The diploma thesis deals with the analysis of the influence of certain types of intersections and at the same time the time of day. The theoretical part describes the introduction to the factors affecting the driver when driving a motor vehicle. Furthermore, the work defines road junctions by law, types of road junctions and research on the issue of road junctions. The practical part of the diploma thesis is devoted to the evaluation of the total number of primary and secondary views of certain types of intersections and their average duration of day or night. At the same time, it evaluates the first direction of view according to the type of road (secondary road / main road) and the subsequent direction of travel. The obtained data are evaluated in the chapter discussion / analysis of results. The evaluated data in the first part showed that the secondary views have a shorter average duration compared to the average duration of the primary views. In the second part, the results of day and night were identical only at the roundabouts, when the drivers made the first direction of view to the left.

Abgelenktes Fahren gehört zu den Hauptursachen von Verkehrsunfällen und kann auf visuelle, manuelle oder kognitive Ablenkungsquellen zurückgeführt werden. Jede dieser Ablenkungsquellen wurde bereits mit negativen Effekten auf die Fahrerleistung in Zusammenhang gebracht. Obschon ein weitgehender Konsens über negative Auswirkungen von visueller/visuell-manueller Ablenkung besteht, sind die Wirkungen kognitiver Ablenkung auf Fahrfehler und Unfälle noch immer umstritten. Viele experimentelle Studien haben negative Auswirkungen kognitiver Ablenkung auf die Fahrerleistung berichtet. Demgegenüber stehen jedoch die Ergebnisse der Mehrzahl vorliegender „naturalistic driving studies“, die kein erhöhtes Unfallrisiko oder sogar protektive Effekte in diesem Zusammenhang fanden. Die aktuelle Entwicklung hin zu Mensch-Fahrzeug-Schnittstellen, die die Bedienung diverser Anwendungen mittels Sprachsteuerung ermöglichen, führt zu einem Anstieg von kognitiver Beanspruchung beim Fahren. Es ist daher von entscheidender Bedeutung, die Auswirkungen kognitiver Ablenkung auf die Fahrerleistung zu erfassen, um den Verantwortungsträgern in der Gesellschaft, den Regierungen und der Industrie eine Risikoabschätzung dieser Funktionen zu ermöglichen und die Sicherheit von Mensch-Fahrzeug-Schnittstellen zu erhöhen. Das Hauptziel dieser Dissertation bestand darin, die Effekte von kognitiver Ablenkung auf die Fahrerleistung zu untersuchen. Verschiedene Arten kognitiver Ablenkung, die sich beim Fahren unter realen Bedingungen häufig auf die Fahrer auswirken, wurden in dieser Arbeit kodiert und analysiert: kognitiv ablenkende Nebenaufgaben (z.B. telefonieren, singen), Fahreremotionen (z.B. Freude, Wut/Frustration, Traurigkeit) und Kombinationen von Fahreremotionen und Nebenaufgaben (z.B. Streit mit dem Beifahrer oder am Telefon). Bei der Untersuchung von Effekten kognitiver Ablenkung auf das Fahren sind Umwelt-, Situations- und Personenfaktoren zu berücksichtigen, da sie Mediator- und Moderatorvariablen bei der Erfassung des relativen Risikos von Ablenkung beim Fahren im Straßenverkehr darstellen. Daher folgte diese Dissertation dem ganzheitlichen Ansatz, so viele relevante Variablen wie möglich zu betrachten, die mit der Ausführung kognitiv ablenkender Tätigkeiten interagieren. Zu diesem Zweck wurden Daten der derzeit umfangreichsten „naturalistic driving study“ (the second Strategic Highway Research Program, SHRP 2) kodiert und analysiert, um möglichst viele Situationen, in denen eine kognitive Beanspruchung die Fahrerleistung potenziell beeinflusste, umfassend zu bewerten. Gleichzeitig wurde eine große Zahl von Mediator- und Moderatorvariablen betrachtet, die beim Fahren im realen Straßenverkehr auftreten (z.B. Einfluss von Kreuzungen, Wetter, etc.). Dieser Ansatz sollte das Verständnis und die externe Validität der Ergebnisse erhöhen und stellt einen wichtigen Schritt hin zu einem vollständigen Modell jener Variablen dar, die entweder zu unangemessen Verhaltensweisen und Unfällen beitragen oder sie reduzieren. Im Rahmen der Dissertation wurden vier Studien durchgeführt, die auf der Grundlage von zwei SHRP 2 Datensätzen die Zusammenhänge zwischen kognitiven und anderen Ablenkungsquellen, Umwelt-, Situations- und Personenfaktoren und Fahrerleistung untersuchten. Weiterhin wurden Kausalfaktoren in 315 vom Fahrer verursachten Unfällen und Beinaheunfällen, die mit Fahrerablenkung, Fahrerbeeinträchtigung oder keinem dieser Faktoren assoziiert waren, analysiert. Die erste Studie untersuchte die Auswirkungen von Wut beim Fahren und Streit mit dem Beifahrer oder jemandem am Telefon auf die Fahrerleistung. Wut beim Fahren ging mit einer Häufung aggressiver Verhaltensweisen einher, jedoch nicht mit einer Erhöhung von Fahrfehlern. Streitgespräche mit dem Beifahrer oder einer Person am Telefon (das heißt, wenn mutmaßlich das höchste Maß an kognitiver Ablenkung vorlag), schienen darüber hinaus mit keiner Form von unangemessenen Verhaltensweisen im Zusammenhang zu stehen. Die zweite Studie untersuchte, wie sich kognitive, visuelle und manuelle Fahrerablenkung, emotionale Beeinträchtigung sowie Umwelt-, Situations- und Persönlichkeitsfaktoren auf die Fahrerleistung auswirken. Ein Zusammenhang zwischen kognitiver Ablenkung und einer Verschlechterung der Fahrerleistung konnte nicht festgestellt werden. Die dritte Studie replizierte und erweiterte Ergebnisse der zweiten Untersuchung auf der Grundlage eines größeren Datensatzes, bestehend aus Fahrsegmenten, die Unfällen, Beinaheunfällen und Baselines vorausgingen und weder emotionale noch andere Fahrerbeeinträchtigungen enthielten. In Übereinstimmung mit den Ergebnissen der ersten und zweiten Studie, wurde keine Assoziation zwischen kognitiver Ablenkung und einer verschlechterten Fahrerleistung festgestellt. Bei der vierten Studie handelte es sich um eine vergleichende Analyse von Risikofaktoren für Unfälle/ Beinaheunfälle, die mit verschiedenen Arten von Ablenkung, Beeinträchtigung oder keinem von beiden, assoziiert waren. Unfälle, denen eine kognitive Ablenkung vorausgegangen war, waren vor allem mit von Ablenkung unabhängigen Fahrfehlern verbunden - genau wie die Unfälle, denen keine beobachtbare Nebentätigkeit vorausgegangen war. Dieses Ergebnis lässt vermuten, dass in früheren „naturalistic driving studies“, das Unfallrisiko von kognitiv ablenkenden Nebentätigkeiten eventuell sogar überschätzt wurde. Zusammenfassend legen die Ergebnisse die Schlussfolgerung nahe, dass kognitive Ablenkung durch beobachtbare emotionale Beeinträchtigung, (überwiegend) kognitiv ablenkende Nebenaufgaben oder die Kombination dieser beiden Faktoren, nicht mit sichtbaren negativen Auswirkungen auf die Fahrerleistung im tatsächlichen Straßenverkehr assoziiert werden kann. Im Gegensatz dazu hatten ablenkende Tätigkeiten, die zu Blickabwendungen von der Straße führen, und solche, die mit einem besonders hohen Unfallrisiko assoziiert werden, die größte Wahrscheinlichkeit Fahrfehler und Unfälle zu verursachen. / Driver distractions are among the leading causes of motor vehicle accidents. Such distractions can stem from competing visual, manual, or cognitive resources, all of which have been associated with detrimental effects on driving performance. Although the negative impacts of visual/visual-manual distraction are widely agreed upon, the effects of cognitive load on driving errors and crash risk are still debated. On the one hand, numerous experimental studies have shown adverse effects of cognitive distraction on driving performance. In contrast, most existing naturalistic driving studies have either not revealed increased crash/near-crash risk due to cognitive distraction, or have even reported a safety benefit. The number of in-vehicle tasks placing cognitive load on the driver is increasing in recent years due to the development of auditory human–machine interfaces such as voice control for several functions. This has enhanced the need to assess how cognitive distraction affects driving performance. These results are necessary to provide society, government, and industry with valid risk estimates, which will affect decision making regarding how to enhance the safety of using in-vehicle human-machine interfaces while driving. Therefore, the main objective of this thesis was to investigate how cognitive distraction affects driving performance. Different types of cognitive distraction that commonly affect most drivers in naturalistic conditions were coded and analyzed in the present thesis, including: cognitively distracting secondary tasks (e.g., talking on the phone, singing), driver emotion (e.g., happiness, anger/frustration, sadness), and combinations of driver emotion and secondary task demand (e.g., arguing with a passenger or with someone on the phone). Environmental, situational, and individual factors cannot be ignored when investigating the effects of cognitive distraction on driving performance, as they are mediating and moderating variables for estimating distraction relative risk in naturalistic driving. Therefore, a holistic approach guided this thesis towards incorporating as many important variables as possible that interact with the engagement in cognitively distracting activities. Data from the largest naturalistic driving study ever conducted (the second Strategic Highway Research Program, SHRP 2) were coded and analyzed to comprehensively assess many situations in which cognitive load potentially affected driving performance. Further, the goal was to simultaneously consider many possible mediating and moderating variables existent in real-world traffic (such as intersection influences, weather, etc.). This approach should increase understanding and external validity of the results, as well as represent an important step towards building a complete model depicting variables that contribute to or mitigate aberrant driving behaviors and crash risk. Four different analyses focused on two SHRP 2 data subsets to assess the relationship between cognitive and other distraction sources, environmental, situational, and individual factors, as well as driving performance. In addition, contributing factors in 315 at-fault crash and near-crash events associated with driver distraction, driver impairment, or neither of the two were analyzed. The first study examined driving performance in relation to driving anger as well as arguing with a passenger or with someone on the phone. Results showed that driving anger was associated with more frequent aggressive driving behaviors without increasing driving error frequency. Furthermore, when a conflict arose with a passenger or with someone on the phone (i.e., when the level of cognitive distraction was expected to be highest), there did not appear to be a link to any type of aberrant driving behavior. The second study analyzed driving performance based on cognitive, visual, and manual driver distraction, emotional impairment, as well as environmental, situational, and individual factors. Cognitive distraction was not associated with any decline in driving performance. The purpose of the third analysis was to replicate and extend the second study’s effects based on a larger data sample of driving segments preceding crashes, near-crashes, and matched baselines, of drivers not exhibiting emotional or other impairment types. Corroborating the first and second study’s results, there was no association between cognitive distractions and impaired driving performance. Finally, the fourth study compared the risk factors of crashes/near-crashes associated with either different driver distraction types, impairment, or neither. Crashes preceded by cognitive distraction were mainly associated with driving errors unrelated to the secondary task demands, as were the crashes preceded by no observable secondary task. This finding suggests that previous studies analyzing naturalistic driving data may have even overestimated the crash risk of cognitively distracting secondary task engagement. In summary, this thesis provides compelling evidence that cognitive distraction, either through observable emotional impairment, (mainly) cognitively distracting secondary tasks, or the combination of both, has no apparent relation with poorer driving performance observable in real-world traffic. On the contrary, distracting activities requiring the driver’s gaze to move away from the forward roadway and those associated with a particularly high crash risk had the highest chances of causing driving errors and crashes.

When developing new active safety systems or improving existing systems, conducting performance evaluations is necessary. By performing these evaluations during early development stages, potential problems can be identified and mitigated before the system moves into the production phase. Testing active safety systems can be difficult since the characteristic scenarios may have complex interactions. Using real vehicles for performing these types of scenarios is difficult, expensive, and potentially dangerous. Alternative methods, such as using inflatable targets, scale models, computer simulations or driving simulators, also suffer from drawbacks. Consequently, using virtual reality as an alternative to the traditional methods has been proposed. In this case, a real vehicle is driven while wearing a head-mounted display that presents the scenario to the driver. This research aims to investigate the potential of such technology. Specifically, this work investigates how the chosen technology affects the driver. This investigation has been conducted through a literature review. A test platform was constructed, and two user studies using normal drivers were performed. The first study focused on the effects of visual time delays on driver behavior. This study revealed that lateral behavior changes with added time delays, whereas longitudinal behavior appears unaffected. The second study investigated how driver behavior is affected by different modes of virtuality. This study demonstrated that drivers perceived mixed reality as more difficult than virtual reality. The main contribution of this work is the detailed understanding of how time delays and different modes of virtuality affect drivers. This is important knowledge for selecting which scenarios are suitable for evaluation using virtual reality. / <p>The series name <em>Linköping Studies in Science and Technology Licentiate Thesis</em> is incorrect. The correct series name is <em>Linköping Studies in Science and Technology Thesis</em>.</p>

Advanced Vehicle Systems promise improved safety and comfort for drivers. Steady advancements in technology are resulting in increasing levels of vehicle automation capabilities, furthering safety benefits. In fact, some of these vehicle automation systems are already deployed and available, but with promised benefits, such systems can potentially change driving behaviors. There is evidence that drivers have increased secondary task engagements while driving with automated vehicle systems, but there is a need for a clearer scientific understanding of any potential correlations between the use of automated vehicle systems and potentially negative driver behaviors. Therefore, this thesis aims to understand the state of knowledge on automated vehicle systems and their possible impact on drivers’ distraction behaviors. I have conducted two systematic literature reviews to examine this question. This thesis reports these reviews and examines the effects of secondary task engagement on driving behaviors such as take-over times, visual attention, trust, and workload, and discusses the implications on driver safety.

Civil infrastructure serves as the driving force behind the evolution of a safe, sustainable, and efficient environment. However, the way information about civil infrastructure has been communicated to the public has been insufficient. Since every human is intrinsically different, designing, and dispersing information about civil infrastructure that accommodates everyone, while also being direct and concise has been a challenge for policymakers and other federal, state, local, and tribal civil engineering stakeholders. Though there has been a plethora of research conducted on message design and communication in other disciplines, little research has been done in the US that focuses on designing more accessible, actionable civil infrastructure messages. The objective of this research was to investigate how to improve the accessibility of civil infrastructure messages and communication infrastructure to enhance the public's ability to make daily infrastructure decisions. This research study utilized quantitative and qualitative methods to analyze and discuss various ways that civil infrastructure messages can be improved. Results from this study are based on the exploration of three different ways in which civil infrastructure messaging can be improved: policy, transportation/roadway safety, and emergency response. Data sources include eight publicly accessible energy policies from 1978-2022, a publicly available dataset of more than 75 thousand WEAs, and a dataset retrieved from Shealy et al. (2020), which collected data on 300 Virginia drivers in both rural and urban areas. A descriptive policy analysis and Flesch-Kincaid readability test were conducted to historically analyze energy policies and understand their accessibility impacts for research question 1; a brain activation network analysis was conducted and nodal network measures (i.e., network density, degree centrality) were used to investigate the cognitive response Virginia drivers had for various types of non-traditional traffic safety messages for research question 2; and sentiment analysis, emotion detection analysis, as well as a two-phased qualitative coding analysis (i.e., in-vivo coding, focused coding) were conducted to investigate how WEAs can be better designed to increase public attention and engagement for research question 3. The findings from this study demonstrate how emotional content that is present in tweets authored by community members affected by the natural disaster event can be incorporated into the WEA template. The findings from research question 1 identified potential issues with accessibility and energy policy. Also, the findings from this study describe the content included in the parallel documents that federal agencies use to communicate the most important information of a policy. The findings from research question 2 demonstrate that while the various types of non-traditional traffic safety messages produced variances in cognitive response, messages that included negative emotional content or statistics should be further explored on their impact on evoking safer driving behaviors. The findings from research question 3 reported on how emotional content could be incorporated into the template design of WEAs. The implications from this dissertation provide valuable insights for policymakers, civil engineers, transportation engineers, and emergency response stakeholders and the conclusions set the stage for future research to improve the design of more accessible civil infrastructure messages. / Doctor of Philosophy / Civil infrastructure messages are used daily, but improper design can make them difficult to understand or to continue to use over long periods of time. Also, every human is different and interprets information about civil infrastructure, which adds a level of difficulty to designing effective civil infrastructure messages. Though there has been a lot of research on the effectiveness of civil infrastructure, little research has used a human-centered design approach to improve civil infrastructure messages. This study analyzes three different ways to improve civil infrastructure messages: policy, traffic safety, and emergency response. We used publicly available energy policies from 1978-2022, data collected by co-authors from Shealy et al. (2020) to analyze the cognitive response of 300 Virginia drivers to various types of non-traditional traffic safety messages, a publicly available dataset of more than 75 thousand Wireless Emergency Alerts sent by FEMA, and a publicly available data set of more than 9.1 thousand tweets about Hurricane Harvey. To analyze this data, this research study utilized various methods to understand how easy policies are to read, to understand how the brains of Virginia drivers respond to different types of non-traditional traffic safety messages and to identify the differences between tweets and WEAs. Results from this study suggest that parallel documents should be published alongside energy policies to help the public understand the main points of the policy, establish a readability metric to use for all energy policies, continue to investigate non-traditional traffic safety messages that included negative emotional content or statistics, measure the brain activation and observe long-term driving behaviors, use more negative emotional content in templated WEAs, and use social media data to better design templated WEAs. The findings reported from this study can be beneficial for various types of civil infrastructure stakeholders such as policymakers, utilities, US State Departments of Transportation, FEMA, alerting officials, and the public to further explore ways in which the language of civil infrastructure messages can be improved to address accessibility issues with energy policy, traffic safety, and emergency response to the public.

In recent years, the automotive sector has seen a steady increase in the introduction of new Advanced Driving Assistance Systems (ADAS). This trend toward more complex systems will become even more pronounced with regard to Highly Automated Driving (HAD). In addition to the expected benefits of ADAS and HAD (increased comfort, efficiency, and safety), it is important to eliminate risks as much as possible to ensure that the system does not introduce new critical situations or road traffic accidents. Due to the increasing interaction of systems with the driver and their environment, it is no longer sufficient to investigate the system in isolation. There is also a need to investigate how the driver and the environment interact with the new system. Furthermore, the functional scope of the systems is expanding to cover entire application domains, such as highways and in the future rural and urban areas. This results in a significant increase in the number of parameters and scenarios that require testing for approval of these new technologies. This means that the scenario space to be analyzed is constantly expanding, which poses increasing problems for safety assessments. The expected number of test kilometers required to validate HAD is too large to be cost- and time-effective through real-world testing. This is why virtual safety assessments are necessary. In this context, the present thesis investigates whether virtual safety assessments can be efficiently performed today through Monte Carlo simulations using cognitive driver behavior models. The body of the thesis consists of four articles that consider different aspects of the safety assessment. Article 1 derives the cognitive core functions that driver behavior models must implement to display the causes and mechanisms of human error. This way, driver behavior models are able to map all hazard levels of realistic traffic, including normal traffic, critical situations, and road traffic accidents. By mapping the interactions of road users, cognitive models thus form the basis for the virtual safety assessment of ADAS and HAD systems. Due to the lack of existing cognitive driver behavior models that implement these cognitive core functions, the Driver Reaction Model (DReaM), a new driver behavior model, was developed and continuously improved as part of this work. Article 2 outlines a calibration and validation strategy, using DReaM as an example, to investigate whether driver behavior models are suitable for safety assessments, mapping all levels of realistic traffic. Subsequently, Article 3 estimates the time required to perform Monte Carlo studies for safety assessments, again using DReaM as an example. Therefore, an optimistic and pessimistic estimation is generated based on the minimum number of runs (MNR) required to simulate an exemplary traffic scenario. In summary, Articles 1–3 examine the quality of driver behavior models and the time required to perform safety-related studies. This lays the foundation for determining whether efficient safety assessments are feasible. Finally, Article 4 exemplarily assesses an urban Automatic Emergency Braking (AEB) system using DReaM to outline the overall virtual assessment methodology. Based on Article 4 and the findings of Article 1–3, minimal requirements are defined for improving and standardizing the virtual safety assessment process. These requirements aim to improve the reliability of safety assessments and enhance the comparability of results across various studies and models

Unsafe driving habits increase the severity of roadway accidents. The behaviors that are generally associated with unsafe driving are influenced by drivers and their decision to engage in dangerous habits. In order to solve this problem, Departments of Transportation use roadside safety campaigns. To gain a comprehensive understanding of the effectiveness of these campaigns, this research study captured five different metrics of effectiveness to understand what messages are effective and how to target messages to different groups of people. Since reading and interpreting the messages produces cognitive activation among participants, a neuroimaging technology called functional near-infrared spectroscopy (fNIRS) was used to measure neurocognitive activation as a proxy for response. The fNIRS system captures this cognitive activation by measuring change in oxygenated blood (oxy-Hb). An increase in oxy-Hb is a proxy for increased task engagement. The first journal paper provides an understanding of what types of messages are perceived as effective, are misunderstood, are memorable, are considered inappropriate, and cause the greatest increase in cognitive engagement. Overall, drivers perceive messages to be effective at changing behavior, but particular messages are perceived as more effective than others. Messages about distracted driving and driving without a seat belt, messages that are intended to produce a negative emotional response, and messages with statistics are the behaviors, emotions, and themes that are most likely to be perceived to change driver behavior. Messages about distracted driving and messages about statistics are most likely to be remembered by drivers. In general, drivers do not find messages used in safety campaigns to be inappropriate. Drivers elicit more cognitive attention to signs about distracted driving and signs with a humorous emotion. The second journal considers the effectiveness of these messages with different target demographics by further investigating the first journal's results by different dependent variables, including age, gender, and risky driving habits of the participants. In the second study, the results from the first study are further examined to determine if some campaigns are more effective among different demographics of drivers. The behavioral results indicated that females, drivers over 65, low-risk and high-risk drivers, and urban and rural drivers perceive the safety campaigns as more effective. The neurological data revealed that younger drivers had more activation in the ventrolateral prefrontal cortex, an area known for semantics and word processing, which might indicate more cognitive attention to these types of messages. This study provides a unique application of using neuroimaging techniques to understand driver response to safety messages. The recommendations for an effective safety campaign are to use messages about distracted driving, messages with an emotional stimulus, and messages about statistics. Messages about word play and rhyme are recommended for appealing to younger demographics. / Master of Science / Messages like "New year, new you, use your blinker" and "May the 4th be with you, text I will not" are increasingly used to catch drivers' attention. The development and use of these non-traditional safety messages are distinctly different than messages previously displayed on highway signs because the intent of these messages is to modify driver behavior rather than just provide information. Unfortunately, there is little empirical evidence measuring how effective these messages are at changing driver behavior or guidance on how to target messages for specific groups of people. The goal of this study was to understand what types of non-traditional safety messages are effective and how to target these messages to different target audiences. Roadway collisions are made more severe when the cause of the incident involves dangerous driving habits, such as distracted, impaired, or aggressive driving. The problem is made even more severe by the fact that the habits that make driving dangerous are affected by the driver's decision to engage in risky driving behavior. The solution to this problem is to gain an understanding of driver preferences and response, a research effort this study will address. Reading and interpreting the messages produces cognitive activation among participants. The study uses functional near-infrared spectroscopy (fNIRS), which allows researchers to capture this cognitive activation by measuring change in oxygenated blood (oxy-Hb). This provides not only the ability to gain a more detailed understanding of driver response, but the ability to triangulate this with what drivers perceive as effective in changing driver behavior. In the first study, the participants felt that campaigns targeting distracted driving, messages with a negative emotion, and campaigns about statistics were significantly more effective at changing driver behavior compared to other behaviors, emotions, and themes. The neurological data revealed that drivers respond more to campaigns about distracted driving. However, the neurological data indicates that humorous messages and messages that fit under the theme word play and rhyme elicit a greater cognitive response. The second study furthers the first study and revealed that females, drivers over 65, low-risk and high-risk drivers, and urban and rural drivers perceive the safety campaigns as more effective. The neurological data revealed that younger and older males and older high-risk drivers respond with greater peak oxy-Hb when compared to other groups of people. This study advances the applicability of fNIRS in traffic related studies.

The Type-II dilemma zone refers to the segment of roadway approaching an intersection where drivers have difficulty deciding to stop or proceed through at the onset of the circular yellow (CY) indication. Signalized intersection safety can be improved when the dilemma zone is correctly identified and steps are taken to reduce the likelihood that vehicles are caught in it. This research employs driving simulation as a means to collect driver response data at the onset of the CY indication to better understand and describe the dilemma zone. The data obtained was compared against that from previous experiments documented in the literature and the evidence suggests that driving simulator data is valid for describing driver behavior under the given conditions. Fuzzy logic was proposed as a tool to model driver behavior in the dilemma zone, and three such models were developed to describe driver behavior as it relates to the speed and position of the vehicle. These models were shown to be consistent with previous research on this subject and were able to predict driver behavior with up to 90% accuracy. / Graduation date: 2013

Variable Speed Limits (VSL) are often used to improve traffic conditions on congested motorways. VSL can be implemented as mandatory or advisory. The objective of the thesis isto study in detail the effectiveness of VSL. The focus is on both, design parameters and conditions under which VSL are most effective. The MCS system on the E4 motorway inStockholm is used as a case study. The evaluation was conducted using empirical methods (including aggregate data from microwave sensors and other sources, and disaggregate data from a mobile study), and microscopic traffic simulation. The empirical analysis is based on before and after VSL data, including evaluation of individual measures of performance, and multivariate analysis in the form of the fundamental diagram, and speed-density relationships. The results from the empirical study are mixed with an indication that driver behavior has a strong impact on the effectiveness of the system. The microscopic traffic simulation analysis included the development of a platform for testing VSL and more generally motorway control strategies. The simulation platform was calibrated and validated with the empirical data and includes in addition to VSL, and Automatic Incident Detection (AID) system, the ALINEA ramp metering algorithm. The test-platform allows the testing of different control strategies and various combinations of control strategies, under different scenarios and in a controlled environment. The results from the simulation study indicate that driver compliance is an important factor and VSL performance quickly deteriorates as compliance rate drops. Hence, VSL should be implemented as mandatory instead of advisory. In addition, mandatory VSL can be effective both, under incident and moderately congested conditions. A combined VSL and ramp metering strategy can be most effective in reducing travel time, improving traffic conditions on the motorway. Furthermore, the results indicate that such a strategy also has the least impact on the flows entering the motorway from the ramps. / QC20100630

Μία από τις πιο γοργά αναπτυσσόμενες περιοχές της επιστήμης των υπολογιστών είναι η ανάπτυξη έξυπνων συστημάτων που υποστηρίζουν τις αποφάσεις των χρηστών και παρέχουν ένα ευρύ πεδίο υπηρεσιών. Η εξυπνάδα τους βασίζεται στην παρακολούθηση και αποκωδικοποίηση των αναγκών και την προσομοίωση της συμπεριφοράς του χρήστη. Το αντικείμενο της παρούσας διατριβής είναι η παρουσίαση ενός νέου, αξιόπιστου συστήματος δυναμικής αξιολόγησης της συμπεριφοράς του οδηγού και υποστήριξης αυτού σε πραγματικό χρόνο. Πιο συγκεκριμένα, δίνεται έμφαση στην ανοικτή, προσανατολισμένη προς τις υπηρεσίες (service-oriented) αρχιτεκτονική του, στους κανόνες που το διέπουν και στο υλικό και το λογισμικό που του επιτρέπουν να παρέχει διαλειτουργικές υπηρεσίες. Εφαρμόζεται η συστημική προσέγγιση που αρχίζει με τα στοιχεία εισόδου. Τα στοιχεία αυτά αφορούν βασικά τον οδηγό: προσωπικά στοιχεία, προφίλ, καλούς χειρισμούς κ.ά., το αυτοκίνητο: ταχύτητα, επιτάχυνση, επιβράδυνση, γωνία τιμονιού, αριθμός στροφών κινητήρα, σχέση μετάδοσης στο κιβώτιο ταχυτήτων, μοντέλο και τύπος οχήματος και το περιβάλλον: GPS, RFID, κάμερες, αισθητήρες, ασύρματες και δορυφορικές επικοινωνίες κ.ά. Συνεχίζουμε με την ευφυή - αλγοριθμική, στατιστική κ.λπ. - επεξεργασία αυτών των στοιχείων (α) για να εκτιμήσουμε την τρέχουσα κατάσταση του οδηγού και του οχήματος στις συγκεκριμένες περιβαλλοντικές συνθήκες και (β) για να κατανοήσουμε τη συμπεριφορά και να υποστηρίξουμε παθητικά ή ενεργά τον οδηγό κατά τη διάρκεια ενός ταξιδιού. Παράγουμε πρωτότυπα αποτελέσματα, δηλαδή χρήσιμη πληροφορία και πιθανές συμβουλές προς τον οδηγό του οχήματος, στοιχεία για την συμπεριφορά του οδηγού με σκοπό την περαιτέρω χρήση αυτών των πληροφοριών από άλλους φορείς, όπως ασφαλιστικές, εταιρείες, ελεγκτικά όργανα του κράτους κ.λπ. Βέβαια, οι δυνατότητες που προσφέρονται από το προτεινόμενο σύστημα μπορούν να οδηγήσουν σε οφέλη και για τις ασφαλιστικές εταιρείες οι οποίες καλούνται να εκσυγχρονίζουν συνεχώς τον τρόπο με τον οποίο καθορίζουν το ύψος των ασφαλίστρων. Επιπλέον, το προτεινόμενο σύστημα θα μπορούσε να χρησιμοποιηθεί από εταιρείες που διαθέτουν στόλο οχημάτων, προκειμένου να επαληθεύουν και να ελέγχουν την ικανότητα των οδηγών σε πραγματικό χρόνο. Τέλος, το προτεινόμενο σύστημα θα μπορούσε να χρησιμοποιηθεί από το υπουργείο μεταφορών, την τροχαία, τους φορείς τοπικής διοίκησης κ.ά. / One of the fastest growing areas of computer science is the development of intelligent systems that support user decisions and provide a wide range of services. Their intelligence is based on monitoring and decoding of real needs, as well as the simulation of end user’s behavior. The object of this Thesis is the presentation of a new, integrated system for dynamic evaluation of driver behavior. In particular, we emphasize at its open, service-oriented architecture, the incorporated set of rules and the system hardware and software which allow it to provide interoperability. We apply the systematic approach that begins with the input data plus requirements. These data mainly concern the driver: personal data, profile, good practices etc., the vehicle: speed, acceleration, deceleration, steering angle, engine speed, gear ratio in gearbox, model and type, and the environment: GPS, RFID, cameras, wireless and satellite communications, etc. Then, these data are processed applying an intelligent-algorithmic, statistical etc.- approach in order (a) to evaluate the current state of the driver and the car in certain environmental conditions, and (b) to understand the behavior and passively or actively support the driver during a travel. We produce original results, i.e. useful information and possible recommendations to the driver of the vehicle, data concerning the driver behavior and thus, this information can be further used by others, such as insurance companies, audit institutions of state etc. We place great emphasis on cutting-edge technologies that are applied to achieve the required feedback, parameterization and adaptation of the system. Of course, the capabilities offered by the proposed system can lead to clear benefits for various organizations, like insurance companies, which are required to continually update their price policy, companies that have a fleet of vehicles, in order to verify the ability of the drivers and support them in real time. Finally, the proposed system could be used by the Ministry of Transport, the traffic police, the local authorities etc.