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Activity Schedule Model for Tel Aviv Metropolitan: Outline and Application Issues

Activity Schedule Model for Tel Aviv Metropolitan: Outline and Application Issues. by Leonid Kheifits, Boris Shmulyian, Shlomo Bekhor. EMME User’s Conference, Montreal, October 2010. Introduction.

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Activity Schedule Model for Tel Aviv Metropolitan: Outline and Application Issues

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  1. Activity Schedule Model for Tel Aviv Metropolitan: Outline and Application Issues by Leonid Kheifits, Boris Shmulyian, Shlomo Bekhor EMME User’s Conference, Montreal, October 2010

  2. Introduction • The model was developed for MOT of Israel by Cambridge Systematics, Ltd. with participation of local team (data collection and processing and model implementation) • The model is intended for use by transportation planning agencies as a mutual base for analyzing transportation projects of various types: • Congestion pricing, • Parking policy, • Land use and growth management, • Introduction of new public transportation systems (LRT, BRT) • Highway improvements • The model is maintained by Ayalon Highways Company (MOT)

  3. Project area About 1,500 square Km and 3.3 million habitants in 2009

  4. Model Dimensions 1219 TAZ 13,000 regular links 1000 Transit lines Main Modes: car driver, car passenger, taxi, bus, rail, Mass Transit (BRT/LRT) Access modes: walk, transit, Park&Ride, Kiss&Ride

  5. Model Structure Base List of Persons POPULATION GENERATOR Persons By Zone Zonal Data Estimated Models Networks TOUR GENERATOR Full List of Tours Level of Service TRIP GENERATOR ASSIGNMENT UNIT Initial Demand OD Matrices

  6. Population Generator POPULATION GENERATOR Input: • Base list of persons (NTHS) • Zonal constraints for each TAZ for target year • Zonal population • Distribution by gender and age • Average household size • Average number of workers Method: multi-dimensional IPF Output: Synthesized population Software: Stand-alone module (FoxPro) Run time: 10 to 15 minutes

  7. Tour Generator TOUR GENERATOR Reproduces trip patterns observed: Up to 2 home-based tours during a day with 0 or 1 intermediate stop between home and main destination and with 0 or 1 intermediate stop in trip back home Input: • Synthesized population • Zonal attributes • Level of service Method: set of logit models Output: Day travel schedule of each person Software: Integrated C# application

  8. Tour Generator. Flow Chart Car availability Zero One Two + Main Activity Work Education Shopping Other No Tour Time Periods Tour start Tour End Morning AM peak Midday PM peak Evening Morning AM peak Midday PM peak Evening

  9. Tour Generator. Flow Chart Main Destination Destination 1 Destination … Destination … Destination 1 Destination 2 Destination 3 Destination 1219 Tour Main Mode Taxi Driver Passenger Bus Rail MT Walk, P&R, K&R Walk, Bus + MT, P&R, K&R Walk, Bus, P&R, K&R

  10. Tour Generator. Flow Chart Intermediate Stops Before No Stops After Before and After Work Education Shopping Other Location of Intermediate Stops Destination … Destination 1 Destination 2 Destination 3 Destination 1219 Mode Switching at Main Destination Switch to Driver Switch to Passenger Switch to Transit No Mode Switch

  11. Trip Generator TRIP GENERATOR Input: • Day travel schedules • Additional demand • External trips • Trucks and commercial vehicles Output: • Origin-destination matrices by mode and period of day Software: C# application and EMME macro

  12. Assignment Unit ASSIGNMENT UNIT Tasks: • Determination of mode choice set • Assignment of combined modes • LOS calculation and storing • Summarizing results Input data: • O-D matrices by mode and time of day • Road and transit networks and parking data Outputs: • Level of service matrices • Mode choice feasible sets • Highway and transit assignments Software: EMME macros & auxiliary C++ application

  13. Feasible Mode Choice Sets TRIP ASSIGNMENTS Motivation: • Logit mode choice model does not exclude any itinerary • EMME assignment procedure cuts out non-competitive itineraries • The probability of mode choice obtained from logit model cannot be reproduced by assignments • The mode choice set should correspond to both choice model and assignment procedure Solution: • Define “feasible” set of alternatives for each OD pair Possible approaches: • Define rules for excluding “not reasonable alternatives” • Take advantage of consistency of EMME assignments

  14. Example of feasibility rules Destination Side effect: increase in train speed may result in decrease in ridership Origin BUS RAIL Feasibility rule: IVTBUS/ IVTRAIL < k

  15. Adopted Approach Make Trial Assignment Advantages: • Assures consistency of mode choice • Cuts off non-competitive alternatives Mark OD-pairs with no use of given mode as not feasible Save feasibility mask in a matrix for next iteration

  16. Assignment Unit. Modes for assignment Taxi Driver Passenger Bus Rail MT 14 modes of Tour Generator Walk, P&R, K&R Walk, Bus + MT, P&R, K&R Walk, Bus, P&R, K&R Simple Modes Combined Modes 10 modes of AU MT (LRT/BRT) Bus/walk access Rail MT/Bus/walk access Bus P&R access Driver +Taxi Bus Walk access Bus K&R access MT (LRT/BRT) P&R access Rail P&R access MT (LRT/BRT) K&R access Rail K&R access

  17. Assignment for combined modes Should be consistent with mode choice model Two approaches were considered (example of Rail, Park & Ride access):

  18. Combined mode assignment - 1 Origin TAZ • Make auto assignment to obtain access travel time • Make transit assignment to obtain LOS indices • Find the station of boarding • Make auto assignment (Origin-> parking lot) • Make transit assignment (Parking lot -> destination) Roads P&R parking lot- 1 P&R parking lot - 2 Walk Walk RAIL Destination TAZ

  19. Combined mode assignment -2 Origin TAZ • Make auto assignment to obtain access travel time Roads P&R parking lot P&R parking lot Walk Walk RAIL Destination TAZ

  20. Combined mode assignment -2 Origin TAZ • Make auto assignment to obtain access travel time • Create access links (auxiliary transit) to represent car access • Make transit assignment Accesslinks RAIL Destination TAZ

  21. Combined mode assignment -2. Summary • Total number of stations with P&R/K&R access: 190 • Total number of access links: about 10,000 • Average number of outcome access links per TAZ: 53 • Average number of income access links per station with P&R/K&R: 52

  22. LOS calculation and storing LOS indices needed for Tour Generator: • In-vehicle times • Total travel times • Waiting times • Number of transfers • Access/egress times Total of about 80 matrices for one POD including feasibility masks: • Obtained directly from assignments • Obtained from assignments with additional options Use of MatInOut.exe – application for export/import matrices (binary format)

  23. Other Features Disaggregate tour-based approach allows accurate handling of return trips for P&R access modes and accounting for occupancy of parking lots during the day Extended usage of read/write files helped in adding/deleting/modifying access links and in preparation summary files for EXCEL

  24. Model Performance • Disk space required for regular project – about 4GB • Memory required – about 1.5 GB • Run times (Intel® Core™2 CPU 6300, 1.86.GHz): • Tour Generator and Trip Generator for sample of 10% (about 330,000 persons) is: about 1 hour for first iteration that includes car ownership model, and 25 minutes for regular iteration • Assignment Unit is about 45 minutes for one POD: • Auto assignments – (4 assignments): 12 minutes • Transit assignments (53 assignments): 22 minutes • Creation of access links – 3 minutes • Matrix calculations and read/write files: 9 minutes

  25. Conclusions • The structure of modes should be simplified to assure consistency with mode choice model: • Driver, Passenger, Transit, P&R, K&R • Usage of feasible set of modes for each OD proved to be efficient • Opened issue: combined mode assignment with a single mode change point and cut-off of unreasonable alternatives vs. “logit”-type model with several possible mode change points and inclusion of all alternatives

  26. Future developments Next version of the model will include parallel processing: Tour Generator will take advantage of all available processors, and Assignment Unit will work simultaneously with 3 POD (in 3 separate banks)

  27. Expectations Following are the main types of processing required for complex models, which are not covered now by EMME: • Work with groups of objects (define selection set, add/delete/modify entire group) • EMME macro language is irreplaceable in everyday work, but it is not powerful enough for common programming tasks required for complex models • Work with databases • Reporting

  28. Expectations - 2 • Integration of Python may allow implementing future models within EMME • Extended import/export abilities (for matrices DBF, CSV, and binary formats, for networks – EXCEL, csv) would be highly useful • Advance in transit assignment procedures may improve performance of the model

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