Is the “Safety in Numbers” effect tied to specific road types? - A GIS-based approach

The 'Safety in Numbers' (SiN) effect proposes that when the volume of cycling traffic increases, the number of crashes increases less (relative to the cycling volume). A recent meta-analysis supported the general idea of a SiN effect, but also highlighted the heterogeneity of its strength, also see). The authors of this meta-study conclude that the SiN effect is strenger at the macro-level than at the micro-level, but bears no clear relationship to the quality of the cycling infrastructure. The mechanisms producing the SiN effect are still unknown. Possible explanations are (i) that safer street regulations and designs are more likely to ex.ist in societies with more wallcing and bicycling; (ii) changes in the behavior of people walking or bicycling; or (iii) changes in behavior of drivers. However, all of these explanations have their shortcomings. Additionally, some authors have argued that an increase in the number of crashes cannot be ruled out due to the increasing numbe:r of inexpe:rienced or particularly risk-taking cyclists. The:re appears to be little research on the question whether and how the SiN effect may be linked to specific road types featuring different combinations of speed zones and cycling infrastructures. Furthermore, the base rate of cyclists (i.e. the cycling volume) is a highly relevant factor when investigating the distribution of crashes throughout different road types [6]. In our research, we thus use a GIS-based approach aimed at testing the relation between the cycling volume and the number of crashes involving cyclists for roads featuring different speed zones and cycling infrastructures

Attention allocation and subjective risk at un-signaled intersections - A virtual cycling game

The probability of a cycling crash is much higher at intersections as along the road. A number of reasons contribute to this difference, for example car drivers overlooking cyclists when taking a turn. There have been attempts to quantify the risk at prototypical, un-signaled intersections featuring different levels of cycling infrastructure, as well as cyclists' perception of risk of these intersections. However, these attempts are limited to regular, four-arm intersections, although irregular intersections featuring both a higher and a lower nwnber of anns as weil as odd angles are likely to pose additional challenges for cyclists. There appears tobe little research on the question how the complexity and layout of such intersection affects cyclists perception of risk, as weil as their allocation of attention towards the different arms of an intersection. In, we presented a first approach to taclde this issue in a virtua1 reality (VR) based setup. We found evidence that tbe type oftum affected the subjective risk (e.g. with. a higher risk associated with situations requiring a sharp turn or to continue to an offset road), but no effects of the general position of an intersection arm in relation to the cyclist' traveling trajectory. However, the repeated exposure to the same intersection in this stu.dy limits the conclusiveness of the findings. We thus developed a more flexible virtual environment allowing us to investigate the attention allocation and risk. perception at various types of intersections

How to like yourself better, or chocolate less: changing implicit attitudes with one IAT task

The current paper introduces a novel feature of Implicit Association Tests (IATs) by demonstrating their potential to change implicit attitudes. We assume that such changes are driven by associative learning mechanisms caused by carrying out an IAT task. Currently, evaluative conditioning appears to be the only widespread paradigm for changing implicit attitudes. An IAT task could provide an alternative. In two experiments, participants initially reacted to only one IAT task. Implicit preferences subsequently assessed with different implicit measures depended on the initial IAT task. This was shown for implicit self-esteem and for attitudes towards well-known candy brands. Findings are discussed in relation to task-order effects in IATs

The influence of active and passive navigation on spatial memory of drivers and co-drivers

Forschungsergebnisse zu aktiver Navigation legen nahe, dass die Ausführung einer Bewegung für das räumliche Gedächtnis weniger entscheidend sein könnte als die aktive Entscheidung über die Bewegungsrichtung. Demzufolge wäre ein aktiv navigierender Beifahrer gegenüber einem Navigationsanweisungen befolgenden Fahrer nicht zwangsläufig benachteiligt. Diese Hypothese wurde mit zwei Experimentserien untersucht. In Serie 1 wurde in virtueller Umgebungen Bewegungskontrolle als erster Faktor manipuliert und vier anderen Faktoren gegenübergestellt (Experiment 1: Lernintention; Experiment 2: Instruktionsspezifität und Instruktionskontrolle; Experiment 3: Navigationskontrolle). Serie 2 übertrug diese Befunde auf einen realen Kontext. Die Teilnehmer fuhren auf dem Vorder- oder dem Rücksitz eines Tandems und erhielten verschieden ausführliche Kartenausschnitte, um durch einen Park zu navigieren (Experiment 4 und 5). In einem weiteren Experiment ohne Exposition wurden mögliche Konfundierungen der beiden vorherigen Experimente ausgeschlossen (Experiment 6). Die vorliegende Arbeit bestätigt, dass räumliches Lernen in der Forschung zur aktiven Navigation weniger von aktiver Bewegungskontrolle bestimmt wird, sondern mehr von der zwingenden Teilhabe am navigatorischen Prozess. Wenn die Situation Beifahrer zum notwendigen Verarbeitungslevel zwingt, entsteht kein Nachteil in punkto Überblickswissen. Im Kontrast dazu legen die Daten allerdings die Annahme eines grundsätzlichen Vorteils von Fahrern im Hinblick auf Landmarken-Wissen nahe. Die vorliegenden Studien tragen durch den Vergleich verschiedener, relevanter Faktoren in ausbalancierten Versuchspläne zur Klärung der existierenden Diskrepanzen in der aktiven Navigationsforschung bei. Sie zeigen außerdem mittels einer der ersten Realisierungen aktiver Navigation in einer realen Umgebung überhaupt, dass die Erforschung von räumlicher Kognition in virtuellen Umgebungen ökologisch valide Schlüsse auf reale Kontexte ermöglicht

Is the “Safety in Numbers” effect tied to specific road types? - A GIS-based approach

The 'Safety in Numbers' (SiN) effect proposes that when the volume of cycling traffic increases, the number of crashes increases less (relative to the cycling volume). A recent meta-analysis supported the general idea of a SiN effect, but also highlighted the heterogeneity of its strength, also see). The authors of this meta-study conclude that the SiN effect is strenger at the macro-level than at the micro-level, but bears no clear relationship to the quality of the cycling infrastructure. The mechanisms producing the SiN effect are still unknown. Possible explanations are (i) that safer street regulations and designs are more likely to ex.ist in societies with more wallcing and bicycling; (ii) changes in the behavior of people walking or bicycling; or (iii) changes in behavior of drivers. However, all of these explanations have their shortcomings. Additionally, some authors have argued that an increase in the number of crashes cannot be ruled out due to the increasing numbe:r of inexpe:rienced or particularly risk-taking cyclists. The:re appears to be little research on the question whether and how the SiN effect may be linked to specific road types featuring different combinations of speed zones and cycling infrastructures. Furthermore, the base rate of cyclists (i.e. the cycling volume) is a highly relevant factor when investigating the distribution of crashes throughout different road types [6]. In our research, we thus use a GIS-based approach aimed at testing the relation between the cycling volume and the number of crashes involving cyclists for roads featuring different speed zones and cycling infrastructures

Is the “Safety in Numbers” effect tied to specific road types? - A GIS-based approach

The 'Safety in Numbers' (SiN) effect proposes that when the volume of cycling traffic increases, the number of crashes increases less (relative to the cycling volume). A recent meta-analysis supported the general idea of a SiN effect, but also highlighted the heterogeneity of its strength, also see). The authors of this meta-study conclude that the SiN effect is strenger at the macro-level than at the micro-level, but bears no clear relationship to the quality of the cycling infrastructure. The mechanisms producing the SiN effect are still unknown. Possible explanations are (i) that safer street regulations and designs are more likely to ex.ist in societies with more wallcing and bicycling; (ii) changes in the behavior of people walking or bicycling; or (iii) changes in behavior of drivers. However, all of these explanations have their shortcomings. Additionally, some authors have argued that an increase in the number of crashes cannot be ruled out due to the increasing numbe:r of inexpe:rienced or particularly risk-taking cyclists. The:re appears to be little research on the question whether and how the SiN effect may be linked to specific road types featuring different combinations of speed zones and cycling infrastructures. Furthermore, the base rate of cyclists (i.e. the cycling volume) is a highly relevant factor when investigating the distribution of crashes throughout different road types [6]. In our research, we thus use a GIS-based approach aimed at testing the relation between the cycling volume and the number of crashes involving cyclists for roads featuring different speed zones and cycling infrastructures

Attention allocation and subjective risk at un-signaled intersections - A virtual cycling game

The probability of a cycling crash is much higher at intersections as along the road. A number of reasons contribute to this difference, for example car drivers overlooking cyclists when taking a turn. There have been attempts to quantify the risk at prototypical, un-signaled intersections featuring different levels of cycling infrastructure, as well as cyclists' perception of risk of these intersections. However, these attempts are limited to regular, four-arm intersections, although irregular intersections featuring both a higher and a lower nwnber of anns as weil as odd angles are likely to pose additional challenges for cyclists. There appears tobe little research on the question how the complexity and layout of such intersection affects cyclists perception of risk, as weil as their allocation of attention towards the different arms of an intersection. In, we presented a first approach to taclde this issue in a virtua1 reality (VR) based setup. We found evidence that tbe type oftum affected the subjective risk (e.g. with. a higher risk associated with situations requiring a sharp turn or to continue to an offset road), but no effects of the general position of an intersection arm in relation to the cyclist' traveling trajectory. However, the repeated exposure to the same intersection in this stu.dy limits the conclusiveness of the findings. We thus developed a more flexible virtual environment allowing us to investigate the attention allocation and risk. perception at various types of intersections

Risk Perception and Gaze Behavior during Urban Cycling – A Field Study

We investigated the relation of risk perception and gaze behavior during urban cycling in a naturalistic real world setting. Participants rode a bike on five route segments while wearing a mobile eye tracking device, and subsequently marked, described, and rated all areas they thought to be dangerous. Their gaze was focused on the areas they marked as potentially dangerous. The participants’ gaze was also more focused on areas they felt to be subjectively dangerous (but where no accidents had previously occurred) as compared to areas where accidents had occurred (but that also were subjectively dangerous)

Attention allocation and subjective risk at un-signaled intersections - A virtual cycling game

The probability of a cycling crash is much higher at intersections as along the road. A number of reasons contribute to this difference, for example car drivers overlooking cyclists when taking a turn. There have been attempts to quantify the risk at prototypical, un-signaled intersections featuring different levels of cycling infrastructure, as well as cyclists' perception of risk of these intersections. However, these attempts are limited to regular, four-arm intersections, although irregular intersections featuring both a higher and a lower nwnber of anns as weil as odd angles are likely to pose additional challenges for cyclists. There appears tobe little research on the question how the complexity and layout of such intersection affects cyclists perception of risk, as weil as their allocation of attention towards the different arms of an intersection. In, we presented a first approach to taclde this issue in a virtua1 reality (VR) based setup. We found evidence that tbe type oftum affected the subjective risk (e.g. with. a higher risk associated with situations requiring a sharp turn or to continue to an offset road), but no effects of the general position of an intersection arm in relation to the cyclist' traveling trajectory. However, the repeated exposure to the same intersection in this stu.dy limits the conclusiveness of the findings. We thus developed a more flexible virtual environment allowing us to investigate the attention allocation and risk. perception at various types of intersections