Cycling as a mode of transportation has been recording an upward trend in both the U.S. and Europe. Unfortunately, the safety of cyclists has been a point of growing concern. Data from the National Highway Traffic Safety Administration (NHTSA) show that the crashes that occur during the events of motorists overtaking cyclists was one of the leading categories involving cyclists in fatal crashes. In support of the efforts to understand the driving behavior of drivers of motorized vehicles while overtaking cyclists, this research project is aimed at developing an algorithm to identify the overtaking events. Most existing quantitative studies on cycling safety leverage instrumented bicycles or vehicles with sensors for extracting naturalistic driving trajectories. Whereas we use data from a recent research that provides naturalistic driving trajectories of road users collected at select intersections in urban areas in Germany using drones equipped with cameras. Using these videos with a data frequency of 25 Hz, the authors of this study have output inD dataset. The inD dataset contains trajectories of road users that are captured in form of coordinates on a two-dimensional plane obtained from the ariel or bird's eye view of the road. Additionally, the data also captures velocity, acceleration, heading angles, dimensions of driver's vehicle etc. Overtaking can be thought of as four phases of approaching, steering away, passing, and returning. Using the inD dataset, we have developed an algorithm to identify events when a driver of motor vehicle overtakes a cyclist. This work fits into our broader goal to contribute to the body of knowledge for improving road safety of cyclists. The work is expected to provide inputs to governmental/ traffic authorities in aspects such as design of intersections and design of bicycle lanes by providing insights into overtaking events. [from Indroduction]

Mobility has changed in recent years in cities worldwide, th.anks to tb.e strong rise in vehicles of micromobility. Bicycle riding is the most widespread micromobility transport mode, followed by stand-up electric scooters (e-scooters). This increase in its use has also led to an increase in related crashes. Both cyclists and e-scooter riders are vulnerable road users and are lik.ely to sustain severe injuries in crashes, especially with motor vehicles. The crashes consequences involving cyclists and other micromobility users have already investigated using numerical simulation software, such as MADYMO and PC-Crash. Most of them have been focused on bicycles and electric bicycles, whereas only few of tbem have analyzed e-scooter crashes consequences. Posirisuk: et al. [1] carried out a computational prediction ofhead-ground impact k:inematics :in e-scooter falls. Ptak et al. [2] analyzed the e-scooter user kinematics after a crash against SUV when the e-scooter chives into the sidefront of tbe vehicle, a side B-pillar crash and a frontal impact initiated by tbe e-scooter to tbe front-end of the vehicle. However, they did not study the consequ.ences of a car to e-scooter side impact crashes. Xu et al. [3] did study these crashes but considering electric self-balancing scooters that are less widespread than e-scooters. Current study focuses on the consequences of a car to micromobility user (cyclist and e-scooter rider) side impact crashes. The analysis is based on numerical simulations with PC-Crash software.

IAB one solution of micromobility, e-scooters have become a trend in Germany. However, the concems about the safety of e-scooter riders, influence on pedestrians and the parking issues are growing. In 2020, 2,155 e-scooters involved personal injury accidents were recorded in Germany. The number rose to 5,502 in 2021 meaning an increase of 155.31 %. Compared to cyclists (incl. pedelec cyclists), the increasing rate of personal injury accidents in the same period decreased by 8.75 % [1, 2]. Against the background of accidents with e-scooters in cities, prior studies analysed severity and patterns of injuries caused by such accidents [3, 4]. In addition, comparisons are drawn to the consequences of accidents with other vehicles [5, 6]. Some studies also consider the risk of injuries in relation to the miles travelled [7]. The studies provide valuable findings but the approaches focus on the severe consequences of occurred accidents. At the same time, compared to bicycles, the centre of gravity of e-scooters is lower, they are more manoeuvrable and can still reach speeds of up to 20 km/h [8]. The question remains, if these vehicle characteristics are associated with different interaction behaviour. Hence, the aim of the present study is to reveal the riding behaviour profile in different contexts and investigate e-scooter riders' criticality in interaction behaviour compared with cyclists using surrogate safety measures. We aim to figure out if the interaction behaviour of the two modes differ and what the effects of potential differences are for safety considerations in the system of active mobility.

Roads are a significant element of modernity. They are not only sites that facilitate mobility and fluidity needed for modern capitalist economy but also spaces which signify the social relations formed within the system. This conceptualization of the road is central to the project at hand. The aim of this research is to unpack factors influencing route choices of private car owners in the Bisley area in Pietermaritzburg in terms of their primary activities (going to work, shopping etc.) and what socio-political contents inform and frame these rationales. Additionally, this research explores the extent to which crime influences spatial consumption and mobility patterns. The research made use of qualitative approach that sought to interrogate the contexts within which what is considered rational choices are made and provide insight into how private car owners in Bisley area contextualize their decision. In-depth interviews with individuals (owners of private car) from various households in Bisley were conducted. The findings reveal that drivers use routes that provide them with the maximum positive outcomes, and consider their options within multiple factors as they arise out of the conditions on each road and each trip. The study also found variations in terms of the mode of rationality used in situational contexts and their multiplicity. For example, morning traffic prompted the drivers to use instrumental rationality; whereas travel during other parts of the day was not restricted to this form of rationality. The findings of study also in some ways support already existing view that there is a link between spatial consumption and perceptions of crime; however, this requires further interrogation of this theme with systematic data collection appropriate to it. Most importantly consideration of safety on the road definitely shapes decisions of the research participants on which roads and routes to frequent, and at which time of the day. Furthermore, the study through the tracing of participants‟ movements using maps shows the ways in which class and race feature on the roads of the country. The study argues that class rather than race is re-spatialized in post-apartheid South Africa. This was attributed to recent socio-political and economic dynamic developments taking place in South Africa, where the black majority is becoming more affluent. / Thesis (M.Soc.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2011.

Railway level crossing collisions in Australia are a major cause of concern for both rail and road authorities. Despite the fact that the number of railway crash fatalities in Australia has fallen in recent years, level crossing collisions constitute a significant proportion of the national rail toll. Although rail transport is presently one of the safest forms of land transport, collisions at level crossings are three times more likely to involve fatalities as compared to all other types of road crashes (Afxentis, 1994). With many level crossing fatalities and injuries resulting in coronial inquests, litigation and negative media publicity, the actions of rail and road infrastructure providers and the behaviour of motorists, pedestrians and rail users, come under close scrutiny. Historically, research in this area has been plagued by the rail/road interface and the separation of responsibilities between rail and road authorities reflecting the social and political context in which they are contained. With the recent rail reform in Australia, safety at level crossings has become a key priority area. Accordingly, there is a need to better understand the scope and nature of motorist behaviour at level crossings, in order to develop and implement more effective countermeasures for unsafe driving behaviour. However, a number of obstacles have hindered research into the area of level crossing safety. As with many road crashes, the contributing causes and factors are often difficult to determine, however a recent investigation of fatal collisions at level crossings supports the notion that human fault is a major contributor (Australian Transport Safety Bureau, 2002a). Additionally, there is a lack of reliable data available relating to the behavioural characteristics and perceptions of drivers at level crossings. Studies that do exist have lacked a strong theoretical base to guide the interpretation of results. Due to the lack of financial viability of continuing to approach risk management from an engineering perspective, the merits of human factor research need to be examined for suitability. In Australia, there has been considerable recognition regarding the importance of human factor approaches to level crossing safety (Australian Transport Council, 2003). However, little attempt has been made by authorities to scientifically develop and measure the effectiveness of road safety educational interventions. Therefore, there exists a significant need for developing targeted road safety educational interventions to improve current risk management solutions at level crossings. This research program is the first of its kind in investigating motorist behaviour at level crossings and the measuring the effectiveness of educational interventions for improving driving safety. Although other ‘educational’ campaigns exist in this field, no campaign or intervention has been guided by empirical research or theory. This thesis adopted a multidisciplinary approach to theory, reviewing perspectives from psychology, sociology and public health to explain driver behaviour at level crossings. This array of perspectives is necessary due to the variety of behaviours involved in collisions and near-misses at level crossings. The motivation underlying motorist behaviour determines to a large extent how successful behaviour change strategies (e.g. educational interventions) may be. Fishbein’s Integrated Model of Behaviour Change (IM) based largely on the health belief model, theory of reasoned action and theory of planned behaviour (Fishbein, 2000), assisted in the planning and development of a ‘oneoff’ targeted educational intervention specific for three different road user groups and in questionnaire development to ascertain the present context of motorist behaviour at level crossings. As no known research has been conducted that utilizes any psychosocial model to explain or predict level crossing behavior within different road user groups, this research program used this model as an exploratory tool rather than a tool to asses the model’s capacity in explaining such behaviour. The difference between this model and others is the inclusion of two important constructs in driving: skills (or abilities) and environmental factors. Fishbein (2003) suggests that the model recognises the lack of skills (or abilities) and/or environmental constraints may prevent a person from acting on their intentions, in light of the fact that intention is viewed as the primary determinant of behaviour. While the majority of behaviour change theories are limited by a range of conceptual and contextual factors (Parker, 2004), the IM was used to assist this research program as it appeared to be the most applicable model to examining level crossing safety. A variety of data collection methods were used in this research program as much of what is currently known about level crossing collisions is derived from coroner’s findings and statistics. The first study (Study One) was designed to extend this knowledge by undertaking a more thorough examination of contributing factors to level crossing crashes and the road user groups at risk. This study used the method of ‘triangulation’ (i.e. combining research methods to give a range of perspectives) whereby both qualitative (focus groups) and quantitative (modified Delphi technique) research designs were utilised (Barbour, 1999, Bryman, 1992). With the discipline of road safety research requiring methodological strategies that will enhance efforts to conceptualise the multi-faceted nature of motorist behaviour at level crossings, this application provided the robustness required. Results from the Delphi technique indicated that older, younger and heavy vehicle drivers are considered to be three of the highest risk road user groups by experts in the field. For the older driver group, experts agreed that errors in judgment were the most important issue for this group when driving at level crossings. Risk taking by younger drivers, such as trying to beat the train across the crossing, was viewed as the central issue for the younger driver group. Like the younger driver group, a concern by experts with the heavy vehicle group was intentional risk taking at level crossings. However, experts also rated the length of heavy vehicles a major concern due to the possibility of a truck over-hanging a crossing. Results from focus groups with train drivers in Study One indicated that there are unique problems associated with crossings in rural/regional areas compared to urban areas. The metropolitan train drivers generally experienced motorist behaviour at active crossings with flashing lights and boom gates while the regional train drivers experienced behaviours at active crossings with boom gates, crossings with lights only and passive crossings with stationary signs. In the metropolitan train driver group, experiences of motorist behaviour at level crossings included: motorists driving around boom gates, getting stuck under boom gates, queuing over congested crossings and driving through the crossing after the red lights commence flashing. The behaviour of motorists driving around boom gates was noted to occur quite regularly. The majority of metropolitan train drivers reported that it was a common occurrence for motorists to drive through a crossing when the lights are flashing both before and after the booms were activated and some crossings were named as ‘black spots’ (locations where motorists repeatedly violate the road rules). Vehicles protruding into the path of the train and motorists entering congested crossings and then panicking and driving backwards into the boom gates were also mentioned. Regional train drivers indicated that motorists not stopping or giving way to trains is a continual problem at passively controlled crossings (i.e. no boom gates or flashing lights). Regional train drivers generally agreed that the majority of motorists obey protection systems; however some motorists drive through flashing lights or drive around boom gates. Other high risk behaviours included motorists attempting to beat the train across the crossing, speeding up to go through flashing lights, and general risk taking by younger drivers in particular. Motorists not allowing enough time to cross in front of the train or hesitating (stopstarting) at crossings were also noted to be at high risk. There was a general perception by regional train drivers that motorists are unable to judge the speed and distance of an approaching train to determine a safe gap during which to cross. Local motorists were also reported to be a problem at level crossings for regional train drivers. A theme common to regional and metropolitan train drivers was the risk of catastrophic consequence associated with level crossing collisions. The reasons given for this were the threat of derailment, serious property damage, the high risk of a fatality, personal injury and, most earnestly, the potential for enduring psychological consequences. Drivers uniformly spoke about the continual fear they had of being involved in a collision with a heavy vehicle, and many spoke of the effects that such collisions had on train drivers involved. For this reason, train drivers were said to consider any near-miss incident involving trucks particularly serious. The second study undertaken as part of this research program (Study Two), involved formative research as part of the planning, development and delivery of behavioural interventions for each of the three road user groups identified in Study One. This study also used both qualitative and quantitative data collection methods to provide methodological triangulation and ensure reliability of the data. The overall objective of the qualitative data collection was to obtain rich data using a qualitative mode of inquiry, based on the key variables of attitudes, norms, self-efficacy (perceived behavioural control), perceived risk, environmental constraints and the skills/abilities of drivers. The overall objective of the quantitative data collection was to prioritise the issues identified in order to direct and allocate project resources for intervention planning, development and delivery. This combined recruitment strategy was adopted as it was an appropriate and practical data collection strategy within the qualitative and exploration methodology. Information obtained from each of the groups was critical in assisting, guiding, and identifying priority areas for message and material development. The use of focus groups and one-on-one interviews provided insights into why drivers think or do what they do at level crossings. The qualitative component of this study found that for the older driver group, regional drivers hold a greater perception of risk at level crossings than urban older drivers, with many recalling near-misses. Participants from the urban older driver group indicated that level crossings are not as dangerous as other aspects of driving, with many participants being doubtful that motorists are killed while driving at level crossings. Both urban and regional younger drivers tended to hold a low perception of risk for driving at level crossings, however many participants reported having great difficulty in judging the distance a train is from a crossing. Impatience for waiting at level crossings was reported to be the major reason for any risk taking at level crossings in the younger driver group. Complacency and distraction were viewed by heavy vehicle participants as two of the major driver factors that put them at risk at level crossings, while short-stacking (when the trailer of the truck extends onto the crossing), angle of approach (acute or obtuse angle) and lack of advance warning systems were seen as the major engineering problems for driving a truck at level crossings. The quantitative component of this study involving research with train drivers found that at the aggregate train driver level, it is apparent that train drivers consider motorists’ deliberate violations of the road rules and negligently lax approach to hazard detection as the predominant causes of dangerous driving at level crossings. Experts were observed to rank risk taking behaviours slightly lower than train drivers, although they agreed with train drivers that ‘trying to beat the train’ is the single most critical risk taking behaviour observed by motorists. The third study (Study Three) involved three parts. The aim of Part One of this study was to develop targeted interventions specific to each of the three road user groups by using Fishbein’s theoretical model (Integrated Model of Behaviour Change) as a guide. The development of interventions was originally seen as being outside of the scope of this project, however it became intertwined in questionnaire development and thus deemed to be within the realms of the current mode of inquiry. The interventions were designed in the format of a pilot radio road safety advertisement, as this medium was found to be one of the most acceptable to each of the road user groups as identified in the formative research undertaken in Study Two. The interventions were used as a ‘one-off’ awareness raising intervention for each road user group. Part Two involved the investigation of the present context of unsafe driving behaviour at level crossings. This second part involved the examination of the present context of motorist behaviour at level crossings using key constructs from Fishbein’s Integrated Model of Behaviour Change (IM). Part Three involved trialing a pilot road safety radio advertisement using an intervention and control methodology. This part investigated the changes in pre and post-test constructs including intentions, self-reported behaviour, attitudes, norms, selfefficacy/ perceived behaviour control, perceived risks, environment constraints and skills/ability. Results from this third study indicated that younger drivers recognise that level crossings are potentially a highly dangerous intersection yet are still likely to engage in risk taking behaviours. Additionally, their low levels of self-efficacy in driving at level crossings pose challenges for developing interventions with this age group. For the older driver sample, this research confirms the high prevalence of functional impairments such as increasing trouble adjusting to glare and night-time driving, restricted range of motion to their neck and substantial declines in their hearing. While factors contributing to the over-representation of older drivers in collisions at level crossings are likely to be complex and multi-faceted, such functional impairments are expected to play a critical role. The majority of heavy vehicle drivers reported driving safely and intending to drive safely in the future, however, there is a sub-set of drivers that indicate they have in the past and will in the future take risks when traversing crossings. Although this sub-set is relatively small, if generalised to the larger trucking industry it could be problematic for the rail sector and greater public alike. Familiarity was a common factor that was found to play a role in driving intention at level crossings for all three road user groups. This finding supports previous research conducted by Wigglesworth during the 1970’s in Australia (Wigglesworth, 1979). Taken together, the results of the three studies in this research program have a number of implications for level crossing safety in Australia. Although the ultimate goal to improve level crossing safety for all motorists would be to have a combination of engineering, education and enforcement countermeasures, the small number of fatalities in comparison to the national road toll limits this. It must be noted though that the likelihood of creating behavioural change would be increased if risk taking at level crossings by all motorists was detected and penalised, or alternatively, if perceptions of such detection were increased. The instilling of fear in drivers with the threat of punishment via some form of sanction can only be achieved through a combination of a mass media campaign and increasing police presence. Ideally, the aim would be to combine fear of punishment with the guilt associated with the social non-acceptability of disobeying road rules at level crossings. Such findings have direct implications for improving the present context of motorist behaviour at level crossings throughout Australia.

This diploma thesis deals with children‘s reaction time. The theoretical part of this work describes children as vulnerable road users and discusses methods of measuring reaction time. The practical part presents some realized experiments refer to the children’s reaction time and their results evaluation.

The aim of this work is to evaluate current procedures and to refine assessment of impact connected to the drivers' age and traffic accidents. The theoretical part describes senior drivers, traffic accident rate of seniors and decreased ability to drive influenced by age. The practical part is case report of traffic accidents caused by senior drivers, measuring reaction time and the evaluation of these measurements. I have developed some proposals for seniors to prevent the risk of the occurrence of traffic accidents.

The growing population and motorization generate more movements. In many cities, the increase of population and motorization is much greater than the development of the capacity of the transportation network. For unprotected road users, the risk of getting in a traffic accident increases and the risk of being more severely injured in an accident. In March 2020, a pandemic was declared because of a Coronavirus. More people started to work/study from home to prevent the virus from spreading by avoiding unnecessary trips, gatherings, and crowded areas. Therefore, travel behaviours have shifted during the pandemic compared to previous years. This project aims to get knowledge of how mobility and traffic accidents are affected by significant shifts of travel flow, predict the effect of traffic accidents based on mobility, and evaluate the risk of travelling on a particular road segment. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>

<p>  </p> <p>The research described in this dissertation began in response to frequent questions from users of several crosswalks near a university campus. At each crosswalk was a sign indicating that motorists should yield to pedestrians in the crosswalk. That this message was not being interpreted uniformly was a concern at locations where heterogeneous road users (pedestrians, cyclists, and motorists) were interacting. Instead of trying to impose a single interpretation on users of each crosswalk, it was decided to observe and analyze interactions between users of the crosswalk. </p> <p>Several hours of video were recorded of pedestrians and motorists “negotiating” the right of way at the crosswalk. Because these crossing locations were marked but not signalized, they were called “semi-controlled crosswalks”. The negotiations took place during what were called pedestrian-motorist interactions (PMIs). The PMIs observed on video can be characterized as a “zebra-crossing” game, as described in Chapter 4 of this dissertation. </p> <p>Recently, computer vision (CV) algorithms have been extensively used in road users’ detection and tracking at an unparalleled spatial-temporal scale. In this study, CV algorithms have been applied to convert the video recordings into a large-scale spatial-temporal trajectory dataset including 800 pedestrians and cyclists interacting with more than 500 vehicles. Utilizing the trajectory dataset, a spatial-temporal graph convolutional network-based sequence to sequence (ST-GCN-Seq2Seq) algorithm has been developed to reasonably forecast heterogeneous road users’ trajectories and behavior in real time. Combining CV and ST-GCN-Seq2Seq algorithms can help both design an intelligent tracking system and achieve a form of “smart” interaction at semi-controlled crosswalks for heterogeneous road users.</p> <p>Based on road users’ arrival patterns detected from CV algorithms, it is likely that a "smart" control strategy can minimize the delay of pedestrians and motorists at crosswalks.  Therefore, another branch of this study is to investigate the “smart” control strategies at crosswalks using traffic signal controllers. A reinforcement learning framework was proposed as the “smart” control strategy, and several experiments were conducted using microsimulation. The proposed reinforcement learning framework is able to reduce traffic delay (efficiency), considering real-time pedestrian flow rates and vehicle flow rates with appropriate sensors.</p>

Traffic accidents is one of the major causes of fatalities and economic loss around the world. Thus, there is an urgent need for a better understanding about the factors that contribute to accidents so that the accidents can be prevented in the future. The research objective of this thesis is to analyze the traffic accidents between vehicles and unprotected road users (pedestrians and bicycles) in Stockholm, finding spatial distribution patterns, related attributes and examining relationships between accidents and a number of vehicle flows. The data is first analyzed with general statistical analysis to examine the basic characteristics. There is no apparent trend of change among the number of accidents per year, while the numbers of accidents happening from May to October is higher than the rest of the year except for July due to less traffic during holiday period. Most traffic accidents occur in overcast weather, on a dry road surface, or during the day. In the spatial analysis part of the thesis, Global Moran’s I is used to detect whether there is an attribute-related spatial distribution pattern. Hot spot analysis is then applied on the clustered attributes to find significant hot and cold spots over the study area. The conclusions are that road surface conditions and occurrence time during day/night are two related factors that influence traffic accidents while weather is not considered a related attribute since the accidents distribute randomly in terms of weather, of which it is difficult to obtain temporally-aligned, detailed local information for further analysis. Different parameters are selected and discussed during the process. When calculating the distance between two accidents in traffic accident analysis, Manhattan distance is more appropriate than Euclidean distance since traffic accidents are restricted to the road network. The distance band determines scales of analysis tools, with 50 meters on an intersection and 500 meters for a larger region in Stockholm. Most hot spots arise at intersections and roundabouts where different types of traffic flows meet each other. The result of the relationships between traffic accidents and different types of vehicle flows shows that the correlation coefficients between number of traffic accidents and traffic flows are low, meaning that there is no obvious correlation between them, which is also proved by the scatter plots. Poisson regression model is applied on the traffic accident data. As a result, high-risk and low-risk areas in Stockholm are pointed out. Some are consistent with the hot-spot analysis result.