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随着教师住宅外迁、教职工拥有汽车的数量上升和高校与社会的交往越来越密切，校园内的停车问题日显突出。本文以北京航空航天大学校园为例，通过对进出校园的车流量、停车能力与车位分布、停车行为等进行调查、统计和分析，揭示校园内停车行为的规律，包括各时段进出校园的车辆总数、平均停车时间、车位周转率等，发现停车系统中存在的问题、并提出若干建议。该研究对其他高校有借鉴意义。     

Do changes in neighborhood characteristics lead to changes in travel behavior? A structural equations modeling approach

Suburban sprawl has been widely criticized for its contribution to auto dependence. Numerous studies have found that residents in suburban neighborhoods drive more and walk less than their counterparts in traditional environments. However, most studies confirm only an association between the built environment and travel behavior, and have yet to establish the predominant underlying causal link: whether neighborhood design independently influences travel behavior or whether preferences for travel options affect residential choice. That is, residential self-selection may be at work. A few studies have recently addressed the influence of self-selection. However, our understanding of the causality issue is still immature. To address this issue, this study took into account individuals’ self-selection by employing a quasi-longitudinal design and by controlling for residential preferences and travel attitudes. In particular, using data collected from 547 movers currently living in four traditional neighborhoods and four suburban neighborhoods in Northern California, we developed a structural equations model to investigate the relationships among changes in the built environment, changes in auto ownership, and changes in travel behavior. The results provide some encouragement that land-use policies designed to put residents closer to destinations and provide them with alternative transportation options will actually lead to less driving and more walking.

基于神经网络的停车需求预测模型及应用

结合停车需求特点分析了停车需求影响因素,提出了基于主成分分析的BP神经网络停车需求预测模型,该模型主要是通过对城市中心区停车需求的经济、土地、交通的特征分析,利用主成分分析法,明确了影响停车需求的主成分,简化了神经网络的输入样本,消除了网络输入之间的相关性,提高了网络的性能,实现了公共停车需求的准确预测。     

北京市商业建筑物停车需求调查与分析

建筑物停车需求研究是建筑物停车配建研究的重要内容,其受到城市机动车保有量、路网容量、交通体系构成及运行状况等宏观因素的影响,也受到建筑物停车供给水平、停车泊位共享、公交服务水平、消费行为、个人收入水平等微观因素的影响。通过在北京市西单地区对这些微观影响因素进行问卷调查,分析商业建筑停车需求的影响因素,可为商业建筑停车需求研究提供依据。     

上海CBD共享停车方法研究

上海市CBD停车设施规划须遵从＂低供给＂的发展战略,然而上海市停车场（库）配建标准却有可能导致停车供给的多余。为解决这一问题,对实行CBD共享停车的方法进行研究,提出实行共享停车的条件及步骤,并进行实例分析,以达到降低停车用地使用量、减少工程建设总成本、提高整个停车系统使用效率的目的。     

城市快速路指路引导标志的设置研究

关于快速路指路引导标志的设置,当前国内各城市多是参考城市干路进行单一、近距离的路名指引,这就使道路指示不清的现象屡有发生,严重影响了城市快速路骨干交通功能的发挥。快速路作为城市最高等级道路,其指路标志的等级也应高于一般城市道路,为此提出出口＂三级指引＂、入口＂两级指引＂的指路标志设置方法,可供同行参考借鉴。     

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确定性路段拥挤收费对收费路段的交通拥挤缓解有很好的效果,停车收费对抑制区域路网出行需求有重要影响,将两者组合起来系统研究具有重要意义. 本文通过将路段拥挤收费与停车收费进行组合,分析组合收费策略下出行成本和出行需求变化的基础上,建立了双层规划模型. 以收费社会效益最大化为目标,以拥挤收费和停车费可行区间为约束条件作为上层优化模型,下层模型是考虑广义交通出行费用(含行程费用和停车费用)的弹性需求条件下用户平衡模型,进行路段拥挤收费与停车收费组合优化. 设计了模式搜索算法进行求解,得到不同初始步长和迭代精度下模型的最优解. 数值计算结果表明,联合收费使得路网流量分布更加均衡,缓解了收费路段的交通拥挤,同时出行需求得到了一定抑制,证明该模型与算法具有有效性.     

不同市场机制下多种交通方式定价模型研究

停车费和公交票价是平衡交通方式分担的有效交通需求管理措施. 在分析私家车和公交车两种交通方式并存的路网中出行者的旅行选择均衡行为的基础上,建立了路段及停车设施能力约束下等价的网络均衡模型. 然后,给出了不同市场经营机制下多种交通方式的定价模型,其中市场机制包括垄断、寡头竞争和社会最优. 模型考虑了停车费和公交票价定价策略对出行者交通方式和路径选择行为的影响以及出行需求弹性. 最后,给出一算例说明文中所提出模型的应用,并解释了不同市场机制下解的经济含义.     

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现实生活中,驾驶员往往依据自身偏好来选择路径,对不同的路径属性有不同的要求,且对其属性值存在一个可接受范围,而不是一个精确值. 本文对存在驾驶员偏好的最优路径选择问题进行了研究,提出了一种能够综合反映驾驶员偏好的最优路径选择方法. 首先基于可能度和区间数相离度的多属性决策方法,建立驾驶员偏好与路径属性总偏差最小的优化模型,并解出路径属性权重;其次算出各路径的综合属性值,建立路径间的可能度矩阵及其排序向量,据此选出最优路径;最后以算例进行验证,结果表明本文给出的路径选择方法具有很好的可行性和适用性.     

Route Choice Behavior Model with Guidance Information

Using modeling and simulation methods, the driver's route choice behavior under guidance information is explored based on the combination of the decision field theory (DFT) and Bayesian theory. First, based on the Bayesian theory, a road condition dynamic updating model is presented in light of the guidance information and the driver's previous travel experiences. Then, the route choice behavior model under guidance information is formed by the fusion of the process-oriented vehicle dynamic route choice model and the road condition dynamic updating model. The developed model describes a driver's propensity to comply with received guidance information in terms of the interaction between perceived unreliability of the information, his previous travel experiences, preference for different road alternatives, decision-making speed/quality, and route selection criteria. The simulation results show that the combination of the DFT and Bayesian theory can effectively explain the driver's travel dynamics behavior.