Trafiksäkerhet är något som är viktigt för trafikanten och den som arbetar på vägarna. Säkerheten längs vägar är något som har utvecklats, men inte flyttbara säkerhetsbarriär för vägarbeten. Den här rapporten följer ett produktutvecklingsprojekt där ett koncept för en ny trafikbuffert tas fram. Dagens trafikbuffertar är svåra att hantera, inte anpassade för stadsmiljöer och ger en lång stoppsträcka för fordonet vid kollision. Den ger även en hög kraftpåkänning för de inblandade vid kollision då det blir en hög impuls. Utgångspunkten för projektet har varit att minska stoppsträckan och minska de inblandas upplevda kraftpåkänning. Detta genomfördes genom att följa en beprövad produktutvecklingsprocess. Under förstudien upptäcktes att det fanns begränsat med information och forskning inom området, men det finns liknande lösningar som det togs inspiration från. Det togs sedan fram ett koncept som baserades på att använda en del av bilens vikt för att minska stoppsträckan, tillsammans med en deformationszon som ska minska rörelseenergin i bilen och kraftpåkänningen för de iblandade. Ett flertal iterationer gjordes på konceptet för att uppfylla så många krav som möjligt och bli ett realiserbart koncept. Simuleringar gjordes för att säkerställa hållfastheten på konstruktionen och beräkningar för att få en uppfattning om stoppsträckan.  Resultatet är ett koncept som har kortare stoppsträcka i teorin och en lägre kraftpåkänning än dagens använda trafikbuffert. Konceptet kräver vidare arbete för att definiera tillverkningsteknik och få ett godkännande av Trafikverket. Slutsatsen är att användningen av bilens vikt är ett bra sätt att minska stoppsträckan. Dock krävs krocksimuleringar och krocktester för att validera resultaten. / Traffic safety is something that is important for both drivers and road workers. Safety along roads has seen developments, but portable safety barriers for road work have not. This report follows a product development project where a concept for a new traffic buffer is being developed. Current traffic buffers are difficult to handle, not suitable for urban environments, and have a long stopping distance when a collision occurs. They also exert high force on those involved, resulting in a severe impact.  The goal of this project was to reduce the vehicle stopping distance and the perceived force on the passengers involved. A proven product development process was used to achieve this goal. During the preliminary study it was discovered that there was limited information and research in the field, but there are similar solutions that provided inspiration. A concept was then developed based on utilizing a portion of the vehicle's weight to reduce the stopping distance, along with a deformation zone that would reduce the vehicle's kinetic energy and the force on those involved. Several iterations were made on the concept to meet as many requirements as possible and make it a feasible concept. Simulations were conducted to ensure the structural strength and calculations were made to estimate the stopping distance. The result is a concept that requires further work to make it ready for production and approved by the Swedish Transport Administration. The concept has a theoretical stopping distance of 4.5 meters and lower force exertion. In conclusion, utilizing the vehicle's weight is a good way to reduce the stopping distance. However, crash simulations and tests are required to validate the results. / <p>Betygsdatum 2023-06-28</p>

No description available.

Vehicle Action Intention Prediction plays a more and more crucial role in automated driving and traffic safety. It allows automated vehicles to comprehend the other road participants’ current actions, and foresee the upcoming actions, which can significantly reduce the likelihood of traffic accidents, so as to enhance overall road safety. Meanwhile, by anticipating other vehicles’ movements on the road, the ego vehicle can plan its velocity and trajectory in advance, and make more smooth and finer adjustments during the whole driving process, contributing to a more safe and efficient traffic. Furthermore, the intention prediction enables vehicles to respond proactively rather than reactively in traditional ADAS (Advanced Driver Assistance Systems), such as AEB (Automatic Emergency Braking), which facilitates a more preventive and early intervention approach to traffic safety. In normal conditions, traffic behavior is controlled by traffic rules. This thesis explores vehicle behavior using intention prediction models in scenarios where there are no traffic rules. At hand, we have a unique dataset containing vehicle trajectories, collected from 2 cameras installed overhead on a 1-lane narrowing street, where the vehicles need to negotiate their right of way. After pre-processing these data to form specific input structures, we use different classifier models including both traditional methods and deep learning methods to make vehicle action intention predictions. The data was organized in 3-second windows and contained vehicle position and distance to the center of the intersection along with the speed of both vehicles. We compared and evaluated the model performances and found that MLPs (Multi-Layer Perceptrons) and LSTM (Long Short Term Memory) yield the best performance. Furthermore, a feature selection method and features’ importance analysis are also applied to explore which variables influence the model most in order to explain the internal principle of the prediction model. It was found that close to the narrowing street the first and last samples of the position and distance in the time window and the last sample of the speed of both vehicles were found to influence the model performance the most. Further away from the narrowing street, the first and last samples of the position of the vehicle have a higher influence on the model.

This dissertation explores innovative approaches in driving safety through the development of multi-modal learning frameworks that leverage high-frequency, high-resolution driving data and videos to detect safety-critical events (SCEs). The research unfolds across four methodologies, each contributing to advance the field. The introductory chapter sets the stage by outlining the motivations and challenges in driving safety research, highlighting the need for advanced data-driven approaches to improve SCE prediction and detection. The second chapter presents a framework that combines Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) with XGBoost. This approach reduces dependency on domain expertise and effectively manages imbalanced crash data, enhancing the accuracy and reliability of SCE detection. In the third chapter, a two-stream network architecture is introduced, integrating optical flow with TimeSFormer with a multi-head attention mechanism. This innovative combination achieves exceptional detection accuracy, demonstrating its potential for applications in driving safety. The fourth chapter focuses on the Dual Swin Transformer framework, which enables concurrent analysis of video and time-series data, this methodology shows effective in processing driving front videos for improved SCE detection. The fifth chapter explores the integration of corporate labels' semantic meaning into a classification model and introduces ScVLM, a hybrid approach that merges supervised learning with contrastive learning techniques to enhance understanding of driving videos and improve event description rationality for Vision-Language Models (VLMs). This chapter addresses existing model limitations by providing a more comprehensive analysis of driving scenarios. This dissertation addresses the challenges of analyzing multimodal data and paves the way for future advancements in autonomous driving and traffic safety management. It underscores the potential of integrating diverse data sources to enhance driving safety. / Doctor of Philosophy / This dissertation explores new approaches to enhance driving safety by using advanced learning frameworks that combine video data with high-frequency, high-resolution driving information, introducing innovative techniques to predict and detect critical driving events. The introduction chapter outlines the current challenges in driving safety and emphasizes the potential of data-driven methods to improve predictions and prevent accidents. The second chapter describes a method that uses machine learning models to analyze crash data, reducing the need for expert input and effectively handling data imbalances. This approach improves the accuracy of predicting safety-critical events. The third chapter introduces a two-stream network that processes both sensor data and video frames, achieving high accuracy in detecting safety-related driving incidents. The fourth chapter presents a framework that simultaneously analyzes video and time-series data, validated using a comprehensive driving study dataset. This technique enhances the detection of complex driving scenarios. The fifth chapter introduces a hybrid learning approach that improves understanding of driving videos and event descriptions. By combining different learning techniques, this method addresses limitations in existing models. This work tackles challenges in analyzing multimodal data and sets the stage for future advancements in autonomous driving and traffic safety management. It highlights the potential of integrating diverse data types to create safer driving environments.

With an increasing population of super heavy aircraft operating in the National Airspace System and with the introduction of NextGen technologies, the wake vortex problem has become more important for airport capacity and the en-route air traffic operations. The vortices generated by heavy and super heavy aircraft can generate potential hazards to other aircraft on nearby flight paths. Moreover, the design of new airport procedures needs to consider the interactions between aircraft in closer paths. New methods and models are required to examine these effects before new operations are conducted in the National Airspace System (NAS). Reducing wake vortex separations to safe levels between successive aircraft is essential for NextGen operations. One approach taken recently by ICAO and the FAA is to introduce a re-categorization (ReCat) of wake vortex separations to six groups from the existing five groups employed by the FAA in the United States. Reduced aircraft separations can increase capacity in the NAS with corresponding savings in delay times at busy airports. Future NextGen operations are likely to introduce smaller aircraft separations in the en-route and in the terminal area. Such operations would require better methods to identify potential wake hazards from reduced separation operations. This dissertation describes a model to identify potential wake encounters in the future NAS. The goal of the dissertation is to describe the Enhanced Wake Encounter Model (EWEM), a model that employs a detailed NASA-developed wake model to generate wake zones for different aircraft categories under different flight conditions that can be used with aircraft flight path data to identify potential wake encounters. The main contribution of this model is to gain an understanding of potential wake encounters under future NAS operations. / Ph. D.

Most vehicular tunnels, due to their restrictive and confined environment require continuous traffic surveillance and control. This is achieved by a variety of systems like closed circuit TV monitoring, personnel stationed in the tunnel, overheight & speed detectors and others. Traffic flow data were obtained from the Interstate 64(I -64) Hampton Roads Bridge-Tunnel. The data were analyzed and conclusions were drawn about traffic flow behavior at the different tunnel sections. During the operation of a tunnel, capacity reductions are often experienced due to temporary lane closures. These could result from incidences occurring in the tunnel area or due to regular maintenance activities. This work concentrates on the former case. A microcomputer model called Queue and User Cost Evaluation of Work Zones(QUEWZ) was employed in studying lane closure scenarios for the Virginia Tidewater Area Tunnels. In case of significant capacity reductions or complete blockages of directions of travel, it is desired to know beforehand the impact expected on the surrounding network. Traffic management actions like rerouting policies could be implemented and thus alleviate the problem. MASSVAC2, a computer simulation model for mass evacuation under emergency conditions was employed for analyzing different traffic management scenarios. / M.S.

The standard method for assessing traffic safety is to use the predictive method outlined in the Highway Safety Manual (HSM). This method is site-level, data-intensive, and does not account for interactions between sites, making it difficult to assess larger areas. This dissertation develops a corridor-level approach to traffic safety which uses less data than the HSM predictive method and views roadways holistically rather than combinations of individual, independent sites. First, a corridor definition is developed and applied to 10 urban Florida counties with a history of many crashes, resulting in the identification of 1,048 corridors. These corridors were primarily defined using context classification and lane count, with additional considerations for data availability and minimum length. From 2017–2021, these corridors experienced 459,603 unique crashes. After preliminary modeling and scope refinement, 559 corridors received supplemental data collection. Between the two datasets, a total of 11 models were developed using either negative binomial (NB) or random forest (RF) regression. NB models can be used for network screening purposes or identifying the impacts of potential safety improvements, while RF models can be used to identify variables important to the accuracy of the prediction. Potential safety improvements identified from the NB models include increasing proactive law enforcement patrols for dangerous driving behaviors and installing corridor lighting in corridors without lighting. While both NB and RF models were accurate, NB models were recommended due to resulting in a definite equation and overdispersion parameter that could be used with the empirical Bayes (EB) method to improve prediction accuracy. Overall, the corridor-level NB models outperformed the HSM models in terms of accuracy and statistical reliability. Using a corridor-level approach can help agencies quickly network screen their systems to identify high-risk corridors in need of safety improvements or supplement site-level analyses.

<p dir="ltr">Transportation Network Companies (TNCs) have increased significantly over the last decade, changing the urban mobility dynamics by shifting people from other modes of transportation, potentially affecting safety. While TNC companies promised to enhance urban mobility with more convenient end-to-end services, they were found to contribute to externalities like traffic congestion and safety issues. A deeper analysis is required to test the promise of TNC services and their impacts on cities. This study investigated the safety implications of the surge of TNC services in New York City (NYC) from 2017 to 2019. Specifically, we analyzed the changes in traffic safety performances using surrogate safety measures (SSMs) from 2017 to 2019 based on large-scale GPS trajectories generated by TNC vehicles in NYC.</p><p dir="ltr">This research utilized the twenty-eight days of high-quality and large-scale GPS-based trajectories of Uber vehicles to determine the critical surrogate safety measures (SSMs). To determine the potential traffic conflict and safety from SSMs, this research determined the SSMs based on evasive actions. In addition, this research also utilized real-world historical crash events, traffic flow, road conditions, land use, and congestion index to explore the relationship between critical SSMs and accidents. Additionally, this research extends to assess the socioeconomic inequalities from the perspective of increased TNCs and accidents.</p><p dir="ltr">Our findings indicate a significant increase in critical SSM events such as harsh braking and jerking citywide. These increases are particularly pronounced during off-peak hours and in peripheral areas of Manhattan and transportation hubs. Moreover, we observed stronger correlations between SSMs of TNC vehicles and injury/motorist accidents, compared to those involving pedestrians and cyclists. Despite the evident deterioration in SSMs, we noticed that the overall number of accidents in NYC from 2017 to 2019 has remained relatively stable possibly due to the reduction of traffic speeds. As such, a clustering analysis was conducted to unfold the nuanced patterns of SSMs/accident changes. Also, we find the existence of inequality in the increase in accidents and critical SSMs, and Manhattan is higher in inequality, especially in upper Manhattan. Moreover, individuals disadvantaged from low socioeconomic status and those living in deprived areas are experiencing more inequality from accidents and critical SSMs due to increased TNCs and accidents. This research enriches the understanding of how TNC services impact urban traffic safety. The findings of this research may help to get a holistic understanding of the road safety situations due to increased TNCs and accidents and help the policymakers and authorities to make informed decisions to develop a transportation system prioritizing all road users. Additionally, the methodology employed can be adapted for broader traffic safety applications or real-time monitoring of traffic safety performances using anonymous GPS trajectory segments.</p>

Child pedestrian injury and mortality is an issue of significant public health concern in the city of Johannesburg, Gauteng, in South Africa. Since there is a paucity of studies in the last decade or more on fatal childhood traffic and non-traffic injuries in Johannesburg, this study aspires to address the disproportion in this domain of research, and provide more recent, and comprehensive empirical evidence over a ten-year period. The overarching aim of this study was to describe and examine the magnitude, circumstances, and neighbourhood characteristics of fatal pedestrian injuries among children (0-14 years) in Johannesburg for the period from 2001 to 2010. More specifically, the objectives of the study were: firstly, to provide a comprehensive epidemiological description of the magnitude, trends and occurrence of pedestrian mortality among children; secondly, to describe and examine the epidemiology of child pedestrian mortality in relation to children as motor vehicle passengers; thirdly, to describe and examine child pedestrian mortality in relation to non-traffic injuries, in particular, burns and drowning; and fourthly, to assess the influence of neighbourhood characteristics on child pedestrian mortality. The study conceptualised pedestrian road safety within an ecological systems framework. The study used quantitative descriptive, and multivariate logistic regression methods of analysis to examine child pedestrian mortality data. The study drew on data from the National Injury Mortality Surveillance System (NIMSS) and the Census 2001. The main findings indicated that black, male children aged 5 to 9 years (11.02/100 000) are the most vulnerable, and that mortality occurred predominantly during the afternoons and early evenings (12h00-16h00 and 16h00-21h00), over weekends, during school holidays, and to a lesser extent, during non-holiday months. In addition, neighbourhood characteristics that reflected concentrations of disadvantage, single female-headed households and residentially stable areas were associated with child pedestrian mortality. The study findings highlight the need for critical action in terms of investment in child pedestrian safety research, and appropriate prevention initiatives guided by stringent evidenced-based studies, and the design of safe pedestrian, vehicular and urban environments. / Psychology / D. Phil. (Psychology)

Call number: LD2668 .T4 1985 M87 / Master of Science