A comparative life-cycle energy and emissions analysis for intercity passenger transportation in the U.S. by aviation,intercity bus,and automobile

Intercity passenger trips constitute a significant source of energy consumption, greenhouse gas emissions, and criteria pollutant emissions. The most commonly used city-to-city modes in the United States include aircraft, intercity bus, and automobile. This study applies state-of-the-practice models to assess life-cycle fuel consumption and pollutant emissions for intercity trips via aircraft, intercity bus, and automobile. The analyses compare the fuel and emissions impacts of different travel mode scenarios for intercity trips ranging from 200 to 1600 km. Because these modes operate differently with respect to engine technology, fuel type, and vehicle capacity, the modeling techniques and modeling boundaries vary significantly across modes. For aviation systems, much of the energy and emissions are associated with auxiliary equipment activities, infrastructure power supply, and terminal activities, in addition to the vehicle operations between origin/destination. Furthermore, one should not ignore the embodied energy and initial emissions from the manufacturing of the vehicles, and the construction of airports, bus stations, highways and parking lots. Passenger loading factors and travel distances also significantly influence fuel and emissions results on a per-traveler basis. The results show intercity bus is generally the most fuel-efficient mode and produced the lowest per-passenger-trip emissions for the entire range of trip distances examined. Aviation is not a fuel-efficient mode for short trips (<500 km), primarily due to the large energy impacts associated with takeoff and landing, and to some extent from the emissions of ground support equipment associated with any trip distance. However, aviation is more energy efficient and produces less emissions per-passenger-trip than low-occupancy automobiles for trip distances longer than 700–800 km. This study will help inform policy makers and transportation system operators about how differently each intercity system perform across all activities, and provides a basis for future policies designed to encourage mode shifts by range of service. The estimation procedures used in this study can serve as a reference for future analyses of transportation scenarios.     

Long-term impact of the Shinkansen on rail and air demand: analysis with data from Northeast Japan

This paper analyses how the high-speed rail construction in Northeast Japan (Tohoku) has affected total demand and interregional travel patterns. We use annual interregional passenger data from 1989 to 2012 and apply regression analysis with the demand between Tokyo and the Tohoku prefectures as the dependent variable. We distinguish particularly between the ‘Full-’ and the ‘Mini-’ Shinkansen, where the latter are branch services running with reduced speed. We find that the ‘Full-Shinkansen’ quickly increases rail and total public transport trips and generates additional rail demand year on year. The ‘Mini-Shinkansen’ impacts are less pronounced. Furthermore, our analysis shows that the Shinkansen has shifted some demand from air to rail once it started operation and increased rail share gradually. We therefore suggest that predictions of demand impacts should carefully distinguish immediate from gradual impacts. We also discuss differences in regional demand in that not all prefectures have gained equally from Shinkansen construction.     

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.     

城市综合体对社区居民出行行为影响研究

城市综合体作为一种高度复合的新型开发模式，通过改变社区建成环境，对居民日常活动产生了潜移默化的影响.本文从交通规划者视角，对城市综合体产生的社区居民出行行为影响进行了研究.基于问卷调查数据，通过倾向得分匹配预处理，弱化个人信息对于出行特征分析的干扰，以有城市综合体的社区居民自陈述的出行变化作拟纵向分析，初步把握城市综合体对社区居民出行特征产生的影响趋势.而后对有无城市综合体两类社区居民的出行数据进行对比分析.结果显示，城市综合体开发模式可有效促进居民产生更多的短距离慢行出行替代机动化长距离出行以满足弹性需求.本研究可为城市综合体区别于传统单一用地开发模式的交通系统配置提供方向指导.     

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.     

Integrating capacity analysis with high-speed railway timetabling: A minimum cycle time calculation model with flexible overtaking constraints and intelligent enumeration

Compared with most optimization methods for capacity evaluation, integrating capacity analysis with timetabling can reveal the types of train line plans and operating rules that have a positive influence on improving capacity utilization as well as yielding more accurate analyses. For most capacity analyses and cyclic timetabling methods, the cycle time is a constant (e.g., one or two hours). In this paper, we propose a minimum cycle time calculation (MCTC) model based on the periodic event scheduling problem (PESP) for a given train line plan, which is promising for macroscopic train timetabling and capacity analysis. In accordance with train operating rules, a non-collision constraint and a series of flexible overtaking constraints (FOCs) are constructed based on variations of the original binary variables in the PESP. Because of the complexity of the PESP, an iterative approximation (IA) method for integration with the CPLEX solver is proposed. Finally, two hypothetical cases are considered to analyze railway capacity, and several influencing factors are studied, including train regularity, train speed, line plan specifications (train stops), overtaking and train heterogeneity. The MCTC model and IA method are used to test a real-world case involving the timetable of the Beijing–Shanghai high-speed railway in China.     

基于LSTM的舰船运动姿态短期预测

舰船的六自由度运动状态形成复杂的非线性过程,运动姿态会受到耦合作用、不定周期、噪声信号以及混沌特性等因素的干扰,因此很难得到精确的预测结果.为了提升舰船运动姿态的预测精度,利用舰船时间序列的特点,建立了基于长短期记忆单元(LSTM)模型,对其进行了舰船姿态预测仿真,将结果与时间序列分析法的结果进行对比.实例分析表明:基于LSTM模型的预测方法具有精确度高、易实现的特点.这为舰船运动短期预测提供了一个新的思路和方法.