Catálogo de publicaciones - libros

Mobility, transport and the physical forms of urban areas are closely bound up with each other (Cervero and Kockelman in Transportation Research Part D: Transport and Environment 2(3), 199–219, ). Urban structure plays an important role when households and businesses make mobility decisions, and to a considerable degree dictates what forms of transport may or may not be taken. Compact city form with high density and mixed use provide good preconditions for short trips and efficient public transportation, promote walking and cycling, and often render daily car use unnecessary.

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”.

Since Carl Benz invented the automobile in 1886, some very different vehicle concepts have been developed. Some can be regarded as the logical continued development and replacement of former concepts, such as the renunciation of the carriage design and the integration of the wheels and chassis under the (self supporting) body.

There is currently much discussion, development and research, both in expert circles and in the public sphere, concerning automated (or often “autonomous”) vehicles. Within this discourse, personally used vehicles assume a central position, that is to say, focus is geared toward increasing vehicle automation on city streets and highways.

This paper aims to quantify the effects of autonomous driving on the traffic management level. This involves developing a model of autonomous driving that makes it possible to use human-controlled and autonomous vehicles with only minor modifications.

Autonomous vehicles maneuver in traffic through road networks without requiring humans as supervisors or decision makers. Autonomous vehicles increase comfort for their passengers by removing the need for them to perform driving tasks. Autonomous vehicles provide new mobility opportunities for groups of people that thus far have been partially or entirely excluded from participation in public life due to mobility restrictions.

In his meta-analysis, Thomas Winkle documents exemplary analyses of potential safety-enhancing vehicle systems with low degrees of automation. However, a safety prognosis of highly or fully automated vehicles depends on assumptions, as so far no series applications of such features exist. For testing methods to develop and validate safe automated vehicles with reasonable expenditure, the author recommends combining worldwide traffic accident-, weather-, vehicle operation data and traffic simulations. Based on these findings, a realistic evaluation of internationally prospective, and statistically relevant real world traffic scenarios as well as error processes and stochastic models can be analysed (in combination with virtual tests in laboratories and driving simulators) to control critical driving situations.

The degree of vehicle automation is continuously rising in all modes of transport both on public traffic infrastructure and in-house transport within company grounds, in order to improve the productivity, reliability, and flexibility of transport.

This chapter discusses the operational and economic aspects of autonomous mobility-on-demand (AMoD) systems, a transformative and rapidly developing mode of transportation wherein robotic, self-driving vehicles transport customers in a given environment. Specifically, AMoD systems are addressed along three dimensions: (1) modeling, that is analytical models capturing salient dynamic and stochastic features of customer demand, (2) control, that is coordination algorithms for the vehicles aimed at throughput maximization, and (3) economic, that is fleet sizing and financial analyses for case studies of New York City and Singapore. Collectively, the models and methods presented in this chapter enables a rigorous assessment of the of AMoD systems. In particular, the case study of New York City shows that the current taxi demand in Manhattan can be met with about 8000 robotic vehicles (roughly 70 % of the size of the current taxi fleet), while the case study of Singapore suggests that an AMoD system can meet the personal mobility need of the population of Singapore with a number of robotic vehicles roughly equal to 1/3 of the current number of passenger vehicles. Directions for future research on AMoD systems are presented and discussed.

In the case of highly-automated and fully-automated driving it is necessary for the vehicle itself to recognize the limitations of its machine perception, as well as the functional limitations of processing modules based on this perception to react adequately.