Stability enhancement of a high-speed train bogie using active mass inertial actuators

In this study, a method regarding frame lateral vibration control based on the state feedback of an additional oscillator is proposed, so as to improve the bogie hunting stability. The multi-objective optimisation method (MOOP), with two objective functions of the stability index and control effort, is solved by the NSGA-II algorithm to obtain the feedback gains. The frame lateral vibration control can effectively improve the bogie hunting stability according to the linear and non-linear analysis of a high-speed train bogie, in which a fault of the yaw damper and time delay in the control system are considered. The effect of the oscillator suspension parameters and time delay on the system stability and robustness are analysed. The results show that the damped vibration frequency of the oscillator should be equal to the bogie hunting frequency, but a harder oscillator suspension can be used to improve the hunting critical speed margin of the bogie control system. However, just as how the feeding the frame states back directly, a hard oscillator suspension will lead to instability in the control system at a certain time delay. Therefore, the improvement of bogie hunting stability and reduction of control system stability must be considered when optimising the oscillator parameters. For the 350?km/h train bogie covered in this study, the optimal mass, natural frequency and damping ratio of the additional oscillator are acquired.     

Vehicle identification sensors location problem for large networks

Finding the optimal location for sensors is a key problem in flow estimation. There are several location models that have been developed recently for vehicle identification (ID) sensors. However, these location models cannot be applied to large networks because there are many constraints and integer variables. Based on a property of the location problem for vehicle ID sensors, given the initial vehicle ID sensors that are pre-installed and fixed on the network, this article presents a solution that greatly reduces the size of this location problem. An applied example demonstrates that when 8% of the arcs from a real network that are randomly selected have a vehicle ID sensor, the reductions are as large as 97% for the number of remaining constraints in the location model and 84% for the adjusted diameter of the feasible region of target flow. Using these two indices as target functions, two greedy algorithms are presented for solving the vehicle ID sensor location problem. These two algorithms were applied to an example in Mashhad city with 2,526 arcs, 7,157 origin-destination pairs and 121,627 paths. Using these algorithms, installing vehicle ID sensors on 8% of the network arcs results in satisfaction of 99.82% of the constraints in the location model and 97.6% reduction in the adjusted maximum possible error index. This means that deploying a low number of vehicle ID sensors on a real large network, with these greedy algorithms, yields a high level of observability.     

A practical model for transfer optimization in a transit network: Model formulations and solutions

This paper studies the transit network scheduling problem and aims to minimize the waiting time at transfer stations. First, the problem is formulated as a mixed integer programming model that gives the departure times of vehicles in lines so that passengers can transfer between lines at transfer stations with minimum waiting times. Then, the model is expanded to a second model by considering the extra stopping time of vehicles at transfer stations as a new variable set. By calculating the optimal values for these variables, transfers can be better performed. The sizes of the models, compared with the existing models, are small enough that the models can be solved for small- and medium-sized networks using regular MIP solvers, such as CPLEX. Moreover, a genetic algorithm approach is represented to more easily solve larger networks. A simple network is used to describe the models, and a medium-sized, real-life network is used to compare the proposed models with another existing model in the literature. The results demonstrate significant improvement. Finally, a large-scale, real-life network is used as a case study to evaluate the proposed models and the genetic algorithm approach.     

Estimation of origin–destination matrices using link counts and partial path data

Transportation - After several decades of work by several talented researchers, estimation of the origin–destination matrix using traffic data has remained very challenging. This paper...     

Hunting stability analysis of high-speed train bogie under the frame lateral vibration active control

In this paper, we study a multi-objective optimal design of three different frame vibration control configurations and compare their performances in improving the lateral stability of a high-speed train bogie. The existence of the time-delay in the control system and its impact on the bogie hunting stability are also investigated. The continuous time approximation method is used to approximate the time-delay system dynamics and then the root locus curves of the system before and after applying control are depicted. The analysis results show that the three control cases could improve the bogie hunting stability effectively. But the root locus of low- frequency hunting mode of bogie which determinates the system critical speed is different, thus affecting the system stability with the increasing of speed. Based on the stability analysis at different bogie dynamics parameters, the robustness of the control case (1) is the strongest. However, the case (2) is more suitable for the dynamic performance requirements of bogie. For the case (1), the time-delay over 10?ms may lead to instability of the control system which will affect the bogie hunting stability seriously. For the case (2) and (3), the increasing time-delay reduces the hunting stability gradually over the high-speed range. At a certain speed, such as 200?km/h, an appropriate time-delay is favourable to the bogie hunting stability. The mechanism is proposed according to the root locus analysis of time-delay system. At last, the nonlinear bifurcation characteristics of the bogie control system are studied by the numerical integration methods to verify the effects of these active control configurations and the delay on the bogie hunting stability.     

A new fuzzy approach to estimate the O–D matrix from link volumes

Abstract

Estimation of the origin–destination (O–D) trip demand matrix plays a key role in travel analysis and transportation planning and operations. Many researchers have developed different O–D matrix estimation methods using traffic counts, which allow simple data collection as opposed to the costly traditional direct estimation methods based on home and roadside interviews.

In this paper, we present a new fuzzy model to estimate the O–D matrix from traffic counts. Since link data only represent a snapshot situation, resulting in inconsistency of data and poor quality of the estimated O–Ds, the proposed method considers the link data as a fuzzy number that varies within a certain bandwidth. Shafahi and Ramezani's fuzzy assignment method is improved upon and used to assign the estimated O–D matrix, which causes the assigned volumes to be fuzzy numbers similar to what is proposed for observed link counts. The shortest path algorithm of the proposed method is similar to the Floyd–Warshall algorithm, and we call it the Fuzzy Floyd–Warshall Algorithm. A new fuzzy comparing index is proposed by improving the fuzzy comparison method developed by Dubois and Prade to estimate and compare the distance between the assigned and observed link volumes. The O–D estimation model is formulated as a convex minimization problem based on the proposed fuzzy index to minimize the fuzzy distance between the observed and assigned link volumes. A gradient-based method is used to solve the problem. To ensure the original O–D matrix does not change more than necessary during the iterations, a fuzzy rule-based approach is proposed to control the matrix changes.     

Multiclass fuzzy user equilibrium with endogenous membership functions and risk‐taking behaviors

Over the last decades, several approaches have been proposed in the literature to incorporate users' perceptions of travel costs, their bounded rationality, and risk‐taking behaviors into network equilibrium modeling for traffic assignment problem. While theoretically advanced, these models often suffer from high complexity and computational cost and often involve parameters that are difficult to estimate. This study proposes an alternative approach where users' imprecise perceptions of travel times are endogenously constructed as fuzzy sets based on the probability distributions of random link travel times. Two decision rules are proposed accordingly to account for users' heterogeneous risk‐taking behaviors, that is, optimistic and pessimistic rules. The proposed approach, namely, the multiclass fuzzy user equilibrium, can be formulated as a link‐based variational inequality model. The model can be solved efficiently, and parameters involved can be either easily estimated or treated as factors for calibration against observed traffic flow data. Numerical examples show that the proposed model can be solved efficiently even for a large‐scale network of Mashhad, Iran, with 2538 links and 7157 origin–destination pairs. The example also illustrates the calibration capability of the proposed model, highlighting that the model is able to produce much more accurate flow estimates compared with the Wardropian user equilibrium model. Copyright © 2016 John Wiley & Sons, Ltd.