prevalence of musculoskeletal problems. Moreover, several combined effects of parameters were investigated towards the experience of knee pain among drivers. Methods: This study adopted a cross-sectional questionnaire survey method. The survey conducted in 2021 was randomly sampled among 18-year-old and above Malaysian drivers with valid driving license and the survey conducted through social media via an online Google form. To analyse the association and outcomes of the survey, Chi-Square and Binary Logistic Regression tests were used respectively. Results: Overall, 320 drivers were recruited in this study. Data of 180 drivers who frequently stuck during peak hours in congestion was analysed with chi-square test that showed no significant relationship for both the driving exposure variables with the prevalence of knee pain during stop-go motion. Nevertheless, 92 (51.11%) drivers reported commonly experiencing knee pain symptoms from prolonged repetitive driving motion. The total sample of this study tested using regression analysis for combined effects of the parameters showed a significant (P<0.05) correlation of the drivers’ experience of knee pain while driving in heavy traffic. Conclusion: Generally, there are combined variables that contributed towards the occurrence of knee pain during stop-go driving in this study.

Stimulating the following vehicles by the leading ones repeatedly is created by the different reasons such as a sudden drop in the speed of the leading vehicle and lane changing maneuvers that result in stop and go traffic. It leads to several negative effects, such as reduction in safety, and increase in travel delay. When follower driver of platoon receives the last released wave of downstream to upstream in the traffic oscillation, he or she makes different reactions with respect to the released wave. They result in forming different behavioral patterns and behavior diversion from equilibrium driver. The behavior change analysis of the driver-vehicle unit is an indispensable factor for increasing and decreasing frequent repetitions in traffic oscillation. In this paper, employs vehicle trajectory data from Next Generation Simulation (NGSIM) program. The vehicle trajectory data of two freeway sites of the NGSIM program, Interstate 80 (I-80) and US highway 101 (US-101), were used in transportation and traffic research (NGSIM, 2006). Platoons of vehicles identified through a traffic disturbance classify in deceleration phase based on driver behavior. When the follower vehicle receives a deceleration wave, the follower’ s reaction may be to create a high or low speed drop. Driver behavior in deceleration phase leads to congestion classify into four behavioral patterns: under reaction and over reaction based on maneuvering errors of follower driver. Follower vehicles react different responses to deceleration wave in deceleration phase. The reactions result in low and more speed drop between Newell driver, low and more delay time of moving vehicles. A high speed drop, more safe spacing, results in the development of under reaction pattern. Also, a low speed drop, lower safe spacing, results in over reaction. In this paper, behavior diversion parameter and time of deceleration phase are assessed, based on observed behavioral patterns and using data envelopment analysis. Follower vehicles with different numbers in traffic stop and go traffic is considered as decision making units, leading to different decision-making units in the vehicle platoon. Using data envelopment analysis method, efficient units for trajectory data are analyzed, which is based on any behavior that indicates the delay in the vehicle platoon. More performance of behavior diversion parameter and time as result of any decision making unit are identified based on any diffused deceleration wave and follower spacing of receiving deceleration wave that results in the lowest delay time of vehicle platoon of deceleration phase. The results of the analysis show that the most effective decision making unit of overreaction behavioral pattern is created in the vehicle platoon at the same spacing and velocity values but the amount of different acceleration wave and behavioral changes. Based on under reaction pattern, the greatest delay in the platoon is proportional to the different speeds and spacing but the same deceleration wave which represents the most inefficient driving situation of a vehicle following the vehicle platoon. Present results can help traffic engineers in simulating vehicles movements of freeways to evaluate vehicle moves of F level of service for calculating delay and speed drop of vehicle platoon.

Graphene oxide thin layers, graphene oxide:silver nano-composite, graphene oxide:zinc oxide nano-composite and graphene oxide:zinc oxide/graphene oxide:silver bilayer were deposited by spray pyrolysis method. The synthesized thin layers were characterized using X-ray diffraction spectroscopy, field emission scanning electron microscope, energy dispersive x-ray spectroscopy and Raman spectroscopy. The optical properties and the band gap of the thin layers were also studied and calculated using the Tauc equation. Gram-negative bacterium of Escherichia coli was used to study the antibacterial properties of thin layers. The results showed that among the thin layers investigated, GO:ZnO/GO:Ag bilayer had the greatest effect on the inhibition of E. coli growth and was able to control the growth of bacterium after 2 hours.

Traffic oscillation, stop and go traffic, is created by different reasons such as: sudden speed drop of leader vehicle. Stop and go traffic commonly is observed in congested freeways results in traffic oscillation. Many theories had been presented to define congestion traffic based on laws of physics such as: thermodynamics and fluid. But, these theories could not explain the complexity of driving responses in different situations of traffic especially in traffic jams. Unfortunately, because trajectories data are very scarce, our understanding of this type of oscillations in congested traffic is still limited. When the leader vehicle of a platoon drops speed, deceleration waves are released from downstream to upstream. Follower vehicles reacts different behavioral reactions based on personal characteristics. In this paper, behavioral patterns of follower driver were classified based on asymmetric microscopic driving behavior theory and traffic hysteresis in NGSIM trajectories. They were four patterns in deceleration phase and two patterns in acceleration phase. Then, two parameters of last deceleration wave leading to congestion, time and space parameters, τ and δ , were calculated based on Newell’ s car following model. Time of two phases, stop and congestion phases, were identified based on follower vehicle trajectory. In order to calculate time of two phases, two points were identified: point of receiving stop wave leading to congestion and point of entering to congestion. Artificial neural network models were developed to analyze the relationship between the microscopic parameters and time of two phases. Analysis results present spacing difference of follower between stop and congestion phase based on under reaction-timid pattern and spacing difference of follower between deceleration and congestion phase based on over reaction-timid pattern and spacing of leader vehicle at the wave diffusion point are most effective parameters in stop time leading to congestion. One of the main practical applications of this paper can be the addressing one of the main problems of micro simulation soft wares (like Aimsun) due to behavioral patterns.

Delay in recovering the speed of the vehicle results in occurrence of the hysteresis phenomenon in traffic flow the disturbance. The fundamental theories to analyze the hysteresis phenomenon based on driver behavior asymmetry during acceleration, and deceleration phases are open research area and need more developments. In this paper, which is the review of recent efforts in understanding hysteresis; using a Newell`s car following model, are reviewed to study the effects of different parameters on the magnitude of the motioned phenomenon. Moreover, the hysteresis phenomenon based on aggressive and timid driver behavior was identified. Microscopic sensitivity analysis and its results are shown by considering a conducted research based on the modeling of the phenomenon by neural network, and optimizing hidden layers of the neural network using genetic algorithm. Furthermore, in non-stationary traffic flow by using the kinematic wave model in variable wave speed; the magnitude of the hysteresis was estimated by a gradual analysis of the speed- density relationship. The results showed that the shape of traffic hysteresis loops depend on the driving behavior. Also, Spacing and acceleration at the end point of the phenomenon are considered as the most effective parameters in the sensitivity analysis related to driver behavior. Last but not least, it was found that the hysteresis magnitude in the non-stationary condition resulted in the lowest amount.