交通信息对驾驶员逐日路径选择行为影响研究

在无信息、发布历史信息和发布预测信息3种信息条件下,分别建立了驾驶员的路径理解行程时间期望值的更新模型.通过建立一个含有2条平行路径的简单路网,对3种交通信息条件下驾驶员逐日路径选择进行了仿真,结果表明:交通信息对驾驶员的作用与驾驶员路径选择的随机程度和对信息依赖程度有关;在3种交通信息条件下,路网均不能达到用户均衡平衡状态;交通信息的预测方法不同,预测信息对驾驶员路径选择的影响有差异.     

基于地质条件下的公路选线设计探讨

公路选线要受自然条件、技术标准、国家政策、社会环境、道路美学、民族风俗习惯等因素的制约,其中自然条件主要表现为地形、地质等.公路选线时应进行深入调查研究,查明特殊地区和不良地质地段的分布范围、类型、规模及发生、发展的原因和规律,灵活运用选线原则和掌握选线要点.     

A theoretical foundation for the relationship between generalized origin–destination matrix and flow matrix based on ordinal graph trajectories

This paper shows the relationship between flow, generalized origin–destination (OD), and alternative route flow from a set of ordinal graph trajectories. In contrast to traffic assignment methods that employ OD matrix to produce flow matrix, we use ordinal trajectory on a network graph as input and produce both the generalized OD matrix and the flow matrix, with the alternative and substitute route flow matrices as additional outputs. By using linear algebra‐like operations on matrix sets, the relationship between network utilization (in terms of flow, generalized OD, alternative route flow, and desire line) and network structure (in terms of distance matrix and adjacency matrix) are derived. Copyright © 2012 John Wiley & Sons, Ltd.     

Incorporating Driver Behaviors in Network Design Problems: Challenges and Opportunities

The goal of a network design problem (NDP) is to make optimal decisions to achieve a certain objective such as minimizing total travel time or maximizing tolls collected in the network. A critical component to NDP is how travelers make their route choices. Researchers in transportation have adopted human decision theories to describe more accurate route choice behaviors. In this paper, we review the NDP with various route choice models: the random utility model (RUM), random regret-minimization (RRM) model, bounded rationality (BR), cumulative prospect theory (CPT), the fuzzy logic model (FLM) and dynamic learning models. Moreover, we identify challenges in applying behavioral route choice models to NDP and opportunities for future research.     

Adapt-Traf: An adaptive multiagent road traffic management system based on hybrid ant-hierarchical fuzzy model

Usually, road networks are characterized by their great dynamics including different entities in interactions. This leads to more complex road traffic management. This paper proposes an adaptive multiagent system based on the ant colony behavior and the hierarchical fuzzy model. This system allows adjusting efficiently the road traffic according to the real-time changes in road networks by the integration of an adaptive vehicle route guidance system. The proposed system is implemented and simulated under a multiagent platform in order to discuss the improvement of the global road traffic quality in terms of time, fluidity and adaptivity.     

铁路旅客旅行换乘方案优选算法

为给铁路旅客选择合理的旅行方案提供参考,以旅行目标值与换乘目标值之和最小作为目标函数值,考虑旅客从起点站出发、最终到达终到站、在中转站进出平衡的约束条件,建立旅客旅行换乘方案选择问题的数学模型。分别给出旅行时间、换乘次数、票价、距离、到发时刻和综合指数6种目标权值的确定方法,提出最短路法和列车匹配法2种求解方法。最短路法是通过构造并简化旅客运输网络,求出网络上若干条次短路,再根据各条次短路上列车的接续,构造列车换乘方案网络图,根据该网络的目标权值确定最短路,得到最优换乘方案。列车匹配法是根据列车运行图信息直接搜索发、到站的接续列车集合,求其两列车对应的重合停车站,根据重合停车站集合的情况选择迭代步数,剔除显然不利方案,构成换乘方案,根据方案的权值进行比选,得到最优换乘方案。对2种算法均用C#编程实现,并用现有的全路客票数据进行检算。计算结果表明:2种算法均能得到最优换乘方案;列车匹配法所用计算时间比最短路法少160 ms。     

站场形数据结构设计研究

站场形数据结构是微机联锁软件系统的重要组成部分。文中介绍了站场形数据结构的各组成要素，举例说明其设计原理，最后指出了构造站场形数据结构所必须遵循的原则。     

猪屎豆、白灰毛豆在海南中线高速边坡生态恢复中的应用

为了研究猪屎豆和白灰毛豆两种灌木在公路边坡生态恢复中的实际应用效果,2012年在海南省中线高速(S21)选择骨架防护、三维网防护、普通喷播3种典型防护形式坡面进行了猪屎豆、白灰毛豆灌木种子不同配比方案恢复试验.试验结果表明,猪屎豆、白灰毛豆两种灌木的株高、冠幅、胸径、生长密度4个生长指标变化特征与前期种子配比情况有直接关系,其中冠幅和胸径指标正相关,但是与株高和生长密度指标负相关.该结果说明这两种灌木在海南省中线高速边坡(包括普通土质边坡和膨胀土劣质边坡)生态恢复中具有可行性,但是应调整灌木种子用量,确保灌木适宜的生长空间,维持灌木健壮的生长势,同时种子应合理搭配以维持后期边坡物种的多样性.这一研究结果可为海南省中线高速边坡生态恢复提供一定的参考.     

地质选线在杭新景高速公路衢州段的应用实践

杭新景高速公路衢州段处于浙江省西部山区,该地区地质地貌复杂,不良地质类型多样,潜在地质灾害严重。在该高速公路路线设计阶段,贯彻地质选线的理念,进行多方案比选,对不良地质采取避让、处治等措施,不仅可有效降低工程造价,而且可以避免地质灾害的发生,为工程的建设和运营安全打下良好基础。     

智能交通系统中动态路径诱导算法分析

对智能交通中动态路径诱导算法进行较为系统的综述。首先,从微观和宏观角度对动态诱导系统中需要用到的交通参数模型进行分析和比较,并列举几种交通参数模型;然后,对一些经典路网寻优算法进行分析,并引用一些专家学者的研究成果;最后,对动态路径诱导算法的未来发展方向进行探讨。