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

A Bayesian Approach with Prior Mixed Strategy Nash Equilibrium for Vehicle Intention Prediction

The state-of-the-art technology in the field of vehicle automation will lead to a mixed traffic environment in the coming years where Connected and Automated Vehicles (CAVs) have to interact with Human-Driven ones (HVs). In this context, it is necessary to have intention prediction models with the capability of forecasting how the traffic scenario is going to evolve with respect to the physical state of vehicles, the possible maneuvers and the interactions between the traffic participants within the seconds to come. This article presents a Bayesian approach for vehicle intention forecasting, proposing as prior estimate a game-theoretic framework in the form of a Mixed Strategy Nash Equilibrium (MSNE) to model the reciprocal influence between traffic participants. The likelihood is then computed based on the Kullback-Leibler divergence. The game is modeled as a static nonzero-sum polymatrix game with individual preferences, a well known strategic game. Finding the MSNE for these games is in the PPAD \ PLS complexity class, with polynomial-time tractability. The approach shows good results in simulation in the long term horizon (10s), with its computational complexity allowing for online applications

Simulation-based investigation of transport scenarios for Hamburg

This simulation work investigates new means to decrease the modal share of motorized transport in a large urban area in Hamburg, Germany. This was deemed necessary in order to cut down CO2 emissions. The five scenarios simulated with the MATSim [13] framework including an adapted mode choice model strongly suggest that making public transport more attractive is not sufficient to reach this goal, the results display a meager 3%-point change in the share of motorized transport. With introducing additional means to repel motorized transport, an 8%-point change may be within reach. The results also show that by making bike riding more safe, a considerably higher share of biking is possible (+8%-points)

Umfrage zum Forschungsdatenmanagement an der FH Potsdam

Forschungsdaten sind in der Wissenschaft ein wichtiger Teil von Forschungsergebnissen. Mit dem vermehrten Aufkommen von digitalen Daten in Forschungsprozessen geht auch eine Verantwortung im Umgang mit ihnen einher. Forschungsdatenmanagement (FDM) steht für nachhaltige Prozesse bei der aktiven Dokumentation und dient der Qualitätssicherung wissenschaftlicher Erkenntnisse. Hochschulen sind als wissenschaftliche Einrichtungen mitverantwortlich für die Sicherung und nachhaltige Verfügbarmachung von Forschungsdaten. Die Fachhochschule Potsdam (FHP) hat eine Umfrage zum Umgang mit Forschungsdaten im November 2017 initiiert, u.a. mit der Fragestellung, welche unterstützenden Maßnahmen an der FHP etabliert werden müssen, um den Umgang mit Forschungsdaten zu verbessern. Die Umfrageergebnisse zeichnen sich durch eine hohe Beteiligung (24,7%) aus. Die Ergebnisse bilden eine fundierte Basis für die Handlungsempfehlungen an die Hochschulleitung. Die Rohdaten und alle weiteren Prozessierungsschritte wurden dokumentiert und mittels DOI publiziert. Der vorliegende Bericht beschreibt die Konzeption, Methodik, Auswertung und Interpretation. Eines der wichtigsten Ergebnisse stellt die große Bedeutung von Forschungsdaten für die tägliche Arbeit der Daten-Produzenten dar. Ein hoher Prozentsatz der Befragten ist bereit, die eigenen Daten für die Nachnutzung bereitzustellen. Allerdings wurden auch Bedarfe z.B. bezüglich rechtlicher Aspekte, Forschungsdatenmanagementplänen, technischer Infrastruktur etc. formuliert

A Bayesian Approach with Prior Mixed Strategy Nash Equilibrium for Vehicle Intention Prediction

The state-of-the-art technology in the field of vehicle automation will lead to a mixed traffic environment in the coming years where Connected and Automated Vehicles (CAVs) have to interact with Human-Driven ones (HVs). In this context, it is necessary to have intention prediction models with the capability of forecasting how the traffic scenario is going to evolve with respect to the physical state of vehicles, the possible maneuvers and the interactions between the traffic participants within the seconds to come. This article presents a Bayesian approach for vehicle intention forecasting, proposing as prior estimate a game-theoretic framework in the form of a Mixed Strategy Nash Equilibrium (MSNE) to model the reciprocal influence between traffic participants. The likelihood is then computed based on the Kullback-Leibler divergence. The game is modeled as a static nonzero-sum polymatrix game with individual preferences, a well known strategic game. Finding the MSNE for these games is in the PPAD \ PLS complexity class, with polynomial-time tractability. The approach shows good results in simulation in the long term horizon (10s), with its computational complexity allowing for online applications

A Bayesian Approach with A-Priori Mixed Strategy Nash Equilibrium for Vehicle Intention Prediction

The state-of-the-art technology in the field of vehicle automation will lead to a mixed traffic environment in the coming years where Connected and Automated Vehicles (CAVs) have to interact with Human-Driven ones (HVs). In this context, it is necessary to have intention prediction models with the capability of forecasting how the traffic scenario is going to evolve with respect to the physical state of vehicles, the possible maneuvers and the interactions between the traffic participants within the seconds to come. This article presents a Bayesian approach for vehicle intention forecasting, proposing as prior estimate a game-theoretic framework in the form of a Mixed Strategy Nash Equilibrium (MSNE) to model the reciprocal influence between traffic participants. The likelihood is then computed based on the Kullback-Leibler divergence. The game is modeled as a static nonzero-sum polymatrix game with individual preferences, a well known strategic game. Finding the MSNE for these games is in the PPAD \ PLS complexity class, with polynomial-time tractability. The approach shows good results in simulation in the long term horizon (10s), with its computational complexity allowing for online applications

Shaping the future of mobility via online participation and co-creation within RealLab Hamburg

Sustainable mobility transitions depend on everyday citizens using corresponding mobility offers to bring change to life. For this purpose, a mobility lab approach empowering citizens to develop mobility visions and find user-friendly solutions was created and tested within the urban living lab RealLab Hamburg. It relied solely on online interaction and ensured different user groups were included. Its core participation and co-creation method was design thinking, which was combined with other creative tools. Based on the workshop design and the empirical data from its implementation (N=37 within eight mobility labs), it has become clear that the participation approach appears to be effective by drawing on the concepts of information, consultation, participation, and empowerment. Each of the participation steps generated specific goal-oriented and transformation knowledge that can be taken up in a transition process towards a more sustainable, user-centered mobility system