太原市河道覆盖及整治规划研究

阐述太原市山洪沟河及排退水渠的现状及发展 ,研讨河道及排水渠道的规划和复盖中应遵循的原则和存在的一些问题     

成都市道路建设改革与研究

~~成都市道路建设改革与研究@钟强文$成都市市政工程设计研究院!四川成都610015~~~~     

关于哈尔滨市城市防洪工程的建设情况

简要介绍哈尔滨市城市防洪工程建设情况。     

关于南京市街巷路整治的反思

本文阐述南京市街巷支路整治中存在的问题及其对策     

高强力复合土工布在软基路堤中的应用

本文结合工程实例介绍采用高强力复合土工布处理软土地基路堤的设计、施工与应用效果     

成都市城区水域开发工程设计概况

今文介绍了成都市府南河上蓄水、泄洪为一体的水工设施——充水式橡胶坝枢纽工程，并确定了坝址、坝高及坝前所形成的有效水面面积，还讨论了实现城区西侧勾通府南河的设想。     

深圳市“5.24”暴雨洪水灾后的反思

1998年5月24日，深圳市暴雨成灾，经济损失很大。本文总结和分析了暴雨洪水的特性及其成灾的原因，提出防洪减灾的措施和建议。     

提高圩区自然抗洪排涝能力的探讨

本文结合无锡市圩区的自然条件．对圩区地面迳流成因及水量平衡、典型年份水位持续天数及对应降水量、排涝标准与圩区地面标高的关系等问题作了分析探讨；对提高圩区规划地面标准作了准优化计算。     

Driving cycle prediction model based on bus route features

Bus fuel economy is deeply influenced by the driving cycles, which vary for different route conditions. Buses optimized for a standard driving cycle are not necessarily suitable for actual driving conditions, and, therefore, it is critical to predict the driving cycles based on the route conditions. To conveniently predict representative driving cycles of special bus routes, this paper proposed a prediction model based on bus route features, which supports bus optimization. The relations between 27 inter-station characteristics and bus fuel economy were analyzed. According to the analysis, five inter-station route characteristics were abstracted to represent the bus route features, and four inter-station driving characteristics were abstracted to represent the driving cycle features between bus stations. Inter-station driving characteristic equations were established based on the multiple linear regression, reflecting the linear relationships between the five inter-station route characteristics and the four inter-station driving characteristics. Using kinematic segment classification, a basic driving cycle database was established, including 4704 different transmission matrices. Based on the inter-station driving characteristic equations and the basic driving cycle database, the driving cycle prediction model was developed, generating drive cycles by the iterative Markov chain for the assigned bus lines. The model was finally validated by more than 2 years of acquired data. The experimental results show that the predicted driving cycle is consistent with the historical average velocity profile, and the prediction similarity is 78.69%. The proposed model can be an effective way for the driving cycle prediction of bus routes.