公交车辆智能调度研究

根据智能交通系统ITS(Intelligent Transport Systems)研究发展状况以及我国ITS研 究开发的主要内容和公交公司实际运输的基本需求采用遗传算法GA(Genetic Algorithm)进 行了公交车辆智能调度方法的研究.该方法充分利用遗传算法的智能化特征,有效地改善公交 车辆的静态调度,提高公交车辆的运营效率.为避免遗传算法的早熟问题,运用混合遗传算法 HGA(Hybrid Genetic Algorithm),提高遗传算法的收敛性能和优化质量,以确保遗传算法的优化性能.在静态调度方法的基础上,针对公交车辆运营调度管理特点,借助通信技术、计算机技术以及自动控制技术,分析研究了公文车辆的动态调度的实现方法,从而进一步提高公交车辆运营调度管理效率,为公交车辆智能化调度系统的实施做好必要的技术服务.     

C-V2X应用场景的算法建模及评价模型综述

V2X利用无线信息交互技术,实现车与其他实体之间的信息交互,将人、车、路、云交通参与要素有机地联系在一起,构建一个智慧的交通体系。算法的评价基于算法建模,通过建模梳理算法的逻辑思路,输出算法相关的标定参数,进而使算法性能达到更优。基于C-V2X应用场景的算法评价模型,旨在研究如何评判不同应用场景的实现优劣,用于设计出标准的指标参数衡量算法的性能,重点从算法计算效率和算法有效性2个维度考虑,构建起评估算法功能及性能的立体模型,多角度权衡并分析,从而对应用场景的实现给予评估反馈。     

Transit network design by genetic algorithm with elitism

The transit network design problem is concerned with the finding of a set of routes with corresponding schedules for a public transport system. This problem belongs to the class of NP-Hard problem because of the vast search space and multiple constraints whose optimal solution is really difficult to find out. The paper develops a Population based model for the transit network design problem. While designing the transit network, we give preference to maximize the number of satisfied passengers, to minimize the total number of transfers, and to minimize the total travel time of all served passengers. Our approach to the transit network design problem is based on the Genetic Algorithm (GA) optimization. The Genetic Algorithm is similar to evolution strategy which iterates through fitness assessment, selection and breeding, and population reassembly. In this paper, we will show two different experimental results performed on known benchmark problems. We clearly show that results obtained by Genetic Algorithm with increasing population is better than so far best technique which is really difficult for future researchers to beat.     

Evolutionary crew scheduling with adaptive chromosomes

This paper presents an adaptive evolutionary approach incorporating a hybrid genetic algorithm (GA) for public transport crew scheduling problems, which are well-known to be NP-hard. To ensure the search efficiency, a suitable chromosome representation has to be determined first. Unlike a canonical GA for crew scheduling where the chromosome length is fixed, the chromosome length in the proposed approach may vary adaptively during the iterative process, and its initial value is elaborately designated as the lower bound of the number of shifts to be used in an unachievable optimal solution. Next, the hybrid GA with such a short chromosome length is employed to find a feasible schedule. During the GA process, the adaptation on chromosome lengths is achieved by genetic operations of crossover and mutation with removal and replenishment strategies aided by a simple greedy algorithm. If a feasible schedule cannot be found when the GA’s termination condition is met, the GA will restart with one more gene added. The above process is repeated until a feasible solution is found. Computational experiments based on 11 real-world crew scheduling problems in China show that, compared to a fuzzy GA known to be well performed for crew scheduling, better solutions are found for all the testing problems. Moreover, the algorithm works fast, has achieved results close to the lower bounds obtained by a standard linear programming solver in terms of the number of shifts, and has much potential for future developments.     

The Japanese experience with mini‐cars

Despite the early appeal of the light vehicle, increases in the average annual income have allowed consumers to consider a broader range of vehicles so that the negative aspects of mini‐vehicles such as higher noise and vibration levels, the lack of horsepower and instability in certain driving conditions have made light vehicles less tolerable. The “oil shock” shattered economic projections, and people began to acknowledge that living in a world with limited resources was a harsh reality. Concurrently, congestion increased dramatically in urban areas as a result of the popularity of automobiles, and producers made a number of design changes to improve the safety and comfort limitations of light vehicles. Thus, in a world where fuel economy and ease of use gained a greater meaning, light vehicles slowly regained their original appeal.

Light vehicles may play a greater role in the future. Studies indicate that light vehicles tend to be driven by females and elderly people and current trends indicate that the number of female drivers is increasing and that the average age of the Japanese population is getting higher. Furthermore, migration patterns indicate that a greater number of people are moving to smaller cities and their outlying areas as a result of national decentralization policies. The migration pattern may popularize light vehicles because vehicle ownership rates are higher in these areas than in larger cities. Another development which may increase the popularity of light vehicles is that more families are owning more than one car and light vehicles are popular as second vehicles. Moreover, the prospects of low economic growth have tempered the importance of comfortable amenities, and the virtues of maneuverability and fuel economy have become more important.

     

基于GA-LSSVR模型的路网短时交通流预测研究

目前,很多短时交通流预测方法仅利用某一路段历史数据的时间相关性或者道路上下游路段的时空相关性进行交通流预测,未充分考虑路网所有路段之间的时空相关性.提出了一种基于稀疏混合遗传算法优化的最小二乘支持向量回归(LSSVR)模型,并应用于路网短时交通流预测.该预测模型不仅可以自动优化LSSVR模型参数,而且可以从高维路网交通流数据中选择有助于交通流预测的变量子集.实验结果表明,与LSSVR模型相比,所提方法具有更好的预测能力;而且,少量时空变量被选择出来构建预测模型,极大减少了信息冗余,改进了模型可解释性.     

城市轨道交通线路规划优化模型与算法

文章根据我国城市轨道交通规划建设的特点,建立了以轨道交通投资和系统出行总时间最小为目标的轨道交通线路规划的双层规划模型,提出应用成熟的遗传算法进行优化模型求解。方法简单实用,对于我国的轨道交通线路规划具有实用价值。     

收费道路均衡配流的双层规划模型分析

考虑弹性用户需求下,当两点之间的收费额增加时,阻抗会随之增加,交通需求量会相应减少,引入新的交通阻抗函数。根据弹性交通需求下交通均衡配流,考虑一个双层规划模型来考虑道路经营者收费道路费率制定,同时在这个基础上给出了基于遗传一模拟退火的混合优化算法（GASA）求解算法,最后通过一个数值算例加以简单的论证,得到比较合理的费率。     

Operating Rule Classification System of Water Supply Reservoir Based on Learning Classifier System

An operating rule classification system based on lesrning classifier system (LCS), which learns through credit assignment (bucket brigade algorithm, BBA) and rule discovery (genetic algorithm, GA), is established to extract water-supply reservoir operating rules. The proposed system acquires an online identification rate of 95% for training samples and an offline rate of 85% for testing samples in a case study. The performances of the rule classification system are discussed from the rationality of the obtained rules, the impact of training samples on rule extraction, and a comparison between the rule classification system and the artificial neural network (ANN). The results indicate that the LCS is feasible and effective for the system to obtain the reservoir supply operating rules.     

基于遗传算法的信号联锁故障处理中风险问题的研究

以遗传算法优化技术为基础,对铁路信号联锁故障处理中的风险问题因素进行了分析,期望减少电务部门故障处理流程中的风险,从而避免人为造成重大、大事故.