Intercity bus roof structural integrity study. Volume I: executive summary. Final report.

Federal Highway Administration, Washington, D.C.

Intercity bus roof structural integrity study. Volume II: final report.

Federal Highway Administration, Washington, D.C.

The Fairchild ESV family sedan - final report. Volume 1.

National Highway Traffic Safety Administration, Washington, D.C.

The Fairchild ESV family sedan - final report. Volume 2.

National Highway Traffic Safety Administration, Washington, D.C.

Quantification of motor vehicle emission factors from on road measurements

Assessment and prediction of the impact of vehicular traffic emissions on air quality and exposure levels requires knowledge of vehicle emission factors. The aim of this study was quantification of emission factors from an on road, over twelve months measurement program conducted at two sites in Brisbane: 1) freeway type (free flowing traffic at about 100 km/h, fleet dominated by small passenger cars - Tora St); and 2) urban busy road with stop/start traffic mode, fleet comprising a significant fraction of heavy duty vehicles - Ipswich Rd. A physical model linking concentrations measured at the road for specific meteorological conditions with motor vehicle emission factors was applied for data analyses. The focus of the study was on submicrometer particles; however the measurements also included supermicrometer particles, PM2.5, carbon monoxide, sulfur dioxide, oxides of nitrogen. The results of the study are summarised in this paper. In particular, the emission factors for submicrometer particles were 6.08 x 1013 and 5.15 x 1013 particles per vehicle-1 km-1 for Tora St and Ipswich Rd respectively and for supermicrometer particles for Tora St, 1.48 x 109 particles per vehicle-1 km-1. Emission factors of diesel vehicles at both sites were about an order of magnitude higher than emissions from gasoline powered vehicles. For submicrometer particles and gasoline vehicles the emission factors were 6.08 x 1013 and 4.34 x 1013 particles per vehicle-1 km-1 for Tora St and Ipswich Rd, respectively, and for diesel vehicles were 5.35 x 1014 and 2.03 x 1014 particles per vehicle-1 km-1 for Tora St and Ipswich Rd, respectively. For supermicrometer particles at Tora St the emission factors were 2.59 x 109 and 1.53 x 1012 particles per vehicle-1 km-1, for gasoline and diesel vehicles, respectively.

Aging in China and its impact on vehicle design

This study contributes to the growth of design knowledge in China, where vehicle design for the local, older user is in its initial developmental stages. Therefore, this research has explored the travel needs of older Chinese vehicle users in order to assist designers to better understand users’ current and future needs. A triangulation method consisting of interviews, logbook and co-discovery was used to collect multiple forms of data and so explore the research question. Grounded theory has been employed to analyze the research data. This study found that users’ needs are reflected through various ‘meanings’ that they attach to vehicles – meanings that give a tangible expression to their experiences. This study identified six older-user need categories: (i) safety, (ii) utility, (iii) comfort, (iv) identity, (v) emotion and (vi) spirituality. The interrelationships among these six categories are seen as an interactive structure, rather than as a linear or hierarchical arrangement. Chinese cultural values, which are generated from particular local context and users’ social practice, will play a dynamic role in linking and shaping the travel needs of older vehicle users in the future. Moreover, this study structures the older-user needs model into three levels of meaning, to give guidance to vehicle design direction: (i) the practical meaning level, (ii) the social meaning level and (ii) the cultural meaning level. This study suggests that a more comprehensive explanation exists if designers can identify the vehicle’s meaning and property associated with the fulfilled older users’ needs. However, these needs will vary, and must be related to particular technological, social, and cultural contexts. The significance of this study lies in its contributions to the body of knowledge in three areas: research methodology, theory and design. These theoretical contributions provide a series of methodological tools, models and approaches from a vehicle design perspective. These include a conditional/consequential matrix, a travel needs identification model, an older users’ travel-related needs framework, a user information structure model, and an Older-User-Need-Based vehicle design approach. These models suggest a basic framework for the new design process which might assist in the design of new vehicles to fulfil the needs of future, aging Chinese generations. The models have the potential to be transferred to other design domains and different cultural contexts.

Development of a particle number and particle mass vehicle emissions inventory for an urban fleet

Motor vehicles are major emitters of gaseous and particulate pollution in urban areas, and exposure to particulate pollution can have serious health effects, ranging from respiratory and cardiovascular disease to mortality. Motor vehicle tailpipe particle emissions span a broad size range from 0.003-10µm, and are measured as different subsets of particle mass concentrations or particle number count. However, no comprehensive inventories currently exist in the international published literature covering this wide size range. This paper presents the first published comprehensive inventory of motor vehicle tailpipe particle emissions covering the full size range of particles emitted. The inventory was developed for urban South-East Queensland by combining two techniques from distinctly different disciplines, from aerosol science and transport modelling. A comprehensive set of particle emission factors were combined with traffic modelling, and tailpipe particle emissions were quantified for particle number (ultrafine particles), PM1, PM2.5 and PM10 for light and heavy duty vehicles and buses. A second aim of the paper involved using the data derived in this inventory for scenario analyses, to model the particle emission implications of different proportions of passengers travelling in light duty vehicles and buses in the study region, and to derive an estimate of fleet particle emissions in 2026. It was found that heavy duty vehicles (HDVs) in the study region were major emitters of particulate matter pollution, and although they contributed only around 6% of total regional vehicle kilometres travelled, they contributed more than 50% of the region’s particle number (ultrafine particles) and PM1 emissions. With the freight task in the region predicted to double over the next 20 years, this suggests that HDVs need to be a major focus of mitigation efforts. HDVs dominated particle number (ultrafine particles) and PM1 emissions; and LDV PM2.5 and PM10 emissions. Buses contributed approximately 1-2% of regional particle emissions.

Fusion of in-vehicle sensor data to develop Intelligent Driver Training System (IDTS)

The over represented number of novice drivers involved in crashes is alarming. Driver training is one of the interventions aimed at mitigating the number of crashes that involve young drivers. To our knowledge, Advanced Driver Assistance Systems (ADAS) have never been comprehensively used in designing an intelligent driver training system. Currently, there is a need to develop and evaluate ADAS that could assess driving competencies. The aim is to develop an unsupervised system called Intelligent Driver Training System (IDTS) that analyzes crash risks in a given driving situation. In order to design a comprehensive IDTS, data is collected from the Driver, Vehicle and Environment (DVE), synchronized and analyzed. The first implementation phase of this intelligent driver training system deals with synchronizing multiple variables acquired from DVE. RTMaps is used to collect and synchronize data like GPS, vehicle dynamics and driver head movement. After the data synchronization, maneuvers are segmented out as right turn, left turn and overtake. Each maneuver is composed of several individual tasks that are necessary to be performed in a sequential manner. This paper focuses on turn maneuvers. Some of the tasks required in the analysis of ‘turn’ maneuver are: detect the start and end of the turn, detect the indicator status change, check if the indicator was turned on within a safe distance and check the lane keeping during the turn maneuver. This paper proposes a fusion and analysis of heterogeneous data, mainly involved in driving, to determine the risk factor of particular maneuvers within the drive. It also explains the segmentation and risk analysis of the turn maneuver in a drive.