Modern technology for solving advanced transport planning challenges
For many years, 4-step models have been the standard for transport planning. But they fall short on a number of today’s pressing questions, as averages and static demand are no longer enough for today’s urban challenges.
People behave as individuals, small differences in accessibility, infrastructure or personal preferences can lead to vastly different individual decisions. Mobility models should take this into account, working with disaggregated but related trips by individuals, to reproduce effects accurately.
Learn why the technology behind Simunto’s transport planning solution is able to handle today’s and tomorrow’s urban challenges.
Mobility demand is created by individuals, not by averages.
Individual Behavior Representation
- Models demand at the level of individual agents (not aggregate flows)
- Captures full daily activity chains (activity-based demand)
- Enables disaggregate analysis across population segments
Endogenous System Dynamics
- Congestion and travel times arise from network interactions
- Captures feedback between demand and network performance
- Represents temporal dynamics (e.g. peak spreading, departure time choice)
Policy Sensitivity and Scenario Analysis
- Supports evaluation of pricing, parking, and service design measures
- Captures behavioral responses (mode, route, departure time adaptation)
- Enables consistent comparison of scenarios within one framework
Static assignments fail to capture dynamic effects.
Mobility is not only about private cars, but also about mobility sharing and trips with multiple modes.
Sunny morning – People use city bikes in the morning due to sunny weather and crowded public transport.
Rainy evening – People return home late by bus and train due to less favorable weather conditions.
The next day – Bikes aren’t available for travelling without active redistribution.
Full daily mobility patterns need to be taken into account.
What happens when cities implement time-dependent congestion charges?
Trips are not independent – Policies affecting one trip propagate to others.
Output, not input – In 4-step models, one must manually approximate these dependencies. In MATSim, they emerge naturally from simulation.
Fully time-dependent – MATSim supports arbitrary timings and dynamic pricing, without being limited by static time windows.
Benefit from the many advantages MATSim provides:
Fully dynamic and multi-modal
Supports large-scale urban planning
Modular and extensible thanks open-source
Partnering with us allows you to take full advantage of advanced transport planning methodology and turn it into a competitive advantage.