Longer-term effects of ADAS use on driving performance of healthy older drivers and drivers diagnosed with Parkinson’s disease

Oudere automobilisten vormen een groep met unieke kenmerken. Enerzijds hebben ze vaak heel veel rijervaring, anderzijds kan er sprake zijn van leeftijdgerelateerde achteruitgang in visuele, cogitieve en motorische functies die de verkeersveiligheid kan verminderen. Recent op de markt gebrachte "Advanced Driver Assistance Systems"(ADAS), bedoeld om automobilisten in het algemeen te ondersteunen, sluiten niet noodzakelijkerwijze goed aan bij de behoeften van oudere automobilisten, al dan niet met functiebeperkingen. Op grond van hun specifieke ongevalsprofiel en unieke kenmerken kunnen oudere automobilisten in het bijzonder baat hebben bij het vooraf aanbieden van relevante verkeersinformatie, waardoor er in complexe verkeerssituaties minder tijdsdruk is en minder noodzaak is om de aandacht te verdelen. In dit proefschrift wordt een ADAS onderzocht dat informatie geeft over voorrang, snelheid, oversteekmogelijkheden en volgafstand. Het systeem is longitudinaaal onderzocht bij gezonde oudere automobilisten, automobilisten met de ziekte van Parkinson en jonge onervaren automobilisten. Veranderingen in rijgedrag en in subjectieve beoordelingen voortkomend uit het gebruik van ADAS worden onderzocht in relatie tot korte en langere duur van het gebruik, maar ook in relatie tot het weghalen van de ADAS ondersteuning nadat er verscheidene achtereenvolgende sessies gebruik van is gemaakt

UR:BAN – Urbaner Raum: Benutzergerechte Assistenzsysteme und Netzmanagement - Schlussbericht DLR zu dem Teilprojekt Mensch im Verkehr (MV)

Ziel des Projekts UR:BAN war es innovative Fahrerassistenz- und Verkehrsmanagementsysteme für urbane Räume zu entwickeln, zu testen und deren Beitrag zur Verbesserung der Sicherheit und Effizienz zu bewerten. Hierbei wurde der Mensch mit seinen vielfältigen Rollen im Verkehrssystem betrachtet. Die vorliegenden Arbeiten hatten das Ziel, Verhalten der Verkehrsteilnehmer in Interaktion miteinander zu beschreiben sowie in Kooperation mit neuen Systemen realitätsnäher abzubilden. Dazu wurden entsprechende Methoden zur Beschreibung von Interaktion und Kooperation entwickelt. Zum Einsatz kamen zum einen Feldbeobachtungen an der AIM Forschungskreuzung, die das Ziel hatten, die Kooperation zwischen abbiegenden Auto- und Radfahrern zu beschreiben. Parameter zur Kooperationsbeschreibung wurden erarbeitet, die die Quantifizierung dieser Interaktion erlauben. Zum anderen kam die vernetzte Fahrsimulation zum Einsatz mit dem Ziel, die Auswirkung eines Fahrers mit neuer intelligenter Fahrerassistenz auf umgebenen Verkehr zu quantifizieren. Parameter zur Beschreibung mehrerer Fahrer in der Interaktion wurden angewendet und weiterentwickelt. Parameter aus dem Bereich der Signalverarbeitung wurden genutzt, um Verhaltensanpassungen von umgebenden Verkehr an den Fahrer mit intelligenter Fahrerassistenz zu beschreiben. Die gewonnenen Ergebnisse können genutzt werden, um bestehende Interaktionsmodelle in der Fahr- und Verkehrssimulation zu optimieren sowie Fahrerassistenz- und Verkehrsmanagementsysteme hinsichtlich menschzentrierter Aspekte zu gestalten und zu parametrisieren

How crowded is the train? Optimal conditions for providing information on capacity utilization in public transport

Crowding in public transportation (PT) is a major challenge that has both negative economic consequences for providers and negative psychological consequences for passengers. The increasing availability of precise information on capacity rates of PT brings the possibility of providing information on alternative, less crowded route options to passengers. In order to determine when, how and where the information on capacities needs to be displayed to be effective for planning a trip, an online study was administered. The goal of the study was to investigate the effects of socio demographic features of passengers and certain trip characteristics on the perceived usefulness of information on capacity utilization (CU) for passengers. Data was collected via an online questionnaire. Results (N = 204) show that trip purpose, service frequency and trip duration had a significant effect on the perceived usefulness of CU. Differences between people living in a metropole and people living in smaller cities could be observed. Strategies and requirements for providing capacity information effectively will be derived

Information on Capacity Utilization for Public Transportation: Useful for Passengers?

Crowding in public transportation (PT) is a major challenge that has negative economic consequences for providers and negative psychological consequences for passengers. With more and more people using journey planner apps and the increasing availability of precise information on capacity utilization (CU) of PT, providing information on alternative, less crowded route options to passengers, is possible. In order to determine when, how and where the information on CU needs to be displayed and for whom it is especially useful for planning a trip, an online study was administered. An objective of the study was to investigate the use of journey planner apps among public transport users in more detail. In particular, the frequency and timing of app use for different trip purposes were investigated. Another goal was to identify under what circumstances public transport users perceive information on CU as useful. Results (N = 204) show that the frequency and timing of the use of journey planner apps differs depending on the trip purpose. People from metropolitan areas use journey planner apps less often than people from smaller cities. Trip purpose, duration of the trip, and service frequency have an influence on the perceived usefulness of information on CU. Important insights on the use of journey planner apps and the optimal conditions for the target group specific provision of information on CU could be gained with this study. The results can be used to derive strategies and recommendations for PT service providers

Digitally co-developed urban politics and policies: Bringing sustainable mobility solutions to life

Erste Auswertungsergebnisse eines digitalen Planspiels mit Stakeholdern aus der (kommunalen) Mobilitätsentwicklung, in der (a) eine Verhandlungstechnik getestet wurde (Harvard-Konzept, vgl. Fisher/Ury 2018) (b) gemeinsam getragene Mobilitätslösungen identifiziert und ihre Umsetzung skizziert wurde

Analysis of implicit communication of motorists and cyclists in intersection using video and trajectory data

The interaction of automated vehicles with vulnerable road users is one of the greatest challenges in the development of automated driving functions. In order to improve efficiency and ensure the safety of mixed traffic, automated driving functions need to understand the intention of vulnerable road users, to adapt to their driving behavior, and to show its intention. However, this communication may occur in an implicit way, meaning they may communicate with vulnerable road users by using dynamic information, such as speed, distance, etc. Therefore, investigating patterns of implicit communication of human drivers with vulnerable road users is relevant for developing automated driving functions. The aim of this study is to identify the patterns of implicit communication of human drivers with vulnerable road users. For this purpose, the interaction between right-turning motorists and crossing cyclists was investigated at a traffic light controlled urban intersection. In the scenario, motorists and cyclists had a green signal at the same time, but cyclist had right-of-way. Using the Application Platform for Intelligent Mobility (AIM) Research Intersection, trajectory and video data were recorded at an intersection in Braunschweig, Germany. Data had been recorded for four weeks. Based on the criticality metric post encroachment time (PET) and quality of the recorded trajectory, 206 cases of interaction were selected for further analyses. According to the video annotation, when approaching the intersection, three common communication patterns were identified: (1) no yield, motorists, who should yield to cyclists, crossed the intersection first while forcing right-of-way; (2) active yield, motorists, who were in front of cyclists, gave the right-of-way; (3) passive yield, motorists, who were behind cyclists, had to give the right-of-way. The analysis of the trajectory data revealed different patterns of changes in time advantage in these three categories. Additionally, the communication patterns were evaluated with regard to frequency of occurrence, efficiency, and safety. The findings of this study may provide knowledge for the implementation of a communication strategy for automated driving functions, contributing to traffic efficiency as well as ensuring safety in the interaction with vulnerable road users

Cycling through intersections: Patterns affecting safety

Within the past years, a shift in mode of transport has been observed. The popularity of cycling has significantly increased and will increase even more in the time to come. On the downside, cycling is also associated with severe injuries or even fatalities, particularly when involved in a crash with a motorized vehicle. In urban areas, one of the most risky situations is crossing an intersection while a motorized vehicle turns right. The largest part of crashes involving cyclists are caused by wrong behavior when turning / crossing of which more than 40% result in major injuries or even death. In an effort to increase road traffic safety of cyclists in intersections, within the framework of EU-funded project XCYCLE, an infrastructural supported cooperative advanced driver assistance system (C-ADAS) will be developed. The objective is to develop an online risk assessment in order to predict critical encounters between motorized vehicles and cyclists in real-time. Understanding how critical situations/conflicts arise is the key to this situation and risk assessment. Therefore, it is inevitable to quantify and analyze human behavior, determine situational factors, but also analyze objective data, such as video-based recorded trajectory data in order to identify patterns between and within interacting partners (i.e. motorist and cyclist). At the AIM Research Intersection in Braunschweig, trajectory and video data has been collected since summer 2016. Based on the data collected between August 22nd and September 18th, 2017, in an observational study, situational factors contributing to critical situations were analyzed. According to the results, the relative position of cyclist and motorist to each other in the approach to the intersections indicates to contribute to critical situations. This knowledge allows us deciding whether an upcoming situation may end up as an encounter or critical situation already at an early stage possibly enabling a rapid-warning mechanism for motorists. In a next step, trajectory data will be analyzed in order to determine distinct patterns in, for example speed, between critical situations, encounters, cyclists crossing the intersection without interaction partner, and motorists turning at the intersection without interaction partner. Performance indicators used to assess the interaction or cooperation between motorists and cyclists are also defined and will be analyzed. Results of the analyses will be incorporated into the development of an online situation and risk assessment

Zwischen Fahrrad und Flugtaxi: Bürger:innen-Bedarfe an die Hamburger Mobilität 2035

Vorstellung von Zwischenergebnissen zu Mobilitäts-Zukunftsvisionen, Mobilitätsmodi und Anforderungen an spezifische Verkehrsmittel, die Hamburger Bürger:innen für ihre gewünschte Mobilität 2035 formuliert haben

Why so serious? – Comparing two traffic conflict techniques for assessing encounters in shared space

In Germany, approximately 2.7 million crashes occurred in 2019. Especially vulnerable road users (VRU) have a high risk of being seriously injured or killed in traffic. Within the safe system approach, changes to the traffic infrastructure have been implemented to increase VRU safety. The creation of so-called shared spaces, in which all road users are encouraged to negotiate priority, is part of these efforts. Even though the concept has been known and applied for more than 40 years, comparatively little is known about interactions between different road users and methods to quantify interactions in shared spaces. The aim of this study is to investigate similarities and differences in quantifying the level of severity of encounters between pedestrians and motorised vehicles applying the Swedish traffic conflicts technique (STCT) and the pedestrian-vehicle conflicts analysis (PVCA). The STCT integrates the factors conflicting speed (CS) and time-to-accident (TA) to arrive at a severity level. In contrast, with four factors, the PVCA integrates more elements: time-to-collision (TTC, corresponding to TA), severity of evasive action, complexity of evasive action, and distance-to-collision (DTC). Trajectory and video data of a shared space were recorded using the Application Platform for Intelligent Mobile Units (AIM) in Ulm, Germany. 1364 interactions were randomly selected. Due to different exclusion criteria, such as interaction partners not being a car or pedestrian, missing values, and detection errors, 69 encounters were available for analyses. Using the PVCA, nine encounters were classified as critical and 60 as non-critical interactions. In contrast, computing the values based on the STCT, only three of the 69 encounters were categorised as critical. The results of a Spearman rank correlation did not show a significant correlation between the severity categories of the PVCA and severity levels of the STCT (r = 0.03, p = 0.78). An additional analysis of the encounters ranked as critical by the PVCA but as non-critical by the STCT showed that all six encounters had a large temporal distance (> 2 s) combined with very small spatial distance (< 5 m for vehicles and < 2.5 m for pedestrians). While the PVCA and STCT yielded similar results in most encounters, this could not be confirmed for all. Results indicate that spatial distance may contribute to the severity of encounters between pedestrians and vehicles in a shared space