The aim of this study is to investigate which effects the introduction of the new legislation for bicyclestreets has on road users. The focus has been on studying road users' compliance, accessibility andexperience of bicycle streets. Since the legislation came into force in December 2020, vehicles entering abicycle street have a duty to give way to road users on the bicycle street and drivers of motor vehicleshave to adapt their speed to the bicycle traffic. There is also a general speed limit of 30 km/h and parkingis only allowed in designated areas.The report consists of three main sections, the first of which reviews the history and dialogue behind thecurrent legislation and addresses some of the main points of criticisms leveled at it. This was exploredthrough a detailed review of the available documents and studies dealing with bicycle streets. Thesecond part aims to review the conditions for cycle streets in Sweden and to provide an overview of theopportunities and challenges that exist. This part includes, among other things, thorough examinationsof previous studies of bicycle streets, existing bicycle streets in Sweden, similar legislation and aninternational outlook where bicycle streets in other countries in Northern Europe are studied. The lastpart is a case study of one of Sweden's first bicycle streets in Varberg where, among other things,speeds, movement patterns and yielding behavior of road users are studied. This was investigatedthrough radar measurements, video analysis and visual observations on site. Furthermore, two differentsurveys were carried out, one of which aimed to find out the opinions and experiences of road users onthe bicycle street in Varberg. The second survey was of a more general nature and aimed to collect roadusers' opinions on issues related to bicycle streets.The results indicate that there is a general consensus among experts, planners and road users thatbicycle streets have the potential to improve cyclists' accessibility and road safety and, in the long term,to encourage more people to cycle. However, several shortcomings are found in the existing legislationthat many believe prevent the bicycle street from achieving its purpose. For example, there are currentlyno official guidelines on how a bicycle street should be designed and there is also older legislation thatprevents cyclists from being able to use the whole roadway and cycle side by side. The case study revealsseveral interesting findings that confirm several points of criticism of bicycle streets and its legislation.Among other things, the majority of the observed cyclists chose to take up space in the roadway and alsoride side by side. The survey results also indicate that a change in traffic regulations to allow this onbicycle streets is something that is desired by a majority. Based on the results of the study, severalsuggestions are made for regulation changes that would make the bicycle street better serve its purpose.For example, it is suggested that cyclists should always be allowed to use the whole roadway and rideside by side on bicycle streets. It is also recommended that the responsible authorities develop nationalrecommendations for the physical design of cycle lanes as soon as possible. As for other common viewson regulatory changes, such as whether or not overtaking of cyclists should be allowed and what speedlimit should apply on bicycle streets, further studies need to be carried out before any conclusions canbe drawn.

Chevron pavement markings have seen rising interest in the United States as a means to reduce speeds at high-speed locations and improve safety performance. In Atlanta, there are two freeway-to-freeway ramps where chevron markings are being used. A previous study analyzed before-and-after speed data at these ramps and found only a modest reduction on overall vehicle speeds. However, a cursory crash analysis indicated that the ramps had crash reductions of over 60%, suggesting that safety benefits exist even though vehicle speeds are not significantly affected. This research aims to evaluate the safety performance of chevron markings on the two ramps in Atlanta, GA in order to quantify the potential impact of the treatment on safety and to understand the mechanism by which the treatment influences safety. This thesis begins with a literature review covering topics in human factors in safety, past uses of different types of pavement markings, and methods in using crash databases and police reports in accident studies. Next, the thesis presents an in-depth before and after analysis of crash data from crash databases and police reports provided by the Georgia Department of Transportation. And finally, the thesis concludes with a summary of findings and a discussion of further research needs. The results verified that there were 73% and 61% crash reductions in the two study ramps. Chevron markings appear to have benefitted all types of crashes and that they are possibly serving as a warning to drivers of potential upcoming hazards. Unavailability of a number of police reports and errors in crash databases were limitations to this study, and ultimately, new sites should be selected carefully and further studies need to be performed to better understand the treatment's benefits.

Different road users follow different behaviors and intentions in the trajectories that they traverse. Predicting the intent of these road users at intersections would not only help increase the comfort of drive in autonomous vehicles, but also help detect potential accidents. In this thesis, the research objective is to build models that predicts future positions of road users (pedestrians,cyclists and autonomous shuttles) by capturing behaviors endemic to different road users.  Firstly, a constant velocity state space model is used as a benchmark for intent prediction, with a fresh approach to estimate parameters from the data through the EM algorithm. Then, a neural network based LSTM sequence modeling architecture is used to better capture the dynamics of road user movement and their dependence on the spatial area. Inspired by the recent success of transformers and attention in text mining, we then propose a mechanism to capture the road users' social behavior amongst their neighbors. To achieve this, past trajectories of different road users are forward propagated through the LSTM network to obtain representative feature vectors for each road users' behaviour. These feature vectors are then passed through an attention-layer to obtain representations that incorporate information from other road users' feature vectors, which are in-turn used to predict future positions for every road user in the frame. It is seen that the attention based LSTM model slightly outperforms the plain LSTM models, while both substantially outperform the constant velocity model. A comparative qualitative analysis is performed to assess the behaviors that are captured/missed by the different models. The thesis concludes with a dissection of the behaviors captured by the attention module.