Patrick M. Bösch

MacroSim - A macroscopic Mobsim for MATSim

Abstract: The simulation of large-scale scenarios requires high-performance simulations. MATSim, an agent-based transport simulation, is increasingly reaching limits. The traditional approach is to scale the scenarios, i.e. simulating only 10% of a population instead of 100%. This paper suggests MacroSim, a macroscopic mobility simulation module for MATSim, to overcome the current per- formance limits within MATSim. It uses volume-delay functions to estimate travel times for links based on experienced usages of these links. This allows to decouple agents and thus allows a parallelization of the mobility simulation within MATSim per design. A preliminary implementation of MacroSim showed promising results (7 to 50 times faster than the current mobility simulation depending on the scenario size). Given its limitations - most important no back propagation of traffic congestion - MacroSim is suggested as a complementary mobility simulation to the current implementation for cases where scenario size and simulation performance are more important than precise traffic dynamics.

Transport Policy Optimization with Autonomous Vehicles

Autonomous vehicles (AVs), here self-driving and driverless vehicles, SAE levels 4 and 5 are becoming more clearly a reality. Potential services based on AVs and their consequences for the transport system are of increasing importance. This paper investigates policy combinations for a world with such services. The policy measures investigated are pricing of public transport (through subsidies), pricing of private motorized transport (through taxation or mobility pricing), and the organization of AV services (monopoly vs. oligopoly, with or without ride-sharing). Further, the perception of travel times for autonomous private cars is considered. All combinations of policies (respectively two to four levels each) were implemented in a simulation to determine their synergies. The applied model was the agent-based transportation simulation MATSim. The scenario employed for the tests was the agglomeration of Zug, Switzerland. The results suggest that, given the current spatial distribution of the demand and the current transport system, AV systems are only able to reduce travel times at the cost of substantial mode shifts and additional vehicle kilometers driven. Of the tested policy measures, although all showed the expected causality, only the organizational form of the AV service had a statistically significant effect. Therefore, this paper suggests that policy makers should be cautious when confronted with the promises of future transport services. To invest the benefits of automation into an improvement of the existing transport system (e.g., automation of public mass transit or complementing public mass transit with ride-sharing AVs in low-demand areas) might be a good alternative.

Public Transit Route Mapping for Large-Scale Multimodal Networks

For the simulation of public transport, next to a schedule, knowledge of the public transport routes is required. While the schedules are becoming available, the precise network routes often remain unknown and must be reconstructed. For large-scale networks, however, a manual reconstruction becomes unfeasible. This paper presents a route reconstruction algorithm, which requires only the sequence and positions of the public transport stops and the street network. It uses an abstract graph to calculate the least-cost path from a route’s first to its last stop, with the constraint that the path must contain a so-called link candidate for every stop of the route’s stop sequence. The proposed algorithm is implemented explicitly for large-scale, real life networks. The algorithm is able to handle multiple lines and modes, to combine them at the same stop location (e.g., train and bus lines coming together at a train station), to automatically reconstruct missing links in the network, and to provide intelligent and efficient feedback if apparent errors occur. GPS or OSM tracks of the lines can be used to improve results, if available. The open-source algorithm has been tested for Zurich for mapping accuracy. In summary, the new algorithm and its MATSim-based implementation is a powerful, tested tool to reconstruct public transport network routes for large-scale systems.