An Activity Based Model for a Regional City

1. An Activity Based Model for a Regional City

2. Prepared by Mr Len Johnstone of Oriental Consultants and Mr Treerapot Siripiroteof PCBK

3. Phitsanulok CBD.

4. Muang Phitsanulok Phitsanulok Network

5. Snapshot of Phitsanulok in 2007 • Muang Phitsanulok is the capital district (amphoe mueang) of Phitsanulok Province, northern Thailand. • Area 750.810 km² (474,250 rai) • Population = 191,012 Household = 74,069 Pop density = 254.4 per/km2 • GPP(Gross Provincial Product) = 23,624 Million Baht (700 Mil USD) • Muang Phitsanulok is in the North of Thailand about 380 km from Bangkok. • Major Tourist Centre.

6. HOME– SCHOOL Trip SCHOOL– SHOP– HOME Trip HOME– OTHER Trip OTHER –HOME Trip CASE STUDY : Activity based model • Activity based model ,which is used in MuangPhitsanulok , to simulate the travel behavior of individual person for example a student who has a primary activity of studying and other activites such as shopping (Sample of HH 1,200) • Wakes upat 6.00 and Leave home6:30 • Drive his motorcycle to school7:00 • Leave school 16:00 • Stop after school for shopping 16:39 • Arrival at home17:00 • Drive his motorcycle tointernet cafe17:30 • Secondly back home 18:30 • Stays at homebetween18:30 6:30

7. The Phitsanulok Model - Structure

8. The Phitsanulok Model - Structure

9. Socio – economic data, Household data ,Commodity flows , Business and commercial unit , etc. Land use model Activity based model Freight Model Pattern type Location Travel periods Mode choice Route selection Calibration and validation Base year 2008 Future traffic forecast year 2010 2015 and 2020 Dynamic Assignment The Phitsanulok Model - Structure

10. Work Pattern Tour Pattern type model

11. Typical Activity Pattern

12. Population Synthesizer, an Interlude Generate 270,000 Households Number of People, Income and Veh Ownership and Employees

13. Pattern Selection The procedure of choice pattern type uses discrete choice (Multinomial logit model: Monte Carlo (Adler, 1979; Luce, 1959)) for every trip chain as described below: Calculate the probability (P1,P2 , … ,Pk) of selecting any pattern type 1…k (U1+U2+… + Uk) Pj = Uj where Find random number(R) between 0 to 1 Select the pattern type 1…j where if 0 <= R < P1, : select Pattern type no. 1 if P1 <= R < P2 : select Pattern type no. 2 if Pๅ+P2+…+Pk-2 <= R < Pๅ+P2+…+Pk-1 , select Pattern type number k-1 if Pๅ+P2+…+Pk-1 <= R < 1, select Pattern type number k

14. Model Validation in 2007

15. Case study : Muang Phitsanulok Tour duration decisions Utility of each Tour duration todutil[1]=exp(5.32 -1.07*inccat1 -1.41*inccat2 -8.14*inccat3 -7.76*inccat4) todutil[2]=exp(5.21 -0.71*inccat1 -2.02*inccat2 -8.58*inccat3 -8.70*inccat4) todutil[3]=exp(5.79 -1.36*inccat1 -2.43*inccat2 -8.05*inccat3 -7.39*inccat4) . . todutil[13]=exp(5.79 -1.36*inccat1 -2.43*inccat2 -8.05*inccat3 -7.39*inccat4) where Inccat1 :Low level of household income Inccate2 : Med level of household income Inccate3 : Med-high of household income Inccate4 :high of household income

16. Factors to choose any locationI individual choice Distance/travel time Business/commercial /school Density Trip Distribution

17. I Aj = Dj eln(Lij) i =1 Case study : Muang Phitsanulok Location Choice Individual decisions for making trips Pattern typein 1 day( to work , study , or others) Zone1 Individual decisions? Tour duration for each activities in 1 day MR. A Location choice for each activities in 1 day Mr. A 45 yrs old. Position: consultants engineerHousehold income 50,000 bahthas3 cars,total family members3 andhas 1 son still studying Mode choice for each activities in 1 day Location choice? Where Aj : Accessibility of each person to locationj ,from location1….I Dj : Activity quantities at the location j Lij :the sum of exponential Utility for every possible mode (Lij = exp(Uprivate) + exp (Upublic)+ exp(Uwalk))  :the co-efficient of exponential Utility from every possiblemode  : the co-efficient of Activity quantities

18. Home to work place Home to work place Worker full time Worker part time Home to school Home to others Student Others Case study : Muang Phitsanulok Trip distribution The compare Travel distances from home to primary locations distribution between survey and modelled

19. Case study : Muang Phitsanulok Mode Split Individual decisions for making trips Decision mode? Usediscrete choice (multinomial logit model ) for each tour. Cprivate = w2* in vehicle time + (perceived voc*distance)/(VOT*occupancy) Cpublic = w1* walk time + w2* in vehicle time + w3*wait time + fare/VOT Cwalk = w1* walk time Umode i= a*Cmode iwherea isweight factor of cost bymode i

20. Traffic Assignment Traffic assignment uses Dynamics traffic assignment ,moreover the delay at junction will be represented and included in path building stage Route selection techniqueis All or nothing assignment (AoN) + volume average (AVE) ZONE1 ZONE2 Vehicle group(packet) Periods t1 – t2

21. Case study : Muang Phitsanulok Validationt Traffic volume validations

22. 11 12 1086 117 1063 1064 11 Legends Open yr2015 Open yr2020 Model Application-Road improvement plan for Short and Mid term (yr 2015-2020) The application of model

23. Future Traffic Assignment Km./hr. Travel speed summary

24. Comparison of Level of Service Road improvement case Base case: Do nothing case

25. Dynamic Assignment Dynamic assignment result in CBD during peak Dynamic assignment result in CBD during off peak

26. TH THE END