<p>In this thesis report we conducted research study on driver's behavior in T-Intersections using simulated environment. This report describes and discusses correlation analysis of driver's personality traits and style while driving at T-Intersections.</p><p>The experiments were performed on multi user driving simulator under controlled settings, at Linköping University. A total of forty-eight people participated in the study and were divided into groups of four, all driving in the same simulated world.</p><p>During the experiments participants were asked to fill a series of well-known self-report questionnaires. We evaluated questionnaires to get the insight in driver's personality traits and driving style. The self-report questionnaires consist of Schwartz's configural model of 10 values types and NEO-five factor inventory. Also driver's behavior was studied with the help of questionnaires based on driver's behavior, style, conflict avoidance, time horizon and tolerance of uncertainty. Then these 10 Schwartz's values are correlated with the other questionnaires to give the detail insight of the driving habits and personality traits of the drivers.</p>

The growth of serious injuries and fatalities resulting from traffic accidents at intersections is one of the main problems in urban areas. Signal control was proposed as an alternative intersection design on rural roads. There were many reasons behind this, the most outstanding of which was the traffic signals can be used as a cost effective tools for traffic management in urban areas. The LHOVRA technique was intended to improve safety and reduce lost time at signalized intersection along high speed roads. The LHOVRA technique is an isolated traffic control strategy in Sweden which is formed from different concepts. This thesis work is aimed to evaluate the LHOVRA technique with a focus on the O-function. Hence, two different scenarios, one with O-function and one without O-function were implemented in the micro traffic simulation software, VISSIM. VISSIM has been used to simulate the traffic situation of the Gamla Övägen – Albrektsvägen intersection by considering the LHOVRA scenario (with O-function) as well as traditional scenario (without O-function) of the intersection. Field measurements were used as input data for VISSIM simulation. The VISSIM simulation model was calibrated to find a close match between simulated and real data. Finally, a comparison of alternatives was carried out based on traffic performance and traffic safety measurements. The simulation experiment results gained by the comparisons were presented a higher time-to-collision value. The higher time-to-collision value the safer situation is. Both delays and travel time were reduced to primary road traffic.

Esta pesquisa caracterizou o comportamento de motoristas em interseções semaforizadas sob três aspectos: (1) reação frente à mudança do verde para o amarelo; (2) comportamento durante a desaceleração para parar; e (3) comportamento durante a saída do cruzamento semaforizado. Os dados foram coletados em uma interseção localizada em pista de testes no Virginia Tech Transportation Institute, nos EUA. A amostra foi composta por 60 motoristas voluntários igualmente divididos em função do gênero; dos quais 32 tinham idade inferior a 65 anos (\"jovens\"). Foram investigados efeitos da idade, do gênero e da declividade da via sobre as seguintes situações: tomada de decisão entre parar ou prosseguir no amarelo; posição de parada em relação à faixa de retenção; tempo de percepção e reação (TPR) para frenagem e partida do cruzamento; efeito de zonas de opção e de dilema; taxa de desaceleração para parada na interseção; e taxa de aceleração para partida da interseção. As análises indicaram que: (1) os motoristas mais jovens invadiram mais a faixa de retenção que os idosos; (2) mulheres apresentam maiores TPR para decidir partir da interseção; e (3) o TPR é menor no declive tanto para a decisão de frear quanto para a partida do cruzamento. As taxas de desaceleração não apresentaram influência dos fatores avaliados. Por outro lado, constatou-se que a aceleração foi afetada pelo fator declividade. Como resultado final da pesquisa, foram propostos modelos, em função do tempo, que exprimem a desaceleração/aceleração usada pelos motoristas ao frear/acelerar. Foram propostos modelos para o motorista médio e para motoristas desagregados em três grupos em função da agressividade. / The objective of this research was to characterize driver behavior at signalized intersections according to three aspects: (1) reaction at the onset of the amber phase; (2) behavior during the deceleration to stop at the signal; and (3) behavior during the acceleration to leave the intersection at the onset of the green. The data were collected at a signalized intersection on a private highway, at the Virginia Tech Transportation Institute, in the USA. The sample consisted of 60 volunteer drivers, equally divided by gender. The sample was divided into two age groups: younger drivers (age was less than 65) and older drivers. Effects of gender, age group and roadway grade were investigated for the following aspects: decision making at the onset of amber; final stopping position with relation to the stop line; perception/reaction times (PRT) at the onset of the amber and the green lights; effects of dilemma and option zones; and deceleration and acceleration rates used by the drivers. The analyses suggest that: (1) younger drivers tend to stop farther past the stop line, compared to older drivers; (2) women have longer PRT at the onset of the green; and (3) PRT are shorter on downgrade at the onset of both amber and green lights. The observed deceleration rates were not affected by gender, age group or roadway grade. Acceleration rates were found to be influenced by the grade. A set of models that express the acceleration/deceleration rates as a function of time were proposed to represent the average behavior observed for drivers in the sample. Specific models were also proposed for aggressive, non-aggressive and intermediate drivers.

The current driving simulator study investigates the effect of 2 distinct levels of distraction on a drivers’ situational awareness and latent and inherent hazard anticipation. In this study, rear-end crashes were used as the primary crash configuration to target a specific category of crashes due to distraction. The two types of task load used in the experiment was a cognitive distraction (mock cell-phone task) & visual distraction (I-pad task). Forty-eight young participants aged 18-25 years navigated 8 scenarios each in a mixed subject design with task load (cognitive or visual distraction) as a between-subject variable and the presence/absence of distraction representing the within-subject variable. All participants drove 4 scenarios with a distraction and 4 scenarios without any distraction. Physiological variables in the form of Heart rate and heart rate variability was collected for each participant during the practice drives and after each of the 8 experimental drives. After the completion of each experimental drive, participants were asked to fill up a NASA TLX questionnaire which quantifies the overall task load experienced by giving it a score between 1 and 100, where higher scores translate to higher perceived task load. Eye-movements were also recorded for the proportion of latent and inherent hazards anticipated and mitigated for all participants. Standard vehicle data (velocity, acceleration & lane offset) were also collected from the simulator for each participants’ each drive. Analysis of data showed that there was a significant difference in velocity, lane offset and task load index scores across the 2 groups (between-subject factors). The vehicle data, heart rate data and TLX data was analyzed using Mixed subject ANOVA. There was also a logistic regression model devised which showed significant effects of velocity, lane offset, TLX scores and age on a participants’ hazard anticipation abilities. The findings have a major practical implication in reducing drivers’ risk of fatal, serious or near crashes.

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This thesis investigates how various driver signals, signals that a truck driver can interact with, influences fuel consumption and what are the optimal values of these signals in various traffic conditions. More specifically, the objective is to estimate good driver behavior in various traffic conditions and compare bad driver behavior in similar situations to see how performing a specific driver action, changing a driver signal from the bad driver value to the corresponding good driver value impacts the fuel consumption. The result is an AI-based algorithm that utilizes the transformer model architecture to estimate good driver behavior, based on environmental describing signals, as well as fuel consumption. Utilizing these, causal inference is used to estimate how much fuel can be saved by switching a driver signal from a bad driver value to a good driver value.

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In the realm of autonomous vehicles, ensuring safety through advanced anomaly detection is crucial. This thesis integrates Reservoir Computing with temporal-aware data analysis to enhance driver behavior assessment and traffic flow prediction. Our approach combines Reservoir Computing with autoencoder-based feature extraction to analyze driving metrics from vehicle sensors, capturing complex temporal patterns efficiently. Additionally, we extend our analysis to forecast traffic flow dynamics within road networks using the same framework. We evaluate our model using the PEMS-BAY and METRA-LA datasets, encompassing diverse traffic scenarios, along with a GPS dataset of 10,000 taxis, providing real-world driving dynamics. Through a support vector machine (SVM) algorithm, we categorize drivers based on their performance, offering insights for tailored anomaly detection strategies. This research advances anomaly detection for autonomous vehicles, promoting safer driving experiences and the evolution of vehicle safety technologies. By integrating Reservoir Computing with temporal-aware data analysis, this thesis contributes to both driver behavior assessment and traffic flow prediction, addressing critical aspects of autonomous vehicle systems. / Master of Science / Our cities are constantly growing, and traffic congestion is a major challenge. This project explores how innovative technology can help us predict traffic patterns and develop smarter management strategies. Inspired by the rigorous safety systems being developed for self-driving cars, we'll delve into the world of machine learning. By combining advanced techniques for identifying unusual traffic patterns with tools that analyze data over time, we'll gain a deeper understanding of traffic flow and driver behavior. We'll utilize data collected by car sensors, such as speed and turning patterns, to not only predict traffic jams but also see how drivers react in different situations. However, our project has a broader scope than just traffic flow. We aim to leverage this framework to understand driver behavior in general, with a particular focus on its implications for self-driving vehicles. Through meticulous data analysis and sophisticated algorithms, we can categorize drivers based on their performance. This valuable information can be used to develop improved methods for detecting risky situations, ultimately leading to safer roads and smoother traffic flow for everyone. To ensure the effectiveness of our approach, we'll rigorously test it using real-world data from GPS data from taxi fleets and nationally recognized traffic datasets. By harnessing the power of machine learning and tools that can adapt to changing data patterns, this project has the potential to revolutionize traffic management in cities. This paves the way for a future with safer roads, less congestion, and a more positive experience for everyone who lives in and travels through our bustling urban centers.

In this dissertation, new algorithms that address three traffic problems of major importance are developed. First automatic identification and prediction algorithms are developed to identify and predict the occurrence of traffic congestion. The identification algorithms concoct a model to identify speed thresholds by exploiting historical spatiotemporal speed matrices. We employ the speed model to define a cutoff speed separating free-flow from congested traffic. We further enhance our algorithm by utilizing weather and visibility data. To our knowledge, we are the first to include weather and visibility variables in formulating an automatic congestion identification model. We also approach the congestion prediction problem by adopting an algorithm which employs Adaptive Boosting machine learning classifiers again something novel that has not been done previously. The algorithm is promising where it resulted in a true positive rate slightly higher than 0.99 and false positive rate less than 0.001. We next address the issue of travel time modeling. We propose algorithms to model travel time using various machine learning and statistical learning techniques. We obtain travel time models by employing the historical spatiotemporal speed matrices in conjunction with our algorithms. The algorithms yield pertinent information regarding travel time reliability and prediction of travel times. Our proposed algorithms give better predictions compared to the state of practice algorithms. Finally we consider driver safety at signalized intersections and uncontrolled intersections in a connected vehicles environment. For signalized intersections, we exploit datasets collected from four controlled experiments to model the stop-run behavior of the driver at the onset of the yellow indicator for various roadway surface conditions and multiple vehicle types. We further propose a new variable (predictor) related to driver aggressiveness which we estimate by monitoring how drivers respond to yellow indications. The performance of the stop-run models shows improvements after adding the new aggressiveness predictor. The proposed models are practical and easy to implement in advanced driver assistance systems. For uncontrolled intersections, we present a game theory based algorithm that models the intersection as a chicken game to solve the conflicts between vehicles crossing the intersection. The simulation results show a 49% saving in travel time on average relative to a stop control when the vehicles obey the Nash equilibrium of the game. / Ph. D.

Intersection crashes are among the most frequent and lethal crash modes in the United States. Intersection Advanced Driver Assistance Systems (I-ADAS) are an emerging active safety technology which aims to help drivers safely navigate through intersections. One primary function of I-ADAS is to detect oncoming vehicles and in the event of an imminent collision can (a) alert the driver and/or (b) autonomously evade the crash. Another function of I-ADAS may be to detect and prevent imminent traffic signal violations (i.e. running a red light or stop sign) earlier in the intersection approach, while the driver still has time to yield for the traffic control device. This dissertation evaluated the capacity of I-ADAS to prevent U.S. intersection crashes and mitigate associated injuries. I-ADAS was estimated to have the potential to prevent up to 64% of crashes and 79% of vehicles with a seriously injured driver. However, I-ADAS effectiveness was found to be highly dependent on driver behavior, system design, and intersection/roadway characteristics. To generate this result, several studies were performed. First, driver behavior at intersections was examined, including typical, non-crash intersection approach and traversal patterns, the acceleration patterns of drivers prior to real-world crashes, and the frequency, timing, and magnitude of any crash avoidance actions. Second, two large simulation case sets of intersection crashes were generated from U.S. national crash databases. Third, the developed simulation case sets were used to examine I-ADAS performance in real-world crash scenarios. This included examining the capacity of a stop sign violation detection algorithm, investigating the sensor detection needs of I-ADAS technology, and quantifying the proportion of crashes and seriously injuries that are potentially preventable by this crash avoidance technology. / Ph. D. / Intersection crashes account for over 5,000 fatalities each year in the U.S., which places them among the most lethal crash modes. Highly automated vehicles are a rapidly emerging technology, which has the potential to greatly reduce all traffic fatalities. This work evaluated the capacity of intersection advanced driver assistance systems (I-ADAS) to prevent U.S. intersection crashes and mitigate associated injuries. I-ADAS is an emerging technology used by highly automated vehicles to help drivers safely navigate intersections. This technology utilizes onboard sensors to detect oncoming vehicles. If an imminent crash is detected, I-ADAS can respond by (a) warning the driver and/or (b) autonomously braking. Another function of I-ADAS may be to prevent intersection violations altogether, such as running a red light or a stop sign. Preventing and/or mitigating crashes and injuries that occur in intersection crashes are among the highest priority for designers, evaluators, and regulatory agencies. This dissertation has three main components. The first aim of this research was to describe how individuals drive through intersections. This included examining how drivers approach, traverse, and take crash avoidance actions at intersections. The second aim was to develop a dataset of intersection crashes that could be used to examine I-ADAS effectiveness. This was completed by extracting crashes that occurred throughout the U.S., and reconstructing vehicle positions before and after impact. The third aim was to use the extracted dataset of intersection crashes, and consider a scenario where one of the vehicles had been equipped with I-ADAS. Estimates of IADAS effectiveness were then generated based on these results.