Effects of driverless vehicles

Driverless vehicles have the potential to significantly affect the transport system, society, and environment. However, there are still many unanswered questions regarding what the development will look like, and there are several contradictory forces. This paper addresses the effects of driverless vehicles by combining the results from 26 simulation studies. Each simulation study focuses on a particular case, e.g. a certain mobility concept or geographical region. By combining and analysing the results from the 26 simulation studies, an overall picture of the effects of driverless vehicles is presented. In the paper, the following perspectives are considered: what types of application of driverless vehicles have been studied in literature; what effects these simulation studies predict; and what research gaps still exist related to the effects of driverless vehicles. The analysis shows that it is primarily driverless taxi applications in urban areas that have been studied. Some parameters, such as trip cost and waiting time, show small variations between the simulation studies. Other parameters, such as vehicle kilometres travelled (VKT), show larger variations and depend heavily on the assumptions concerning value of time and level of sharing. To increase the understanding of system level effects of driverless vehicles, simulations of more complex applications and aspects such as land use, congestion and energy consumption are considered

Metrics for Quantifying Shareability in Transportation Networks: The Maximum Network Flow Overlap Problem

Cities around the world vary in terms of their transportation networks and travel demand patterns; these variations affect the viability of shared mobility services. This study proposes metrics to quantify the shareability of person-trips in a city, as a function of two inputs--the road network structure and origin-destination (OD) travel demand. The study first conceptualizes a fundamental shareability unit, 'flow overlap'. Flow overlap denotes, for a person-trip traversing a given path, the weighted (by link distance) average number of other trips sharing the links along the original person's path. The study extends this concept to the network level and formulates the Maximum Network Flow Overlap Problem (MNFLOP) to assign all OD trips to paths that maximize network-wide flow overlap. The study utilizes the MNFLOP output to calculate metrics of shareability at various levels of aggregation: person-trip level, OD level, origin or destination level, network level, and link level. The study applies the MNFLOP and associated shareability metrics to different OD demand scenarios in the Sioux Falls network. The computational results verify that (i) MNFLOP assigns person-trips to paths such that flow overlaps significantly increase relative to shortest path assignment, (ii) MNFLOP and its associated shareability metrics can meaningfully differentiate between different OD trip matrices in terms of shareability, and (iii) an MNFLOP-based metric can quantify demand dispersion--a metric of the directionality of demand--in addition to the magnitude of demand, for trips originating or terminating from a single node in the network. The paper also includes an extensive discussion of potential future uses of the MNFLOP and its associated shareability metrics