The following study aimed to understand pure wayfinding search strategies and identify the most efficient strategy when discovering a new environment. Participants performed one drive in a simulated city environment within a driving simulation lab. Their objective was to locate a target within the city, without any navigational aids (maps, GPS, etc.). Efficiency measures, such as number of road segments covered between origin and target, were evaluated. Experience and gender were also analyzed. There was a significant difference of efficiency between search strategies. Experience did not impact a driver’s efficiency. The knowledge from this study can be used in city planning of high tourist areas or major facilities.

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In this thesis, the author developed a retroreflective sheeting selection technique for traffic signs. Previous research was used to determine the luminance needed by drivers (demand luminance). The author used roadways scenarios to determine the amount of luminance the retroreflective sheeting on a sign would produce (supply luminance). A spreadsheet was developed to determine the performance of different retroreflective sheeting types by comparing the demand and supply luminance for specific roadway scenarios. Using the results of previous studies, three demand luminance levels were created: replacement, adequate, and desirable. The replacement level represents the level of luminance when a sign needs to be replaced and is 2.5 cd/m2. The adequate level is the recommended amount of luminance when installing new traffic signs and is 10 cd/m2. The desirable level is the approximate level when additional luminance has diminishing returns and is 30 cd/m2. Supply luminance on a specific traffic sign was determined by evaluating roadway geometries, sign placement, retroreflective sheeting type and vehicle data. The author reviewed roadway geometries in Texas to estimate typical number of lanes, shoulder widths and horizontal curvature in the US. Sign placement from the MUTCD determined the typical lateral placements, sign heights, and sign twists. Vehicle data included vehicle dimensions and headlamp type. Both the supply and demand luminance were determined for a specific viewing distance for a given scenario. The viewing distance is the distance a driver needs to read or recognize a sign to respond properly. In addition, the type of sign, alphanumeric or symbol, determined how this distance was calculated. The author developed four sign groups to calculate the distance required to read and respond to a traffic sign, including 1) Stop required, 2) Reduction in speed required, 3) Read the message provided, and 4) Change of lane required. For symbol signs, the minimum required visibility distance (MRVD) was determined for the sign group and for text signs, the viewing distance at a legibility index (LI) of 30 ft/in was found. At these distances, the author calculated the supply luminance and then compared it to the demand luminance levels to determine the performance level. The author developed the Retroreflective Sheeting Selection Spreadsheet (RSSS) to allow others to use the methodology presented in this thesis. RSSS allows users to input the roadway data, vehicle data, and sign data. RSSS takes this information and looks up the supply luminance for the scenario. RSSS then compares the supply luminance to the demand luminance levels and outputs the retroreflective sheeting performance level for the scenario.

The dissertation examines the relationship between the number of lane changes, the speed of the ramp lane, and the location upstream of the ramp split. Analyses indicate the number of lane changes exhibits a parabolic relationship with respect to the ramp lane speed, and the number of lane changes exhibits gamma-distributed relationship with respect to the distance upstream of the ramp. The macroscopic lane changing model presented is best characterized as the development of generalized lane-changing relationships, and provides a starting point from which more complex corridor-level models can be developed. This study also identifies an unusual car-following behavior exhibited by certain lane-changing drivers. When the target lane is moving slowly, some lane-changing drivers will slow down, causing a disruption in their initial lane. Regression analysis is used to estimate the speed upstream of the initial lane to indicate the disruption is responsible for the lateral propagation of congestion. The lane choice of exiting vehicles is also studied. Lane choice appears to be a function of origin/destination, and freeway speed. As speeds in the general purpose lanes decrease, exiting vehicles are more likely to wait longer to move into the exit ramp lanes, resulting in an increased lane changing density. Results from this study are expected to have the greatest impact on microscopic lane-change model validation. Additionally, results have implications for design and safety issues associated with freeway ramps. As data collection technologies improve and data becomes increasingly available, this research provides the basis for the further development of more elaborate lane-changing models.

In comparison to conventional cruise control, adaptive cruise control (ACC) vehicles are capable of sensing forward traffic and slowing to accommodate as necessary. When no forward vehicles are present, ACC function is the same as conventional cruise control. However, with ACC, when a slower vehicle is detected, the ACC system will decelerate and follow at a selected time-based distance. While slowing to follow, the driver will experience a system-controlled deceleration of the ACC vehicle. An experiment was conducted to evaluate driver preferences for the distance at which the primary deceleration occurs and the level of deceleration that is obtained. Driver intervention was required in one trial and driver response behavior was measured. Ten men and ten women in two age groups evaluated the decelerations from a cruise speed of 70mph to a following speed of 55mph behind a confederate lead vehicle on the highway. Evaluations can be made using four scales: Good vs. Bad, Comfortable vs. Uncomfortable, Jerky vs. Smooth, and Early vs. Late. Decelerations of approximately 0.06g which occur approximately 200ft to 250ft behind the lead vehicle were most preferred. Prior to intervention, foot position ranged from a point directly below the brake pedal to 16.4in from the brake pedal. Foot motion began between 21.12s time-to-collision (TTC) and 3.97s TTC. Eighty percent of the participants paused to "cover" the brake before final motion to activate the brake. The older age group intervened (braked) later than the younger age group. Driver braking after intervention ranged from 0.16g to 0.32g. / Master of Science

This thesis presents an econometric test of the offsetting behavior hypothesis concerning drivers of light trucks relative to cars. The main objective is to determine whether drivers of light trucks offset perceived safety benefits associated with larger vehicles by driving more aggressively than drivers of cars, subsequently causing more fatal crashes, holding all else constant. An empirical model using data on pedestrian fatalities across the United States over a five-year period is developed and analyzed in order to capture the desired results. Estimates provide substantial evidence in support of the offsetting behavior hypothesis. To strengthen the case for driver offsetting behavior beyond previous studies, the model is estimated again using pedalcyclist fatalities. The results also point to interesting conclusions regarding the effects of increased speed limits on the behavior of drivers.

The research objectives were (1) to perform a detailed review of the literature on speech recognition technology and the attentional demands of driving; (2) to develop decision tools that assist designers of in-vehicle systems; (3) to experimentally examine automatic speech recognition (ASR) design parameters, input modalities, and driver ages; and (4) to provide human factors recommendations for the use of speech recognition technology in automotive applications. Two experiments were conducted to determine the effects of ASR design parameters, input modality, and age on driving performance, system usability, and driver preference/acceptance. Eye movement behavior, steering input behavior, speed maintenance behavior, reaction time to forward scene event, task completion time, and task completion errors when driving and performing in-vehicle tasks were measured. Driver preference/acceptance subjective data were also recorded. The results showed that ASR design parameters significantly affected measures of driving performance, system usability, and driver preference/acceptance. However, from a practical viewpoint, ASR design parameters had a nominal effect on driving performance. Differences measured in driving performance brought on by changes in ASR system design parameters were small enough that alternative ASR system designs could be considered without impacting driving performance. No benefits could be claimed for ASR systems improving driving safety/performance compared to current manual-control systems. Speech recognition system design demonstrated a moderate influence on the usability of in-vehicle tasks. Criteria such as task completion times and task completion errors were shown to be different between speech-input and manual-input control methods, and under different ASR design configurations. Therefore, trade-offs between ASR system designs, and between speech-input and manual-input systems, could be evaluated in terms of usability. Finally, ASR system design had a nominal effect on driver preference/acceptance. Further research is warranted to determine if long-term use of ASR systems with less than optimal design parameters would result in significantly lower values for driver preference/acceptance compared to data collected in this research effort. Human factors recommendations for the use of ASR technology in automotive applications are included. The recommendations are based on the empirical research and the literature review on speech recognition technology and the attentional demands of driving. / Ph. D.

Master of Science / Department of Civil Engineering / Sunanda Dissanayake / Motorists generally follow the guidance provided by the pavement markings while traveling on roads. Under certain circumstances, construction joints may be necessary in concrete pavements, which are generally designed to be coincident with the pavement markings. At some locations, however, the construction joints may not exactly match the pavement markings. These situations may create confusion in the minds of drivers, which may lead them to follow joints instead of the markings. In the absence of detailed studies on this topic, an effort was made in the present study to evaluate the effects of unmatched longitudinal construction joints and pavement markings on the lateral position of vehicles. Sites having the characteristics of unmatched longitudinal construction joints and pavement markings were identified, and detailed data were collected at one of the sites. Video camera technique was used for capturing the movements of vehicles along the test site for longer durations. The video tapes were later reduced in the laboratory to extract necessary information. The distance to the right side of the vehicles from right curb of the road, the type of vehicle, presence of vehicles in the adjacent lane, weather and light conditions, and the movement of the vehicles immediately after traversing the section of the road having unmatched longitudinal construction joints and pavement markings were the main parameters observed while reducing the data. Two surveys were also conducted for gathering the opinions of some practitioners and engineers on the issue. Statistical analyses were carried out using t-tests to evaluate if there were differences. Several comparisons were made for different types of vehicles based on various conditions. The analysis results indicated that there was a statistically significant difference between the actual and expected distances to the center-line of vehicles, implying that the lateral position of vehicles may have been affected by the joints. A model was also developed to determine the lateral position of the vehicles by considering the parameters used in the analysis. Based on the survey results and analysis of field data, it was found that the lateral position of vehicles may have been affected by the unmatched joints and pavement markings.

Reductions in speed and, more critically, speed variability between vehicles are thought to reduce crash risk in work zones. Numerous factors, such as lane width and lateral clearance and activity level, have been shown to influence speed but very little research has considered how multiple factors might interact to affect driver performance in work zones. This study evaluated the effect of work zone barrier type, presence of a lateral buffer, and work zone activity level on measures of speed and lane position. Twelve middle aged and twelve senior subjects drove in a National Advanced Driving Simulator (NADS) MiniSim. The subjects drove faster and with less variability in work zones with concrete barriers. Measures of speed and lane position were more heterogeneous across groups with 42-inch channelizers compared to drums. Speed was reduced and more variable in work areas with a high level of activity than in areas with a low level of activity. On the whole, the presence of a lateral buffer reduced speed variability in the high activity areas but this response was not uniform across all drivers. This research demonstrates that driving simulators can be used to evaluate how work zone factors may interact with one another to affect driver performance for different driver groups. While the results from this study corresponded to observations from actual work zones, the driving simulator must be validated with on-road data before generalizations can be made.

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