盾构隧道穿越高强度强富水灰岩上浮段工程风险分析及应对措施

针对盾构穿越土岩交界面、高强度富水灰岩段及灰岩岩溶区所存在的风险进行分析,合理做好盾构选型,通过工程类比法并结合工程实际提出相应处理措施。采取全土压掘进模式、合理的掘进参数及渣土改良等手段控制土岩交界面上方管线及地表变形;穿越高强度强富水灰岩段,为防止掘进过程中产生"喷涌"现象,采取盾构半敞开式掘进模式、洞内补偿注浆封堵后方来水、地表注射聚氨酯和渣土改良等措施;针对高强度硬岩,通过合理选用刀具、控制盾构掘进参数减少刀具的磨损和破坏。穿越石灰岩岩溶区,针对溶洞的大小、与隧道相对位置关系和充填类型采取地面袖阀管注浆预处理,并结合管片预留孔进行注浆处理,通过采取上述综合技术措施,保证了盾构机在高强度富水灰岩上浮段的顺利掘进。     

Comparing the self-assessed and examiner-assessed driving skills of Japanese driving school students

Previous research about subjective driving skills has revealed that drivers, especially younger males, tend to rate their driving skills more highly than those of the average driver. This study examines the accuracy of Japanese novice drivers' self-evaluation of their driving skills by comparing their self-assessments with assessments made by a driving examiner. We also examined the effects of gender and age on the accuracy of driver self-evaluation of their skills.     

Role of steering wheel feedback on driver performance: driving simulator and modeling analysis

When driving in curves, how do drivers use the force appearing on the steering wheel? As it carries information related to lateral acceleration, this force could be necessary for drivers to tune their internal model of vehicle dynamics; alternatively, being opposed to the drivers' efforts, it could just help them stabilize the steering wheel position. To assess these two hypotheses, we designed an experiment on a motion-based driving simulator. The steering characteristics of the vehicle were modified in the course of driving, unknown to drivers. Results obtained with standard drivers showed a surprisingly wide range of adaptation, except for exaggerated modifications of the steering force feedback. A two-level driver model, combining a preview of vehicle dynamics and a neuromuscular steering control, reproduces these experimental results qualitatively and indicates that adaptation occurs at the haptic level rather than in the internal model of vehicle dynamics. This effect is related to other theories on the manual control of dynamics systems, wherein force feedback characteristics are abstracted at the position control level. This research also illustrates the use of driving simulation for the study of driver behavior and future intelligent steering assistance systems.     

Commuters’ exposure to PM2.5 and CO2 in metro carriages of Shanghai metro system

Air quality inside transportation carriages has become a public concern. A comprehensive measurement campaign was conducted to examine the commuters’ exposure to PM_2.5 (d_p ⩽ 2.5 μm) and CO₂ in Shanghai metro system under different conditions. The PM_2.5 and CO₂ concentrations inside all the measured metro lines were observed at 84 ± 42 μg/m³ and 1253.1 ± 449.1 ppm, respectively. The factors that determine the in-carriage PM_2.5 and CO₂ concentrations were quantitatively investigated. The metro in-carriage PM_2.5 concentrations were significantly affected by the ventilation systems, out-carriage PM_2.5 concentrations and the passenger numbers. The largest in-carriage PM_2.5 and CO₂ concentrations were observed at 132 μg/m³ and 1855.0 ppm inside the carriages equipped with the oldest ventilation systems. The average PM_2.5 and CO₂ concentrations increased by 24.14% and 9.93% as the metro was driven from underground to overground. The average in-carriage PM_2.5 concentrations increased by 17.19% and CO₂ concentration decreased by 16.97% as the metro was driven from urban to the suburban area. It was found that PM_2.5 concentration is proportional to the on-board passenger number at a ratio of 0.4 μg/m³·passenger. A mass-balance model was developed to estimate the in-carriage PM_2.5 concentration under different driving conditions.     

Eco-driving key factors that influence fuel consumption in heavy-truck fleets: A Colombian case

This research identifies key variables that influence fuel consumption that might be improved through eco-driving training programs under three circumstances that have been scarcely studied before: (a) heavy- and medium-duty truck fleets, (b) long-distance freight transport, and (c) the Latin American region. Based on statistical analyses that include multivariate regression of operational variables on fuel consumption, the impacts of an eco-driving training campaign were measured by comparing ex ante and ex post data. Operational variables are grouped into driving errors, trip conditions, driver behavior, driver profile, and vehicle attributes.The methodology is applied in a freight fleet with nationwide transport operations located in Colombia, where the steepness of its roads plays an important role in fuel consumption. The fleet, composed of 18 trucks, is equipped with state-of-the-art real-time data logger systems. During four months, 517 trips traveling a total distance of 292,512 km and carrying a total of 10,034 tons were analyzed.The results show a baseline average fuel consumption (FC) of 1.716 liters per ton-100 km. A different logistics performance indicator, which measures FC in liters per ton transported each 100 km, shows an average of 3.115. After the eco-driving campaign, reductions of 6.8% and 5.5% were obtained. Drivers’ experience, driving errors, average speed, and weight-capacity ratio, among others, were found to be highly relevant to FC. In particular, driving errors such as acceleration, braking and speed excesses are the most sensitive to eco-driving training, showing reductions of up to 96% on the average number of events per trip.     

Personalised assistance for fuel-efficient driving

Recent advances in technology are changing the way how everyday activities are performed. Technologies in the traffic domain provide diverse instruments of gathering and analysing data for more fuel-efficient, safe, and convenient travelling for both drivers and passengers. In this article, we propose a reference architecture for a context-aware driving assistant system. Moreover, we exemplify this architecture with a real prototype of a driving assistance system called Driving coach. This prototype collects, fuses and analyses diverse information, like digital map, weather, traffic situation, as well as vehicle information to provide drivers in-depth information regarding their previous trip along with personalised hints to improve their fuel-efficient driving in the future. The Driving coach system monitors its own performance, as well as driver feedback to correct itself to serve the driver more appropriately.     

Interface design considerations for an in-vehicle eco-driving assistance system

This high-fidelity driving simulator study used a paired comparison design to investigate the effectiveness of 12 potential eco-driving interfaces. Previous work has demonstrated fuel economy improvements through the provision of in-vehicle eco-driving guidance using a visual or haptic interface. This study uses an eco-driving assistance system that advises the driver of the most fuel efficient accelerator pedal angle, in real time. Assistance was provided to drivers through a visual dashboard display, a multimodal visual dashboard and auditory tone combination, or a haptic accelerator pedal. The style of advice delivery was varied within each modality. The effectiveness of the eco-driving guidance was assessed via subjective feedback, and objectively through the pedal angle error between system-requested and participant-selected accelerator pedal angle. Comparisons amongst the six haptic systems suggest that drivers are guided best by a force feedback system, where a driver experiences a step change in force applied against their foot when they accelerate inefficiently. Subjective impressions also identified this system as more effective than a stiffness feedback system involving a more gradual change in pedal feedback. For interfaces with a visual component, drivers produced smaller pedal errors with an in-vehicle visual display containing second order information on the required rate of change of pedal angle, in addition to current fuel economy information. This was supported by subjective feedback. The presence of complementary audio alerts improved eco-driving performance and reduced visual distraction from the roadway. The results of this study can inform the further development of an in-vehicle assistance system that supports ‘green’ driving.     

Effects of driving style on the fuel consumption of city buses under different road conditions and vehicle masses

The variance in fuel consumption caused by driving style (DS) difference exceeds 10% and reaches a maximum of 20% under different road conditions, even for experienced bus drivers. To study the influence of DS on fuel consumption, a method for summarizing DS characteristic parameters on the basis of vehicle-engine combined model is proposed. With this method, the author proposes 26 DS characteristic parameters related to fuel consumption in the accelerating, normal running, and decelerating processes of vehicles. The influence of DS characteristic parameters on fuel consumption under different road conditions and vehicle masses is quantitatively analyzed on the basis of real driving data over 100,000 km. Analysis results show that the influence of DS characteristic parameters on fuel consumption changes with road condition and vehicle mass, with road condition serving a more important function. However, the DS characteristics in the accelerating process of vehicles are decisive for fuel consumption under different conditions. This study also calculates the minimum sample size necessary for analyzing the effect of DS characteristics on fuel consumption. The statistical analysis based on the real driving data over 2500 km can determine the influence of DS on fuel consumption under a given power-train configuration and road condition. The analysis results can be employed to evaluate the fuel consumption of drivers, as well as to guide the design of Driver Advisory System for Eco-driving directly.     

Categorization of the lane change decision process on freeways

Traffic operations for new road layouts are often simulated using microscopic traffic simulation packages. These traffic simulation packages usually simulate traffic on freeways by a combination of a car-following model and a lane change model. The car-following models have gained attention of researchers and are well calibrated versus data. The proposed lane change models are often representations of assumed reasonable behavior, not necessarily corresponding to reality. The current simulation packages apply solely one specific type of model for car-following or lane changing for all vehicles during the simulation. This paper investigates the decision process of lane changing maneuvers for a variety of drivers based on a two-stage test-drive. Participants are asked to take a drive on a freeway in the Netherlands in a camera-equipped vehicle. Afterwards, the drivers are asked to comment on their choices related to lane and speed choice, while watching the video. This paper reveals that different drivers have completely different strategies to choose lanes, and the choices to change lane are related to their speed choice. Four distinct strategies are empirically found. These strategies differ not only in parameter values, as is currently being modeled in most simulation packages, but also in their reasoning. Most remarkably, all drivers perceive their strategy as an obvious behavior and expect all other drivers to drive in a similar way. In addition to the interviews of the participants in the test-drive, 11 people who did not take part in the experiment were interviewed and questioned on lane change decisions. Moreover, the findings of this study have been presented to various groups of audience with different backgrounds (about 150 people). Their comments and feedback on the derived driving strategies have added some value to this study. The findings in this paper form a starting point for developing a novel lane change model which considers four different driving strategies among the drivers on freeway. This is a significant contribution in the area of driving behavior modeling, since the existing microscopic simulators consider only one type of lane change models for all drivers during the simulation. This could lead to significant changes in the way lane changes on freeways are modeled.     

Interface design of eco-driving support systems – Truck drivers’ preferences and behavioural compliance

The aim of the study was to investigate the perceived usefulness of various types of in-vehicle feedback and advice on fuel efficient driving. Twenty-four professional truck drivers participated in a driving simulator study. Two eco-driving support systems were included in the experiment: one that provided continuous information and one that provided intermittent information. After the simulator session, the participants were interviewed about their experiences of the various constituents of the systems. In general, the participants had a positive attitude towards eco-driving support systems and behavioural data indicated that they tended to comply with the advice given. However, different drivers had very different preferences with respect to what type of information they found useful. The majority of the participants preferred simple and clear information. The eco-driving constituents that were rated as most useful were advice on gas pedal pressure, speed guidance, feedback on manoeuvres, fuel consumption information and simple statistics. It is concluded that customisable user interfaces are recommended for eco-driving support systems for trucks.