“…The source code of MATSim and its official extensions, including the parking module used in this research, is open and available at https://github.com/matsim-org/matsim. As an agentbased, flexible and pluggable open-source software, its coevolutionary algorithms and customizable scoring functions provide a very versatile base to extend an existing model with parking search behavior [20]. Furthermore, it might be possible to integrate some of the existing parking choice scoring functionality described above with the approach, if required.…”
Parking is a major constraint for car users and therefore an important factor in mode choice decisions. In this paper we introduce a model to simulate parking search behavior for cars within a multi-agent transport simulation, including full simulation of all steps of parking search, such as walking to and from the vehicle. This is combined with the capabilities of privately owned autonomous vehicles (AVs), which may park automatically, often in other locations than conventional cars, once they are not in use. Three different strategies for AVs to park are developed: (1) Conventional parking search, (2) parking at a designated AV lot, and (3) empty cruising, where vehicles do not use any parking space, but keep on driving. We apply the simulation model to a residential neighborhood in central Berlin, where parking pressure is generally high and apply different shares of AV usage to the synthetic population used. This allows a detailed evaluation of effects for both AV and conventional vehicle owners. Results suggest that the usage of designated parking lots may be the most beneficial solution for most users, with both vehicle wait times and parking search durations being the lowest.
“…The source code of MATSim and its official extensions, including the parking module used in this research, is open and available at https://github.com/matsim-org/matsim. As an agentbased, flexible and pluggable open-source software, its coevolutionary algorithms and customizable scoring functions provide a very versatile base to extend an existing model with parking search behavior [20]. Furthermore, it might be possible to integrate some of the existing parking choice scoring functionality described above with the approach, if required.…”
Parking is a major constraint for car users and therefore an important factor in mode choice decisions. In this paper we introduce a model to simulate parking search behavior for cars within a multi-agent transport simulation, including full simulation of all steps of parking search, such as walking to and from the vehicle. This is combined with the capabilities of privately owned autonomous vehicles (AVs), which may park automatically, often in other locations than conventional cars, once they are not in use. Three different strategies for AVs to park are developed: (1) Conventional parking search, (2) parking at a designated AV lot, and (3) empty cruising, where vehicles do not use any parking space, but keep on driving. We apply the simulation model to a residential neighborhood in central Berlin, where parking pressure is generally high and apply different shares of AV usage to the synthetic population used. This allows a detailed evaluation of effects for both AV and conventional vehicle owners. Results suggest that the usage of designated parking lots may be the most beneficial solution for most users, with both vehicle wait times and parking search durations being the lowest.
“…The simulation is based on agents, which are equipped with (typically) day-long plans consisting of activities, such as home or work, and legs, where the mode and the route is defined. At the end of a day, plans are scored and, up to a certain point, modified for the next day (Horni et al 2016a).…”
Fleets of shared Autonomous Vehicles (AVs) could replace private cars by providing a taxi-like service but at a cost similar to driving a private car. On the one hand, large Autonomous Taxi (AT) fleets may result in increased road capacity and lower demand for parking spaces. On the other hand, an increase in vehicle trips is very likely, as travelling becomes more convenient and affordable, and additionally, ATs need to drive unoccupied between requests. This study evaluates the impact of a city-wide introduction of ATs on traffic congestion. The analysis is based on a multi-agent transport simulation (MATSim) of Berlin (Germany) and the neighbouring Brandenburg area. The central focus is on precise simulation of both real-time AT operation and mixed autonomous/conventional vehicle traffic flow. Different ratios of replacing private car trips with AT trips are used to estimate the possible effects at different stages of introducing such services. The obtained results suggest that large fleets operating in cities may have a positive effect on traffic if road capacity increases according to current predictions. ATs will practically eliminate traffic congestion, even in the city centre, despite the increase in traffic volume. However, given no flow capacity improvement, such services cannot be introduced on a large scale, since the induced additional traffic volume will intensify today’s congestion.
“…The files are normal XML and readable using many parsers. The choice to use the Multi-Agent Transport Simulation (MATSim) infrastructure is because the populations are frequently used for large-scale mesoscopic transport models using the agent-based MATSim [2].Fig. 1Distribution of study areas across South Africa.…”
Section: Datamentioning
confidence: 99%
“…For example, the housingType is a standard Java string, while the homeCoordWGS is a specific coordinate class in the MATSim open source project [2].…”
This article presents the procedure followed to generate complete synthetic populations from the South African National Census. The populations are accurate at both household and individual level, and were generated for nine major metropolitan and provincial areas. The disaggregate description of the population is useful in a variety of modelling contexts, especially if one wants to observe or study the distributional effects of, for example, policy measures. That is, studies in which equity and equality are of concern. The datasets are publicly available from https://doi.org/10.17632/dh4gcm7ckb.1.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
