Matthias Heinrichs

There are innumerable models within the transportation field, which offer a variety of detailed and specific analyses (e.g., traffic flow, fuel consumption, mode choice). However, to date there is an absence of a model providing a holistic understanding of the entire transport system. Existing models are reliant on simple assumptions outside of their specific focus to generate such system-wide results. Against this background this paper presents a new interactive tool for the holistic evaluation of multiple aspects of the transportation system integrating research results from a variety of scientific disciplines and models. It aims to provide both a holistic understanding of the transport system and an interactive tool allowing the wider public to engage with state-of-the-art scientific research. For this purpose, it is based on a unique data eco-system that provides interfaces for transport demand, noise pollution, and emission models, and outlines the results of the model chain in an appealing and easily understandable manner. This paper illustrates the features and the advantages of this new tool using the results of the project Connected Mobility for Liveable Places. The project aims to develop a people-centered, inclusive, and sustainable system of mobility of both people and goods.

In new residential neighborhoods, planners often implement mobility concepts consisting of various measures and instruments, which intend to serve as alternatives for private cars and thus limit the negative external effects of motorized transport. However, there is a lack of studies regarding the impacts of such concepts. Accordingly, we analyze which actual effects mobility measures have on transport and land consumption. Investigating a newly developed low-car neighborhood with 361 households in Berlin, Germany, we surveyed the residents (45 survey participants) and simulated transport. Our results show that planning ideal and lived reality differ—new residents bring more cars than expected and planning intentions differ from what residents want and use. Only strong measures may change mobility behavior but better information and mobility management could help to increase awareness, acceptance and use of new mobility options.

Parking is a vital component of today's transportation system and descriptive data are therefore of great importance for urban planning and traffic management. However, data quality is often low: managed parking places may only be partially inventoried, or parking at the curbside and on private ground may be missing. This paper presents a processing chain in which remote sensing data and statistical methods are combined to provide parking area estimates. First, parking spaces and other traffic areas are detected from aerial imagery using a convolutional neural network. Individual image segmentations are fused to increase completeness. Next, a Gamma hurdle model is estimated using the detected parking areas and OpenStreetMap and land use data to predict the parking area adjacent to streets. A systematic relationship is found between the road length and type and the parking area obtained. It is suggested that these results are informative to those needing information on parking in structurally similar regions.

Mobility is a must for human life on this planet, because important activities like working or shopping cannot be done from home for everyone. Present modes of transports contributes significantly to green house gas emissions while the efforts to reduce these emissions can be improved in many countries. Pathways to a more sustainable form of mobility can be modelled using travel demand models to aid decision makers. However, to project human behavior into the future one should analyze the changes in the past to understand the drivers in mobility change. Mobility surveys provide sets of activity diaries, which show changes in travel behavior over time. Those activity diaries are one of the inputs in activity-based demand generation models like travel activity pattern simulation (TAPAS). This paper shows a method of using probability distributions between person and diary groups. It offers an opportunity for an increased heterogeneity in travel behavior without sacrificing too much accuracy. Additionally it will present the use case of temporal back- and forecasting of changes in activity choices of existing mobility survey data. The results show the possibilities within this approach together with its limits and pitfalls.

Decisions concerning household car ownership and the corresponding usage by the household members have significant implications on vehicle usage, fuel consumption and vehicle emissions. In this context, long-term and short-term choices which are strongly interrelated with one another play an important role. The long-term aspects involve the number of vehicles and their different types owned by a household as well as the assignment of a main driver, acting as the primary user, to each vehicle. The short-term dimension is represented by the vehicle allocation within a household at a daily level. In order to better understand the vehicle allocation process in the household context, the paper at hand investigates the importance of the short-term and long-term aspects in this process and explores several approaches to model them. For this purpose, four different methods for car allocation within a household, which strongly differ in their complexity, are implemented into a microscopic agent-based travel demand model and subsequently evaluated. The respective approaches are the following: (1) random car allocation, (2) car allocation by age, (3) car allocation by main driver assignment, and (4) car allocation by household optimization. Given a population of a bigger region that is described by a set of attributes, these various models determine which person of a household uses one of the available cars within the household for his/her daily trips. The simulations show that all four implementations of car allocation result in good representations (with deviations of less than 10%) of observed travel behaviour, their results being closer to each other than initially expected. Model (4), which optimizes car allocation for the entire household, shows the best results when compared to real-world data, while model (3) allows for the adaptation of changes in car ownership and/or socio-demographic and socio-economic attributes of the population.