September, 2012 An Activity Based Model for a Regional City 1
An Activity Based Model for a Regional CityAn Activity Based Model for a Regional City
September, 2012 An Activity Based Model for a Regional City 2
Prepared by
Mr Len Johnstone of Oriental Consultants and
Mr Treerapot Siripiroteof PCBK
An Activity Based Model for a Regional City
Phitsanulok CBD.
An Activity Based Model for a Regional City
Muang Phitsanulok
Phitsanulok Network
September, 2012 An Activity Based Model for a Regional City 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.
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• 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
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The Phitsanulok Model - Structure
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The Phitsanulok Model - Structure
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The Phitsanulok Model - Structure
Land use model
Freight Model
Activity based model
Tra
vel
peri
ods
Socio – economic data, Household data ,Commodity flows , Business and commercial unit , etc.
Pattern type
Location
Mode choiceRoute selection
Calibration and validation
Base year 2008
Future traffic forecast year
2010 2015 and 2020
Dynamic Assignment
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Pattern type model Work Pattern Tour
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Typical Activity Pattern
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Population Synthesizer, an InterludeGenerate 270,000 HouseholdsNumber of People, Income and Veh Ownership and Employees
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The procedure of choice pattern type uses discrete choice (Multinomial The procedure of choice pattern type uses discrete choice (Multinomial logit model:logit model: Monte Monte Carlo (Adler, 1979; Luce, 1959)) for every trip Carlo (Adler, 1979; Luce, 1959)) for every trip chain as described below: chain as described below:
Calculate the probability (PCalculate the probability (P11,P,P22 , … ,Pk) of selecting , … ,Pk) of selecting any any pattern type 1…pattern type 1…kk
(U1+U2+… + Uk)(U1+U2+… + Uk)
PPj j = = UUjj
wherewhere
Find random number(R) between 0 toFind random number(R) between 0 to 1 1
Select the pattern type 1…j whereSelect the pattern type 1…j where
if 0 <= R < Pif 0 <= R < P11, : select Pattern type no. 1, : select Pattern type no. 1
if P1 <= R < Pif P1 <= R < P22 : select Pattern type no. 2 : select Pattern type no. 2
if Pif Pๅๅ+P+P22+…+P+…+Pk-2k-2 <= R < P <= R < Pๅๅ+P+P22+…+P+…+Pk-1k-1 , select Pattern type number k-1 , select Pattern type number k-1
if Pif Pๅๅ+P+P22+…+P+…+Pk-1k-1 <= R < 1, select Pattern type number k <= R < 1, select Pattern type number k
Pattern Selection
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Model Validation in 2007
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
Household income
(baht/household/
month)
(USD/household/
month)
1 < 5,000 < 145
2 5,000-14,999 145-429
3 15,000 – 29,999 430 – 834
4 >= 30,000 >= 835
Tour duration decisions
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Trip Distribution
Factors to choose any location I
individual choice
Distance/travel timeBusiness/commercial
/school Density
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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 1Individual
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
Aj = Dj eln(Lij)
i =1
I
WhereAj : Accessibility of each person to location j ,from location 1….IDj : Activity quantities at the location jLij : the sum of exponential Utility for every possible mode (Lij = 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
Case study : Muang Phitsanulok
The compare Travel distances from home to primary locations distribution between survey and modelled
กลุ่��มทำ�งนประจำ�
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29Trip length (km)
Frequency (%)
ทำ��ง�นประจำ��(สำ��รวจำ)ทำ��ง�นประจำ��(แบบจำ��ลอง)
กลุ่��มทำ�งนไม�ประจำ�
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29Trip length (km)
Frequency (%)
ทำ��ง�นไม่�ประจำ��(สำ��รวจำ)ทำ��ง�นไม่�ประจำ��(แบบจำ��ลอง)
Worker full time
Worker part time
กลุ่��มน�กเร�ยน/น�กศึ�กษ
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29Trip length (km)
Frequency (%)
น�กเร�ยน(สำ��รวจำ)น�กเร�ยน(แบบจำ��ลอง)
กลุ่��มอื่��นๆ
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
45.0%
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29Trip length (km)
Frequency (%)
กล��ม่อ��นๆ (สำ��รวจำ)กล��ม่อ��นๆ (แบบจำ��ลอง)
Student
Others
Home to work place
Home to work place
Home to school
Home to others
Trip distribution
Individual decisions for making trips
Decision mode?Use discrete 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 i where a is weight factor of cost
by mode i
Case study : Muang Phitsanulok Mode Split
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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
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Case study : Muang Phitsanulok
0200
400600
8001000
12001400
16001800
2000
0 200 400 600 800 1000 1200 1400 1600 1800 2000
Observed volume (PCU/hr)
Mod
elled vo
lume (PC
U/hr) R2 = 0.9386
0
500
1000
1500
2000
0 200 400 600 800 1000 1200 1400 1600 1800 2000
Traffic volume validations
Validationt
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
An Activity Based Model for a Regional City
Km./hr.
Travel speed summary
Future Traffic Assignment
An Activity Based Model for a Regional City
Road improvement case
Base case: Do nothing case
Comparison of Level of Service
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
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TH
THE END