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Historically, rear seat occupants have been at a lower risk of serious injury and fatality in motor vehicle crashes than their front seat counterparts. However, many passive safety advancements that have occurred over the past few decades such as advanced airbag and seatbelt technology primarily benefit occupants of the front seat. Indeed, safety for front seat occupants has improved drastically in the 21st century, but has it improved so much that the front seat is now safer than the rear? Today, rear-seated occupants account for 10% of all passenger vehicle fatalities. In this era focused on achieving zero traffic deaths, the safety of rear-seated occupants must be further addressed. This dissertation analyzed U.S. national crash data to quantify the risk of injury and fatality to rear-seated passenger vehicle occupants while accounting for the influence of associated crash, vehicle, and occupant characteristics such as crash severity, vehicle model year, and occupant age/sex. In rear impacts, the risk of moderate-to-fatal injury was greater for rear-seated occupants than their front-seated counterparts. In high-severity rear impact crashes, catastrophic occupant compartment collapse can occur and carries with it a great fatality risk. In frontal impacts, there is evidence that the rear versus front seat relative risk of fatality has been increasing in vehicle model years 2007 and newer. Rear-seated occupants often sustained serious thoracic, abdomen, and/or head injuries that are generally related to seatbelt use. Seatbelt pretensioners and load limiters – commonplace technology in the front seating positions – aim to mitigate these types of injuries but are rarely provided as standard safety equipment in the rear seats of vehicles today. Finally, in side impacts, injury and fatality risks to rear- and front-seated occupants are more similar than in the other crash modes studied, though disparities in protection remain, especially in near-side vehicle-to-vehicle crashes. Finally, this work projects great injury reduction benefits if a rear seat belt reminder system were to be widely implemented in the U.S. vehicle fleet. This dissertation presents a comprehensive investigation of the factors that contribute to rear-seated occupant injury and/or fatality through retrospective studies on rear, front, and side impacts. The overall goal of this dissertation is to better quantify the current risk of injury to rear-seated occupants under a variety of crash conditions, compare this to the current risk to front-seated occupants, and, when possible, identify how exactly injuries are occurring and ways in which they may be prevented in the future. The findings can benefit automakers who seek to improve the effectiveness of rear seat safety systems as well as regulatory agencies seeking to improve was vehicle tests targeting rear seat passenger vehicle safety. / Doctor of Philosophy / Historically, if a passenger vehicle such as a sedan or SUV is in a crash, occupants who are rear-seated were less likely to be hurt than someone who was front-seated. In other words, rear-seated occupants have been at a lower risk of injury than front-seated occupants. Indeed, safety for front seat occupants has improved drastically in the 21st century due to advancements in airbag and seatbelt technologies, among others, but has it improved so much that the front seat is now safer than the rear? Today, of all vehicle occupants who are killed in crashes on U.S. roadways, 10% are rear-seated. During this time when conversations surrounding vehicle safety are focused on achieving zero traffic deaths, the safety of rear-seated occupants must be further studied. This dissertation looked at national databases of all police-reported crashes that occur each year in the United States. The risk of injury to rear-seated passenger vehicle occupants was quantified and compared to that of front-seated occupants. Factors that may increase or decrease this risk of injury and fatality such as crash type, vehicle type, and occupant demographics were further explored and reported. In vehicles that were rear-ended, the risk of injury was greater for rear-seated occupants than their front-seated counterparts. When a vehicle crashes into something front-first (the most common type of impact in a vehicle crash), evidence is presented that the risk of fatality is greater in the rear seats than the front seats in model year 2007 and newer vehicles which generally are equipped with the most recent airbag and seatbelt technology. When a vehicle is hit on either of its sides, the risk of injury is closer between rear- and front-seated occupants than it was in the rear-end or frontal crashes previously studied. That said, differences in occupant protection were still observed between the rear and front seats, especially when the occupants studied were seated on the closest side of impact, or the near-side, and the vehicle was struck by another vehicle rather than sliding into an object such as a pole. Finally, this work projects great injury reduction benefits if a rear seat belt reminder system were to be widely implemented in the U.S. vehicle fleet. This dissertation presents a comprehensive investigation of the factors that contribute to rear-seated occupant injury and/or fatality through retrospective studies on rear, front, and side impacts. The overall goal of this dissertation is to better quantify the current risk of injury to rear-seated occupants under a variety of crash conditions, compare this to the current risk to front-seated occupants, and, when possible, identify how exactly injuries are occurring and ways in which they may be prevented in the future. The findings can benefit automakers who seek to improve the effectiveness of rear seat safety systems as well as regulatory agencies seeking to improve was vehicle tests targeting rear seat passenger vehicle safety.

Food inflation in South Africa has been viewed as an important source of underlying inflationary pressures in the economy due to its persistence beyond that of other commodities. Although several studies found food to be one of the factors that influence purchase decisions, there still appears to be an absence of research that directly links food inflation to consumers’ decisions, especially when financing the purchase of new passenger vehicles in South Africa. In this regard, this study investigated whether the increase in the prices of food products has a significant effect on passenger vehicle purchases in South Africa. Leaning on the literature that argues that economic factors do not play much of a role in passenger vehicle purchase decisions in South Africa, it was hypothesised that there is no supported relationship between food inflation and passenger vehicle purchases in South Africa. Using secondary time series data, the Pearson correlation test revealed a negative but insignificant relationship between food inflation and vehicle purchases in South Africa. The ordinary least squares estimate of the purchase function, taking into account several economic factors that influence passenger vehicle purchase decisions in the literature, showed that disposable income of households along with vehicle purchases of the previous period are to be considered as main determinants of vehicle purchases in South Africa. In addition, it was also revealed that new vehicle prices are also a significant determinant of vehicle purchases. The Johansen cointegration test revealed that the variables in the vehicle purchase function were cointegrated in the long run. The vector error correction model showed a long-run relationship, albeit insignificant, between food inflation and vehicle purchases and no relationship between the two variables in the short run. The Granger causality test revealed that food inflation and vehicle purchases are independent from each other, meaning that no causal effect was found between the variables, regardless of the direction of the test. This study concluded that economic factors such as interest rate and fuel price have an insignificant influence on passenger vehicle purchases in South Africa. In the same line, the impact of food inflation on passenger vehicle purchases in South Africa was found to be insignificant, therefore, the conclusion was drawn that the increase in the prices of food products will not play a considerable role in consumers’ decisions regarding passenger vehicle purchase in South Africa. / Business Management / M. Com. (Business Management)

With the transport sector switching to electric energy to reduce greenhouse gas emission, the supply and demand in the energy system are impacted by this transition. Meanwhile, there are not a lot of studies focus on the electrification of the vehicle fleet in Sweden. To fill up the knowledge gap, the paper aims to identify the total required electrical energy and power for the electrification of the vehicle fleet in Sweden. This includes switching passenger vehicles, light and heavy trucks, and buses to battery electric vehicles. An Electric Vehicle Power Demand Model is designed to answer the research question. It is a simplified model that can calculate energy consumption and power demand from an electric vehicle fleet. To simulate the charging schedule, four scenarios are created with differences in charge speed and the use of smart or unregulated charging. Based on the model, the electric vehicle fleet consumes 20.4 TWh of electricity per year, accounting for 14.7% of total demand in Sweden. Combing the vehicle fleet with other energy services, an average hourly peak load of 16.2 GW in summer and 24.3 in winter can be seen, while the available capacity in Sweden is around 27.1. The result indicates that the current Swedish energy system is capable of handling demand from charging the electric vehicle fleet in terms of power capacity for most times. However, undersupply may happen in some extreme condition during the winter due to higher consumption from other energy services. Furthermore, with the increasing share of renewable power in the system, the availability of these power plants can have a direct impact on the supply. This requires smart charging to shift the charging events to prevent peak hours, which can potentially decrease the peak loads up to 2 GW in EV charging demand during peak hours. However, the actual effect of it still requires more study. Lastly, the model created for the research can be used as a research or decision-making tool to estimate the impact of a group of electric vehicles in the future, therefore, contribute to the development of the sustainable energy transition.

An accurate estimate of the mass of a passenger vehicle is important for several safety systems and environmental aspects. In this thesis, an algorithm for estimating the mass of a passenger vehicle using the recursive least squares methodis presented. The algorithm is based on a physical model of the vehicle and is designed to be able to run in real-time onboard a vehicle and uses the wheel torque signal calculated in the electrical control unit in the engine. Therefore no estimation of the powertrain is needed. This is one contribution that distinguishes this thesis from previous work on the same topic, which has used the engine torque. The benefit of this is that no estimation of the dynamics in the powertrain is needed. The drawback of using this method is that the algorithm is dependenton the accuracy of the estimation done in the engine electrical control unit. Two different versions of the recursive least squares method (RLS) have been developed - one with a single forgetting factor and one with two forgetting factors. The estimation performance of the two versions are compared on several different real-world driving scenarios, which include driving on country roads, highways, and city roads, and different loads in the vehicle. The algorithm with a single forgetting factor estimates the mass with an average error for all tests of 4.42% and the algorithm with multiple forgetting factors estimates the mass with an average error of 4.15 %, which is in line with state-of-the-art algorithms that are presented in other studies. In a sensitivity analysis, it is shown that the algorithms are robust to changes in the drag coefficient. The single forgetting factor algorithm is robust to changes in the rolling resistance coefficient whereas the multiple forgetting factor algorithm needs the rolling resistance coefficient to be estimated with fairly good accuracy. Both versions of the algorithm need to know the wheel radius with an accuracy of 90 %. The results show that the algorithms estimate the mass accurately for all three different driving scenarios and estimate highway roads best with an average error of 2.83 % and 2.69 % for the single forgetting factor algorithm and the multiple forgetting factor algorithm, respectively. The results indicate it is possible to use either algorithm in a real-world scenario, where the choice of which algorithm depends on sought-after robustness.