基于行程时间影响的关键路段识别与查找

从路段实际功能出发，提出基于路段与路径行程时间序列的相关性识别关键路段的方法.借鉴蒙特卡洛思想，以真实数据构造10万条随机路径验证该方法的可行性，并识别出对上海市路网行程时间有关键影响的路段集合.以上述集合为参照，利用模糊聚类及迭代累计平方和算法提取路段行程时间序列特征并构造两个新变量，结合基础属性建立二项Logit模型，从而主动查找关键路段.比较该模型与基础模型、随机分类器查找效果表明：基于最大归一化行程时间曲线聚类，其结果对关键路段识别模型的性能有提升效用；行程时间对数差分序列的结构性变点在路网和路段级别均有明显时间聚集特性，虽然其个数与路段关键性无明显关系，但其与常见波动程度指标相关性小，可保留用于描述行程时间波动常发性和聚集性.     

含碎石化层的沥青加铺路面结构分析

利用ABAQUS有限元软件建立了含碎石化层的沥青加铺路面结构模型,研究土基、旧路基层与碎石化层模量以及碎石化层和加铺层厚度对含碎石化层沥青加铺层路面结构的力学响应的影响,确定碎石化模量的控制范围.结果表明,荷载作用中心点及附近一定区域,沥青加铺层层底受拉;沥青加铺层层底拉应力对土基模量、旧路基层模量、碎石化层厚度不敏感,但当碎石化层模量较小(接近300MPa)或加铺层厚(大于20cm)时层底拉应力均较大,在重载作用下更大.因此碎石化道路必须验算沥青层层底拉应力指标.为使沥青层层底拉应力峰值不至于过大甚至超过容许拉应力,使得受拉区域控制在一定范围以内,同时为降低加铺层竖向剪应力及土基顶面压应变,并达到防治反射裂缝的效果,碎石化层的模量宜控制在500～1000MPa.     

Assessment of factors associated with travel time reliability and prediction: an empirical analysis using probabilistic reasoning approach

Significant efforts have been made in modeling a travel time distribution and establishing measures of travel time reliability (TTR). However, the literature on evaluating the factors affecting TTR is not well established. Accordingly, this paper presents an empirical analysis to determine potential factors that are associated with TTR. This study mainly applies the Bayesian Networks model to assess the probabilistic association between road geometry, traffic data, and TTR. The results from this model reveal that land use characteristics, intersection factors, and posted speed limits are directly associated with TTR. Evaluating the strength of the association between TTR and the directly related variables, the log odds ratio analysis indicates that the land use factor has the highest impact (0.83) followed by the intersection factor (0.57). The findings from this study can provide valuable resources to planners and traffic operators in their decision-making to improve TTR with quantitative evidence.     

Policy packages for modal shift and CO2 reduction in Lille,France

This paper proposes different policy scenarios to cut CO₂ emissions caused by the urban mobility of passengers. More precisely, we compare the effects of the ‘direct tool’ of carbon tax, to a combination of ‘indirect tools’ – not originally aimed at reducing CO₂ (i.e. congestion charging, parking charges and a reduction in public transport travel time) in terms of CO₂ impacts through a change in the modal split. In our model, modal choices depend on individual characteristics, trip features (including the effects of policy tools), and land use at origin and destination zones. Personal “CO₂ emissions budgets” resulting from the trips observed in the metropolitan area of Lille (France) in 2006 are calculated and compared to the situation related to the different policy scenarios. We find that an increase of 50% in parking charges combined with a cordon toll of €1.20 and a 10% travel time decrease in public transport services (made after recycling toll-revenues) is the winning scenario. The combined effects of all the policy scenarios are superior to their separate effects.     

考虑空车调配协调的货物列车开行方案优化研究

为实现空车调配与货物列车开行方案协调优化，结合基本运行图架构与车流径路，构建货运时空服务拓展网络。考虑配空与装卸取送、集编发等环节的时间接续要求，节点与区段不对流空车要求，以重车流全程运送与空车配送等广义总费用最少为目标，建立整数规划弧路模型。针对既有算法设计局限性，结合重车或空车配空的时间接续要求，提出将不同的 k 短路重车流方案与空车配空方案相关联的改进可行解构造方法，设计混合差分进化求解算法。实例研究表明，考虑空车调配进行重车、空车流组织协调优化，能够减少空车走行费用，及时满足装车需求，有效保证作业车流配合中转车流集结编组及时挂线，提高方案可实施性。     

逐日路径演化中出行者信息偏好的实验分析

在逐日出行过程中,出行者对各类信息的偏好是一个动态变化的过程,不同出行者对同类信息的偏好也不同.本研究搭建不同信息条件下逐日路径选择实验场景,用信息偏好系数表征不同类别信息对出行者感知时间影响的相对权重,研究出行者信息偏好的演化规律与分布特征.实验结果表明,出行个体信息偏好的演化存在3种类型,出行群体对感知时间的信息偏好系数先震荡增加后逐步稳定,出行个体对感知时间的信息偏好系数在出行群体中成正态分布.与只提供完全历史信息相比,同时提供基于指数平滑法的系统预测信息会降低出行者对感知时间的偏好.     

基于特征子序列修正的公交刷卡滞后时间推算

针对一票制公共交通IC卡刷卡系统存在的滞后时间问题,提出基于特征子序列修正的时差匹配推算方法。依托GPS车辆到站信息序列和刷卡聚类序列,分析首末站和中途站站间运行时间分布的差异化特征,分别从两序列中提取大时间差和连续小时间差等特征子序列;利用两种子序列间一对一或一对多的对应关系分别进行初步搜索和二次修正,兼顾匹配过程的效率和精度。将推算流程应用于山西省大同市公共汽车系统,结果显示,抽样车辆的刷卡匹配率均达到99%以上,并且针对不同类型的公共汽车线路具有较强的适应性。     

Time use in travel surveys and time use surveys – Two sides of the same coin?

An in-depth understanding of travel behaviour determinants, including the relationship to non-travel activities, is the foundation for modelling and policy making. National Travel Surveys (NTS) and time use surveys (TUS) are two major data sources for travel behaviour and activity participation. The aim of this paper is to systematically compare both survey types regarding travel activities and non-travel activities. The analyses are based on the German National Travel Survey and the German National Time Use Survey from 2002.The number of trips and daily travel time for mobile respondents were computed as the main travel estimates. The number of trips per person is higher in the German TUS when changes in location without a trip are included. Location changes without a trip are consecutive non-trip activities with different locations but without a trip in-between. The daily travel time is consistently higher in the German TUS. The main reason for this difference is the 10-min interval used. Differences in travel estimates between the German TUS and NTS result from several interaction effects. Activity time in NTS is comparable with TUS for subsistence activities.Our analyses confirm that both survey types have advantages and disadvantages. TUS provide reliable travel estimates. The number of trips even seems preferable to NTS if missed trips are properly identified and considered. Daily travel times are somewhat exaggerated due to the 10-min interval. The fixed time interval is the most important limitation of TUS data. The result is that trip times in TUS do not represent actual trip times very well and should be treated with caution.We can use NTS activity data for subsistence activities between the first trip and the last trip. This can potentially benefit activity-based approaches since most activities before the first trip and after the last trip are typical home-based activities which are rarely substituted by out-of-home activities.     

基于时延特性建模的多断面短时交通流预测

针对现有交通流预测方法未充分考虑多断面车流演变规律，提出基于时延特性建模的时空相关性计算方法. 该方法采用对不同断面、不同时刻交通流的分布相似性度量，对输入的车辆到达数据序列进行切割构建时空相似度矩阵，得到相邻断面之间的时延参数. 基于时延特性建模，将多断面之间的流量信息进行融合，使用长短时记忆(LSTM)网络进行流量预测. 通过对实际路段数据的预测和结果分析，验证所提方法的有效性和实用性.     

智能网联车环境下高速匝道汇入车流轨迹优化模型

为了解决传统匝道控制车流汇入时车辆需要减速至停止，从而造成延误时间过长的问题，提出了一种智能网联车环境下的高速匝道汇入车辆轨迹优化的两阶段优化模型，其中，第1 阶段优化车辆进入匝道口的时序；第2 阶段基于第1 阶段的最优时序，优化车辆轨迹. 根据所构建的模型设计了一种启发式算法优化车辆通过匝道冲突区域的时序，然后结合 GPOPS工具优化车辆的轨迹.为了验证所提出方法的有效性，将所提出的方法应用到20 min 随机到达的车流，进行仿真实验.实验结果表明，与先进先出的方法相比，本文所提出的方法能够使总延误减少59.7%，总油耗减少10.5%，说明该方法能够实现车辆以较高的速度通过匝道冲突区域，有效地减少了车辆汇入延误，同时也节约了油耗.