What is it?
A dynamic transit operations and assignment simulation model (see links below). The model can be applied to multi-modal metropolitan transit networks. It has been applied in a large range of research projects and real-world applications.
The agent-based transit assignment model implemented in BusMezzo simulates the travel decisions of individual passengers and the movements of individual vehicles. The dynamic and stochastic representation of the transit system is integrated into Mezzo, an event-based mesoscopic traffic simulation tool.
I have introduced the main modelling components during the course of my PhD studies. Ever since, the model was used by researchers and planners in a number of projects for forecasting passenger flows and testing operational measures prior to their implementation.
What can it be used for?
Key model capabilities include:
- Traffic dynamics. Each individual vehicle is affected mesoscopically by traffic conditions on links and at intersections.
- Transit operations. Sources of uncertainty such as traffic dynamics, flow-dependent dwell times and propagation of delays through vehicle scheduling are modeled explicitly. A library of holding control strategies is available.
- En-route passenger decisions. Each individual passenger takes a sequence of travel decisions based on his/her expectations which are based on service provision, real-time information (if applicable) and past experience (when using iterative network loading). A library of real-time information provision alternatives is available.
- Disruptions. Simulating unplanned partial capacity reductions on network links.
- Day to day learning. Performing an iterative network loading where individual travelers gain experience about service attributes, including waiting times, on-board crowding and the reliability of real-time information.
Case studies include multi-modal (bus, tram, light rail, underground and commuter train) networks in the metropolitan areas of Stockholm and Amsterdam. Running times are less than a minute once a choice-set has been generated (need to be done once for a given network topology)
BusMezzo was applied in the following studies (see related links below):
- Design and assessment of real-time control strategies for high-frequency bus lines (Stockholm, Sweden; Tel Aviv, Israel)
- Optimization of real-time transfer coordination with passenger predictions (tram lines in The Hague, The Netherlands)
- Evaluation of investments in network extensions (Stockholm, Sweden; Amsterdam, The Netherlands)
- Network robustness, including identifying critical links and locations for adding reserve capacity (Stockholm, Sweden)
- Evaluating the impact of real-time information, including its reliability and under disruptions (Stockholm,. Sweden)
- Analyzing assignment results in comparison to conventional transit assignment models
- Model validation
The model has been used by researchers in Sweden, Germany, The Netherlands, Israel, Poland and the United Kingdom.
Want to use the model?
You can access the BusMezzo Application file here
You will need two manuals to master all the input and output files:
- Mezzo File Formats provide you with all the information on the underlying network and traffic representation. BusMezzo Input-Output format v2.3 describes the transit-related input required and the output generated by the BusMezzo simulation.
You may wanna try out a network to get acquainted with the file formats. You can access BusMezzo networks on this link. The Amsterdam metro network is used as the primary illustration network. The folder also includes a version of the Spiess and Florian network (see their seminal paper on frequency-based transit assignment) and Stockholm-based networks, among others. You can run a network by locating it in the same folder as the application file and simply double click the batch file. You can also use a GUI version (only for some output visualization purposes) which you may use along with the QT files that are available in the same folder.
You can also of course generate your own network! If you have access to Visum, you may want to make use of a the Visum->BM importer tool developed by Rafał Kucharski and Arkadiusz Drabicki from Politechnika Krakowska. The tool is available on Github, see the README.md for important notes on how to use it: Github network importer tool
Note that since Visum allows importing networks from GTFS, you can convert GTFS networks to BM networks via Visum. Of course, you may be interested in specifying additional input, depending on the application.
Want to develop additional functionalities?
All you need is to install Microsoft Visual Studio and QT libraries on your computer to be able to run BusMezzo in the development environment. This will allow you to modify and add functionalities to the simulation code.
(Bus)Mezzo is written in C++ and it supports all major platforms (Linux, macOS and Windows).
To set-up everything you need, follow carefully the instruction on this webpage given by Mezzo developer, Wilco Burghout.
You can request access to the latest version of the code by searching for “Mezzo mesoscopic traffic simulation” from Github or contact the developers for further information.
Want more information?
Check out the links below and welcome to contact me at: o.cats(remove this)@tudelft.nl
In any case, will be nice to hear from you if you plan or already use/develop BusMezzo.
Scientific publications about model developments:
- Cats O. and West J. (2020). Learning and Adaptation in Dynamic Transit Assignment Models for Congested Networks. Transportation Research Record.
- Cats O., Burghout W., Toledo T. and Koutsopoulos H.N. (2010). Mesoscopic Modeling of Bus Public Transportation. Transportation Research Record, 2188, 9-18.
- Toledo T., Cats O., Burghout W. and Koutsopoulos H.N. (2010). Mesoscopic Simulation for Transit Operations. Transportation Research Part C, 18(6), 896-908.
Scientific publications about public transport service design, operations and control applications of the model:
- Hatzenbühler J., Cats O. and Jenelius E. (2020) Transitioning towards the Deployment of Line-based Autonomous Buses: Consequences for Service Frequency and Vehicle Capacity. Transportation Research Part A, 138, 491-507.
- Laskaris G., Rinaldi M., Cats O., Jenelius E. and Viti F. (2019). Multiline Holding based Control for Lines Merging to a Shared Transit Corridor. Transportmetrica B, 7(1), 1062-1095.
- van der Werff E., van Oort N., Cats O. and Hoogendoorn S. (2019). Robust Control for Regulating Frequent Bus Services: Supporting the Implementation of Headway-based Holding Strategies. Transportation Research Record, 2673 (9), 654-665.
- Gavriilidou A. and Cats O. (2018). Reconciling Transfer Synchronization and Service Regularity: Real-time Control Strategies using Passenger Data.Transportmetrica A, Accepted.
- Cats O., Mach Rufi F. and Koutsopoulos H.N. (2014). Optimizing the Number and Location of Time Point Stops. Public Transport, 6 (3), 215-235.
- Cats O., Larijani A.N., Ólafsdóttir A., Burghout W., Andreasson I. and Koutsopoulos H.N. (2012). Holding Control Strategies: A Simulation-Based Evaluation and Guidelines for Implementation. Transportation Research Record, 2274, 100-108.
- Cats O., Larijani A.N., Burghout W. and Koutsopoulos H.N (2011). Impacts of Holding Control Strategies on Transit Performance: A Bus Simulation Model Analysis. Transportation Research Record, 2216, 51-58.
Scientific publications about public transport dynamic assignment model applications:
- Drabicki A., Kucharski R., Cats O. and Szarata A. (2020). Modelling the Effects of Real-time Crowding Information in Urban Public Transport Systems. Transportmetrica A. In press.
- Cats O. and Glück S. (2019). Frequency and Vehicle Capacity Determination Using a Dynamic Transit Assignment Model. Transportation Research Record, in press.
- Malandri C., Fonzone A. and Cats O. (2018). Recovery Time and Propagation Effects of Passenger Transport Disruption. Physica A, 505, 7-17.
- Gentile G., Florian M., Hamdouch Y., Cats O. and Nuzzolo A. (2016). The Theory of Transit Assignment: Basic Modelling Frameworks. In: Modeling Public Transport Passenger Flows in the Era of Intelligent Transport Systems, G. Gentile and K. Nökel, pp. 287-386. Springer International Publishing. ISBN 978-3-319-25082-3.
- Chandakas E., Leurent F. and Cats O. (2016). Applications and Future Developments: Modeling Software and Advanced Applications. In: Modeling Public Transport Passenger Flows in the Era of Intelligent Transport Systems, G. Gentile and K. Nökel, pp. 521-560. Springer International Publishing. ISBN 978-3-319-25082-3
- Cats O. and Jenelius E. (2016). Beyond a Complete Failure: The Impact of Partial Capacity Degradation on Public Transport Network Vulnerability. Transportmetrica B: Transport Dynamics. In press.
- Cats O., West J. and Eliasson J. (2016). A Dynamic Stochastic Model for Evaluating Congestion and Crowding Effects in Transit Systems. Transportation Research Part B, 89, 43-57.
- Cats O. and Hartl M. (2016). Modelling Public Transport On-board Congestion: Comparing Schedule-based and Agent-based Assignment Approaches and their Implications. Journal of Advanced Transportation. In press.
- Cats O. and Jenelius E. (2015). Planning for the Unexpected: The Value of Reserve Capacity for Public Transport Network Robustness. Transportation Research Part A, 81, 47-61.
- Jenelius E. and Cats O. (2015). The Value of New Public Transport Links for Network Robustness and Redundancy. Transportmetrica A: Transport Science, 11 (9), 819-835.
- Cats O. and Gkioulou Z. (2015). Modelling the Impacts of Public Transport Reliability and Travel Information on Passengers’ Waiting Time Uncertainty. EURO Journal of Transportation and Logistics. In press, DOI 10.1007/s13676-014-0070-4.
- Cats O. and Jenelius E. (2014). Dynamic Vulnerability Analysis of Public Transport Networks: Mitigation Effects of Real-Time Information. Networks and Spatial Economics, 14, 435-463.
- Cats O., Koutsopoulos H.N., Burghout W. and Toledo T. (2011). Effect of Real-Time Transit Information on Dynamic Passenger Path Choice. Transportation Research Record, 2217, 46-54.
ADAPT-IT Analysis and Development of Attractive Public Transport through Information Technology
The project develops a decision-support system that will facilitate proactive and predictive public transport operations and traveler experience. It involves the development of a simulation model to evaluate alternative operations strategies to support real-time control center decisions with a focus on passenger-based control and transfer coordination. In cooperation with Technion – Israel Institute of Technology, Keolis bus operator and Stockholm City.
TRANS-FORM Smart Transfers through Unravelling Urban Form and Travel Flow Dynamics
The project will develop a multi-level approach for monitoring, mapping, analyzing and managing urban dynamics in relation to interchanging travel flows. Analysis of pedestrian and traveler flows at the hub, urban and regional networks. Control strategies incorporate information on passenger flows and service disruptions. The consortium consists of EPFL, IBM Research, LiU, BTH and ETRA. Local implementation partners are HTM and City of The Hague.
SMART Simulation and Modelling of Autonomous Road Transport
The project develops a mesoscopic simulation model of fleets of automated vehicles. Automated operations of Demand Responsive Transport will be developed and modeled to examine their performance and potential under alternative circumstances and design. The project is performed at KTH and funded by the Swedish Transport Administration via CTR.
Other projects where the model is used are iQMobility partners include KTH, Scania and INIT) devoted to the design of autonomous bus services and a project commissioned by the Stockholm County Council where the model is extended to model the interaction between crowding along metro platforms and the distribution of on-board crowding.