Using MATSim for Public Transport Analysis

Transcription

1 February 13, 2014, Hasselt. ORDERin F Seminar 3 Using MATSim for Public Transport Analysis Marcel Rieser Senozon AG [email protected]

2 Agenda 2 MATSim The Berlin Model Public Transport in Berlin Analyzing Activity-Based Travel Demand in Switzerland

3 About Senozon 3 Founded 2010 as ETH Spinoff by Michael Balmer & Marcel Rieser Operational since 1/2011 Specialized on MATSim applications Building MATSim models for clients Maintaining our own model of Switzerland (8 Mio. agents) Via: Visualization and analysis software for MATSim data

4 Senozon 4 Senozon AG ETH Spinoff since 1/2011 Senozon Deutschland GmbH since 6/2013 Michael Balmer Dr.sc.ETH, CEO Marcel Rieser Dr.Ing., CTO Thomas Haupt Dipl.-Wi.-Ing, GF Daniel Röder M. Sc. Operational & Scientific Advisory Board Jan Fülscher, lic.oec.publ. «Mister Startup» Kay W. Axhausen, Prof.Dr. ETH Zürich Kai Nagel, Prof.Dr. TU Berlin

5 MATSim

6 MATSim Multi-Agent Transport Simulation 6 Agent-based simulation of people s mobility behavior Multi-modal (private cars, public transport) Typically: simulate one day with agents activities and trips Open Source Written in Java Being developed since 2000, 60+ PhD years Global community (users in Germany, Switzerland, UK, USA, Canada, South Africa, Japan, Australia, Venezuela, )

7 Example: Zurich 7

8 MATSim: Inputs 8 Simulation Inputs: Network (Roads, Rails): Infrastructure Public Transport Schedule: Provided Services Facilities (Buildings): Locations where Activities can be performed Car-Counts: for Calibration Travel Demand

9 MATSim: Travel Demand 9 Synthetic population with daily plans <person id="241 > <plan score="123 > <act type="home" link="5834" end_time="07:00" /> <leg mode="car" trav_time="00:25 > <route> </route> </leg> <act type="work" link="5844" dur="09:00" /> <leg mode="car" trav_time="00:14 > <route> </route> </leg> <act type="home" link="5834" /> </plan> </person>

10 MATSim: Plans Execution 10 Mobility simulation moves agents around according to their plans Agents can get stuck in traffic A bus might get stuck in traffic Agents might miss a connecting train Agents may arrive too late for work Agents may arrive at a shop once it has already closed The agents plans are just plans! The reality (simulation) can differ!

11 MATSim: Replanning 11 Agents can modify their plan Departure time choice Transport mode choice Route choice (Secondary activity location choice) Agents can try out multiple variants for their day plan to find a good one What is a good plan?

12 MATSim: Scoring 12 Executed plans get a score (generalized utility) Performing activity: positive utility Travelling: negative utility Being late: high negative utility Paying a toll: negative utility Goal of each agent: Create a plan that gets scored as high as possible The generalized utility function can be parameterized with agents attributes

13 MATSim: Controller 13 Iteratively perform plans executing, plans scoring, replanning MATSim not only simulates traffic MATSim simulates mobility behavior incl. behavior changes MATSim optimizes travel demand

14 Demand optimization: detailed automatic modal split 14 car users transit users train stops other transit stops

15 Demand optimization: detailed automatic modal split 15 car users transit users train stops other transit stops

16 Demand optimization: detailed automatic modal split 16 High share of transit users High share of car users This is the outcome of the model, not input! car users transit users train stops other transit stops

17 The Berlin Model

18 The Berlin model 18 Built for BVG Berliner Verkehrsbetriebe Joint project together with PTV (Berlin) Creating a static, macroscopic model (VISUM) and a dynamic, microscopic model (MATSim) based on the same data

19 A new airport will come (eventually) 19 TXL BER Map data OpenStreetMap contributors, CC-BY-SA. Rendering: maps.skobbler.com

20 Why? 20 BVG, Berlin Public Transport Company large interest in updated model of new situation worked with PTV VISUM, Static Assignment Model very interested in MATSim requested an activity- and agent-based model of new situation

21 Problem 21 PTV VISUM MATSim

22 Solution: Provide converters 22 Edit Scenario convert Simulate with MATSim Analyze with VISUM convert Analyze with agentbased specific tools

23 Converting Infrastructure Data 23 Network Transit schedule Transit vehicle types Land use information All exported from VISUM in ASCII file format Converted to MATSim XML-based file formats Post-processing / cleaning of converted data Enriching of converted data (e.g. door operation modes for vehicle types)

24 Converting Demand Data 24 VISUM model uses ~100 OD matrices (4 modes * 23 activity groups, plus long distance, freight, airport and tourist traffic) Additional data set: SrV Survey (travel diary) Create a new, activity-based demand based on land-use and SrV survey reflecting the VISUM demand as good as possible create population based on land-use (inhabitants per zone) assign socio-demographic attributes and activity chains based on SrV weighted draw from OD matrices for all activity locations assign coordinates for activities within zone s block-level land-use data

25 Model Details 25 Network: links Population: 4,5 million agents Public Transport: 530 lines, 96 transit vehicle types

26 Model Visualization 26 MOVIE

27 Converting Results 27 PTV VISUM offers a passenger survey module import, validate, analyze reported transit connections used to import all simulated agents transit trips Good for reporting / comparison of overall statistics Loss of information about individual agents

28 Analyzing Results 28 Analysis GUI: Specific, agent-based analyses. Output for Excel, GIS.

29 Solution: Provide converters 29 Edit Scenario convert Simulate with MATSim Analyze with VISUM convert Analyze with agentbased specific tools

30 Public Transport in Berlin

31 Usages of AB-Model in Berlin 31 Analyzing current travel behavior Marketing & Planning Operations Predictions / Case Studies

32 Analyzing Travel Behavior 32 Activity types and locations of passengers of bus line 245

33 Marketing / Planning 33 Identify persons not using public transport, although public transport would be a viable alternative for their trips. Where are they located? What lines would they use if they used public transport?

34 Marketing / Planning 34 What stops would they frequent if they used public transport? What parts of a line would they travel? a few a lot

35 Marketing / Planning 35 Heat map of trip start locations of potential users Number of trips starting per cell [in % of max value] <10% 10% 50% 100%

36 Marketing / Planning: Missing services? 36 Direction of potential trips Current services Current services are not covering all directions well enough

37 Operations 37 The simulation was calibrated to reflect the observed schedule of two lines Average difference in travel time (7-9 o clock) from stop to stop between simulation and operational data (left: bus line 187, right: bus line M41)

38 Predictions & Case Studies 38 in public transport travel demand, (assuming the new airport is open )

39 Analyzing Activity-Based Travel Demand in Switzerland

40 Analyzing a situation 40 First step to improve a situation: Analyze existing situation What travel demand exists? Why is it using public transport, or why the car? At what time is the demand the highest?

41 Data from an Activity-Based Model 41 Prepare a table with one row per trip Start time, end time Start location, end location From activity, to activity Travel time Travel distance Beeline distance Transport mode

42 Scenario Analysis: Live Demo 42

43 43

44 Conclusions 44 Activity- and agent-based, microscopic models can answer much more questions regarding the behavior of (potential) public transport passengers Traditional tools can be used as graphical editors for infrastructure data (network, schedules, zones, ), or for analyzing aggregated results Agent-based models can build upon traditional models, given some additional data for disaggregation of data is available although the other way makes much more sense

45 Thanks for your attention! 45 Questions? Marcel Rieser Senozon AG