信号控制对动态路线选择的影响研究

以动态路线选择模型为基础的先进的出行者信息系统（ＡＴＩＳ）的实施必然对城市交通控制系统产生影响，同时交通控制系统的控制方案对路线诱导信息“运行时间”的估计也发生作用，影响用户对最优路线的造选择。对两系统的相互关系进行了分析，并建立了两系统相互关系模型最后给出了实际案例分析。     

一种基于垂直字符边界特征的车牌定位方法

提出了一种基于垂直字符边界点特征的车牌定位方法，该方法能有效定位车牌，定位精度高达９８％，定位时间不超过２ｓ，并能有效地克服光线和天气条件等影响。     

温度和麻醉对神经几种兴奋性指标的影响——兼论何者可以正确反映兴奋性

在蟾蜍坐骨神经-腓神经观察了温度和麻醉对几种兴奋性指标的影响。结果表明,标准电量(a)随兴奋性的变化而有规律地变化。兴奋性愈高。a值愈小。而Weiss式中的b、基强度(Rh)、时值(Chr)及强度—时间曲线均不能正确反映兴奋性。细胞膜时间常数(RC)与兴奋性的变化无规律性联系。结论,a是兴奋性的可靠定量指标。     

Comparing travel mode and trip chain choices between holidays and weekdays

Choices of travel mode and trip chain as well as their interplays have long drawn the interests of researchers. However, few studies have examined the differences in the travel behaviors between holidays and weekdays. This paper compares the choice of travel mode and trip chain between holidays and weekdays tours using travel survey data from Beijing, China. Nested Logit (NL) models with alternative nesting structures are estimated to analyze the decision process of travelers. Results show that there are at least three differences between commuting-based tours on weekdays and non-commuting tours on holidays. First, the decision structures in weekday and holiday tours are opposite. In weekday tours people prefer to decide on trip chain pattern prior to choosing travel mode, whereas in holiday tours travel mode is chosen first. Second, holiday tours show stronger dependency on cars than weekday tours. Third, travelers on holidays are more sensitive to changes in tour time than to the changes in tour cost, while commuters on weekdays are more sensitive to tour cost. Findings are helpful for improving travel activity modeling and designing differential transportation system management strategies for weekdays and holidays.     

Multi-step prediction of experienced travel times using agent-based modeling

This paper develops an agent-based modeling approach to predict multi-step ahead experienced travel times using real-time and historical spatiotemporal traffic data. At the microscopic level, each agent represents an expert in a decision-making system. Each expert predicts the travel time for each time interval according to experiences from a historical dataset. A set of agent interactions is developed to preserve agents that correspond to traffic patterns similar to the real-time measurements and replace invalid agents or agents associated with negligible weights with new agents. Consequently, the aggregation of each agent’s recommendation (predicted travel time with associated weight) provides a macroscopic level of output, namely the predicted travel time distribution. Probe vehicle data from a 95-mile freeway stretch along I-64 and I-264 are used to test different predictors. The results show that the agent-based modeling approach produces the least prediction error compared to other state-of-the-practice and state-of-the-art methods (instantaneous travel time, historical average and k-nearest neighbor), and maintains less than a 9% prediction error for trip departures up to 60 min into the future for a two-hour trip. Moreover, the confidence boundaries of the predicted travel times demonstrate that the proposed approach also provides high accuracy in predicting travel time confidence intervals. Finally, the proposed approach does not require offline training thus making it easily transferable to other locations and the fast algorithm computation allows the proposed approach to be implemented in real-time applications in Traffic Management Centers.     

Estimating travel time functions for urban roads: options and issues

Travel time functions specify the relationship between the travel time on a road and the volume of traffic on the road. Until recently, the parameters of travel time functions were rarely estimated in practice; however, a compelling case can be made for the empirical examination of these functions. This paper reviews, and qualitatively evaluates, a range of options for developing a set of travel time functions. A hierarchy of travel time functions is defined based on four levels of network detail: area, corridor, route and link. This hierarchy is illustrated by considering the development of travel time functions for Adelaide. Alternative sources of data for estimating travel time functions are identified.

In general, the costs and benefits increase as the travel time functions are estimated at finer levels of network detail. The costs of developing travel time functions include data acquisition costs and analysis costs. The benefits include the potential for reducing prediction errors, the degree of application flexibility and the policy sensitivity of the travel time functions.     

时间多项式进化算法在旅行商问题中的研究

提出了一种模拟生物遗传的进化算法，并将该算法应用于旅行商问题得到了较好的结果，根据达尔文进化论的优化过程，结合自然选择原则提出了启发式算法，该算法的时间复杂性与快速排序策略相当。在文中利用该算法求解中国旅行商问题得到目前的最佳结果。     

京唐城际铁路速度目标值选择

速度目标值是城际铁路最主要技术标准,不同的速度目标值决定了基础设施、设备及各项配套设施的选择。从京唐城际铁路功能定位出发,通过对客流特点、功能定位、时间目标值需求、经济技术比选等多方面因素的分析,综合得出京唐城际铁路速度目标值推荐采用350 km/h的方案。     

Daily rhythms of suburban commuters’ movements in the Tallinn metropolitan area: Case study with mobile positioning data

The objective of this study is to analyse the diurnal rhythms of city life and its spatial differences in Tallinn, using mobile telephone positioning data. The positioning experiment was carried out in April 2006 over an 8-day period and 15-min intervals, with a random sample of 277 respondents living in new residential areas outside the city of Tallinn.The investigation of the space–time movements and daily distances of respondents showed that the majority of respondents had a similar temporal rhythm related to work, school, services and leisure in the city. Because of the different timing of those activities, the mobile positioning data made it possible to map functional differences in the city. The advantages and disadvantages of mobile positioning data in mapping urban life are discussed in the final section of the study.