奔驰A200加装带照明的梅赛德斯三叉星徽

<正>加装说明:本文和大家分享一下2018年上市的奔驰新款A200加装带前部照明的梅赛德斯三叉星徽(如图1所示)的方法和步骤。此加装文档适用于奔驰型号为177.1,本文以新款A200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.拆下散热器格栅中的梅赛德斯三叉星徽。2.如果适用,完全卸下空气过滤器外壳并将其连同接线一起放到一旁。不要将空气过滤器外壳的上部与空气过滤器外壳的下部分开。     

An investigation of commuting trip timing and mode choice in the Greater Toronto Area: Application of a joint discrete-continuous model

The trip timing and mode choice are two critical decisions of individual commuters mostly define peak period traffic congestion in urban areas. Due to the increasing evidence in many North American cities that the duration of the congested peak travelling periods is expanding (peak spreading), it becomes necessary and natural to investigate these two commuting decisions jointly. In addition to being considered jointly with mode choice decisions, trip timing must also be modelled as a continuous variable in order to precisely capture peak spreading trends in a policy sensitive transportation demand model. However, in the literature to date, these two fundamental decisions have largely been treated separately or in some cases as integrated discrete decisions for joint investigation. In this paper, a discrete-continuous econometric model is used to investigate the joint decisions of trip timing and mode choice for commuting trips in the Greater Toronto Area (GTA). The joint model, with a multinomial logit model for mode choice and a continuous time hazard model for trip timing, allows for unrestricted correlation between the unobserved factors influencing these two decisions. Models are estimated by occupation groups using 2001 travel survey data for the GTA. Across all occupation groups, strong correlations between unobserved factors influencing mode choice and trip timing are found. Furthermore, the estimated model proves that it sufficiently captures the peak spreading phenomenon and is capable of being applied within the activity-based travel demand model framework.     

Forecasting bus ridership using a “Blended Approach”

Transportation - As sources of “Big Data” continue to grow, transportation planners and researchers seek to utilize these new resources. Given the current dependency on traditional...     

Longitudinal analysis of activity generation in the Greater Toronto and Hamilton Area

Transportation - This paper presents a longitudinal analysis of activity generation behaviour in the Greater Toronto and Hamilton Area (GTHA) between 1996 and 2016 for various activity types: work,...     

Travelers’ compliance with social routing advice: evidence from SP and RP experiments

Transportation - This study examines to what extent travel information can be used to direct travelers to system-optimal routes that may be sub-optimal for them personally, but contribute to...     

Carsharing: the impact of system characteristics on its potential to replace private car trips and reduce car ownership

Transportation - This paper aims to explore the potential of carsharing in replacing private car trips and reducing car ownership and how this is affected by its attributes. To that affect, a...     

Air–sea gas exchange at extreme wind speeds measured by autonomous oceanographic floats

Measurements of the air–sea fluxes of N₂ and O₂ were made in winds of 15–57 m s^− 1 beneath Hurricane Frances using two types of air-deployed neutrally buoyant and profiling underwater floats. Two “Lagrangian floats” measured O₂ and total gas tension (GT) in pre-storm and post-storm profiles and in the actively turbulent mixed layer during the storm. A single “EM-APEX float” profiled continuously from 30 to 200 m before, during and after the storm. All floats measured temperature and salinity. N₂ concentrations were computed from GT and O₂ after correcting for instrumental effects. Gas fluxes were computed by three methods. First, a one-dimensional mixed layer budget diagnosed the changes in mixed layer concentrations given the pre-storm profile and a time varying mixed layer depth. This model was calibrated using temperature and salinity data. The difference between the predicted mixed layer concentrations of O₂ and N₂ and those measured was attributed to air–sea gas fluxes F_BO and F_BN. Second, the covariance flux F_CO(z) = wO₂′(z) was computed, where w is the vertical motion of the water-following Lagrangian floats, O₂′ is a high-pass filtered O₂ concentration and (z) is an average over covariance pairs as a function of depth. The profile F_CO(z) was extrapolated to the surface to yield the surface O₂ flux F_CO(0). Third, a deficit of O₂ was found in the upper few meters of the ocean at the height of the storm. A flux F_SO, moving O₂ out of the ocean, was calculated by dividing this deficit by the residence time of the water in this layer, inferred from the Lagrangian floats. The three methods gave generally consistent results. At the highest winds, gas transfer is dominated by bubbles created by surface wave breaking, injected into the ocean by large-scale turbulent eddies and dissolving near 10-m depth. This conclusion is supported by observations of fluxes into the ocean despite its supersaturation; by the molar flux ratio F_BO/F_BN, which is closer to that of air rather than that appropriate for Schmidt number scaling; by O₂ increases at about 10-m depth along the water trajectories accompanied by a reduction in void fraction as measured by conductivity; and from the profile of F_CO(z), which peaks near 10 m instead of at the surface.At the highest winds O₂ and N₂ are injected into the ocean by bubbles dissolving at depth. This, plus entrainment of gas-rich water from below, supersaturates the mixed layer causing gas to flux out of the near-surface ocean. A net influx of gas results from the balance of these two competing processes. At lower speeds, the total gas fluxes, F_BO, F_BN and F_CO(0), are out of the ocean and downgradient.     

Minimization of Pedaling Induced Energy Losses in Off-road Bicycle Rear Suspension Systems

This paper presents the results of an optimization analysis performed on off-road bicycles in which the energy loss induced as a result of pedaling action was minimized. A previously developed computer-based dynamic system model (Wang and Hull, Vehicle System Dynamics, 25:3, 1996) was used to evaluate the power dissipated by a single pivot point rear suspension while pedalling uphill on a smooth surface. By systematically varying the location of the pivot point, the relationship between power dissipated and pivot location was determined. The optimal location was defined as the location which resulted in the least power dissipated. The simulation results show that the power dissipated was very dependent on the height above the bottom bracket but not the fore-aft location of the pivot point. If the pivot point is constrained to the seat tube, then the optimal pivot point was found to be 11 cm above the bottom bracket. Compared to a commercially available design, the optimal pivot point reduced the power dissipated from 6.9 to 1.2 Watts. Furthermore, the optimal pivot point was found to be very insensitive to pedaling mechanics, and both the spring and damping parameter values. The optimal pivot point did, however, have a linear dependence on the height of the chainline; as the chainline height increased so too did the optimal pivot point height.     

Calibration of large-scale transport planning models: a structured approach

Transportation - Traditionally, transport planning model systems are estimated and calibrated in an unstructured way, which does not allow for interactions among included parameters to be...     

A novel approach for systematically calibrating transport planning model systems

Calibration of a transport planning model system is a complex process. While trial-and-error methods and modelling expertise are still the backbone of calibration of transport models, analytical approaches automating the calibration process can improve the accuracy of the models. Introducing a model to guide modellers in the calibration process of large-scale transport planning model systems is the core of this study, where a systematic model for choosing the most appropriate models and parameters is discussed. The effectiveness of the proposed model is investigated by comparing three scenarios which are built on the Travel/Activity Scheduler for Household Agents model as a large-scale agent-based model system.