1. September, 2012An Activity Based Model for a Regional City1

2. September, 2012An Activity Based Model for a Regional City2 Prepared by Mr Len Johnstone of Oriental Consultants and Mr Treerapot Siripiroteof PCBK

3. An Activity Based Model for a Regional City Phitsanulok CBD.

4. An Activity Based Model for a Regional City Muang Phitsanulok Phitsanulok Network

5. September, 2012An Activity Based Model for a Regional City5 Snapshot of Phitsanulok in 2007 Muang Phitsanulok is the capital district (amphoe mueang) of Phitsanulok Province, northern Thailand.amphoe mueangPhitsanulok ProvinceThailand Area 750.810 km² (474,250 rai)rai Population = 191,012 Household = 74,069 Pop density = 254.4 per/km 2 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. September, 2012An Activity Based Model for a Regional City6 Activity based model,which is used in Muang Phitsanulok, 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 up at 6.00 and Leave home 6:30 Drive his motorcycle to school 7:00 Leave school 16:00 Stop after school for shopping 16:39 Arrival at home 17:00 Drive his motorcycle to internet cafe 17:30 Secondly back home 18:30 Stays at home between 18:30 6:30 CASE STUDY : Activity based model HOME – SCHOOL Trip SCHOOL – SHOP – HOME Trip HOME – OTHER TripOTHER –HOME Trip

7. September, 2012An Activity Based Model for a Regional City7 The Phitsanulok Model - Structure

8. September, 2012An Activity Based Model for a Regional City8 The Phitsanulok Model - Structure

9. September, 2012An Activity Based Model for a Regional City9 The Phitsanulok Model - Structure Land use model Freight Model Activity based model Travel periods Socio – economic data, Household data,Commodity flows, Business and commercial unit, etc. Pattern type Location Mode choice Route selection Calibration and validation Base year 2008 Future traffic forecast year 2010 2015 and 2020 Dynamic Assignment

10. September, 2012An Activity Based Model for a Regional City10 Pattern type model Work Pattern Tour

11. September, 2012An Activity Based Model for a Regional City11 Typical Activity Pattern

12. September, 2012An Activity Based Model for a Regional City12 Population Synthesizer, an Interlude Generate 270,000 Households Number of People, Income and Veh Ownership and Employees

13. September, 2012An Activity Based Model for a Regional City13 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 (P 1,P 2, …,Pk) of selecting any pattern type 1…k (U1+U2+… + Uk) P j = U j P j = U j where where Find random number(R) between 0 to 1 Select the pattern type 1…j where if 0 <= R < P 1, : select Pattern type no. 1 if 0 <= R < P 1, : select Pattern type no. 1 if P1 <= R < P 2 : select Pattern type no. 2 if P ๅ +P 2 +…+P k-2 <= R < P ๅ +P 2 +…+P k-1, select Pattern type number k-1 if P ๅ +P 2 +…+P k-1 <= R < 1, select Pattern type number k Pattern Selection

14. September, 2012An Activity Based Model for a Regional City14 Model Validation in 2007

15. where Inccat1 : Low level of household income Inccate2 : Med level of household income Inccate3 : Med-high of household income Inccate4 : high of household income 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) Case study : Muang Phitsanulok inccat no. Household income (baht/househol d/month) (USD/household/ month) 1< 5,000< 145 25,000-14,999145-429 315,000 – 29,999430 – 834 4>= 30,000>= 835 Tour duration decisions

16. September, 2012An Activity Based Model for a Regional City16 Trip Distribution Factors to choose any location I individual choice Distance/travel time Business/commercial /school Density

17. September, 2012An Activity Based Model for a Regional City17 Individual decisions for making trips MR. A Mr. A 45 yrs old. Position: consultants engineer Household income 50,000 baht has 3 cars, total family members 3 and has 1 son still studying Zone 1 Individual decisions ? Pattern type in 1 day ( to work, study, or others) Tour duration for each activities in 1 day Mode choice for each activities in 1 day Location choice for each activities in 1 day A j =   D j e  ln(Lij) i =1 I Where A j : Accessibility of each person to location j,from location 1….I D j : Activity quantities at the location j L ij : the sum of exponential Utility for every possible mode (L ij = exp(Uprivate) + exp (Upublic) + exp(Uwalk) )  : the co-efficient of exponential Utility from every possible mode  : the co-efficient of Activity quantities Location choice? Case study : Muang Phitsanulok Location Choice

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

19. Individual decisions for making trips Decision mode? Use discrete choice (multinomial logit model ) for each tour. C private = w 2 * in vehicle time + (perceived voc*distance)/(VOT*occupancy) C public = w 1 * walk time + w 2 * in vehicle time + w 3 *wait time + fare/VOT C walk = w 1 * walk time U mode i = a*C mode i where a is weight factor of cost by mode i Case study : Muang Phitsanulok Mode Split

20. September, 2012An Activity Based Model for a Regional City20 Traffic assignment uses Dynamics traffic assignment,moreover the delay at junction will be represented and included in path building stage Route selection technique is All or nothing assignment (AoN) + volume average (AVE) Traffic Assignment ZONE 1 ZONE 2 Vehicle group (packet) Periods t1 – t2

21. September, 2012An Activity Based Model for a Regional City21 Case study : Muang Phitsanulok Traffic volume validations Validationt

22. An Activity Based Model for a Regional City The application of model Model Application -Road improvement plan for Short and Mid term (yr 2015-2020) Legends Open yr 2015 Open yr 2020 11 1064 1063 117 11 12 1086

23. An Activity Based Model for a Regional City Km./ hr. Travel speed summary Future Traffic Assignment

24. An Activity Based Model for a Regional City Road improvement case Base case: Do nothing case Comparison of Level of Service

25. An Activity Based Model for a Regional City Dynamic assignment result in CBD during peak Dynamic assignment result in CBD during off peak Dynamic Assignment

26. September, 2012An Activity Based Model for a Regional City26 TH THE END