Transport Demand Management in Turkey: A Genetic Algorithm Approach

Abstract

This article proposes new models for estimating transport demand using a genetic algorithm (GA) approach. Based on population, gross national product and number of vehicles, four forms of the genetic algorithm transport planning (GATP) model are developed – one exponential and the others taking quadratic forms – and applied to Turkey. The best fit models in terms of minimum total average relative errors in the test period are selected for future estimation. Demand management strategies are proposed based on three scenarios: restricting private car use, restricting truck use and the simultaneous management of private car use and goods movement. Results show that the GATP model may be used to estimate transport demand in terms of passenger-kilometers traveled (pass-km), vehicle-kilometers traveled (veh-km) and ton-kilometers completed (ton-km). Results also show that the third scenario – simultaneous restrictions on private car use and goods movement – could reduce total veh-km by about 35% by 2025 in this study of Turkish rural roads.     

A genetic algorithm-based method for improving quality of travel time prediction intervals

The transportation literature is rich in the application of neural networks for travel time prediction. The uncertainty prevailing in operation of transportation systems, however, highly degrades prediction performance of neural networks. Prediction intervals for neural network outcomes can properly represent the uncertainty associated with the predictions. This paper studies an application of the delta technique for the construction of prediction intervals for bus and freeway travel times. The quality of these intervals strongly depends on the neural network structure and a training hyperparameter. A genetic algorithm–based method is developed that automates the neural network model selection and adjustment of the hyperparameter. Model selection and parameter adjustment is carried out through minimization of a prediction interval-based cost function, which depends on the width and coverage probability of constructed prediction intervals. Experiments conducted using the bus and freeway travel time datasets demonstrate the suitability of the proposed method for improving the quality of constructed prediction intervals in terms of their length and coverage probability.     

基于皮尔-遗传神经网络的高路堤沉降预测研究

为确保高速公路的建设质量，许多高速公路进行了路基沉降观测工作，以便对路堤填筑完成以后的沉降发展做出准确的预测，高路堤沉降预测模型较多，但均较难达到理想的精度。文章则依据高路堤填土施工期路基沉降实测资料，考虑到沉降单调增长的特殊性，根据位移分解原理，采用皮尔曲线提取沉降趋势，用基于免疫进化的新型遗传神经网络模型外推偏差，从而提出了一种高路堤沉降预测的新型智能方法。实际工程证明了所提智能预测方法的有效性和可行性。     

Designing H∞/GH 2 static-output feedback controller for vehicle suspensions using linear matrix inequalities and genetic algorithms

This paper presents an approach to design the H_∞/GH ₂ static-output feedback controller for vehicle suspensions by using linear matrix inequalities (LMIs) and genetic algorithms (GAs). Three main performance requirements for an advanced vehicle suspension are considered in this paper. Among these requirements, the ride-comfort performance is optimized by minimizing the H_∞ norm of the transfer function from the road disturbance to the sprung mass acceleration, while the road-holding performance and the suspension deflection limitation are guaranteed by constraining the generalized H₂ (GH ₂) norms of the transfer functions from the road disturbance to the dynamic tyre load and the suspension deflection to be less than their hard limits, respectively. At the same time, the controller saturation problem is considered by constraining its peak response output to be less than a given limit using the GH ₂ norm as well. A four-degree-of-freedom half-car model with active suspension system is applied in this paper. Several kinds of H_∞/GH ₂ static-output feedback controllers, which use the available sprung mass velocities or the suspension deflections as feedback signals, are obtained by using the GAs to search for the possible control gain matrices and then resolving the LMIs together with the minimization optimization problem. These designed H_∞/GH ₂ static-output feedback controllers are validated by numerical simulations on both the bump and the random road responses which show that the designed H_∞/GH ₂ static-output feedback controllers can achieve similar or even better active suspension performances compared with the state-feedback control case in spite of their simplicities.     

Isight平台理论与方法及其在产品结构设计与优化中的应用研究

Isight平台是集成、自动化和优化设计过程的通用软件框架,广泛应用于产品结构设计和优化工作中.文章不仅对其理论和方法进行了详细的描述,而且给出了一个工业实例,以说明该平台的应用.结果表明,Isight平台能够集成多种类型的软件,并基于其数据集成和数据处理等能力实现自动分析,与传统的产品设计和优化方法相比具有更大的优势...     

遗传算法在有竞争的物流配送中心选址中的应用

在分析有竞争的物流配送中心选址特点的基础上,引入了一种有竞争的物流配送中心选址模型。由于该模型是一个组合优化问题,具有NP难的性质,用常规算法对其进行求解时,经常会陷入局部优化解。针对这一情况,提出一种求解该模型的混合遗传算法,该算法主要利用Drezner算法的局部搜索性能来提高遗传算法的寻优能力。研究结果表明该算法能很好地处理有竞争的物流配送中心选址问题。     

基于遗传算法的膜片弹簧优化研究

膜片弹簧离合器是现今手动挡汽车的主要离合器形式,针对膜片弹簧在轴向受压过程中,弹簧大端压紧力与变形量的关系,新膜片弹簧工作点的压紧力与摩擦片最大磨损点所对应的膜片弹簧压紧力之间变化平缓以及驾驶员平均操纵力最小这两个矛盾问题的优化.利用权重系数法对其权重进行分配,并采用遗传算法对压紧力变化的平均值最小与驾驶员操纵力最小进...     

基于汽车ABS/ASR/ACC集成化系统的ABS参考车速确定方法的研究

分析了前人提出的汽车制动防抱系统控制中多种参考车速算法，比较了它们的优缺点。提出了参考车速的递推算法，给出了计算框图，进行了误差分析。在轿车上进行了大量的试验验证，结果表明该算法大大提高了参考车速确定的准确度及其自适应性，完善了ABS控制效果。