Hitting the sweet spot: Variability in commute lengths and vehicle emissions across a diverse state

In this paper we consider travel across Virginia and identify sustainability “sweet spots” where commute lengths and vehicle emissions per mile combine to maximize green travel in terms of total CO₂ emissions associated with commuting. The analysis is conducted across local voter precincts (N = 2373 in the state) because they are a useful proxy for neighborhoods and well-sized for implementing policy designed to encourage sustainable travel behavior. Virginia is especially appropriate for an examination of variability in sustainable travel behavior and technologies because the state’s transportation, demographic, and political patterns are particularly diverse and have been changing rapidly. We identify four Virginia precinct-based sustainability clusters: Sweet Spots, Emerging Sweet Spots, Neutral and Non-sustaining. A model of demographic differences among the clusters shows that sustainability outcomes, understood in terms of both local commute behavior and vehicle emissions, are significantly associated with the diverse demography and politics of the state. We also look at changes in transportation sustainability and socio-demographic trends within the clusters over the past half-decade, showing that differences in sustainability and demographic metrics are actually accelerating within the state over time. We conclude with a discussion of the implications of the differences among the clusters for developing and implementing effective transportation sustainability policies across the state.     

Prediction of travel time variability for cost-benefit analysis

Unreliable travel times cause substantial costs to travelers. Nevertheless, they are often not taken into account in cost-benefit analyses (CBA), or only in very rough ways. This paper aims at providing simple rules to predict variability, based on travel time data from Dutch highways. Two different concepts of travel time variability are used, which differ in their assumptions on information availability to drivers. The first measure is based on the assumption that, for a given road link and given time of day, the expected travel time is constant across all working days (rough information: RI). In the second case, expected travel times are assumed to reflect day-specific factors such as weather conditions or weekdays (fine information: FI). For both definitions of variability, we find that the mean travel time is a good predictor. On average, longer delays are associated with higher variability. However, the derivative of variability with respect to delays is decreasing in delays. It can be shown that this result relates to differences in the relative shares of observed traffic ‘regimes’ (free-flow, congested, hyper-congested) in the mean delay. For most CBAs, no information on the relative shares of the traffic regimes is available. A non-linear model based on mean travel times can then be used as an approximation.     

The impact of land-use mix on residents' travel energy consumption: New evidence from Beijing

Policies that encourage mixed land use are widely believed to make transport more energy efficient. However, few studies have directly examined the impacts of land-use heterogeneity on travel energy consumption at the individual level. Moreover, the definition and measures of land-use heterogeneity are debated. This paper aims to fill these gaps using the large city of Beijing, China, as a case study. Three types of land use are examined in terms of their effects on individual residents’ travel energy consumption. The results suggest that high land-use diversity and a good jobs-housing balance significantly reduces commuting travel. Interestingly, highly heterogeneous retail and housing areas may have high travel energy use, as residents are more likely to go shopping. There are obvious spatial variations in these effects. Residents of suburban ‘newtowns’, where the jobs-housing balance is particularly good, consume less travel energy. The results suggest that decreased use of conventional planning patterns, such as the socialist danwei system, and increasing urban sprawl, bring new challenges to achieving transport efficiency. Mixed land-use policies can be an effective solution to these challenges.     

Formal scientific research of traffic collision data utilizing GIS

This paper explores the tenuous link between speeding behavior and accident causation, one that has not been well established in the international literature to date. Taking advantage of established engineering conventions and formulae, we were able to set up an a priori hypothesis suitable for testing. Utilizing this formal scientific method (which GIS researchers have been criticised for not using) we establish a statistical link for this relationship. Our methodology can be used to support all police intervention strategies, including the controversial photo radar systems. The results from our research have been entered into a GIS in order to create a map for spatial display. This map illustrates the relative probability or risk of collision occurrence resulting from speeding at all intersections and interchanges within the scope of the study. It is suggested that this methodology could easily be maintained with periodic updates of data, thus creating a dynamic model from which to monitor traffic safety within the city. Furthermore, this model can be utilized to study specific strategies, allowing for the scrutiny of before, during and after effects. The study area is the entire city of Calgary, Alberta, Canada, and includes all traffic collisions occurring during the year of 1994.     

Do cooperative systems make drivers’ car-following behavior safer?

The main goal of in-vehicle technologies and co-operative services is to reduce congestion and increase traffic safety. This is achieved by alerting drivers on risky traffic conditions ahead of them and by exchanging traffic and safety related information for the particular road segment with nearby vehicles. Road capacity, level of service, safety, and air pollution are impacted to a large extent by car-following behavior of drivers. Car-following behavior is an essential component of micro-simulation models. This paper investigates the impact of an infrastructure-to-vehicle (I2V) co-operative system on drivers’ car-following behavior. Test drivers in this experiment drove an instrumented vehicle with and without the system. Collected trajectory data of the subject vehicle and the vehicle in front, as well as socio-demographic characteristics of the test drivers were used to estimate car-following models capturing their driving behavior with and without the I2V system. The results show that the co-operative system harmonized the behavior of drivers and reduced the range of acceleration and deceleration differences among them. The observed impact of the system was largest on the older group of drivers.     

5-羟色胺转运体基因多态性和抑郁症的发病、性别、严重程度及自杀行为的相关性

目的探讨中国汉族人群5-羟色胺转运体启动区(5-HTTLPR)基因多态性和抑郁症的发病、性别、严重程度及自杀是否相关。方法应用聚合酶链式反应(PCR)扩增技术测定150例抑郁症患者和150例正常对照者的5-HT-TLPR基因型和等位基因,分别验证各种基因型与中、重度抑郁症发病、性别及自杀行为的相关性。结果病例组SS、LS基因型及S等位基因频率均高于对照组(26.0% vs.20.0%;52.7% vs.46.0%;52.3% vs.43.0%;P均<0.05);两组性别分层比较,女性S等位基因频率高于对照组(55.3% vs.43.6%,P<0.05);病例组严重程度分层比较差异无统计学意义(P>0.05);病例组有无自杀行为分层比较,有自杀行为患者SS基因型频率(37.3% vs.20.2%)及S等位基因频率(61.8% vs.47.5%)均高于无自杀行为患者(P<0.05);抑郁自杀组性别分层比较未显示显著性差异(P>0.05);抑郁自杀组病情程度分层比较,重度抑郁自杀者SS基因型频率(42.5% vs.18.2%)及S等位基因频率(68.7% vs.36.4%)均高于中度抑郁自杀者(P<0.05)。结论在中国汉族人群中,5-HTTLPR多态性和抑郁症相关。S等位基因可能是抑郁症的易感基因,特别是女性,SS型可能是抑郁症易感基因型;S等位基因可能是抑郁症患者自杀的危险基因,SS基因型人群可能是抑郁症患者自杀的危险人群,特别是携带S等位基因的重度抑郁症患者更易自杀。     

半潜式钻井平台用钢DH36安定行为试验研究

为了给半潜式钻井平台的安全性和寿命评价提供可靠试验依据,在室温下对半潜式钻井平台用钢DH36钢进行了考虑加载波形、加载速率和应力比影响的多工况单轴棘轮安定试验。结果表明:循环硬化速率随循环周次的增加快速下降,在低于某一值的循环应力作用下,应变将最终趋于棘轮饱和状态;正弦应力波比三角波更快地趋于安定,采用较低的加载速率可以加速材料趋向饱和棘轮状态;峰值应力固定不变时,棘轮应变对应力比历史无明显的记忆性;波形和加载速率对饱和棘轮应变的影响有限,饱和棘轮应变随着应力峰值的增加而增大。     

Within day rescheduling microsimulation combined with macrosimulated traffic

The concept of rescheduling is essential to activity-based modeling in order to calculate effects of both unexpected incidents and adaptation of individuals to traffic demand management measures. When collaboration between individuals is involved or timetable based public transportation modes are chosen, rescheduling becomes complex. This paper describes a new framework to investigate algorithms for rescheduling at a large scale. The framework allows to explicitly model the information flow between traffic information services and travelers. It combines macroscopic traffic assignment with microscopic simulation of agents adapting their schedules. Perception filtering is introduced to allow for traveler specific interpretation of perceived macroscopic data and for information going unnoticed; perception filters feed person specific short term predictions about the environment required for schedule adaptation. Individuals are assumed to maximize schedule utility. Initial agendas are created by the FEATHERS activity-based schedule generator for mutually independent individuals using an undisturbed loaded transportation network. The new framework allows both actor behavior and external phenomena to influence the transportation network state; individuals interpret the state changes via perception filtering and start adapting their schedules, again affecting the network via updated traffic demand. The first rescheduling mechanism that has been investigated uses marginal utility that monotonically decreases with activity duration and a monotonically converging relaxation algorithm to efficiently determine the new activity timing. The current framework implementation is aimed to support re-timing, re-location and activity re-sequencing; re-routing at the level of the individual however, requires microscopic travel simulation.     

波浪中破损船舶的运动响应研究

波浪中破损船舶的运动会同时受到波浪激励和进出水的影响,而船体运动也会影响进出水过程,二者的相互影响机理十分复杂.本文重点研究波浪中破舱进出水对船舶运动响应的影响,文中首先基于势流理论建立了考虑破舱进出水的4DOF(横荡-垂荡-横摇-纵摇)相互耦合时域预报方法,在计算中假设舱内的液面水平,利用修正的伯努利方程模拟破舱进/出水,利用Ikeda's经验公式修正阻尼系数.然后以一艘ITTC破损稳性标模为例,研究了波浪中考虑破舱进出水的数学模型以及破舱进出水对运动响应的影响,并研究了不同自由度、破舱口位置对运动响应的影响.研究表明,本文基于势流理论建立的时域预报方法可以定量的预报破损船舶的运动响应.     

Modeling the behavioral determinants of travel behavior: An application of latent transition analysis

This paper applies the relatively new method of latent class transition analysis to explore the notion that qualitative differences in travel behavior patterns are substantively meaningful and therefore relevant from explanatory point of view. For example, because the bicycle may function as an important access and egress mode, a car user who also (occasionally) uses the bicycle may be more likely to switch to a public transit profile than someone who only uses the car. Data from the Dutch mobility panel are used to inductively reveal travel behavior patterns and model transitions in these patterns over time. Additionally, the effects of seven exogenous variables, including two important life events (i.e. moving house and changing jobs), on cluster membership and the transition probabilities are assessed. The results show that multiple-mode users compared to single-mode users are more likely to switch from one behavioral profile to another. In addition, age, the residential environment, moving house and changing jobs have strong influences on the transition probabilities between the revealed behavioral patterns over time.