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Nowadays, among the microscopic traffic flow modeling approaches, the car-following models are increasingly used by transportation experts to utilize appropriate intelligent transportation systems. Unlike previous works, where the reaction delay is considered to be fixed, in this paper, a modified neural network approach is proposed to simulate and predict the carfollowing behavior based on the instantaneous reaction delay of the driver-vehicle unit as the human effects. This reaction delay is calculated based on a proposed idea, and the model is developed based on this feature as an input. In this modeling, the inputs and outputs are chosen with respect to the reaction delay to train the neural network model. Using the field data, the performance of the model is calculated and compared with the responses of some existing neural network car-following models. Considering the difference between the responses of the actual plant and the predicted model as the error, comparison shows that the error in the proposed model is significantly smaller than that that in the other models.

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Recent researches on advanced driver-assistance systems indicate great advances in terms of safety and comfort in automated driving. Advanced driver-assistance systems use control systems to perform most of the maneuvers as performed by the driver in the past. One of the useful advanced driver-assistance systems is automatic lane change system in order to avoid accidents. This study designs the controller of an automatic lane change system for an autonomous vehicle. The control law in this study is adaptive sliding mode control. To avoid chattering in adaptive sliding mode control, fuzzy boundary layer is used. Also, adaptive law is used for sliding-based switching gain. This adaptive controlling law is used to avoid the calculation of upper bound of system uncertainties. In this study, based on the boundary conditions, the vehicle lane change path planning and different maneuver periods are evaluated. To simulate the designed controller, CarSim-Simulink joint simulation model is used. This linkage leads to a full non-linear vehicle model. The results of simulation show excellent tracking for dry road conditions and acceptable tracking in icy and wet roads in some maneuvers of above 4-s long.

Studies have shown the detection of emerging contaminants (ECs), of which pharmaceuticals are a subset, in surface waters across the United States. The objective of this study was to develop methods, and apply them, to evaluate the potential for food chain transfer when EC-containing waters are used for crop irrigation. Greenhouse experiments were performed in which select food crops were irrigated with water spiked with three antibiotics. Field experiments, at two different sites, were conducted. Select crops were irrigated with wastewater effluent known to contain ECs, EC-free well water, and Colorado River water containing trace-level ECs. The results of the greenhouse studies show the potential for uptake of one or more of the antibiotics evaluated, albeit at very low levels. In those food crops watered with wastewater effluent, only an industrial flavoring agent, N,N'-dimethylphenethylamine (DMPEA), was consistently found. None of the evaluated contaminants were found in crops irrigated with Colorado River water.