Intermediate modes of transport, such as shared vehicles or ride sharing, are starting to increase their market share at the expense of traditional modes of car, public transport, and taxi. In the advent of autonomous vehicles, single occupancy shared vehicles are expected to substitute at least in part private conventional vehicle trips. The objective of this paper is to estimate the impact of shared autonomous vehicles on average trip duration and vehicle-km traveled in a large metropolitan area. A stated preference online survey was designed to gather data on the willingness to use shared autonomous vehicles. Then, commute trips and home-based other trips were generated microscopically for a synthetic population in the greater Munich metropolitan area. Individuals who traveled by auto were selected to switch from a conventional vehicle to a shared autonomous vehicle subject to their willingness to use them. The effect of shared autonomous vehicles on urban mobility was assessed through traffic simulations in MATSim with a varying autonomous taxi fleet size. The results indicated that the total traveled distance increased by up to 8% after autonomous fleets were introduced. Current travel demand can still be satisfied with an acceptable waiting time when 10 conventional vehicles are replaced with 4 shared autonomous vehicles.

The most common travel demand model type is the trip-based model, despite major shortcomings due to its aggregate nature. Activity-based models overcome many of the limitations of the trip-based model, but implementing and calibrating an activity-based model is labor-intensive and running an activity-based model often takes long runtimes. This paper proposes a hybrid called MITO (Microsimulation Transport Orchestrator) that overcomes some of the limitations of trip-based models, yet is easier to implement than an activity-based model. MITO uses microsimulation to simulate each household and person individually. After trip generation, the travel time budget in minutes is calculated for every household. This budget influences destination choice; i.e., people who spent a lot of time commuting are less likely to do much other travel, while people who telecommute might compensate by additional discretionary travel. Mode choice uses a nested logit model, and time-of-day choice schedules trips in 1-minute intervals. Three case studies demonstrate how individuals may be traced through the entire model system from trip generation to the assignment.

Integrated land-use/transport models have five decades of history of both widely recognized successful implementations and implementations that remained far behind their originally stated goals. This paper summarizes the state of the art of integrated land-use/transport modeling and reports on findings from the Symposium for the Integration of Land-Use and Transport Models in Raitenhaslach, which is near Munich, in 2016. From these sources, the paper identifies major challenges in integrated land-use/transport modeling and proposes paths that support successful implementations. Particular attention is given to the coordination of short- and long-term decisions, the technical integration of models, microscopic versus macroscopic frameworks and appropriate levels of model complexity. The paper concludes with five themes that require further research to ensure that integrated land-use/transport models will keep up with modeling needs in the future.

In this paper, we develop a synthetic population as the first step in implementing an integrated land use/transport model. The model is agent-based, where every household, person, dwelling, and job is treated as an individual object. Therefore, detailed socioeconomic and demographic attributes are required to support the model. The Iterative Proportional Updating (IPU) procedure is selected for the optimization phase. The original IPU algorithm has been improved to handle three geographical resolutions simultaneously with very little computational time. For the allocation phase, we use Monte Carlo sampling. We applied our approach to the greater Munich metropolitan area. Based on the available data in the control totals and microdata, we selected 47 attributes at the municipality level, 13 attributes at the county level, and 14 additional attributes at the borough level for the city of Munich. Attributes are aggregated at the household, dwelling, and person level. The algorithm is able to synthesize 4.5 million persons in 2.1 million households in less than 1.5 h. Directions regarding how to handle multiple geographical resolutions and how to balance the amount and order of attributes to avoid overfitting are presented.