Rita Cyganski

Automated Driving: How It Could Enter Our Cities and How This Might Af fect Our Mobility Decisions

The recent attention given to automated vehicles suggests that self-driving vehicles or driverless cars are about to become reality in the near, if not very near, future. While technologically this impression may well be justified, the emerging discussion discovers other related aspects that needs to be addressed alongside the technological challenges. Examples are legal and ethical aspects (Gasser et al. 2012; Beiker et al. 2012), user acceptance (Fraedrich, Lenz 2014) or the implications on the transport system that might result from the largescale uptake of self-driving vehicles (Zachariah et al. 2013; Spieser et al. 2014; Fagnant, Kockelman 2014). Somewhat less interest has been attached to the possible implications of this new ‘transport tool’ for people’s mobility decisions. In this article, we focus on the possible implications of automated vehicles for the mobility choices of people, in terms of both their everyday mobility, specifically the choice of transport mode, as well as their longer term residential location decisions. The question is: which influence can we expect from automated vehicles on the choices we make for everyday mobility and also longer term residential location? This interest arises from the specific properties of automated vehicles. In particular, the potential benefit of spending time in the vehicle with activities other than driving may lead to significant changes in how we make use of time while traveling and its resultant valuation (Continental 2013; Solokow 2013; Silberg et al. 2013; Silberg et al. 2012; Munsch 2014; Cyganski et al. 2015). As a result, people might reconsider their choice of transport mode, number of trips and distance of travel. What is more, changes in the value of travel time may influence the choice of residential location. The idea, for example, that time in a vehicle does not have to be spent on driving-related tasks, but instead permits other activities, may provoke a complete reappraisal of the time factor (Silberg et al. 2012). This could then lead to users considering different (further away) destinations for their daily journeys, or even changing their choice of home location, as the long commutes would then be seen more favorably. If we take these connected factors to their logical conclusion, in the end it would bepossible to dissolve the time factor as a limiting variable of urban planning. However, such changes can be expected not only as a result of individual choices but also the type of automated mobility concepts. The use of a vehicle on demand as an alternative to privately-owned cars might lead to an increase in multimodal behavior, or contribute to an increase in carpooling. The use of a fully automated private car might instead increase car use and mileage covered given that time spent in the vehicle is often viewed positively. As these examples show, there is a great need to specify and clarify what kind of automated vehicle or rather automation scenario is under consideration. To acknowledge these considerations, the article starts with an outline of current scenarios for the deployment of automated vehicles in urban areas (section 2). It moves on to discuss the possible implications of these scenarios on mode choice (section 3), followed by considerations of the effects on longer term residential location choices (section 4). The methodological basis of the discussion is a stated preference experiment and a systematic review of existing scenarios (for more information see Cyganski 2015 and Heinrichs 2015). It draws on the work by the authors in the recent Villa Ladenburg project of the Daimler and Benz Foundation, in which an interdisciplinary team explored a wide range of challenges and frame conditions associated with automated driving (see Maurer et al. 2015). The article concludes with some observations concerning the findings including the levels of uncertainty.

Disaggregated Car Fleets in Microscopic Travel Demand Modelling

Microscopic travel demand models take the characteristics of every individual person of the modeled population into account for computing the travel demand for the modeled region. The real world mobility of individuals strongly depends on the specific available car, if any. However, mode choice models usually take a standard average car as reference. This paper shows an integrated approach to model the travel demand with respect to car specific attributes. The proposed work uses a synthetic population for the German capital of Berlin and simulates the travel demand for different examples that replicate car specific changes in fuel price, fleet distribution and entrance restriction. Some of these car-specific measures influence the travel behavior on a level that cannot be modeled when using an average car at all. Furthermore, the results show significant changes in usage of specific car segments, which would be difficult to model using an averaged car.

Demographic Effects on Passenger Transport Demand

Demographic change in Germany will lead to a remarkable change in the composition of the population, particularly in terms of the ageing of society with the prospect of a population decrease. These factors are the main determinants for future travel demand. This chapter describes the procedure of modelling transport for 2030 and illustrates the results with regard to the infrastructure. The findings of the Rostock Center serve as the main basis for the data in the transport model, belonging to the family of microscopic activity-based demand models. The future passenger transport demand will decrease in most areas. The workload of the road infrastructure will decrease correspondingly. Differences of the impact between the two demographic scenarios are low. Findings show significant spatial differences. The western part of Mecklenburg-Western Pomerania, for example, will experience some trip increases, but most of the regions will face a decrease in transport demand. Public transport in Mecklenburg-Western Pomerania will not gain any more passengers in the future. We therefore conclude that the planning process for infrastructure should include a proper approach that assesses the options of disassembling streets and restructuring them. Criteria for making this decision should not be the workload of the road, but rather accessibility questions. Future land use management will also play an important role.

Autonome Fahrzeuge und autonomes Fahren aus Sicht der Nachfragemodellierung

Luis G. Willumsen, einer der renommiertesten Wissenschaftler fur die Verkehrsmodellierung, stellte 2013 auf einer Fachkonferenz fest: „We can no longer ignore them [autonome Fahrzeuge], if planning horizon is 10+ years“ [37]. Doch Arbeiten, die versuchen, die Auswirkungen der autonomen Fahrzeuge auf die Alltagsmobilitat der potenziellen Nutzer und konkret deren Verkehrsmittelwahl zu antizipieren, finden sich erst selten [11], [19], [37]. Allerdings erlaubt ein Blick auf die individuellen Treiber unseres taglichen Mobilitatsverhaltens bereits jetzt vorsichtige Aussagen zu etwaigen Verhaltensanderungen durch die Einfuhrung autonomer Fahrzeuge. Analogieschlusse zur Nutzung bekannter Verkehrsmittel und ihre Ubertragung in Verkehrsnachfragemodelle lassen erste quantitative Aussagen uber Auswirkungen auf die Gesamtverkehrsnachfrage zu. Die Nachfragemodellierung ermoglicht es hierbei, zwischen unterschiedlichen raumlichen Kontexten und Nutzergruppen zu unterscheiden und verschiedene Szenarien zum Einsatz solcher Systeme zu evaluieren.

Who gets the key first? Car allocation in activity-based modelling

ABSTRACT 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.

Simulation of automated transport offers for the city of Brunswick

Abstract The introduction of automated vehicles in the transport system is widely expected to have significant impact on traffic flow and safety, mobility behavior, car ownership and modal usage. Not only the replacement of conventional passenger cars by automated ones but also other forms of mobility offers are being discussed, such as the introduction of vehicle-on-demand fleets. When investigating the effects of such systems, the changes in the perceived travel time have to be regarded. Within this paper, the effects of introducing automated vehicles as well as vehicle-on-demand and shared vehicle-on-demand offers are presented. Eight different simulation settings with different fleet sizes and shares of private automated vehicles were evaluated using an agent-based demand model. The factors for value of time were obtained from a stated preference user survey. The results show only minor changes in the modal split and the amount of rides for the city of Brunswick due to the relatively small travel distances prevailing.

Introduction of car sharing into existing car fleets in microscopic travel demand modelling

Microscopic travel demand models take the characteristics of every individual person of the modelled population into account for computing the travel demand for the modelled region. Car sharing is an old concept, but the combination of a car sharing fleet parked in a public space with smartphone services to find available cars nearby offers a new mobility service. It enables people to use a fleet operator’s cars by providing individual mobility on demand. However, integrating this mobility option into microscopic travel demand models still is a difficult task due to a lack of data. This paper shows an integrated approach to model car sharing as a new mode for transport within a travel demand model using disaggregated car fleets with car-specific attributes. The necessary parameters for mode choice are estimated from various surveys and integrated into an existing multinominal logit model. The proposed work is used to simulate the travel demand of a synthetic population for the German capital of Berlin. A comparison with the survey results shows that the proposed integration of car sharing meets the real-world data. Furthermore, it is shown that the mode choice reacts well for access restrictions for specific car segments and local accessibility influencing the trip lengths.

Automated Vehicles and Automated Driving from a Demand Modeling Perspective

In 2013 Willumsen, one of the most renowned researchers in transport modeling, stated, regarding automated vehicles: “We can no longer ignore them, if [the] planning horizon is 10+ years”.