Researchers have postulated that the implementation of driving automation systems could reduce the prevalence of driver errors, or at least mitigate the severity of their consequences. While driving automation systems are becoming increasingly common on new vehicles, drivers seem to know very little about them. The following dissertation describes an investigation of driver behavior and behavioral adaptation while using driving automation systems in order to improve consumer education and training. This dissertation uses data collected from test track environments and two naturalistic driving studies, the Virginia Connected Corridor 50 (VCC50) Vehicle Naturalistic Driving Study and the NHTSA Level 2 Naturalistic Driving Study (L2 NDS), to investigate driver behavior with driving automation systems and make suggestions for modifications to current consumer education practices. Results from the test track study indicated that while training strategy elicited limited differences in knowledge and no difference in driver behaviors or attitudes, operator behaviors and attitudes were heavily influenced by time and experience with the driving automation. The naturalistic assessment of VCC50 data showed that drivers tended to activate systems more frequently in appropriate roadway environments. However, drivers spent more time looking away from the road while driving automation systems were active and drivers were more likely be observed browsing on their cell phones while using driving automation systems. The analysis of L2 NDS showed that drivers' time gap preferences changes as drivers gain experience using the driving automation systems. Additionally, driver eye glance behavior was significantly different with automation use and indicated the potential for an adaptive trend with increased exposure to the system for both glances away from the roadway and glances to the instrument panel. The penultimate chapter of this work presents training guidelines and recommendations for consumer education with driving automation systems based on this and other research that has been conducted on driver interaction with driving automation systems. The results of this research indicate that driver training should be a key focus in future efforts to ensure the continued safe use of driving automation systems as they continue to emerge in the vehicle fleet. / Doctor of Philosophy / While driving automation systems are becoming increasingly common on new vehicles, drivers seem to know very little about them. Previous studies have found that owners of vehicles equipped with advanced technologies have demonstrated misperceptions or lack of awareness about system limitations, which may impact driver comfort with and reliance on these systems. Partial driving automation systems are designed to assist drivers in some vehicle operation demands, they are not, however, designed to completely remove the driver from the driving task. The following dissertation describes an investigation of driver behavioral adaptation while using driving automation systems with the goal of improving consumer education and training.

This research is focused on driver behavior in traffic, especially during car-following situations and safety critical events. Driving behavior is considered as a human decision process in this research which provides opportunities for an artificial driver agent simulator to learn according to naturalistic driving data. This thesis presents two mechine learning methodologies that can be applied to simulate driver naturalistic driving behavior including risk-taking behavior during an incident and lateral evasive behavior which have not yet been captured in existing literature. Two special machine learning approaches Backpropagation (BP) neural network and Neuro-Fuzzy Actor Critic Reinforcement Learning (NFACRL) are proposed to model driver behavior during car-following situation and safety critical events separately. In addition to that, as part of the research, state-of-the-art car-following models are also analyzed and compared to BP neural network approach. Also, driver heterogeneity analyzed by NFACRL method is discussed. Finally, it presents the findings and limitations drawn from each of the specific issues, along with recommendations for further research. / Master of Science

This thesis explores the possibility of identifying risk behaviour patterns among forklift drivers through the analysis of telemetry data using unsupervised clustering algorithms. The objective is to predict whether certain behaviour patterns increase the risk of accidents. With the increasing accessibility of Internet of Things technology, data from forklifts has become more available, allowing for the study of driver behaviour. The telemetry data utilised is sourced from Toyota Material Handling Manufacturer Sweden’s internal database, collected from Data Handling Units that are installed on forklifts across Europe. This data, referred to as shock data, is triggered when a force is applied to the forklift, such as a collision. The thesis investigates combinations of various clustering algorithms and dataset modifications. The evaluation of the results is conducted using several quantitative measures and visualisation, along with analysis of time distribution, geographical placement, comparison of forklift models, and comparison with "no-shock" data. The evaluation yields K-Prototypes and K-Means as the best performing algorithms, while indicating that soft clustering and density-based clustering are not well-suited for the data. The identified best performing algorithms reveal two recurring driver behaviour patterns: the first one being driving forward at high speed with the lift motor idle, and the second pattern being driving backward at low speed while lowering the forks. Furthermore, a majority of the data points remain unclassified into specific behaviour patterns, suggesting that the dataset or methods used may not be sufficient enough. The inclusion of additional featuers, such as steering angle and forklift height, should be considered for exploration in future work. The thesis demonstrates the feasibility of identifying risk behaviour patterns, with potential for future research expanding on the findings to further contribute to the prevention of workplace accidents involving forklifts.

Advanced Driver Assistance Systems (ADAS) offer the possibility of helping drivers to fulfill their driving tasks. Automated vehicles are capable of communicating with surrounding vehicles (V2V) and infrastructure (V2I) in order to collect and provide essential information about driving environment. Studies have proved that automated vehicles have a potential to decrease traffic congestion on road networks by reducing the time headway, enhancing the traffic capacity and improving the safety margins in car following. Furthermore, vehicle movement and driver’s behavior of conventional vehicles will be affected by the presence of automated vehicles in traffic networks. Despite different encouraging factors, automated driving raises some concerns such as possible loss of situation awareness, overreliance on automation and degrading driving skills in absence of practice. Moreover, coping with complex scenarios, such as merging at ramps and overtaking, in terms of interaction between automated vehicles and conventional vehicles need more research. This thesis work aims to investigate the effects of automated vehicles on driver’s behavior and traffic performance. A broad literature review in the area of driving simulators and psychological studies was performed to examine the automated vehicle effects on driver’s behavior. Findings from the literature survey, which has been served as setup values in the simulation study of the current work, reveal that the conventional vehicles, which are driving close to the platoon of automated vehicles with short time headway, tend to reduce their time headway and spend more time under their critical time headway. Additionally, driving highly automated vehicles is tedious in a long run, reduce situation awareness and can intensify driver drowsiness, exclusively in light traffic. In order to investigate the influences of automated vehicles on traffic performance, a microscopic simulation case study consisting of different penetration rates of automated vehicles (0, 50 and 100 percentages) was conducted in VISSIM software. The scenario network is a three-lane autobahn segment of 2.9 kilometers including an off-ramp, on-ramp and a roundabout with some surrounding urban roads. Outputs of the microscopic simulation in this study reveal that the positive effects of automated vehicles on roads are especially highlighted when the network is crowded (e.g. peak hours). This can definitely count as a constructive point for the future of road networks with higher demands. In details, average density of autobahn segment remarkably decreased by 8.09% during p.m. peak hours in scenario with automated vehicles. Besides, Smoother traffic flow with less queue in the weaving segment was observed. Result of the scenario with 50% share of automated vehicles moreover shows a feasible interaction between conventional vehicles and automated vehicles. Meaningful outputs of this case study, based on the input data from literature review, demonstrate the capability of VISSIM software to simulate the presence of automated vehicles in great extent, not only as an automated vehicle scenario but also a share of them, in traffic network. The validity of the output values nonetheless needs future research work on urban and rural roads with different traffic conditions.

Este trabalho apresenta um estudo a respeito dos acidentes de trânsito envolvendo o transporte público urbano por ônibus e objetiva, principalmente, a relação dos acidentes versus comportamento dos condutores. Os dados utilizados, a partir do estudo de caso realizado na cidade de Ribeirão Preto-SP, têm duas origens: dados da operação do sistema (quantidade de horas operadas, quilometragem e frota) e dados gerados pelo monitoramento por câmeras. Este último tem como principais variáveis o comportamento dos condutores durante a condução dos veículos. Através de índices de exposição, utilizando as variáveis da operação do sistema, juntamente com os acidentes por linha, foi possível identificar quais as linhas com piores indicadores, ou seja, quais linhas merecem maior atenção na criação de intervenções ou campanhas para redução do número de acidentes. Foram tratados aproximadamente 72 mil dados e a partir dos dados extraídos e processados estatisticamente para obtenção das variáveis mais significativas com relação aos acidentes. A variável com maior peso foi a utilização de telefones celulares durante a condução e que tem alta utilização nos horários de pico, da ordem de 53% dos eventos ocorrem em horários de maior movimento de passageiros e de tráfego intenso. O tempo de utilização do celular durante a condução do ônibus é majoritariamente maior que 5 minutos, ou seja, 33% dos eventos mostram que os condutores utilizam o telefone celular por mais de 5 minutos. Criou-se uma taxonomia do comportamento dos condutores, baseando-se, principalmente, no banco de dados e tem como função instituir uma base teórica dos comportamentos, ajudando a descrevê-los e entendê-los. É dessa maneira que a segunda variável foi discutida. O avanço do sinal amarelo, com nível de significância alta, não representa em sua totalidade um comportamento decidido (decisões conscientes do condutor), mas algumas vezes comportamento involuntário (falhas e lapsos). Essa distinção de comportamento decidido ou involuntário é complexa, mas sabemos que decisões conscientes são mais frequentes. Este trabalho identificou quais as linhas que necessitam de intervenções e quais os problemas com o comportamento dos condutores, direcionando o operador do sistema de transporte às campanhas necessárias para redução dos acidentes, ou mesmo possibilitando outras empresas a replicarem as análises para a sua realidade operacional. / This work presents a study about the traffic accidents involving urban public transport by bus and objective, especially the relation of accidents versus driver behavior. The data used from the case study in the city of Ribeirão Preto, have two sources: System operation data (number of operated hours, mileage and fleet) and data generated by the monitoring camera system. The latter\'s main variables driver behavior while driving the vehicle. Through levels of exposure, using the system operating variables, along with accidents per line, it was possible to identify lines with worse indicators, or which lines deserve close attention in setting up operations or campaigns to reduce the number of accidents. Approximately 72,000 data were treated and statistically processed to obtain the most significant variables in relation to accidents. The most significant variable was the use of mobile phones while driving and which has high utilization during peak hours, the order of 53% of events occur when there is a large number of passengers and traffic jam. The utilization of the cellphone while driving the bus is overwhelmingly greater than 5 minutes, i.e., 33% of the events showed that drivers use the mobile phone for more than 5 minutes. Has been created drivers behavior taxonomy, based mainly in the database and with aim to establish a theoretical basis of bus drivers behavior, helping to describe and understand them. This is how the second significant variable was discussed. The advance of the yellow sign is not totally a decided behavior (conscious decisions of the driver), but sometimes involuntary behavior (failures and lapses). This decided or involuntary behavior distinction is complex, but we know that conscious decisions are more frequent. This work identified which lines needed intervention and what are the problems with the behavior of drivers, orienting the operator of the transportation system to needed campaigns to reduce accidents, or even allowing other companies to replicate the analysis to their operational reality.

Previous research regarding the relationship between speeding behavior and crashes suggests that drivers who engage in frequent and extreme speeding behavior are over-involved in crashes. However, many of these earlier studies relied on estimates of prevailing and pre-crash speeds, and as a result, their conclusions have been questioned. Over the last several years automotive manufacturers have begun installing airbag systems that collect and maintain accurate pre-crash speeds. Though, patterns of driver speeding behavior are also necessary to discern whether drivers who regularly participate in speeding have increased risk of crash involvement. This dissertation presents a framework and methods for quantifying and analyzing individual driver behavior using instrumented vehicles. The goals of the research were threefold: 1) Develop processing methods and observational coding systems for quantifying driver speeding using instrumented vehicle data; 2) Develop a framework for analyzing aggregate and individual driver speeding behavior; and 3) Explore the potential application of behavioral safety concepts to transportation safety problems. Quantitative assessments of driver speeding behavior could be used in combination with event data recorder data to analyze crash risk. Additionally, speed behavior models could aid in the early identification of problem behavior as well as in the development of targeted countermeasure programs. For this research, 172 instrumented vehicles from the Commute Atlanta program were utilized to collect individual driver speeding behavior. Continuous monitoring capabilities allowed the capture of speed and location for every second of vehicle operation. Driver speeds were then matched to road networks and subsequently to posted speed limits using a geographic information system. This allowed differences between the drivers speed and the posted speed. Several processes were developed to assess the accuracy and the completeness of the data prior to analysis. Finally, metrics and analysis frameworks were tested for their potential usefulness in future behavioral risk analysis. The results of the research were both positive and staggering. On average, nearly 40% of all driving activity by the sample population was above the posted speed limit. The amount and extent of speeding was highest for young drivers. Trends indicate that speeding behavior decreases in amount and extent as age increases.

In this research the problem of incident congestion on surface street networks is addressed. Microscopic simulation is used to simulate incident scenarios on various corridors in the Tampa Bay area. The effect of the three factors, namely, network, speed and signal strategies on the traffic flow is studied. The network performance is based on Highway Capacity Manual specified measures of effectiveness prepared by the Transportation Research Board. Three inherently different city corridors, high, medium and low volume, are used to test the strategies developed. The strategies investigated include varying speed limits during incidents and using pre-timed and semi-actuated signals that respond to real time traffic volumes. The effectiveness measures are total delay in vehicle minutes, average speed in miles per hour and average travel time in seconds. Different facilities on a network include intersections; both signalized and unsignalized, local highways and arterials. The outputs from the simulation model is used to set up a factorial design to study the interaction between network type, signal strategy and speed strategy with the measures of effectiveness being the response variables. This type of corridor analysis is unique and provides decision support for local transportation planning departments for making corridor enhancements. In most city, state or county planning departments road planning is merely based on projected traffic demand using existing static models and does not factor necessary adjustments for incidents. Another unique aspect of this research is that variable speed limits are tested on surface streets. Such a test is not available in the literature. With dynamic message signs, next generation communication networks for traffic signal control and ITS technologies available, it is possible to implement the strategies suggested in this research.

Since the creation of the automobile, there has been an effort to create and implement mechanical and electronic devices that would improve vehicle safety. In recent years, electronic technologies have become more efficient and cost effective, therefore creating a great spike in widespread implementation. These safety related devices have to be tested for their reliability and amount of help they provide the driver with. The end user (the driver) has to be involved for a successful device. This research presents the methodology used to evaluate the effectiveness of the rearview video system (RVS) used in vehicles, especially in large commercial trucks and effectively the methodology for a more complete investigation of the problem of correctly implementing a safety device. The focus of this research is backing crashes that involve large trucks. The countermeasure tested was a rearview video system which provides a rear view to the driver in real time. A traditional crash data analysis is almost impossible since there is not enough data to perform it, and no data are available for the use of this system since it is fairly new to the market. A driver experiment under controlled conditions was used to create and collect the data necessary for the analysis. The experiment yielded a total of 71 crashes out of 270 maneuvers (26.3%). When analyzed, three backing neuvers yielded different probabilities of having a backing crash with and without the RVS. The increase in stop rate ranged from 46.67 percent to 4.44 percent. This is interpreted as crash reduction due to the device. Driver behavior was observed during the experiment and measured for significant differences. The drivers needed on average 6.47 seconds more time for the maneuvers with the RVS in use. They spent less time looking at mirrors and did it less frequently in order to accommodate the additional glance location presented to them. Overall they seemed to be able to manage their time with some exceptions. The driver acceptance of the device was also measured with a survey given to them after they completed the test. Overall in all measures the majority of drivers agreed that the system helps in reducing the rear blind spot and thus it is a helpful device in reducing backing crashes since it will help them avoid potential hazards while backing. The majority also stated that they would like to have the device in their truck for every day operations. These results show an acceptance of the device and therefore the maximization of the device's use and potential benefits. The RVS is therefore effective in reducing potential backing crashes. The results presented here are limited, and inferences are made with the experiment conditions in mind. General application of the results is possible, with certain assumptions and restrictions.

Drivers are expected to traverse through an intersection in malfunctioning flash mode in the same manner as a stop-sign controlled intersection. Red/red flash corresponds to four-way stop control and yellow/red flash corresponds to two-way stop control. However, at a red flashing signal there is no assurance that a driver can see the cross street indication (i.e., yellow or red flash) and therefore does not know if the intersection is operating as a two-way or four-way flash. In addition, some drivers appear unclear on the rules at a flashing signal. This confusion makes the intersection more accident prone. This study builds upon several previous studies, using data extracted from existing files. The objective of this study is to determine the level of drivers' understanding of the flash control through an analysis of violation rates and types at recorded intersection in malfunction flash. Comparing these violation rates to those at comparable stop-control intersections will help illustrate the difference in drivers' understanding of these similar intersection control devices.

abstract: Alternative fuel vehicles (AFVs) have seen increased attention as a way to reduce reliance on petroleum for transportation, but adoption rates lag behind conventional vehicles. One crucial barrier to their proliferation is the lack of a convenient refueling infrastructure, and there is not a consensus on how to locate initial stations. Some approaches recommend placing stations near where early adopters live. An alternate group of methods places stations along busy travel routes that drivers from across the metropolitan area traverse each day. To assess which theoretical approach is most appropriate, drivers of compressed natural gas (CNG) vehicles in Southern California were surveyed at stations while they refueled. Through GIS analysis, results demonstrate that respondents refueled on the way between their origins and destinations ten times more often than they refueled near their home, when no station satisfied both criteria. Freeway interchanges, which carry high daily passing traffic volumes in metropolitan areas, can be appropriate locations for initial stations based on these results. Stations cannot actually be built directly at these interchange sites, so suitable locations on nearby street networks must be chosen. A network GIS method is developed to assess street network locations' ability to capture all traffic passing through 72 interchanges in greater Los Angeles, using deviation from a driver's shortest path as the metric to assess a candidate site's suitability. There is variation in the ability of these locations to capture passing traffic both within and across interchanges, but only 7% of sites near interchanges can conveniently capture all travel directions passing through the interchange, indicating that an ad hoc station location strategy is unlikely to succeed. Surveys were then conducted at CNG stations near freeway interchanges to assess how drivers perceive and access refueling stations in these environments. Through comparative analysis of drivers' perceptions of stations, consideration of their choice sets, and the observed frequency of the use of a freeway to both access and leave these stations, results indicate that initial AFV stations near freeway interchanges can play an important role in regional AFV infrastructure. / Dissertation/Thesis / Doctoral Dissertation Geography 2015