Does supervision in multi-day travel surveys lead to higher quality? A comparison of two independent surveys

The aim of this paper is to analyze whether the level of supervision influences quality of response and results of multi-day surveys. For that reason, we compare a multi-day survey (seven consecutive days) with children and high level of supervision in Austria (UTS) with data of the same age group out of the Mobility Survey in the Greater Stuttgart Region (MOSt). The results show that supervision during the survey process has nearly no influence on fatigue effects. Nevertheless, the quality of answers in supervised surveys is be better in terms of reporting intermodality or trip stages

Workshop synthesis: Measuring attitudes and perceptions in large scale (quantitative) surveys

This paper presents the main outcomes of the workshop A5 Measuring attitudes and perceptions in large scale (quantitative) surveys. There is a big need in the community to include more questions about psychological factors in surveys, because the travel behavior is getting more divers. The workshop discussion revealed that while there are different survey methods to capture information about the participants’ psychology, Likert scales are mainly used because they are easy to implement and to fill in. However, the reliability of the results must be assessed in light of the survey method and type of analysis. Further research is needed to combine quantitative surveys with methods to collect psychological information

Does Supervision in Multiday Travel Surveys Lead to Higher Quality? A Comparison of Two Independent Surveys

The aim of this paper is to analyze whether the level of supervision influences quality of response and results of multi-day surveys. For that reason, we compare a multi-day survey (seven consecutive days) with children and high level of supervision in Austria (UTS) with data of the same age group out of the Mobility Survey in the Greater Stuttgart Region (MOSt). The results show that supervision during the survey process has nearly no influence on fatigue effects. Nevertheless, the quality of answers in supervised surveys is be better in terms of reporting intermodality or trip stages

Integration von neuen Mobilitätsformen in Verkehrserhebungen und Verkehrsmodellierung

New technologies and forms of mobility are gradually becoming part of today’s transportation supply. These new forms of mobility include, for example, car- or bikesharing and ridepooling, as well as new technologies such as electric mobility or automation. To take these new forms of mobility into account in transport planning tools, it is advisable to adapt both surveys and models

Elektromobilität: Integration von Elektromobilität in die Verkehrsplanung – Welche Anpassungen unserer Werkzeuge brauchen wir?

Die Integration von Elektromobilität in die Verkehrsplanung und im Speziellen in Verkehrserhebungen und Verkehrsnachfragemodellierung, kann mit einigen Anpassungen und der Verwendung von agen-tenbasierten Modellen gut durchgeführt werden. Dabei sind die Charakteristika der Nutzenden/Besit-zenden von elektrisch betriebenen Fahrzeugen, die Eigenschaften der Elektrofahrzeuge v.a. hinsicht-lich Reichweite und die zusätzliche Berücksichtigung der Ladevorgänge bzw. Ladeinfrastruktur zu be-rücksichtigen. Zur Abbildung der Ladevorgänge sind Erhebungs- und Modellierungszeiträume notwen-dig, die einen so langen Zeitraum umfassen, so dass hinreichend viele Ladevorgänge und somit eine Fahrleistung jenseits der Reichweiten vorhanden sind. Nur so können Variationen im Verkehrsverhal-ten und bei Ladestrategien berücksichtigt werden

Mikroskopische Modellierung des Außenverkehrs eines Planungsraums

Im Rahmen der mikroskopischen Verkehrsnachfragemodellierung wird ein Modell vorgestellt, das den für den Planungsraum relevanten Verkehr der in den Außenzellen wohnenden Personen abbildet. Voraussetzung dafür ist die Übertragbarkeit von individuellen Mobilitätsdaten, die auf Basis der Daten des Deutschen Mobilitätspanels (MOP) analysiert wird. Darüber hinaus wird eine Methode zur Abgrenzung von Untersuchungsgebieten für beliebige Planungsräume in Deutschland dargelegt

Usage and User Characteristics - Insights from MOIA, Europe’s Largest Ridepooling Service

New, i.e., shared and digitized, mobility services have been entering urban mobility markets around the globe. Among these new offerings is ridepooling, a mobility solution that bundles requests from passengers with similar routes in real-time and matches them with a vehicle. Ridepooling is quite novel in Germany and knowledge about users, changes in travel behavior, and impacts on the urban traffic system is scarce. To address this gap, we conducted an online survey among users and non-users of MOIA, a German ridepooling provider. Over 12,000 respondents completed the survey. The article presents results on ridepooling users’ characteristics and usage patterns. We found that MOIA users cover all age groups and are multimodal travelers—which leads us to assume that ridepooling enriches mobility portfolios and also serves as an alternative to the private car. MOIA is mostly used occasionally and, in particular, during the evening or the night. A specific focus of the article lies on users with mobility impairments as well as how and by whom ridepooling is used on work-related trips. Both topics are particularly relevant in light of changing travel patterns and transforming urban transport systems towards more sustainability

Representation of Work-Related Trip Patterns in Household and Commercial Travel Surveys

This paper considers which work-related trip patterns are included in household travel surveys and which in commercial travel surveys and if there are certain patterns that are distinctly underrepresented in either one. The study is structured as a comparison between data from a household travel survey and data from a commercial travel survey. Both surveys were conducted in Germany and within close temporal proximity. We applied cluster analysis to identify differences in the data and identify work-related travel patterns. The results show that work-related travel patterns are quite complex. Although some patterns are covered in both surveys, mobile workers’ travel patterns in particular are not represented well in the household travel survey. Furthermore, our analysis shows that not all commercial trips are generated by motorized vehicles and a considerable share of work-related trips are undertaken using public transport or active modes of transport that are not covered by the commercial travel survey. The results indicate that researchers and transport planners creating travel demand models need to pay more attention to work-related travel behavior and acknowledge that depending on the area of study, traditional household travel surveys may not provide a complete sample of the population; however, simply adding data on commercial trips from commercial travel demand models to data from household travel surveys does not provide a complete picture of work-related travel either

Representation of Work-Related Trip Patterns in Household and Commercial Travel Surveys

This paper considers which work-related trip patterns are included in household travel surveys and which in commercial travel surveys and if there are certain patterns that are distinctly underrepresented in either one. The study is structured as a comparison between data from a household travel survey and data from a commercial travel survey. Both surveys were conducted in Germany and within close temporal proximity. We applied cluster analysis to identify differences in the data and identify work-related travel patterns. The results show that work-related travel patterns are quite complex. Although some patterns are covered in both surveys, mobile workers’ travel patterns in particular are not represented well in the household travel survey. Furthermore, our analysis shows that not all commercial trips are generated by motorized vehicles and a considerable share of work-related trips are undertaken using public transport or active modes of transport that are not covered by the commercial travel survey. The results indicate that researchers and transport planners creating travel demand models need to pay more attention to work-related travel behavior and acknowledge that depending on the area of study, traditional household travel surveys may not provide a complete sample of the population; however, simply adding data on commercial trips from commercial travel demand models to data from household travel surveys does not provide a complete picture of work-related travel either