Human factors requirements for real-time motorist information displays. Volume 3: summary of systems in the United States. Final report.

Federal Highway Administration, Office of Research, Washington, D.C.

Human factors requirements for real-time motorist information displays. Volume 4: bibliography and selected annotations: visual systems. Final report.

Federal Highway Administration, Office of Research, Washington, D.C.

Human factors requirements for real-time motorist information displays. Volume 5: bibliography and selected annotations: audio systems. Final report.

Federal Highway Administration, Office of Research, Washington, D.C.

Human factors requirements for real-time motorist information displays. Vol. 1 - design guide. Final report.

Federal Highway Administration, Office of Research, Washington, D.C.

Regulatory signs for lane occupancy and parking. Final report.

Mode of access: Internet.

Seasonal variations of outdoor air pollution and factors driving them in the school environment in rural Bhutan

A quantitative understanding of outdoor air quality in school environments is crucial given that air pollution levels inside classrooms are significantly influenced by outdoor pollution sources. To date, only a handful of studies have been conducted on this important topic in developing countries. The aim of this study was to quantify pollutant levels in the outdoor environment of a school in Bhutan and assess the factors driving them. Measurements were conducted for 16 weeks, spanning the wet and dry seasons, in a rural school in Bhutan. PM10, PM2.5, particle number (PN) and CO were measured daily using real-time instruments, while weekly samples for volatile organic compounds (VOCs), carbonyls and NO2 were collected using a passive sampling method. Overall mean PM10 and PM2.5 concentrations (µg/m3) were 27 and 13 for the wet, and 36 and 29 for the dry season, respectively. Only wet season data were available for PN concentrations, with a mean of 2.56 × 103 particles/cm3. Mean CO concentrations were below the detection limit of the instrumentation for the entire measurement period. Only low levels of eight VOCs were detected in both the wet and dry seasons, which presented different seasonal patterns in terms of the concentration of different compounds. The notable carbonyls were formaldehyde and hexaldehyde, with mean concentrations (µg/m3) of 2.37 and 2.41 for the wet, and 6.22 and 0.34 for the dry season, respectively. Mean NO2 cocentration for the dry season was 1.7 µg/m3, while it was below the detection limit of the instrumentation for the wet season. The pollutant concentrations were associated with a number of factors, such as cleaning and combustion activities in and around the school. A comparison with other school studies showed comparable results with a few of the studies, but in general, we found lower pollutant concentrations in the present study.

The assessment of change in rural landscape and the analysis of the driving forces. Proposal of a spatial model for the rural built environment

The presented study carried out an analysis on rural landscape changes. In particular the study focuses on the understanding of driving forces acting on the rural built environment using a statistical spatial model implemented through GIS techniques. It is well known that the study of landscape changes is essential for a conscious decision making in land planning. From a bibliography review results a general lack of studies dealing with the modeling of rural built environment and hence a theoretical modelling approach for such purpose is needed. The advancement in technology and modernity in building construction and agriculture have gradually changed the rural built environment. In addition, the phenomenon of urbanization of a determined the construction of new volumes that occurred beside abandoned or derelict rural buildings. Consequently there are two types of transformation dynamics affecting mainly the rural built environment that can be observed: the conversion of rural buildings and the increasing of building numbers. It is the specific aim of the presented study to propose a methodology for the development of a spatial model that allows the identification of driving forces that acted on the behaviours of the building allocation. In fact one of the most concerning dynamic nowadays is related to an irrational expansion of buildings sprawl across landscape. The proposed methodology is composed by some conceptual steps that cover different aspects related to the development of a spatial model: the selection of a response variable that better describe the phenomenon under study, the identification of possible driving forces, the sampling methodology concerning the collection of data, the most suitable algorithm to be adopted in relation to statistical theory and method used, the calibration process and evaluation of the model. A different combination of factors in various parts of the territory generated favourable or less favourable conditions for the building allocation and the existence of buildings represents the evidence of such optimum. Conversely the absence of buildings expresses a combination of agents which is not suitable for building allocation. Presence or absence of buildings can be adopted as indicators of such driving conditions, since they represent the expression of the action of driving forces in the land suitability sorting process. The existence of correlation between site selection and hypothetical driving forces, evaluated by means of modeling techniques, provides an evidence of which driving forces are involved in the allocation dynamic and an insight on their level of influence into the process. GIS software by means of spatial analysis tools allows to associate the concept of presence and absence with point futures generating a point process. Presence or absence of buildings at some site locations represent the expression of these driving factors interaction. In case of presences, points represent locations of real existing buildings, conversely absences represent locations were buildings are not existent and so they are generated by a stochastic mechanism. Possible driving forces are selected and the existence of a causal relationship with building allocations is assessed through a spatial model. The adoption of empirical statistical models provides a mechanism for the explanatory variable analysis and for the identification of key driving variables behind the site selection process for new building allocation. The model developed by following the methodology is applied to a case study to test the validity of the methodology. In particular the study area for the testing of the methodology is represented by the New District of Imola characterized by a prevailing agricultural production vocation and were transformation dynamic intensively occurred. The development of the model involved the identification of predictive variables (related to geomorphologic, socio-economic, structural and infrastructural systems of landscape) capable of representing the driving forces responsible for landscape changes.. The calibration of the model is carried out referring to spatial data regarding the periurban and rural area of the study area within the 1975-2005 time period by means of Generalised linear model. The resulting output from the model fit is continuous grid surface where cells assume values ranged from 0 to 1 of probability of building occurrences along the rural and periurban area of the study area. Hence the response variable assesses the changes in the rural built environment occurred in such time interval and is correlated to the selected explanatory variables by means of a generalized linear model using logistic regression. Comparing the probability map obtained from the model to the actual rural building distribution in 2005, the interpretation capability of the model can be evaluated. The proposed model can be also applied to the interpretation of trends which occurred in other study areas, and also referring to different time intervals, depending on the availability of data. The use of suitable data in terms of time, information, and spatial resolution and the costs related to data acquisition, pre-processing, and survey are among the most critical aspects of model implementation. Future in-depth studies can focus on using the proposed model to predict short/medium-range future scenarios for the rural built environment distribution in the study area. In order to predict future scenarios it is necessary to assume that the driving forces do not change and that their levels of influence within the model are not far from those assessed for the time interval used for the calibration.

Identifying influences of driving behaviour : could the Australian work driving setting be unique?

Work-related driving safety is an emerging concern for Australian and overseas organisations. An in depth investigation was undertaken into a group of fleet drivers’ attitudes regarding what personal and environment factors have the greatest impact upon driving behaviours. A number of new and unique factors not previously identified were found including: vehicle features, vehicle ownership, road conditions, weather, etc. The major findings of the study are discussed in regards to practical solutions to improve fleet safety.

Sustainable transport and the impact on the built environment : Future trends and directions

We love the automobile and the independence that it gives us. We are more mobile than we have ever been before in recorded history. In Australia 80% of journeys are by private motor vehicle. But it is becoming increasingly obvious that this era has a very limited lifespan. Fuel prices have skyrocketed recently with no end in sight. In spite of massive amounts of road construction, our cities are becoming increasingly congested. We desperately need to address climate change and the automobile is a major contributor. Carbon trading schemes will put even more upward pressure on fuel prices. At some point in the near future, most of us will need to reconsider our automobile usage whether we like it or not. The time to plan for the future is now. But what will happen to our mobility when access to cheap and available petroleum becomes a thing of the past? Will we start driving electric/hydrogen/ethanol vehicles? Or will we flock to public transport? Will our public transport systems cope with a massive increase in demand? Will thousands of people take to alternatives such as bicycles? If so, where do we put them? How do we change our roads to cope? How do we change our buildings to suit? Will we need recharging stations in our car park for example? Some countries are less reliant on the car than others e.g. Holland and Germany. How can the rest of the world learn from them? This paper discusses many of the likely outcomes of the inevitable shift away from society’s reliance on petroleum and examines the expected impact on the built environment. It also looks at ways in which the built environment can be planned to help ease the transition to a fossil free world. 1.

Creating a proactive decision-support environment in managing road traffic noise

Properly designed decision support environments encourage proactive and objective decision making. The work presented in this paper inquires into developing a decision support environment and a tool to facilitate objective decision making in dealing with road traffic noise. The decision support methodology incorporates traffic amelioration strategies both within and outside the road reserve. The project is funded by the CRC for Construction Innovation and conducted jointly by the RMIT University and the Queensland Department of Main Roads (MR) in collaboration with the Queensland Department of Public Works, Arup Pty Ltd., and the Queensland University of Technology. In this paper, the proposed decision support framework is presented in the way of a flowchart which enabled the development of the decision support tool (DST). The underpinning concept is to establish and retain an information warehouse for each critical road segment (noise corridor) for a given planning horizon. It is understood that, in current practice, some components of the approach described are already in place but not fully integrated and supported. It provides an integrated user-friendly interface between traffic noise modeling software, noise management criteria and cost databases.

Assessment of Speech Dialog Systems using Multi-Modal Cognitive Load Analysis and Driving Performance Metrics

In this paper, cognitive load analysis via acoustic- and CAN-Bus-based driver performance metrics is employed to assess two different commercial speech dialog systems (SDS) during in-vehicle use. Several metrics are proposed to measure increases in stress, distraction and cognitive load and we compare these measures with statistical analysis of the speech recognition component of each SDS. It is found that care must be taken when designing an SDS as it may increase cognitive load which can be observed through increased speech response delay (SRD), changes in speech production due to negative emotion towards the SDS, and decreased driving performance on lateral control tasks. From this study, guidelines are presented for designing systems which are to be used in vehicular environments.

Driving performance on monotonous roads

Driving on motorways has largely been reduced to a lane-keeping task with cruise control. Rapidly, drivers are likely to get bored with such a task and take their attention away from the road. This is of concern in terms of road safety – particularly for professional drivers - since inattention has been identified as one of the main contributing factors to road crashes and is estimated to be involved in 20 to 30% of these crashes. Furthermore, drivers are not aware that their vigilance level has decreased and that their driving performance is impaired. Intelligent Transportation System (ITS) intervention can be used as a countermeasure against vigilance decrement. This paper aims to identify a variety of metrics impacted during monotonous driving - ranging from vehicle data to physiological variables - and relate them to two monotonous factors namely the monotony of the road design (straightness) and the monotony of the environment (landscape, signage, traffic). Data are collected in a driving simulator instrumented with an eye tracking system, a heart rate monitor and an electrodermal activity device (N=25 participants). The two monotonous factors are varied (high and low) leading to the use of four different driving scenarios (40 minutes each). We show with Generalised Linear Mixed Models that driver performance decreases faster when the road is monotonous. We also highlight that road monotony impairs a variety of driving performance and vigilance measures, ranging from speed, lateral position of the vehicle to physiological measurements such as heart rate variability, blink frequency and electrodermal activity. This study informs road designers of the importance of having a varied road environment. It also provides a range of metrics that can be used to detect in real-time the impairment of driving performance on monotonous roads. Such knowledge could result in the development of an in-vehicle device warning drivers at early signs of driving performance impairment on monotonous roads.

Assessment of pile driving refusal using the standard test (SPT)

The design of driven pile foundations involves an iterative process requiring an initial estimate of the refusal level to determine the depth of boreholes for subsequent analyses. Current procedures for determining borehole depths incorporate parameters typically unknown at the investigation stage. Thus, a quantifiable procedure more applicable at this preliminary stage would provide greater confidence in estimating the founding level of driven piles. This paper examines the effectiveness of the Standard Penetration Test (SPT) in directly estimating driven pile refusal levels. A number of significant correlations were obtained between SPT information and pile penetration records demonstrating the potential application of the SPT. Results indicated pile penetration was generally best described as a function of both the pile toe and cumulative shaft SPT values. The influence of the toe SPT increased when piles penetrated rock. A refusal criteria was established from the results to guide both the estimation of borehole depths and likely pile lengths during the design stage.

An assessment system for evaluation of driving competencies

Automobiles have deeply impacted the way in which we travel but they have also contributed to many deaths and injury due to crashes. A number of reasons for these crashes have been pointed out by researchers. Inexperience has been identified as a contributing factor to road crashes. Driver’s driving abilities also play a vital role in judging the road environment and reacting in-time to avoid any possible collision. Therefore driver’s perceptual and motor skills remain the key factors impacting on road safety. Our failure to understand what is really important for learners, in terms of competent driving, is one of the many challenges for building better training programs. Driver training is one of the interventions aimed at decreasing the number of crashes that involve young drivers. Currently, there is a need to develop comprehensive driver evaluation system that benefits from the advances in Driver Assistance Systems. A multidisciplinary approach is necessary to explain how driving abilities evolves with on-road driving experience. To our knowledge, driver assistance systems have never been comprehensively used in a driver training context to assess the safety aspect of driving. The aim and novelty of this thesis is to develop and evaluate an Intelligent Driver Training System (IDTS) as an automated assessment tool that will help drivers and their trainers to comprehensively view complex driving manoeuvres and potentially provide effective feedback by post processing the data recorded during driving. This system is designed to help driver trainers to accurately evaluate driver performance and has the potential to provide valuable feedback to the drivers. Since driving is dependent on fuzzy inputs from the driver (i.e. approximate distance calculation from the other vehicles, approximate assumption of the other vehicle speed), it is necessary that the evaluation system is based on criteria and rules that handles uncertain and fuzzy characteristics of the driving tasks. Therefore, the proposed IDTS utilizes fuzzy set theory for the assessment of driver performance. The proposed research program focuses on integrating the multi-sensory information acquired from the vehicle, driver and environment to assess driving competencies. After information acquisition, the current research focuses on automated segmentation of the selected manoeuvres from the driving scenario. This leads to the creation of a model that determines a “competency” criterion through the driving performance protocol used by driver trainers (i.e. expert knowledge) to assess drivers. This is achieved by comprehensively evaluating and assessing the data stream acquired from multiple in-vehicle sensors using fuzzy rules and classifying the driving manoeuvres (i.e. overtake, lane change, T-crossing and turn) between low and high competency. The fuzzy rules use parameters such as following distance, gaze depth and scan area, distance with respect to lanes and excessive acceleration or braking during the manoeuvres to assess competency. These rules that identify driving competency were initially designed with the help of expert’s knowledge (i.e. driver trainers). In-order to fine tune these rules and the parameters that define these rules, a driving experiment was conducted to identify the empirical differences between novice and experienced drivers. The results from the driving experiment indicated that significant differences existed between novice and experienced driver, in terms of their gaze pattern and duration, speed, stop time at the T-crossing, lane keeping and the time spent in lanes while performing the selected manoeuvres. These differences were used to refine the fuzzy membership functions and rules that govern the assessments of the driving tasks. Next, this research focused on providing an integrated visual assessment interface to both driver trainers and their trainees. By providing a rich set of interactive graphical interfaces, displaying information about the driving tasks, Intelligent Driver Training System (IDTS) visualisation module has the potential to give empirical feedback to its users. Lastly, the validation of the IDTS system’s assessment was conducted by comparing IDTS objective assessments, for the driving experiment, with the subjective assessments of the driver trainers for particular manoeuvres. Results show that not only IDTS was able to match the subjective assessments made by driver trainers during the driving experiment but also identified some additional driving manoeuvres performed in low competency that were not identified by the driver trainers due to increased mental workload of trainers when assessing multiple variables that constitute driving. The validation of IDTS emphasized the need for an automated assessment tool that can segment the manoeuvres from the driving scenario, further investigate the variables within that manoeuvre to determine the manoeuvre’s competency and provide integrated visualisation regarding the manoeuvre to its users (i.e. trainers and trainees). Through analysis and validation it was shown that IDTS is a useful assistance tool for driver trainers to empirically assess and potentially provide feedback regarding the manoeuvres undertaken by the drivers.