Abstract

Gegenstand der Arbeit ist die Entwicklung eines tour-basierten Verkehrsnachfragemodells zur Abbildung von Aktivitätenketten unter Berücksichtigung von raum-zeitlichen constraints. Den theoretischen Hintergrund bildet ein hierarchisch organisierter Entscheidungsprozess, um die theoretisch möglichen Entscheidungskombinationen zu reduzieren und damit eine wahrscheinlichkeitsbasierte Berechnung zu ermöglichen. Als Beispieltour dient die Aktivitätenkette ‚Wohnen-Arbeit-Sekundäraktivität-Wohnen’, auf deren Basis auch die statistischen Analysen der Mobilitätsbefragung Santiagos durchgeführt werden. Unter Verwendung eines GIS werden so genannte ‚Suchräume’ (Aktionsräume in denen Sekundäraktivitäten durchgeführt werden) ermittelt. Ein Ergebnis der Datenanalyse sind Grenzwerte der maximalen täglichen Reisezeit für eine Reihe von Modus-Kombinationen. Die Zeitfenster von Startzeiten und Aktivitätendauer werden in Abhängigkeit sozioökonomischer Gruppen ermittelt. Die Bestimmung der Suchräume erfolgt in Abhängigkeit von Arbeitsdauer sowie Distanz zwischen Wohn- und Arbeitsort. Beide Kriterien erwiesen sich in der Analyse als statistisch signifikant. Der Vergleich zwischen Modell und Empirie (Santiagos Mobilitätsbefragung) deutet darauf hin, dass die Suchräume geeignet sind und die Mehrheit der beobachteten Zielwahlentscheidungen beinhalten. Zur Berechnung der Wahrscheinlichkeitspfade (unter Verwendung der Programmsyntax von SPSS) wird ein im Umfang auf sieben Ziele reduziertes Alternativenset pro Wohn- und Arbeitsstandort bestimmt. Dabei werden Erreichbarkeit und Attraktivität der Ziele innerhalb des Suchraumes berücksichtigt. Die erzielten Ergebnisse stützen das Argument, dass die raum-zeitlichen constraints (tägliche Reisezeit, Suchräume) eine effektive Reduktion der kombinatorischen Vielfalt zulassen. Die Erfahrungen aus der Berechnung der Beispieltour eignen sich zum Übertrag auf weitere Tour-Typen, um eine Modellierung der städtischen Gesamtverkehrsnachfrage zu ermöglichen. In this thesis we develop a tour-based approach for modeling activity and travel pattern considering time-space constraints. A hierarchical structure of choice-making builds theoretical background for the model and is based on a set of axiomatic rules. Our central argument is that the time-space constraints can be used for reducing the number of choices and, respectively, control the combinatorics associated with the probabilistic approach. The empirical analysis of our use case, a tour of type ‘Home-Work-SecondaryActivity-Home’, is based on Santiago’s travel survey. In addition, we apply GIS to estimate the so-called search spaces (potential areas where secondary activities are realized) and justify their sizes with the empirical findings. From the data analysis we identify thresholds for the tour-based maximum daily travel times considering a set of mode combinations. We define regimes of starting times and duration of activities depending on socio-economic user groups. The estimation of search spaces is realized considering the time spent at work as well as the distance between the home and work locations. Both criteria were found to be statistically significant. The comparison of modeled results with survey observations allowed concluding that the search spaces are realistic since they capture most of the observed trip destinations. For the estimation of spatial path flows of activities and trips (using SPSS programming language), we define a final choice set of no more than seven alternatives per primary location considering zone-based accessibility and land-use attractiveness. The obtained results support the argument that time-space constraints (daily travel time, search spaces) allow an effective control of combinatorial complexity. Basing on the experience obtained in process of modeling the exemplary tour, the approach can be applied to further tour types offering the possibility to estimate the entire transport demand of Santiago city.

Abstract

Electric vehicles (EVs) have been discussed as a promising alternative to conventional cars and as a potential solution for mitigating environmental problems caused by road transport. This study analyzes the potential of the commercial use of EVs and the factors influencing user acceptance in commercial fleets. Empirical data on the daily use of EVs was collected by conducting online surveys, interviews, and workshops with the users. This paper summarizes the insights on potential advances and challenges related to the use of the vehicles in commercial fleets from a user perspective. The results suggest that EVs meet the mobility needs of different commercial users, bringing reputational and environmental related benefits for the companies. Main challenges for the deployment of EVs are related rather to the periphery of the EVs (e.g., infrastructure, market characteristics, and lack of experts in related service sectors as well as psychological barriers). Finally, policy and management recommendations in different fields drawn from a synthesis of the data collected within the study are introduced in this paper.

Abstract

Greenhouse gas emissions, in particular CO2 emissions, are a major environmental problem caused mainly by the transportation and the energy sectors. Electric vehicles have been proposed as a solution for mitigating greenhouse gas emissions in road transport. At the same time their potential emission reduction depends on the emissions from the generation of electricity used to charge the vehicles. This study analyzes indirect emissions of electric vehicles to examine optimization potential using real-world data. The results of the study suggest that charging during the daytime is associated with the usage of more electricity from renewable energy sources than charging in typical non-work hours. However, due to the highly violate character of renewable energy sources the differences are rather small. Also, CO2 emissions per kilometer driven depend on driving patterns influencing the energy demand of the vehicles. Accordingly, optimization potential using renewable energy-oriented time course of charging is found to be rather small with an average greenhouse gas emission reduction of 4%. Thus to achieve the potential of electric vehicles to solve environmental issues requires the optimization of driving and charging patterns as well as measures for reducing the carbon intensity in the electricity grid.

Abstract

It is hoped that Automated Driving (AD) will make alternatives to the private car more attractive and facilitate the transition to sustainable transport. However, this expectation may underestimate both the resistance of private automobility and the unintended consequences of automated driving. Whether AD will contribute to sustainable mobility depends largely on its implementation and how its risks are prevented. This paper provides empirical insights into the design of acceptable forms of AD by investigating specific use cases with respect to the requirements of different mobility configurations. We pay special attention to people who travel with children. Our use cases comprise three probable types, covering the spectrum from demand-responsive transport (DRT) to private vehicles. Our results include the identification of mobility configurations and an analysis of AD use cases considering several empirically derived criteria: improved accessibility, ease of daily life and well-being, and improvement of the traffic situation and the transport system. Our analysis is based on a qualitative study in the Berlin area, Germany. The discussion focuses on the usefulness of AD against the background of different user perspectives, sustainability, and societal requirements, as well as an evaluation of AD in terms of its acceptability. We conclude that automated mobility use cases should meet the requirements of different mobility configurations to promote the transformation from private to shared automobility and, eventually, less automobility overall. Document type: Article