Weekly travel-activity behavior

This paper describes a methodology for the analysis of multiday travel-activity patterns in which each multiday travel-activity pattern is characterized as a set of daily travel-activity pattern types. This approach facilitates identification of general weekly travel-activity pattern types as well as examination of the relationships between weekly travel-activity behavior and the hypothesized determinants of that behavior. This methodology is an extension of one developed previously by the author for analysis of daily travel-activity patterns. The methodology is applied to a sample of 112 weekly travel-activity patterns of employed people.The paper develops and examines a number of hypotheses concerning weekly and daily travel-activity patterns and their determinants. The empirical results show that general classes of weekly travel-activity behavior can be identified and that these classes of behavior are related to particular individual and household sociodemographic characteristics. The results also show that, for employed people, daily travel-activity pattern type selection is independent of the day-of-the-week, although there appear to be some specific day-of-week differences in the data used in this research. Further, the empirical results do not reject the hypothesis that the selection of daily travel-activity behavior in a two-stage process (selection of weekly behavior followed by conditional selection of daily behavior), is not influenced by socio-demographic characteristics beyond their impact on selection of the weekly pattern type.     

Intrapersonal variability in daily urban travel behavior: Some additional evidence

Researchers have used multiday travel data sets recently to examine day-to-day variability in travel behavior. This work has shown that there is considerable day-to-day variation in individuals' urban travel behavior in terms of such indicators of behavior as trip frequency, trip chaining, departure time from home, and route choice. These previous studies have also shown that there are a number of important implications of the observed day-to-day variability in travel behavior. For example, it has been shown that it may be possible to improve model parameter estimation precision, without increasing the cost of data collection, by drawing a multiday sample (rather than a single day sample) of traveler behavior, if there is considerable day-to-day variability in the phenomenon being modeled. This paper examines day-to-day variability in urban travel using a three-day travel data set collected recently in Seattle, WA. This research replicates and extends previous work dealing with day-to-day variability in trip-making behavior that was conducted with data collected in Reading, England, in the early 1970s. The present research extends the earlier work by examining day-to-day variations in trip chaining and daily travel time in addition to the variation in trip generation rates. Further, the present paper examines day-to-day variations in travel across the members of two-person households. This paper finds considerable day-to-day variability in the trip frequency, trip chaining and daily travel time of the sample persons and concludes that, in terms of trip frequency, the level of day-to-day variability is very comparable to that observed previously with a data set collected almost 20 years earlier in Reading, England. The paper also finds that day-to-day variability in daily travel time is similar in magnitude to that in daily trip rates. The analysis shows that the level of day-to-day variability is about the same for home-based and non-homebased trips, thus indicating that day-to-day variability in total trip-making is attributable to variation in both home-based and non-home-based trips. Day-to-day variability in the travel behaviors of members of two-person households was also found to be substantial.     

Spatial heterogeneity and temporal dynamics of particles, bacteria, and pEPS in Arctic winter sea ice

Abundances of particles, total bacteria, and particulate extracellular polymeric substances (pEPS) in Arctic sea ice were tracked through a winter season to examine the impact of combined extremes of low temperature and high salinity on the prokaryotic microbial community. Three horizons, centered at depths of 25, 45, and 65 cm from the ice surface, with mean seasonal temperatures of − 20, − 17, and − 13 °C, respectively, were sampled 16 times over the course of 12 weeks. Microscopic counts of bacteria (stained with DAPI) and particles (stained with acridine orange) reflected the dynamic conditions of the growing ice sheet, with greater abundances and variability in the upper ice horizons compared to the lower. The trend of higher particle and bacterial abundances in the upper ice was corroborated by several full-depth profiles taken during the expedition, which also displayed significantly decreasing cell abundance with depth. Bacterial abundance declined slowly and significantly with time in the upper and middle ice horizons, but not in the lowest, suggesting that much of the prokaryotic microbial community is resilient to extreme environmental conditions. We found that pEPS concentrations increased significantly with time and with decreasing temperatures in all depth horizons, which may lend support to the argument that sea ice bacteria produce EPS in situ as a cryoprotectant.     

新款奔驰A200L加装后视摄像头

<正>加装说明:本文和大家分享一下2018年上市的新款奔驰A200L加装倒车影像后视摄像头(如图1所示)的方法和步骤。此加装适用于奔驰型号为117.1,本文以新款A200L为例进行介绍。加装步骤:此步骤总体来说分为两大类即拆卸和安装,那么我们先来说拆卸部分。1.松开左侧和右侧后座区中的侧面饰件,如图2所示,然后推到一旁。不必拆下后座区中的C柱饰板和座椅靠背。     

奔驰新款E300 coupe加装带Mercedes-Benz字样的发光车门槛

<正>加装说明:本文和大家分享新上市的奔驰新款E300coupe加装带Mercedes-Benz字样的发光车门槛(如图1所示)的方法和步骤。此加装适用于奔驰型号为238.3的车型。加装步骤:此步骤总体来说分为两大类即拆卸和安装,那么我们先来说拆卸部分。1.松开驾驶员侧保险丝和继电器模块。断开蓄电池接地线。拆下驾驶员侧仪表板护盖。     

A tour-based model of travel mode choice

This paper presents a new tour-based mode choice model. The model is agent-based: both households and individuals are modelled within an object-oriented, microsimulation framework. The model is household-based in that inter-personal household constraints on vehicle usage are modelled, and the auto passenger mode is modelled as a joint decision between the driver and the passenger(s) to ride-share. Decisions are modelled using a random utility framework. Utility signals are used to communicate preferences among the agents and to make trade-offs among competing demands. Each person is assumed to choose the best combination of modes available to execute each tour, subject to auto availability constraints that are determined at the household level. The households allocations of resources (i.e., cars to drivers and drivers to ride-sharing passengers) are based on maximizing overall household utility, subject to current household resource levels. The model is activity-based: it is designed for integration within a household-based activity scheduling microsimulator. The model is both chain-based and trip-based. It is trip-based in that the ultimate output of the model is a chosen, feasible travel mode for each trip in the simulation. These trip modes are, however, determined through a chain-based analysis. A key organizing principle in the model is that if a car is to be used on a tour, it must be used for the entire chain, since the car must be returned home at the end of the tour. No such constraint, however, exists with respect to other modes such as walk and transit. The paper presents the full conceptual model and estimation results for an initial empirical prototype. Because of the complex nature of the model decision structure, choice probabilities are simulated from direct generation of random utilities rather than through an analytical probability expression.     

State of the art survey: active and semi-active suspension control

This survey paper aims to provide some insight into the design of suspension control system within the context of existing literature and share observations on current hardware implementation of active and semi-active suspension systems. It reviews the performance envelop of active, semi-active, and passive suspensions with a focus on linear quadratic-based optimisation including a specific example. The paper further discusses various design aspects including other design techniques, the decoupling of load and road disturbances, the decoupling of pitch and heave modes, the use of an inerter as an additional design element, and the application of preview. Various production and near production suspension systems were examined and described according to the features they offer, including self-levelling, variable damping, variable geometry, and anti-roll damping and stiffness. The lessons learned from these analytical insights and related hardware implementations are valuable and can be applied towards future active or semi-active suspension design.