modelling car trip generation in the developing world - the tale of two cities

School of somethingFACULTY OF OTHER

Institute for Transport StudiesFACULTY OF ENVIRONMENT

Modelling Car Trip Generation in the

Developing World: The Tale of Two Cities

Mr. Andrew Bwambale, ITS

Dr. Charisma F. Choudhury, ITS

Dr. Nobuhiro Sanko, Kobe University

• Motivation

• Study Objectives

• Study Area

• Data

• Modelling Framework

• Results

• Conclusions

Outline

Data sourcesMotivation

• Models are key to understanding and solving complex

transport problems; however, there are limitations imposed by

data collection budget constraints in developing countries.

• Could transferable models be a possible solution?

• Besides transferability, what are the limitation of current trip

generation models?

• Data shortages in the application context

• Possible Endogeneity between car ownership

and trip generation (Simultaneity)

Study Objectives

(1) How does the household car ownership affect the

household car trip rate in the context of developing

countries?

(2) How can we account for the potential endogeneity in car

trip generation models?

(3) How can we account for data limitations associated with

modelling car trip generation? and

(4) How transferable are the models between two cities that

have similarity in socio-demographics?

Data sourcesStudy Area

Focus will be on spatial

transferability between

Nairobi and Dar-es-Salaam.

These areas are thought to

have largely similar socio-

demographics.

Household travel survey data

collected by JICA from both

cities has been used in this

study.

Data sourcesData

Survey period

Population (million)

Survey area (km2)

Population density (persons/km2)

Total number of households in the survey area ('000)

Number of households surveyed

Number of traffic analysis zones (TAZ)

Survey region

Survey lead

House ownership (%)

Yes

No

Household car ownership (%)

0

1

2

3+

Mean S.D Mean S.D

Household income in USD 385.80 377.20 110.87 194.85

Household size 3.33 1.65 4.40 1.83

Number of workers per household 1.51 0.79 1.24 0.80

Driving licence holders per household 0.60 0.84 0.43 1.04

Number of children per household 0.70 0.87 0.93 0.94

Number of students per household 0.61 0.86 1.02 1.07

650 (in 2004)

Dar-es-Salaam (Tanzania)

2007

3.0 (in 2007)

1687

1796 (in 2007)

708 (in 2007)

Nairobi (Kenya)

2004

2.7 (in 2004)

696

3817 (in 2004)

7676

164

Dar-es-salaam city

Japan International

Cooperation

Agency (JICA)

8588

104

Nairobi city

Japan International

Cooperation

Agency (JICA)

8.80 52.80

91.20 47.20

79.19 94.12

4.40 0.33

1.69 0.04

14.72 5.51

Mobile phone CDRData

Nairobi Dar-es-Salaam

Data sourcesModelling Framework

Four ordered response probit car trip generation models have

been estimated for each city.

• Model 1 (Car trip generation models with car ownership as an

explanatory variable);

nnn Xy '*

)1(

𝑗 =

0, 𝑖𝑓 𝑦𝑛∗ ≤ 𝜇0

1, 𝑖𝑓 𝜇0 <𝑦𝑛

∗ ≤ 𝜇1

2, 𝑖𝑓 𝜇1 <𝑦𝑛

∗ ≤ 𝜇2

3+, 𝑖𝑓 𝑦𝑛

∗ > 𝜇2

Household socio-economic variables

Including # of cars

yn*

# of car trips


• Model 2 (Car trip generation models without car ownership as an

explanatory variable);

'

*' nnn Xy

)4(

𝑗 =

0, 𝑖𝑓 𝑦𝑛∗ ≤ 𝜏0

1, 𝑖𝑓 𝜏0 <𝑦𝑛

∗ ≤ 𝜏1

2, 𝑖𝑓 𝜏1 <𝑦𝑛∗ ≤ 𝜏2


∗ > 𝜏2


Including # of cars

yn*

# of car trips


• Model 3 (Two stage models estimated sequentially; first stage - car

ownership model and second stage - car trip generation model);

ntnnn

ncnn

zXyStage

XzStage

''

**

''

*

.':2

':1

)5( a

)5( b

𝑖 =

0, 𝑖𝑓 𝑧𝑛∗ ≤ 𝜎0

1, 𝑖𝑓 𝜎0 <𝑧𝑛

∗ ≤ 𝜎1

2, 𝑖𝑓 𝜎1 <𝑧𝑛∗ ≤ 𝜎2

3+, 𝑖𝑓 𝑧𝑛

∗ > 𝜎2

𝑗 =

0, 𝑖𝑓 𝑦𝑛∗ ≤ 𝛿0

1, 𝑖𝑓 𝛿0 <𝑦𝑛

∗ ≤ 𝛿1

2, 𝑖𝑓 𝛿1 <𝑦𝑛∗ ≤ 𝛿2


∗ > 𝛿2

# of carsyn*

# of car trips

zn*


Including # of cars

Stage 1

Stage 1

Stage 2

Stage 2

Stage 2


• Model 4 (Joint car trip generation and car ownership models –

Simultaneous BOP models);

tnnnn

cnnn

zXy

Xz

**

*

.'

'

)6(

𝑖 =

0, 𝑖𝑓 𝑧𝑛∗ ≤ ∝0

1, 𝑖𝑓 ∝0 < 𝑧𝑛

∗ ≤ ∝1

2, 𝑖𝑓 ∝1 < 𝑧𝑛∗ ≤ ∝2

3+, 𝑖𝑓 𝑧𝑛

∗ >∝2

𝑗 =

0, 𝑖𝑓 𝑦𝑛∗ ≤ 𝜃0

1, 𝑖𝑓 𝜃0 <𝑦𝑛

∗ ≤ 𝜃1

2, 𝑖𝑓 𝜃1 <𝑦𝑛∗ ≤ 𝜃2


∗ > 𝜃2

# of cars # of car trips

zn*


Including # of cars

yn*The BOP model



Including # of cars

yn*

# of car trips

Model 1 Model 2 Model 3 Model 4

Is car ownership data required in the estimation context?

Is car ownership data required in the application context?

Data sourcesResults

• Models 1 and 2

Variable Est. Z ≈ t-stat.a

Est. Z ≈ t-stat.a

Est. Z ≈ t-stat.a

Est. Z ≈ t-stat.a

Monthly household income ('000) US dollars 1.121 19.32 0.290 2.81 1.794 37.02 0.539 5.64

Dummies related to number of workers per household

Number of workers = 1 0.090 0.60** 0.540 2.89 -0.037 -0.26** 0.383 2.27

Number of workers = 2 0.519 3.45 0.707 3.73 0.324 2.29 0.552 3.21

Number of workers = 3 and above 0.576 3.63 0.733 3.36 0.387 2.59 0.552 2.77

Dummies related to number of driving license holders per

household

Number of driving license holders = 1 or 2 0.927 15.79 0.716 8.17 1.314 24.24 1.205 16.23

Number of driving license holders = 3 1.333 11.55 1.042 6.68 1.721 15.42 1.700 12.32


Number of driving license holders = 5 and above 2.116 6.73 1.564 8.38 2.418 7.72 2.583 15.33

Dummies related to number of cars owned per household

Number of car owned = 1 1.287 25.71 1.543 17.54 - - - -

Number of car owned = 2 1.523 19.7 1.364 5.44 - - - -

Number of car owned = 3 and above 1.222 10.91 2.366 3.06 - - - -

-

-

-

Model 1 Model 2

(-0.46)

(-1.03)

(-0.66)

(1.18)

(0.12)

(0.52)

(-1.46)

(1.50)

3.14

Nairobi Dar-es-Salaam t-stat. diff

11.71

(-1.90)

2.00


7.03

(1.51)

-2.53

(0.61)

(-1.88)

(-0.78)

(-0.58)


Including # of cars

yn*

# of car trips

Data sourcesResults

• Models 3 and 4

Variable Est. Z ≈ t-stat.a

Est. Z ≈ t-stat.a

Est. Z ≈ t-stat.a

Est. Z ≈ t-stat.a

Household car ownership model:

Monthly household income ('000) US dollars 1.942 37.47 0.719 8.24 1.956 37.73 0.709 8.17

House ownership 0.490 9.39 0.234 3.54 0.480 9.17 0.240 3.66


Number of workers = 1 -0.335 -2.96 -0.355 -3.64 -0.364 -3.27 -0.362 -3.79

Number of workers = 2 -0.402 -3.52 -0.296 -2.83 -0.458 -4.07 -0.316 -3.07

Number of workers = 3 and above -0.299 -2.41 -0.280 -1.99 -0.354 -2.88 -0.292 -2.10


household

Number of driving license holders = 1 or 2 1.249 24.51 1.468 21.44 1.229 23.94 1.441 21.20




Dummies related to household size

Household size = 2 or 3 0.096 1.30** 0.384 1.10** 0.070 0.96** 0.322 0.99**

Household size = 4 0.246 3.21 0.470 1.34** 0.226 2.98 0.400 1.23**

Household size = 5+ 0.306 3.97 0.484 1.39** 0.298 3.92 0.431 1.34**

12.06

3.04

(0.13)

(-0.69)

(-0.10)

-2.57

-2.28

-3.38

-2.83

(-0.81)

(-0.62)

(-0.50)


Model 3


(-0.01)

(-0.93)

(-0.33)

-2.48

-2.34

-3.50

-3.19

(-0.76)

(-0.52)

(-0.40)

Model 4

t-stat. diff

12.34

2.87

Data sourcesResults

• Models 3 and 4 cont’d

Household car trip generation model:

Monthly household income ('000) US dollars 0.846 4.31 0.256 1.25** 0.954 4.53 0.345 1.66*


Number of workers = 1 0.126 0.86** 0.524 2.74 0.173 1.07** 0.637 2.54

Number of workers = 2 0.494 3.39 0.655 3.55 0.618 3.80 0.797 3.23

Number of workers = 3 and above 0.510 3.36 0.635 3.08 0.641 3.81 0.823 3.14


household

Number of driving license holders = 1 or 2 0.727 5.63 0.645 1.77* 0.821 5.95 0.674 1.84*


Number of driving license holders = 4 1.177 4.78 1.031 1.72* 1.362 5.28 1.128 1.88*


0.463 4.99 0.376 1.56** 0.553 5.32 0.535 1.63**

Correlation coefficient ( ) - - - - 0.043 0.414** 0.295 1.128**

(-0.22)

2.06

(-1.55)

(-0.60)

(-0.58)

(0.38)

(0.03)

(0.36)

(0.05)

(-0.90)

(-1.65)

(-0.69)

(-0.49)

(0.21)

(-0.02)

(0.22)

(-0.14)

(0.34)

2.08

corr

,

Data sourcesResults

• Overall goodness of fit measures

Summary statistics

,

,

Chi-square stat. (14,0.05), (11,0.05)

Adjusted ρ2

Nairobi Dar-es-SalaamNairobiDar-es-Salaam

Model 1 Model 2

0.378 0.276

-3741.25 -1107.14

-6030.70 -1543.36

4578.91 872.43

19.68 19.68

0.439 0.378

5322.22 1193.90

23.68 23.68

-3369.59 -946.41

-6030.70 -1543.36

)ˆ(LL

)0(LL

)ˆ()0(2 LLLL

)ˆ(LL

)ˆ()0(2 LLLL

Summary statistics

Household car ownership model:

- - - -

- - - -

- - - -Chi-square stat. (15,0.05) - - - -

Adjusted ρ2 - - - -

Household car trip generation model:

,

,

Chi-square stat. (12,0.05), (28,0.05)

Adjusted ρ2

,


Model 3

4603.60

21.03

0.380


Model 4

0.035 0.020

-7069.91 -2180.09

544.20 143.20

41.34 41.34

-6797.81 -2108.49

-3356.55

-5780.96

4848.81

25.00

0.417

-1140.11

-1830.26

1380.29

25.00

0.369

-3728.90

-6030.70

-1105.90

-1543.36

874.92

21.03

0.276

00 ,11, 22 , ,

)0(LL

)0(LL

)ˆ()0(2 LLLL

)ˆ(LL

)ˆ,ˆ( LL

)ˆ,ˆ()0(2 LLLL )ˆ,ˆ,ˆ()0(2 LLLL

>

>

0.37 0.20Trip generation component

>

>

Data sourcesResults

• Overall model spatial transferability (individual parameters are

relatively transferable)

Description Nairobi to Dar-es-Salaam Dar-es-Salaam to Nairobi

Model 1 525.66 2079.72

Model 2 630.54 2776.196

Model 3

(car owenership sub-model)443.85 2833.88

Model 3

(car trip generation sub-model)655.69 2957.16

Model 4 951.82 4689.43

Transferability Test Statistic

Better transferability in this direction

Nairobi models are better.

(434.10)

(733.38)

(2655.00)

(3474.48)

(car ownership component)

(trip generation component)

Data sourcesConclusions

• In both cities, car ownership has been found to have a

statistically significant positive influence on car trip

generation.

• Models 1, 3 and 4.

• The problem associated with potential endogeneity in

modelling trip generation and car ownership can be

addressed using Model structures 3 and 4.

• Model 3: Endogeneity due to variable omission.

• Model 4: Endogeneity due to variable omission and simultaneity.

Data sourcesConclusions

• Possible ways of addressing the lack of car ownership

data for car trip generation modelling in the application

context can be addressed using Model structures 2, 3

and 4, though Model structure 4 is a better option.

• Though all the four models have most of their parameters

individually transferrable between the two cities, none of

the models is wholly transferrable between the two cities.

• Improvement of transferability scores

• Treatment of missing data as a latent variable

Further research

Questions?

modelling car trip generation in the developing world - the tale of two cities

Environment

data sources data

car ownership data

data sources results

data limitations

data sources motivation

data shortages

stage car ownership

household car ownership