Tobias Kuhnimhof

Who Made Peak Car, and How? A Breakdown of Trends over Four Decades in Four Countries

This paper investigates the contribution of underlying trends to per-capita car travel development since the 1970s in France, Germany, Great Britain, and the USA. In these countries, after a long period of growth, car travel began to show signs of stagnation — or even decrease — after the 1990s. Our paper breaks down underlying demographic and travel trends for two study periods: first, a period of per-capita car travel growth (until the mid-1990s); second, a period of stagnation or decrease in car travel (beginning around the turn of the millennium). Two patterns of development emerge: (1) in France and the USA, the reversal in the trend in car travel per capita was due mainly to trend changes in total travel demand by drivers; (2) in Germany and Great Britain, the levelling off of motorisation, and shifts to other modes, played a much larger role. Ageing has in recent years gained weight in shaping per-capita car travel trends. In Europe, the continued increase of car availability for seniors has had a damping effect on peak car. Even though all age classes have contributed to peak car, young adults stand out in this regard and therefore deserve special attention.

Users of Transport Modes and Multimodal Travel Behavior Steps Toward Understanding Travelers' Options and Choices

Results are presented from analysis of individual mode choice behavior in the longitudinal section in Germany. The findings show that about half of German drivers also use public transport. Because they use different modes, they can be characterized as multimodals. This group will constitute an increasing share of the public transport clientele in coming decades because the decline of captive public transport riders is foreseeable. Therefore it is necessary to understand multimodal behavior because in an environment where travelers have increasing options, it is important to know how they make use of their options. It was found that multimodals employ public transport for specific purposes, whereas the car is universal. Less than 20% of multimodals use public transport on a regular basis, for example, to commute. Most multimodals use it occasionally. Multimodals opt for public transport in specific situations because it is the better option and not because there is no car available. Although for families ...

Decomposing Young Germans’ Altered Car Use Patterns: Lower Incomes, More Students, Decrease in Car Travel by Men, and More Multimodality

Since the turn of the millennium, car ownership and car travel among young German adults have decreased noticeably. This paper analyzes these changes in young Germans’ mobility behavior on the basis of a mobility diary survey and an income and expenditure survey. The decrease in car travel by young adults is linked to lower car ownership in this group. However, behavioral changes among car owners are far more important with regard to their overall decrease in car travel. Logistic regression was applied to identify the attributes of young households that are associated with low and altering car ownership. This model indicated that structural changes in the population concerning income, employment, household composition, and residential location account for the majority of the decrease in car ownership among young households. However, the model also showed that, all things being equal, the probability of car ownership has changed. Specifically, the gender gap for car ownership has almost disappeared because young men are less likely to own a car today than in the 1990s. The study also investigated changes in car use by car owners. The results showed that men have reduced their total travel and that both men and women have reduced their car mode share and exhibit increasingly multimodal behavior.

Multimodal Travel Choices of Bicyclists: Multiday Data Analysis of Bicycle Use in Germany

Cycling demand has been growing in recent years in Germany. Today, during an average week, about 30% of the German population, including all age classes and rural as well as urban populations, use the bicycle as a means of transportation. Cyclists use their bicycles on average 3 days per week for about 30% of their trips. The bicycle, however, is a mode used for specific segments of everyday travel, particularly short distances, unlike the car, which is almost universal. Hence, cyclists exhibit multimodal travel behavior (i.e., they combine the bicycle with other modes and switch to public transport or the car when the bicycle is not suitable). The domain of the bicycle as a means of transport is a radius around home that on average is about 3 to 5 km, dependent on the urban form. Within this radius, the car with its flexibility is the only serious competitor to nonmotorized modes. For the neighborhood-oriented travel of cyclists, public transport performs well only in specific niches, such as travel at night or commuting to school or university. Here, public transport and the bicycle are competitors. Nevertheless, from a system perspective they are allies: each provides important components for a multimodal mobility tool kit that enables travelers to cultivate mobility that is less automobile oriented.

Multiday Multiagent Model of Travel Behavior with Activity Scheduling

A 7-day multiagent model of travel demand is presented. Multiday modeling has several strong points and expands the applicability of travel demand models. It allows for incorporating the interrelationship of individual budgets and travel behavior, and the model output provides high flexibility for the further use of the data. Multiday models allow simulation of individual mode use over time and identification of mode clienteles. The challenge of multiday modeling is the realistic representation of behavioral variability and stability and dealing with a large number of possible combinations when generating activity schedules. The presented activity generation approach has two steps: (a) deriving agendas of intended activities from observed activity schedules and assigning the agendas to the agents in the model and (b) transforming these agendas to feasible activity schedules with specific activity start times and activity destinations by using a greedy algorithm. This activity generation module was incorporated in a 7-day travel demand model. The model was applied for predicting changes in mode use in the German city of Erlangen caused by demographic development and growth in car ownership within the next decade.

Generating Internationally Comparable Figures on Long-Distance Travel for Europe

This paper discusses available household survey data on long-distance travel (LDT) and presents harmonized figures on LDT in Europe. First, there is a comparison of the results of different household travel surveys about LDT. The findings of this comparison have important implications for the methodology of surveying LDT: conventional mobility diary surveys are better than LDT surveys in capturing journeys up to 200 km. LDT surveys perform better in capturing travel only beyond 400 km. Second, the paper presents the first internationally comparable figures on LDT demand for Europe. These figures have been compiled by using different sources of information, avoiding the drawbacks of the different surveys. The results indicate that in northern and central Europe there is higher demand for LDT than in the south. Moreover, the results illustrate how policies and public-transport supply affect the modal split in LDT: in countries with a high-quality railroad supply, such as Switzerland, the railroad share is significantly higher than in other countries. Countries with a liberalized, interurban bus market, in contrast, have a significantly higher bus mode share.

Nonresponse, Selectivity, and Data Quality in Travel Surveys: Experiences from Analyzing Recruitment for the German Mobility Panel

Since 1994, the German Mobility Panel (MOP) has been one of the oldest multiday and multiperiod travel surveys. Along with the need to understand behavioral processes, the demand for such detailed and extensive data is increasing. However, for respondents to take part in a survey like the MOP is often time-consuming. Therefore, with generally declining response rates, it is even more difficult to recruit a suitable sample of test participants for a multiday or multiperiod survey. If a selective nonresponse problem exists, this calls into question the representativeness of such a survey. For the MOP, respondents were recruited in a multistage process that provided an opportunity to study selectivity. The findings indicate that sociodemographics dominate selective nonresponse. There is also some mobility-related selectivity because people for whom mobility is an everyday issue take a stronger interest in the survey topic and are therefore more likely to participate in the survey. It was also found that recr...

Do You Go to the Movies During Your Lunch Break?: Trip-Context Data-Based Modeling of Activities

Specific information about travelers’ activities is often vital for transportation planners. For example, in modeling destination choice should only those attractions where a desired activity can be pursued be included? Destination choice modeling may improve when more specific activity information is available, especially for leisure trips. Destinations for leisure trips can be places to visit people, cinemas, and restaurants. In surveys, however, respondents are often asked to categorize their trips into generic purposes such as work, shopping, or simply leisure, without specifying the leisure activity. Hence, there is often a gap between trip-purpose information elicited from respondents and specific activity information, which would enhance destination modeling. A model is presented that bridges this gap with respect to leisure activity engagement. Common experience provides an idea of who is likely to conduct a particular leisure activity and at what time. The approach taken here was to quantify this likelihood by evaluating a data set of more than 20,000 leisure trips that provided specific activity information. This provided the calibration for the model IRMA, which simulates a specific activity for each trip based on trip-context data. IRMA was applied to model activities for leisure trips in a data set from Tuebingen, Germany. The result of the application of IRMA was consistent with common experience and provided vital activity information that helped to improve destination choice modeling. The approach to infer activity information from available trip data has proven useful in bridging the gap between information that is needed and information that is available.

Potential Fleet Size of Private Autonomous Vehicles in Germany and the US

There are high expectations on autonomous vehicles promising a safer, more efficient and comfortable (auto)mobility experience. On the other hand it is important to discuss possible rebound effects going along with such a development. New user groups e.g. people who do not hold a driving license today, or are currently unable to drive because of physical and/or age-related constraints suddenly are enabled to “drive” a passenger car. In addition the past has shown that increasing efficiency and enhancing the comfort leads to a higher travel demand and subsequently more vehicle miles traveled. To support the research on the impact of autonomous vehicles on the transport system it is important to analyze the potential share of autonomous vehicles (AVs) on the passenger vehicle fleet in the future. The paper presents results from modelling private autonomous vehicle scenarios for the year 2035 for Germany and the US to estimate the number of vehicles within the fleet equipped with automation technologies Level 4 and higher (SAE in SAE International Standard J3016, 2014). A vehicle technology diffusion model has been developed to model an evolutionary and a rather revolutionary scenario which are distinguished by different market entry dates and AV technology take rates. Differentiating by passenger car segment, we introduce autonomous vehicles among new vehicles from 2022 resp. 2025 onward assuming an s-shaped market-take-up until 2035.

A systemic view on autonomous vehicles:Policy aspects for a sustainable transportation planning

Abstract Over the past few years, many studies have provided detailed descriptions of the potential benefits associated with the introduction of autonomous vehicles, such as improvements in traffic flows, local and global emissions, traffic safety, cost efficiency of public and private transport operations, etc. Additionally, the mobilization of mobility-impaired people and the independent car use of travelers without a driver’s license have been identified as potential benefits for users. However, merely estimating the benefits of these direct (or first-order) effects is unlikely to show the full picture of the consequences that will emerge once autonomous vehicles enter the roads. In this paper, we therefore put emphasis on discussing systemic (or second-order) effects. The paper presents a conceptual exploration of these effects based on literature and research findings to date. We show that these systemic effects have the potential – especially in urban areas and without adequate policy intervention – to eliminate at least some of the benefits initially associated with autonomous vehicles. Following this systemic view on autonomous vehicles, we discuss policy aspects for responsible authorities and planners on how to prepare transportation systems for the challenges related to the introduction of autonomous vehicles, and conclude with areas of research that seem highly important in terms of further investigation in this context.