September, 2012An Activity Based Model for a Regional City1

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


Prepared by

Mr Len Johnstone of Oriental Consultants and

Mr Treerapot Siripiroteof PCBK

An Activity Based Model for a Regional City

Phitsanulok CBD.


Muang Phitsanulok

Phitsanulok Network


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.

http://en.wikipedia.org/wiki/Amphoe_mueang

http://en.wikipedia.org/wiki/Phitsanulok_Province

http://en.wikipedia.org/wiki/Thailand

http://en.wikipedia.org/wiki/Rai_%28area%29


• 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


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


Pattern type model Work Pattern Tour


Typical Activity Pattern


Population Synthesizer, an InterludeGenerate 270,000 HouseholdsNumber of People, Income and Veh Ownership and Employees


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


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


Trip Distribution

Factors to choose any location I

individual choice

Distance/travel timeBusiness/commercial

/school Density


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


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


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



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


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


Km./hr.

Travel speed summary

Future Traffic Assignment


Road improvement case

Base case: Do nothing case

Comparison of Level of Service


Dynamic assignment result in CBD during peak

Dynamic assignment result in CBD during off peak

Dynamic Assignment


TH

THE END

Documents

September, 2012An Activity Based Model for a Regional City1