Airport road access wayfinding refers to a process in which a driver makes a decision to navigate using information support systems in order to arrive to airport successfully. The purpose of this research is to evaluate senior drivers’ behaviour of alternative airport road access designs. In order to evaluate the impact of wayfinding, the combination of simulated driving and completion of a questionnaire were performed. Quantitative data was acquired to give significant results justifying the research outcomes and allow non-biased interpretation of the research results. It represents the process within the development of the methodology and the concept of airport road access design and driving behaviour. Wayfinding complexity varied due to differing levels of road-side furniture. The simulated driving parameters measured were driving mistakes and performances of senior drivers. Three types of driving scenarios were designed consisting of 3.8 miles of airport road access. 40 senior drivers volunteered to undertake these tasks. The questionnaire was used as a supporting study to increase the reliability and validity of the research. Respondents who volunteered for the simulated driving test were encouraged to participate in the questionnaire sessions. The questionnaire was answered after each simulation test was completed. The Mean, Standard Deviation (SD) and Two-Way ANOVA test were used to analyse the results and discussed with reference to the use of the driving simulation. The results confirmed that age group has no significant effect of airport road access complexity design on driving behaviour. Although many studies have been conducted on wayfinding in general, a detailed evaluation on airport road access wayfinding network and driving behaviour in respect of senior drivers were still unexplored domains.

Driver behaviour has become an important aspect of transport research and over the years a considerable number of car following models have been developed. However, many of these models do not accurately simulate actual driving behaviour due to a lack of suitable qualitative and quantitative data. Moreover, the inclusion of socioeconomic variables in the existing models to ascertain the effect on car following behaviour is lacking. This research underlines the need to further investigate driving behaviour and car following models and to develop techniques to provide a better understanding of driver-vehicle interactions during car following. It investigates data collection techniques and develop better techniques to enhance and improve the collection of microscopic driver behaviour and traffic flow data. This study developed a novel data collection technique which involved instrumenting a private vehicle with front and rear advanced radar sensors, both forward and rear facing video-audio recorders connected to GPS based time series speed and distance measurement devices, an in-vehicle computer logging vehicle speed and a CAN monitoring interface user program to provide real time monitoring and display of data. This system has been utilised to collect a more enhanced and reliable microscopic driver behaviour data in three consecutive vehicles movements which represents an improvement from previously used systems. Three different versions of the GHR car following model were produced for: car following car, truck following car and car following truck. Further analysis of the GHR model showed that in the case of car following car, car drivers responses to the lead car are more obviously stronger than in the case of truck following a car. A distance-based car following model and distance-based two-leader car following model that predict the safe following distance of following vehicles were developed to provide a better understanding of driver behaviour. An extension of these models to include gender, corridor (road) type and vehicle occupancy showed evidence of statistical significance of these variables on driver behaviour. A bus following model that predicts the “following distance” also has been calibrated to describe the interactions between a bus and a car within urban-rural driving conditions. In addition, data analysis showed that drivers were inconsistent with their driving behaviour and that there was variability in driving behaviour across the drivers observed in keeping a safe or desired following distance. This study provides a platform for a number of future research agendas including data collection techniques for collection of driver behaviour data; evaluation of different ITS technologies; impact assessment of ACC on driver safety and improvement of traffic microscopic simulation tools in order to strengthen their ability to simulate realistic transport problems for efficient and effective transportation systems.

<p>Speed choice is one of the more characteristic features of driver behaviour. The speed a driver chooses to travel at determines the degree of difficulty he or she operates under. Higher speeds lead to more accidents, higher accident risk and more severe consequences of an accident. The present thesis examines factors that are associated with drivers’ speed choice. Repeated measures of drivers’ speed showed a reasonably high correlation, but also that stability in speed varied with road layout between measurement sites. Effects of police enforcement were studied on roads with temporary reduced speed limits (from 50 km/h to 30 km/h) during school hours. Lower speeds were found on roads with enforcement and drivers observed on one such road showed a higher perceived probability of detection than did drivers observed on a non-enforced road. However, in a laboratory study higher driving speeds and lower accident risk was associated with enforced roads. Drivers not informed about existing speed limits overestimated the limits to a large extent and chose driving speeds above the limit as did drivers informed about the limits. In an on-the-road survey, fast drivers reported higher driving speed, thought a higher percentage of other drivers were speeding and had a more positive attitude towards speeding than did slower drivers. The results suggest that drivers’ travel speed is influenced by road factors, other road users and enforcement. Furthermore, drivers’ own judgements of what is an appropriate speed are also important for speed choice.</p>

Speed choice is one of the more characteristic features of driver behaviour. The speed a driver chooses to travel at determines the degree of difficulty he or she operates under. Higher speeds lead to more accidents, higher accident risk and more severe consequences of an accident. The present thesis examines factors that are associated with drivers’ speed choice. Repeated measures of drivers’ speed showed a reasonably high correlation, but also that stability in speed varied with road layout between measurement sites. Effects of police enforcement were studied on roads with temporary reduced speed limits (from 50 km/h to 30 km/h) during school hours. Lower speeds were found on roads with enforcement and drivers observed on one such road showed a higher perceived probability of detection than did drivers observed on a non-enforced road. However, in a laboratory study higher driving speeds and lower accident risk was associated with enforced roads. Drivers not informed about existing speed limits overestimated the limits to a large extent and chose driving speeds above the limit as did drivers informed about the limits. In an on-the-road survey, fast drivers reported higher driving speed, thought a higher percentage of other drivers were speeding and had a more positive attitude towards speeding than did slower drivers. The results suggest that drivers’ travel speed is influenced by road factors, other road users and enforcement. Furthermore, drivers’ own judgements of what is an appropriate speed are also important for speed choice.

Introduction: Although several studies have examined self-regulatory practices in older drivers, most have relied on self-report. Blanchard (2008) was the first to examine actual driving patterns more objectively (using in-vehicle devices), and the associations between driver perceptions and self-regulatory practices. However, her sample of older drivers living in Southwestern Ontario was only monitored for one week between June and October. Winter conditions in northern climates appear to influence the driving patterns of older adults, however the only evidence to date is based on self-report (e.g., Sabback & Mann, 2005). Purposes: The aims of the thesis were to: 1) replicate Blanchard’s findings on the associations between driver perceptions and self-regulatory practices in older drivers; and 2) extend this investigation by examining driving over a longer monitoring period in the winter. Methods: A convenience sample of 47 drivers aged 65 to 91 (49% female) from Southwestern Ontario was monitored for two consecutive weeks between late November and March. Driving data was collected using two electronic devices (one with GPS), which were installed at the first of two home visits. Information on weather and road conditions was collected from archives and descriptions in participant trip logs. Participants completed questionnaires concerning background and usual driving habits. Driver perceptions were assessed using the Driving Comfort (DCS) and Perceived Driving Abilities (PDA) scales, while self-reported usual practices were examined using the Situational Driving Frequency (SDF) and Avoidance (SDA) scales. Functional driving-related abilities were assessed using the AAA/CAA’s Roadwise Review and interviews were conducted at the second home visit, at which point devices were removed and trip logs collected. Results: Driver perceptions (particularly night comfort) were significantly related to multiple indictors of driving (distance, duration, radius from home and night driving) in the expected directions. Men had higher comfort scores and better perceptions of their driving abilities and concurrently drove more often, greater distances and further from home. Participants drove on average five days a week over the winter monitoring period. Over half the 94-day monitoring period had inclement weather, while 67% of the period had poor road conditions. Nonetheless, all 46 participants drove at least once in bad weather and 73% did so in darkness. Distance driven at night varied by month of participation, with people driving more at night during December (average 50 km), compared to March (average of only 13 km). Those with lower daytime comfort scores (>50%) scores drove less on days with inclement weather (p=.03). The sample was also more likely to make social trips on clear days (p=.002) and out-of-town trips on days with good road conditions (p=.02). Conclusions: The study replicated Blanchard’s (2008) findings that driver perceptions are strongly associated with actual behaviour, regardless of the season. And both studies indicate that older drivers may not self-regulate as much as they say they do on avoidance questionnaires. Driving was fairly consistent over the two weeks, except for radius and night distance and the additional week of monitoring was more likely to capture night driving. Nonetheless, the present study provides only a snapshot of behaviour and findings should not be generalized beyond urban dwelling, well-educated, healthy and active older drivers from one part of Canada. Further studies, with larger more diverse samples (living in different regions) and longer monitoring periods, are required to advance our knowledge of self-regulatory practices in older drivers and related decision-making processes.

Introduction: Although several studies have examined self-regulatory practices in older drivers, most have relied on self-report. Blanchard (2008) was the first to examine actual driving patterns more objectively (using in-vehicle devices), and the associations between driver perceptions and self-regulatory practices. However, her sample of older drivers living in Southwestern Ontario was only monitored for one week between June and October. Winter conditions in northern climates appear to influence the driving patterns of older adults, however the only evidence to date is based on self-report (e.g., Sabback & Mann, 2005). Purposes: The aims of the thesis were to: 1) replicate Blanchard’s findings on the associations between driver perceptions and self-regulatory practices in older drivers; and 2) extend this investigation by examining driving over a longer monitoring period in the winter. Methods: A convenience sample of 47 drivers aged 65 to 91 (49% female) from Southwestern Ontario was monitored for two consecutive weeks between late November and March. Driving data was collected using two electronic devices (one with GPS), which were installed at the first of two home visits. Information on weather and road conditions was collected from archives and descriptions in participant trip logs. Participants completed questionnaires concerning background and usual driving habits. Driver perceptions were assessed using the Driving Comfort (DCS) and Perceived Driving Abilities (PDA) scales, while self-reported usual practices were examined using the Situational Driving Frequency (SDF) and Avoidance (SDA) scales. Functional driving-related abilities were assessed using the AAA/CAA’s Roadwise Review and interviews were conducted at the second home visit, at which point devices were removed and trip logs collected. Results: Driver perceptions (particularly night comfort) were significantly related to multiple indictors of driving (distance, duration, radius from home and night driving) in the expected directions. Men had higher comfort scores and better perceptions of their driving abilities and concurrently drove more often, greater distances and further from home. Participants drove on average five days a week over the winter monitoring period. Over half the 94-day monitoring period had inclement weather, while 67% of the period had poor road conditions. Nonetheless, all 46 participants drove at least once in bad weather and 73% did so in darkness. Distance driven at night varied by month of participation, with people driving more at night during December (average 50 km), compared to March (average of only 13 km). Those with lower daytime comfort scores (>50%) scores drove less on days with inclement weather (p=.03). The sample was also more likely to make social trips on clear days (p=.002) and out-of-town trips on days with good road conditions (p=.02). Conclusions: The study replicated Blanchard’s (2008) findings that driver perceptions are strongly associated with actual behaviour, regardless of the season. And both studies indicate that older drivers may not self-regulate as much as they say they do on avoidance questionnaires. Driving was fairly consistent over the two weeks, except for radius and night distance and the additional week of monitoring was more likely to capture night driving. Nonetheless, the present study provides only a snapshot of behaviour and findings should not be generalized beyond urban dwelling, well-educated, healthy and active older drivers from one part of Canada. Further studies, with larger more diverse samples (living in different regions) and longer monitoring periods, are required to advance our knowledge of self-regulatory practices in older drivers and related decision-making processes.

This thesis investigates the effect of a feedback-reward system on speeding and tailgating behaviours. Data utilized in this study were collected from 37 participants through a field trial commissioned by Transport Canada. In this trial, a feedback-reward system was investigated, which provided feedback and rewards to the drivers based on speed limit compliance and safe headway maintenance. The trial consisted of three phases: baseline, intervention, and post-intervention. During the intervention phase, real-time feedback was provided on an in-vehicle display. Participants also accumulated reward points and could view related information on a website. Results indicate that the intervention resulted in a significant increase in speed limit compliance, and this positive effect, although dampened, was still apparent after system removal. Similarly, results on headway compliance rate indicate a positive intervention effect, however, this effect did not sustain after system removal. These findings have implications for developing better aids to improve driving behaviour.

This thesis investigates the effect of a feedback-reward system on speeding and tailgating behaviours. Data utilized in this study were collected from 37 participants through a field trial commissioned by Transport Canada. In this trial, a feedback-reward system was investigated, which provided feedback and rewards to the drivers based on speed limit compliance and safe headway maintenance. The trial consisted of three phases: baseline, intervention, and post-intervention. During the intervention phase, real-time feedback was provided on an in-vehicle display. Participants also accumulated reward points and could view related information on a website. Results indicate that the intervention resulted in a significant increase in speed limit compliance, and this positive effect, although dampened, was still apparent after system removal. Similarly, results on headway compliance rate indicate a positive intervention effect, however, this effect did not sustain after system removal. These findings have implications for developing better aids to improve driving behaviour.

Parking a vehicle can often lead to frustration, air pollution and congestion due to limited availability of parking spaces. With increasing population density this problem can certainly increase unless addressed. Parking lots occupy large areas of scarce land resource therefore it is necessary to identify the driving behaviour in a parking lot to improve it further. This Paper tries study the driving behaviour in the parking lot and for this endeavours it conducted direct observation in three parking lots and used GPS data that was collected prior to this study by the University of Dalarna. To evaluate the driving behaviour in the parking lot direct observation was conducted to obtain overall indices of the parking lot vehicles movement. The parking route taken by the driver was compared with the optimal path to identify the driving behaviour in parking lot in terms of distance. The collected data was evaluated, filtered and analysed to identify the route, the distance and the time the vehicle takes to find a parking space. The outcome of the study shows that driving behaviour in the parking lots varies significantly among the parking user where most of the observed vehicles took unnecessary long time to complete their parking. The study shows that 56% of the 430 observed vehicles demonstrated inefficient driving behaviour as they took long driving path rather the than the optimal path. The study trace this behaviour to two factors, first, the absent of parking guidance in the parking lots and the second is the selectivity of the drivers when choosing the parking space. The study also shows that the ability of GPS data to identify the driving behaviour in the parking lots varies based on the time interval and the type of the device that is being used. The small the time interval the more accurate the GPS data in detecting the driving behaviour in the parking lots.

Realism is a very sought feature in interactive driving simulators for traffic studies, since a nonrealistic simulation could produce non-realistic human drivers behaviours. Since cars driven by artificial intelligence (AI) are one of the main components of a traffic simulation, they play an important role in making up the overall sense of realism. A good understanding of how the AI influences human drivers is thus important in avoiding biases in traffic studies with simulators, and might also come useful in simulators for traffic education, in order to induce certain behaviours in the students. The purpose of this study was to build a driving simulation with multiple AI-driven cars, and let human testers use it, in order to analyse if and how a more polite lane-change behaviour, a more realistic lateral alignment, and a slower average speed affect the perception and the behaviour of human drivers. The simulator was developed upon low-cost hardware infrastructure previously used for other traffic studies. Since the existing software is very specific and hard to modify, a new simulation software was built from scratch for this study, using the Unity3D engine and implementing design patterns developed in previous studies, in order to produce a more flexible and modifiable infrastructure than what had been done in the past studies. The test subjects gave a generally good feedback on the simulator as a whole, and cars which politely changed lanes were regarded as behaving in a slightly more realistic way. Some insights were also obtained about user perception, mainly consisting in a difficulty in perceiving absolute speeds, whereas relative speeds were estimated more accurately.