Small communities lack effective transit planning methods that integrate diverse forms of knowledge, foster collaboration, and envision better transit futures. To address this need, this paper presents a case study of a project conducted in Benton Harbor, Michigan. The case study demonstrates a collaborative and data-driven scenario planning process conducted for a small region, and evaluates it through a mixed-methods research design. Through the use of quantitative normative service scenarios and qualitative exploratory scenarios, the project generated financially and operationally feasible proposals that community leaders can implement in the future, and also fostered constructive dialogue among transit stakeholders. Survey data show that participants experienced high levels of learning, engaged in quality deliberation, and are generally optimistic about the potential for improved transit. The project's approach can be replicated elsewhere through the use of five essential elements: a steering committee, stakeholder analysis, a series of engagement workshops, normative and exploratory scenarios, and interaction between data and modeling. Collaborative planning with scenarios can help the transportation field address the need to foster collaboration and epistemic inclusion in a changing world.
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This paper addresses the problem of actuation and design of a controller for a 2-DoF Cable-Driven Serial Chain (CDSC) with variable configuration based on adaptive neuro-fuzzy systems. The mentioned structure uses several light cables to actuate rigid links of a passive planar serial robot, performing a 2-DoF motion pattern, which can apply tensile forces for moving the links and thus produce the necessary torque to track the desired reference model by the robot's end-effector. Additionally, cable attachment point coordinates of the CDSC are considered variable configurations. So, a novel concept is expressed in the calculation of the CDSC's Jacobian matrix. In this mechanism, upon formulating the sliding mode error dynamics, an Adaptive Neural Network (ANN) is used to cope with complex mathematical calculations of the CDSC dynamics. Then, the coordinates of cables are considered as variables that depend on the main states of the plant. These co-states are adaptively determined by applying an appropriate fuzzy system. As a result, an Adaptive Neuro-Fuzzy Controller (ANFC) algorithm is introduced for the mentioned redundant mechanism. Finally, by defining a new Cost of Redundancy (CoR), an optimal configuration of the cables is found, so the maximum Degrees of Redundancy (DoR) is obtained. The simulation results indicate that using the ANFC algorithm leads to a reduction of 2.23% in the CoR for the 2-DoF CDSC.
Optimization includes two types of structural and parametric. The parametric optimization includes classical and state-space methods. In this paper, the desired cost function is minimized by using a state-space parametric optimization method. In state-space method, canonical observable realization is used to minimize of the desired cost function. Cost function an integral quadratic performance is assumed. Finally, by using evaluated equations, two examples by using a MATLAB are simulated. The results show the efficiency of evaluated equations.
This study intends to investigate a new control structure using a robust model reference hybrid fuzzy controller. The main aim of the present study is to improve the transient response of the system subject to uncertainty meanwhile guaranteeing the stability of whole system. The control signal incl udes fuzzy, classic and robust terms. Hereby, by using an appropriate LYAPUNOV function the adaptation laws of the parameters of fuzzy and robust controller are derived and also, asymptotic stability of whole system is proved. As well, a novel adaptive method based on stability theorem is introduced to obtain the reasonable gains for classic controller. To show the efficiency of the suggested controlling approach, the simulation is applied on inverted pendulum, magnetic levitation and DC servo motor systems. The results reveal the proper and acceptable performance of the presented method. Finally, using the root mean square error (RMSE) criterion it is shown that transient response of system is improved compared to that of the classic controller in which the control signal is not included.
This study intends to investigate a dynamic modeling and design of controller for a planar serial chain, performing 2-DoF, in interaction with a cable-driven robot. The under study system can be used as a rehabilitation setup which is helpful for those with arm disability. The latter goal can be achieved by applying the positive tensions of the cable-driven robot which are designed based on feedback linearization approach. To this end, the system dynamics formulation is developed using Lagrange approach and then the so-called Wrench-Closure Workspace (WCW) analysis is performed. Moreover, in the feedback linearization approach, the PD and PID controllers are used as auxiliary controllers input and the stability of the system is guaranteed as a whole. From the simulation results it follows that, in the presence of bounded disturbance based on Roots Mean Square Error (RMSE) criteria, the PID controller has better performance and tracking error of the 2-DoF robot joints are improved 15.29% and 24.32%, respectively.
This study intends to investigate the dynamic model estimation and the design of an adaptive neural network based controller for a passive planar robot, performing 2-DoF motion pattern which is in interaction with an actuated cable-driven robot. In fact, the main goal of applying this structure is to use a number of light cables to drive serial robot links and track the desired reference model by the robot’s end-effector. The under study system can be used as a rehabilitation setup which is helpful for those with arm disability. In this way, upon applying sliding mode error dynamics, it is necessary to determine a vector that contains the matrices related to the robot dynamics. However, finding these matrices requires the use of computational approaches such as Newton-Euler or Lagrange. In addition, since the purpose of this paper is to express comprehensive methods, so with increasing the number of links and degrees of freedom of the robot, finding the dynamics of the robot becomes more difficult. Therefore, the Adaptive Neural Network (ANN) with specific inputs has been used for estimation unknown matrices of the system and the controller design has been performed based on it. So, the main idea in using an adaptive controller is the fact there is no pre-knowledge for the dynamic modeling of the system since the human arm could have different dynamic properties. Hence, the controller is formed by an ANN and robust term. In this way, the adaptation laws of the parameters are extracted by Lyapunov approach, and as a result, as aforementioned, the asymptotic stability of the whole of the system is guaranteed. Simulation results certify the efficiency of the proposed method. Finally, using the Roots Mean Square Error (RMSE) criteria, it has been revealed that, in the presence of bounded disturbance with different amplitude, adding the robust term to the controller leads to improve the tracking error about 34% and 62%, respectively.
This paper presents a regulation control system for Serial Chain Robots (SCRs) using Reinforcement Learning (RL) algorithms. The main challenge is the high dimensionality of the state and action spaces due to the multiple degrees-of-freedom in SCRs. Traditional optimal control methods rely on mathematical models and solving differential equations, while RL offers a computationally intensive alternative. In this study, a controller is proposed with key features: reduced reliance on robot dynamics knowledge, applicability to various robots (SCRs and Cable-Driven Serial Chain Robots (CDSCRs)), and the ability to limit torque. This method avoids complex computations of Coriolis vectors by modifying variables in the robot's dynamics to achieve a zero equilibrium point for regulation error. Actor-critic Neural Network (NN) based on RL methods is used for the adaptation laws of the regulator torque, while a MLP-NN is employed for the desired steady-state torque. Stability of the controller designs is ensured using suitable Lyapunov functions. Simulations on a 4-DoF CDSCR, representing a finger segment of the human body, validate the proposed control method's effectiveness.
Pedestrian and bicyclist safety in school zones is critical because of the vulnerability of children and adult pedestrians to vehicle crashes. This paper explores vehicle–pedestrian/bike crash severity within a 15-min walking time buffer around schools in Detroit, Michigan, and San Jose, California—cities with high pedestrian/bike fatality rates. Using 2016–2020 crash data, we employed random-parameter multinomial logit models with heterogeneity in means and variances to understand unobserved relationships between variables. Key random parameters identified include the number of buffer zones that each crash falls into, daylight conditions, and the number of units involved in a crash, all significantly affecting injury severity. Spatial stability was investigated to see if variable effects were consistent across locations. Results revealed spatial instability across Detroit and San Jose. Factors such as Covid lockdown, dark lighting, arterial road presence, bicycle crashes, and the number of units involved showed stable effects with varying magnitudes in both cities. Network buffer zones highlighted that crash proximity to multiple schools affects injury severity. Additionally, the study found that various behavioral, roadway, weather, lighting, and school-related factors influence injury severity in school zones. These findings provide valuable insights for policymakers and planners to develop countermeasures, making school areas safer for children, adult pedestrians, and bicyclists.
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