Are Pedelec crashes different to bicycle crashes?: A comparison of national accident data in Germany

Since 2014, a distinction between Pedelec (electrical support up to 25 km/h) and bicycle crashes is made in official police reported accidents with personal injuries in Germany. Yet, no comparative analysis using national data is available, moreover some estimation was done how Pedelec crashes may look like based on bicycle crashes. Hence, the present study aims to compare real-world crashes with personal injuries with both vehicle types - Pedelec and bicycle and show similarities and differences of the vehicle classes. Nearly a decade of reporting allows furthermore to have a closer look at the accident figures in a time series and to estimate possible trends

Are Pedelec crashes different to bicycle crashes?: A comparison of national accident data in Germany

Since 2014, a distinction between Pedelec (electrical support up to 25 km/h) and bicycle crashes is made in official police reported accidents with personal injuries in Germany. Yet, no comparative analysis using national data is available, moreover some estimation was done how Pedelec crashes may look like based on bicycle crashes. Hence, the present study aims to compare real-world crashes with personal injuries with both vehicle types - Pedelec and bicycle and show similarities and differences of the vehicle classes. Nearly a decade of reporting allows furthermore to have a closer look at the accident figures in a time series and to estimate possible trends

Are Pedelec crashes different to bicycle crashes?: A comparison of national accident data in Germany

Since 2014, a distinction between Pedelec (electrical support up to 25 km/h) and bicycle crashes is made in official police reported accidents with personal injuries in Germany. Yet, no comparative analysis using national data is available, moreover some estimation was done how Pedelec crashes may look like based on bicycle crashes. Hence, the present study aims to compare real-world crashes with personal injuries with both vehicle types - Pedelec and bicycle and show similarities and differences of the vehicle classes. Nearly a decade of reporting allows furthermore to have a closer look at the accident figures in a time series and to estimate possible trends

In-depth crash investigation setup in Campinas, Sao Paulo, Brazil

Although road infrastructure is developed extensively Brazil is still one of the countries with the most dangerous roads in the world. In order to stop the increasing trend of traffic fatalities of the last few years and to improve traffic safety on Brazilian roads a pilot study on behalf of SAE Brazil started in March 2016 with the goal to lay the foundations for a long-term research activity. Piloting for an in-depth accident investigation the city of Campinas, roughly 100 km north of São Paulo was chosen. The pilot project was carried out with the local partner, the Empresa Municipal de Desenvolvimento de Campinas (EMDEC). The paper reports on the initial training of evidence based accident data collection on-spot, the implementation of the new digital database, the data collection and the first results. An outlook on the planned long-term accident investigations is given

How to evaluate the accident situation in India?

The increasing economics in India has an enormous growth of its road traffic. As observed from official Indian accident statistics the number of road fatalities are one of the highest worldwide. In contrast to most industrialized nations they have an rapidly increasing trend. To come along with this trend it becomes more than essential to understand the traffic accident situation. The official Indian accident statistics gives a glimpse of only basic information. Therefore more detailed data is needed. By using In-depth accident data and officially representative statistics the current accident situation can be evaluated in India, if a suitable weighting methodology is considered. Hence in 2009/2010 a pilot study with the collaboration partner JP-Research India pvt. Ldt. was gathered in Tamil Nadu in south of India. In-depth accident investigations were done around the Coimbatore area on four highways. At first, the collected data is evaluated. Due to consequent and continuous further development based on the first approach a methodology similar to NASS/CDS/GES in the US and GIDAS in Germany was developed. Of course all relevant accident related parameters including pictures and severity information were collected. As a matter of fact based on scaled sketches and reconstruction benefit analyses can be done in order to analyze the accident scenery in India. As a first outcome influence from infrastructure, missing education and vehicle safety were identified as key parameters in order to reduce the number of accidents and casualties. To compare the accident situation against international standards an accident classification for left hand traffic was developed based on the German Insurance classification system. Looking into detail additional accident types were identified and added to create an Indian accident type catalogue. The positive results encouraged several OEMs to participate in this investigation and together with BOSCH a consortium was established in 2010/11. Within one year from beginning in May 2011 about 200 highway accidents were collected, reported and reconstructed using the new standard. Hence a first good overview of the accident situation is available for the Coimbatore Tamil Nadu area. The major target for establishing accident investigations is the extension towards other states of India and urban areas to achieve a better overview of the accident scenery. Therefore local and national authorities have to be embedded in order to strengthen the awareness against traffic safety

Did a higher distribution of pedelecs results in more severe accidents in Germany

The study aimed at estimating the impact of pedelecs (with an assumed higher speed than bicycles) on the traffic accident severity in Germany for different penetration rates. The analysis shows that in many real situations (68%) an electrical support of bicycles has no influence on the sequence of accident events. Taking into account a number of unreported "single bicycle accidents", the adoption of similar traffic behavior and similar age distribution, the authors determined a shift of 400 former slightly to seriously injured cyclists in Germany per year. Overall this would be an increase of approximately 2.3% in case of 10% of pedelec penetration with the pessimistic assumption of 10 km/h speed increase although first natural driving studies predict a much lower average speed increase of pedelecs. The hypothesis verbalized in the initial question whether a higher distribution of pedelecs will result in more severe accidents in Germany is not verified. The study shows that electrical support didn"t result in higher collision speed in general. In many accident situations, the speed of pedelecs has only a minor influence on the accident severity. Further research focusing on a possible change of driver behavior especially in new target groups (elderly people) will be needed

Is there a broken trend in traffic safety in Germany? Model based approach describing the relation between traffic fatalities in Germany and environmental conditions

The declining trend since 1991 in the number of killed people was broken in 2011 when overall 4 009 people died in traffic accidents in Germany. The question arises if there is a stagnating trend of fatalities in Germany in future? By breaking down the accidents with casualties towards a monthly view one can see a decreasing trend of fatalities in the warmer months especially since 2009. When comparing against winter months higher deviations are observed. In December 2011 an increase of 191 traffic deaths were registered (181 in 2010 compared to 372 in 2011). Further analyses of different accident influences were evaluated and their possibility of drastic change from one year to the other was determined. As seen weather- and environmental conditions are one of the major contributing factors and are one of the causes for the increased number of fatalities. To support the underlying assumption a model had been created to calculate the number of traffic deaths on a daily basis approach. As an input, road conditions projected through weather parameters and also different driving behaviors on weekdays or holidays were used. As a result, estimates of daily fatality with up to 75% precision can be achieved out of the 2009, 2010 and 2011 data. Further on it shows that weather and street conditions have a high influence on the overall resulting number of traffic accidents with casualties, and especially to the number of fatalities. Hence it is estimated that approximately 3 300 people were killed in traffic accidents in Germany in 2013 which would be again a reduction of another 13% compared to 2012. Therefore an answer to the question will be that the decreasing trend in traffic fatalities in Germany somehow is not broken when environmental conditions are included in national statistics. Their effects will become more visible in future accident statistics and it is estimated variances of 5% to 8% of the annual number of traffic fatalities in Germany will be seen

A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling

Abstract Radiomics applies machine learning algorithms to quantitative imaging data to characterise the tumour phenotype and predict clinical outcome. For the development of radiomics risk models, a variety of different algorithms is available and it is not clear which one gives optimal results. Therefore, we assessed the performance of 11 machine learning algorithms combined with 12 feature selection methods by the concordance index (C-Index), to predict loco-regional tumour control (LRC) and overall survival for patients with head and neck squamous cell carcinoma. The considered algorithms are able to deal with continuous time-to-event survival data. Feature selection and model building were performed on a multicentre cohort (213 patients) and validated using an independent cohort (80 patients). We found several combinations of machine learning algorithms and feature selection methods which achieve similar results, e.g., MSR-RF: C-Index = 0.71 and BT-COX: C-Index = 0.70 in combination with Spearman feature selection. Using the best performing models, patients were stratified into groups of low and high risk of recurrence. Significant differences in LRC were obtained between both groups on the validation cohort. Based on the presented analysis, we identified a subset of algorithms which should be considered in future radiomics studies to develop stable and clinically relevant predictive models for time-to-event endpoints