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Study of Car-Bicycle Safety at Signalized Intersections from Multi-Aspects

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Study of Car-Bicycle Safety at Signalized Intersections from Multi-Aspects Study of Car-Bicycle Safety at Signalized Intersections from Multi-Aspects vorgelegt von M.Eng. Peipei Liu geb. in Yantai, China von der Fakultät V - Verkehrs- und Maschinensysteme der Technischen Universität Berlin zur Erlangung des akademischen Grades Doktor der Ingenieurwissenschaften - Dr.-Ing. - genehmigte Dissertation Promotionsausschuss: Vorsitzende: Prof. Dr.-Ing. Nele Rußwinkel Gutachterin: Prof. Dr. rer. nat. Stefanie Marker Gutachter: Dr.-Ing. Heiko Johannsen Tag der wissenschaftlichen Aussprache: 04.05.2018 Berlin, 2019 Acknowledgements I would like to express my deepest gratitude to my supervisor Prof. Dr. rer. nat. Stefanie Marker for her guidance, encouragement, patience and support throughout this work. This thesis is a result of her great mentoring. I would also like to extend this gratitude to Prof. Dr. rer. nat. Volker Schindler for providing me the opportunity to do this work. I am obliged to my colleagues in the FVB group (Fahrerverhaltensbeobachtung für energetische Optimierung und Unfallvermeidung, TU Berlin): Dipl.-Ing. Jan Grüner, Benjamin Rippel, M.Sc., Dipl.-Ing. Jens-Olav Jerratsch, Dipl-Phys. Dipl.-Inf. John Exner and Daniel Dreyhaupt, M.Sc.. They have supported and helped me over the last years. Their kindness and humor have provided me a relaxed research environment. This group is a resource of not only professional advices but also friendship. I also want to thank my colleague Dipl.-Ing. Danny Klinder in the KFZ group (Fachgebiet Kraftfahrzeuge, TU Berlin), who aided in the preparation of the instrumented car for the field experiment. I would like to thank Mr. Thomas Wende from the Berlin Police for providing the accident data as well as for his consult, and Mr. Wolfgang Grafenhorst from the Senate Department for Urban Development and the Environment (Berlin) for providing the traffic volume data as well as for his consult. Moreover, the studies presented in this work owe gratitude to all the participants in the field experiments and the online-survey. My special gratitude goes to Mr. Bernd Meurer for proofreading this thesis. I am grateful to him and his Wife Yanzhai Chai for their continuous care, motivation and help during my stay in Berlin. Furthermore, I greatly acknowledge CSC (China Scholarship Council) and TU Berlin for funding my research and my life. Finally, I would like to thank my family. Words cannot express how grateful I am to them. I thank my parents for their understanding and my parents-in-law for their encouragement. I am extremely grateful to my beloved husband Liang Kong. Without his love, support, and sacrifice, I would never have been where I am now. Most importantly, I want to thank my new-born daughter Sophia Kong, who brought me a lot of happiness in the last phase of my doctoral studies. I Abstract Cycling is increasing in popularity in urban areas due to its individual, social and environmental benefits. However, cyclists are among the most vulnerable road users. Especially at intersections, many bicycles collide with passenger cars despite the control of traffic signals. This thesis focuses on car-bicycle safety at signalized intersections. Various factors contribute to the occurrence of road accidents, namely factors from the human, vehicle and environment aspects. The aim of this thesis is to explore potential measures to reduce car-bicycle accidents at signalized intersection from multi-aspects. In accident prevention, one must know the world of accidents. Given that few accident analysis has specifically focused on car-bicycle accidents at signalized intersections, this thesis analyze two accident databases. The analysis deals with the characteristics of car-bicycle accidents at signalized intersections. It reveals possible accident scenarios, frequencies of each scenario and common accident causes of accidents. Naturalistic Driving Observation (NDO) is a fast-growing method for traffic safety studies. Despite of many NDO studies, few have particularly investigated interactions between car drivers and bicyclists at signalized intersections. This thesis carries out a Quasi-NDO study in order to investigate the interactions between car drivers and cyclists at signalized intersections from perspective of car drivers. A car is instrumented with various sensors. The instrumented car is used to collect the data of driving behaviors and environment, while twenty-two participants separately drive this car in real traffic. The collected driving behaviors include dynamic driving data, drivers´ body movements and eye movements. A self-programmed Graphical User Interface and a video annotation tool are used for data analysis in order to detect car-bicycle conflicts. In addition, the collected eye movement data is analyzed in the scenario (i.e. right-hook scenario), where car turns right and bicycle goes through. With 146 detected right-hook events, the analysis reveals bicycle-scanning strategies of car drivers. According to the bicycle-scanning strategies, potential suggestions are proposed to mitigate the risk of this scenario. Road users´ perceived risk influences individual behaviors, and therefore it plays an important role in traffic safety. Through an online survey, the perceived risk of car drivers and cyclists (no matter the consequences of the crash) are investigated for seventeen common car-bicycle scenarios at signalized intersections. A comparison between the subjective perceived risk und the objective risk shows that a discrepancy exists among both car drivers and cyclists. Moreover, it shows that cyclists tend to perceive less risk than car drivers do. The implications of these results are vital for improvement of car-bicycle safety at signalized intersections. Safety effects of bicycle facilities are controversial, especially at intersections. By use of the Negative Binomial model, safety effects of bicycle facilities on car-bicycle crash risk at signalized intersections are estimated. The effects of other intersection factors are simultaneously considered. The estimation results shows that bicycle lanes have positive safety effects, while bicycle paths have negative safety effects. II Kurzfassung Aufgrund seiner Vorteile für Individuum, Gesellschaft und Umweltnutzen nutzen immer mehr Menschen das Fahrrad in Innenstädten als Verkehrsmittel. Fahrradfahrer gehören jedoch zu den am meisten gefährdeten Verkehrsteilnehmern. Besonders an Knotenpunkten treten trotz der Steuerung durch Lichtsignalanlagen (LSA) zahlreiche Unfälle zwischen Pkw und Fahrrädern auf. Im Rahmen der vorliegenden Arbeit wird die Sicherheit von Pkw- und Fahrradfahrern an Knotenpunkten mit LSA fokussiert. Verschiedene Faktoren von Menschen, Fahrzeug und Umwelt könnten Verkehrsunfälle verursachen. Ziel dieser Arbeit ist, aus den diversen Aspekten mögliche Maßnahmen zur Reduzierung von Unfällen zwischen Pkw und Fahrrädern an Knotenpunkten mit LSA zu ermitteln. Für die Unfallprävention muss man die Details der Unfälle kennen. Wegen fehlender Unfallforschung, die speziell Unfälle zwischen Pkw und Fahrrad an Kontenpunkten mit LSA betrachtet, analysiert diese Arbeit zwei Unfalldatenbanken. Diese Analyse konzentriert sich auf die Merkmale der Unfälle zwischen Pkw und Fahrrädern an Kontenpunkten mit LSA. Hierbei werden die möglichen Unfallszenarien, Häufigkeiten jedes Szenarios und häufige Unfallursachen aufgezeigt. Naturalistic Driving Observation (NDO) ist eine stark wachsende Methode im Bereich der Verkehrssicherheit. Obwohl viele NDO-Studien durchgeführt wurden, hat keine insbesondere die Interaktionen zwischen Pkw- Fahrern und Radfahrern an Knotenpunkten mit LSA untersucht. Eine Quasi-NDO-Studie wird in dieser Arbeit durchgeführt, um aus Sicht der Pkw-Fahrer die Interaktionen zwischen Pkw- und Radfahrern an Knotenpunkten mit LSA zu untersuchen. Ein Auto wird mit verschiedenen Sensoren ausgerüstet. Mit diesem Auto werden die Fahrerverhaltens- und Umgebungsdaten von 22 Teilnehmern im Realverkehr erhoben. Die aufgezeichneten Fahrerverhaltensdaten enthalten dynamische Fahrdaten, Köperbewegungen und Augenbewegungen der Fahrer. Eine selbstprogrammierte grafische Benutzeroberfläche und eine Software zur Videoannotation werden zur Datenanalyse verwendet, um Konflikte zwischen Pkw und Fahrrad zu identifizieren. Zusätzlich werden die erhobenen Augenbewegungsdaten in dem Szenario bzw. Rechtsabbiege-Szenario analysiert, in dem der Pkw rechts abbiegt und das Fahrrad geradeaus fährt. Mit 146 erkannten Rechtsabbiege-Ereignissen zeigt diese Analyse auf, wie die Pkw-Fahrer die Radfahrer erkennen. Basierend auf diesen Fahrrad-Scan-Strategien werden Möglichkeiten zur Verringerung des Risikos in dem Szenario abgeleitet. Risikowahrnehmung der Verkehrsteilnehmer beeinflusst ihr Verhalten und spielt eine wichtige Rolle für die Verkehrssicherheit. Mit Hilfe einer Online-Umfrage wird die Risikowahrnehmung der Pkw- und Radfahrer (unabhängig von den Unfallfolgen) für 17 häufige Konflikt-Szenarien zwischen Pkw und Fahrrad erforscht. Ein Vergleich zwischen dem subjektiv wahrgenommenen Risiko und dem objektiven Risiko zeigt, dass eine Diskrepanz sowohl bei Pkw-Fahrern als auch bei Radfahrern besteht. Weiterhin zeigt sich, dass Radfahrer dazu neigen, ein geringeres Risiko als Pkw-Fahrer wahrzunehmen. Diese Ergebnisse sind für die Verbesserung der Sicherheit von Pkw- und Fahrradfahrern an Knotenpunkten mit LSA von großer Bedeutung. Es ist umstritten, wie Radverkehrsanlagen vor allem an Knotenpunkten die Verkehrssicherheit beeinflussen. Mittels des negativen Binomialmodells werden die Auswirkungen von Radstreifen und Radwegen
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