The planning of on-demand services requires the formation of vehicle schedules consisting of service trips and empty trips. This paper presents an algorithm for building vehicle schedules that uses time-dependent demand matrices (= service trips) as input and determines time-dependent empty trip matrices and the number of required vehicles as a result. The presented approach is intended for long-term, strategic transport planning. For this purpose, it provides planners with an estimate of vehicle fleet size and distance travelled by on-demand services. The algorithm can be applied to integer and non-integer demand matrices and is therefore particularly suitable for macroscopic travel demand models. Two case studies illustrate potential applications of the algorithm and feature that on-demand services can be considered in macroscopic travel demand models.

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As the introduction of fully automated vehicles enhances the attractiveness of carsharing and ridesharing systems, cities and regions may want to examine the effects of this development. This paper presents a framework for how to integrate those services in traditional macroscopic travel demand models, which are commonly used to evaluate the impacts of changed transport supply. Addressed topics are (1) the implementation of direct and intermodal ridesharing into the demand modeling process, presenting two approaches for the latter, (2) the pooling of ridesharing trips and (3) the scheduling of automated and shared vehicles. The first approach for integrating intermodal ridesharing includes ridesharing as an additional transport system, which uses the road network and which is integrated in the timetable-based public transport assignment. The second approach uses direct-link connections between traffic zones and suitable public transport transfer stops for the ridesharing feeder trips instead. Using the second approach, preliminary results of a test scenario for the Stuttgart region are presented.

Automated vehicles (AV) will change transport supply and influence travel demand. To evaluate those changes, existing travel demand models need to be extended. This paper presents ways of integrating characteristics of AV into traditional macroscopic travel demand models based on the four-step algorithm. It discusses two model extensions. The first extension allows incorporating impacts of AV on traffic flow performance by assigning specific passenger car unit factors that depend on roadway type and the capabilities of the vehicles. The second extension enables travel demand models to calculate demand changes caused by a different perception of travel time as the active driving time is reduced. The presented methods are applied to a use case of a regional macroscopic travel demand model. The basic assumption is that AV are considered highly but not fully automated and still require a driver for parts of the trip. Model results indicate that first-generation AV, probably being rather cautious, may decrease traffic performance. Further developed AV will improve performance on some parts of the network. Together with a reduction in active driving time, cars will become even more attractive, resulting in a modal shift towards car. Both circumstances lead to an increase in time spent and distance traveled.