Field-Based Prediction Models for Stop Penalty in Traffic Signal Timing Optimization

Transportation agencies optimize signals to improve safety, mobility, and the environment. One commonly used objective function to optimize signals is the Performance Index (PI), a linear combination of delays and stops that can be balanced to minimize fuel consumption (FC). The critical component of the PI is the stop penalty “K,” which expresses an FC stop equivalency estimated in seconds of pure delay. This study applies vehicular trajectory and FC data collected in the field, for a large fleet of modern vehicles, to compute the K-factor. The tested vehicles were classified into seven homogenous groups by using the k-prototype algorithm. Furthermore, multigene genetic programming (MGGP) is utilized to develop prediction models for the K-factor. The proposed K-factor models are expressed as functions of various parameters that impact its value, including vehicle type, cruising speed, road gradient, driving behavior, idling FC, and the deceleration duration. A parametric analysis is carried out to check the developed models’ quality in capturing the individual impact of the included parameters on the K-factor. The developed models showed an excellent performance in estimating the K-factor under multiple conditions. Future research shall evaluate the findings by using field-based K-values in optimizing signals to reduce FC.

Relative controllability of a stochastic system using fractional delayed sine and cosine matrices

In this paper, we study the relative controllability of a fractional stochastic system with pure delay in finite dimensional stochastic spaces. A set of sufficient conditions is obtained for relative exact controllability using fixed point theory, fractional calculus (including fractional delayed linear operators and Grammian matrices) and local assumptions on nonlinear terms. Finally, an example is given to illustrate our theory.

Multivariable predictive functional control of a compound power-split hybrid electric vehicle

The Corun hybrid system (CHS) is a deeply coupled multiple-input–multiple-output (MIMO) hybrid system. The two inputs are the torques of the two motors. The two outputs are the carrier speed and transmission output torque. Using the traditional control method, the multi-objective control quality cannot be guaranteed because of the adopted static decoupling method and proportional–integral–derivative (PID) controllers. In this paper, the problems of the traditional control method are carefully analyzed, and a new control method is proposed. Instead of static decoupling, dynamic decoupling is adopted to improve the decoupling control effect. A predictive functional controller instead of a PID controller is adopted to deal with the pure delay caused by controller area network (CAN) communication. The tracking effect of the target value is further improved by predictive functional controllers. For the two decoupled subsystems, that is, the integral system and the second-order underdamped system, two predictive functional controllers are designed. The new control method was verified by simulations and tests. The results show that the new control method is superior to the traditional control method for CHS.

Finite-Time Stability of Hadamard Fractional Differential Equations in Weighted Banach Spaces

The main purpose of this paper is to investigate the finite-time stability of Hadamard fractional differential equations (HFDEs). Firstly, the standard definition of finite-time stability of HFDEs in compatible Banach space are proposed. In light of the method of successive approximation and Beesack inequality with weakly singular kernel, the criteria for finite-time stability of linear and nonlinear HFDEs are established, respectively. Then with regard to linear HFDEs with pure delay, a novel fractional delayed matrix function (also called delayed Mittag-Leffler matrix function) is given. Specific to nonlinear HFDEs with constant time delay, both Beesack inequality and Hölder inequality are utilized in the framework of the generalized Lipschitz condition. Finally, several indispensable simulations are implemented to verify the effectiveness and practicability of the main results.

Dynamic stand for assessing the time spent on human operator thinking forms

The paper considers design features and functionality of the dynamic stand for assessing the time spent on human operator thinking forms during the hidden (latent) period as information passes through the brain according to experimental data working in Man-Machine Systems. This research is of interest as such a system is intellectual, so it is necessary to determine the pure delay time spent to implement thinking forms for assessing the quality of decisions made by a human operator in the control process. The study shows that a number of Wiener orthogonal G-functionals provides the most complete and rigorous human operator dynamics description and mathematical capabilities for calculating the necessary delay time.