The energy consumption of passenger vehicles is affected by the physical properties of the environment. The ambient temperature in particular has a significant impact on the operating energy consumption. To quantify the impact of a changed climate on vehicles with different drivetrain systems, we set up a model that calculates the change in energy demand with respect to multiple global warming levels. In particular, the effect of rising temperatures on the energy consumption of battery electric vehicles and vehicles with internal combustion engines was investigated. Our results indicate that climate change will likely lead to a rise in energy consumption of vehicles with an internal combustion engine. This is mostly due to the increase in cabin climatization needs caused by the higher ambient temperatures. At a global warming level (GWL) of 4.0 °C, the calculated annual energy consumption on average is 2.1% higher than without taking the climate-change-related changes in temperature into account. Battery electric vehicles, on the other hand, are expected to have a lower overall energy consumption (up to −2.4% at 4 °C GWL) in cold and moderate climate zones. They benefit from the lower heating needs during winter caused by global warming.

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.

This article investigates if the impact of weather conditions on mode choice (walking, riding a bike, driving a car, and using public transport) differs across spatial areas. For this purpose, a survey-based data set with more than 500,000 trips in Germany was enriched with weather conditions prevailing at the closest weather station to the point of departure at the moment of the start of the trip. In addition, the points of departure of each trip were classified into seven different spatial areas. The analysis relied on separate multinomial logit models carried out for each spatial area with mode choice as the dependent variable. The independent variables consisted of non-weather-related factors such as sex, age, car availability, level of education, etc., and various weather-related variables such as air temperature, amount of precipitation, and wind speed. The results show that weather conditions have a rather marginal impact on mode choice, with the exception of riding a bike, which constitutes the mode of transport that is most affected by weather conditions in all spatial areas. However, the impacts tend to be smaller in densely populated urban metropolises than in peripheral, rural areas. In particular, precipitation and wind speed do not appear to affect cyclists in metropolitan areas as much as in peripheral, rural regions.

Bicycle usage is significantly affected by weather conditions. Climate change is, therefore, expected to have an impact on the volume of bicycle traffic, which is an important factor in the planning and design of bicycle infrastructures. To predict bicycle traffic in a changed climate in the city of Berlin, this paper compares a traditional statistical approach to three machine learning models. For this purpose, a cross-validation procedure is developed that evaluates model performance on the basis of prediction accuracy. XGBoost showed the best performance and is used for the prediction of bicycle counts. Our results indicate that we can expect an overall annual increase in bicycle traffic of 1–4% in the city of Berlin due to the changes in local weather conditions caused by global climate change. The biggest changes are expected to occur in the winter season with increases of 11–14% due to rising temperatures and only slight increases in precipitation.

Online shopping has gained popularity in many countries over the past two decades. As a result, the question of whether it reduces the overall transportation volume from fewer personal shopping trips or increases it because of more freight transportation, especially in the courier, express, and parcel segment, is a hot topic in transportation research. Yet few studies have applied a holistic approach to distinguishing online-shopping behavior with regard to different product categories, the distances traveled to purchase different products on physical shopping trips, the means of transportation used on these trips, and the freight transportation caused by online shopping. In addition, the outbreak of the COVID-19 pandemic in spring 2020 led to a dramatic increase in online shopping in many countries, with people trying to avoid physical shopping trips for health reasons. Against this background, this article attempts to clarify the net effects of online shopping on transportation volume both before and during the pandemic. For this purpose, two online surveys were conducted to capture online-shopping behavior and its effects on transportation before (2018) and during the pandemic (2021). The investigation area was Germany. The analysis showed that the pandemic generated more online shopping and more personal shopping trips. However, the overall mileage resulting from personal shopping trips, freight transportation, and pick-up and drop-off trips caused by online shopping remained relatively constant, as people traveled shorter distances during the pandemic.

This paper explores the potential of public webcams as a source of data for transport research. Eight different open-source object detection models were tested on three publicly accessible webcams located in the city of Brunswick, Germany. Fifteen images at different lighting conditions (bright light, dusk, and night) were selected from each webcam and manually labelled with regard to the following six categories: cars, persons, bicycles, trucks, trams, and buses. The manual counts in these six categories were then compared to the number of counts found by the object detection models. The results show that public webcams constitute a useful source of data for transport research. In bright light conditions, applying out-of-the-box object detection models can yield reliable counts of cars or persons in public squares, streets, and junctions. However, the detection of cars and persons was not reliably accurate at dusk or night. Thus, different object detection models might have to be used to generate accurate counts in different lighting conditions. Furthermore, the object detection models worked less well for identifying trams, buses, bicycles, and trucks. Hence fine-tuning and adapting the models to the specific webcams might be needed to achieve satisfactory results for these four types of traffic participants.