Stefan Trommer

Driver Assistance Systems for Transport System Efficiency: Influencing Factors on User Acceptance

A major success factor for the implementation of intelligent transportation systems (ITS) is knowledge about the users reaction and response to such technologies. The objective of each development process is to achieve a broad acceptance among those using the system. User acceptance is a key success factor, so it should be taken into account in early development stages. This applies especially for technical solutions requiring frequent interactions, for example, to influence the drivers behavior toward a more efficient driving style. The complexity of developments in the field of ITS raises the question: At what point do users start feeling overloaded and no longer able to handle all functionalities, ultimately rejecting using them? A Europe-wide study was carried out (N = 5,807) focusing on drivers’ perceptions of cooperative systems that offer assistance on fuel efficiency. The advanced driver assistance systems (ADAS) used in the study have the potential to change driving behavior in the long term and thus have a sustainable impact to reduce fuel consumption and traffic emissions. ADAS functionalities provide up-to-date traffic information, which is disseminated by traffic management to help traffic flow in a more energy efficient way. Six ADAS functions were assessed in pre-, on-, and post-trip driving situations. The chosen research framework was used to compare the users’ perception using a set of relevant acceptance factors that were developed in this study. The results showed significant effects between acceptance factors and the influencing variables. The interrelations between acceptance factors were analyzed using a research model developed in this study. The concept proves to be suitable for studying acceptance of ITS solutions aiming to save fuel.

How Autonomous Driving May Affect the Value of Travel Time Savings for Commuting

Autonomous driving is being discussed as a promising solution for transportation-related issues and might bring some improvement for users of the system. For instance, especially high mileage commuters might compensate for some of their time spent traveling as they will be able to undertake other activities while going to work. At the same time, there are still many uncertainties and little empirical data on the impact of autonomous driving on mode choices. This study addresses the impact of autonomous driving on value of travel time savings (VTTS) and mode choices for commuting trips using stated-choice experiments. Two use cases were addressed – a privately owned, and a shared autonomous vehicle – compared with other modes of transportation. The collected data were analyzed by performing a mixed logit model. The results show that mode-related factors such as time elements, especially in-vehicle time and cost, play a crucial role for mode choices that include autonomous vehicles. The study provides empirical evidence that autonomous driving may lead to a reduction in VTTS for commuting trips. It was found that driving autonomously in a privately owned vehicle might reduce the VTTS by 31% compared with driving manually, and is perceived similarly to in-vehicle time in public transportation. Furthermore, riding in a shared autonomous vehicle is perceived 10% less negatively than driving manually. The study provides important insights into VTTS by autonomous driving for commuting trips and could be a base for future research to build upon.

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.