1 WATCH + SAKURA France-Japan Integrated Action Program (Party - PowerPoint PPT Presentation

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WATCH + SAKURA France-Japan Integrated Action Program ＝花見 (Party under cherry blossom) 2

Inter-Temporal and Inter-Regional Analysis of Household Car and Motorcycle Ownership Behaviours in Asian Big Cities SAKURA Project July 2004 Nagoya University Nobuhiro Sanko, Hiroaki Maesoba, Dilum Dissanayake Toshiyuki Yamamoto, and Takayuki Morikawa 3

INTRODUCTION Economic Growth Income Increase Vehicle Ownership Increase 4

CASE STUDY CITIES We are HERE 5

CASE STUDY CITIES Nagoya, Japan (1981, 1991, 2001) Bangkok, Thailand (1995/96) Manila, Philippines (1996) Kuala Lumpur, Malaysia (1997) 6

Car Ownership in Case Study Cities (1960 ~ 1995) 400 ants nhabitant 1000 Inhabi 300 ars/1000 I 200 100 Car 0 1960 1965 1970 1975 1980 1985 1990 1995 Year Nagoya Bangkok Kuala Lumpur Manila 7

Car Ownership Forecast around the World mln units) 50 45 OECD OECD アメリカ U.S.A. 40 非OECD Others 0 自動車保有台数(億台） 35 Total 10 計 Number of Cars Owned ( 30 25 20 15 10 5 0 2000 2020 2040 2060 2080 2100 (Yr) 図 -3.2 世界の自動車保有台数の将来予測 Increasing Trend in Developing Courtiers 8

INTRODUCTION Vehicle Ownership Increase  can cause traffic congestions and environmental problems Some Countermeasures Considered •Investment in road infrastructure and public transit systems •Regulations against vehicle ownership and usage •Technical innovation in vehicle performance However, understanding vehicle ownership behaviours is the key and prerequisite. 9

OBJECTIVES  Modelling and comparing vehicle ownership behaviours in the case study cities (Nagoya, Bangkok, Kuala Lumpur and Manila)  Obtaining insights into the effects of accessibility on vehicle ownership behaviours  Evaluating temporal and spatial transferability of vehicle ownership models 10

MODELLING FRAMEWORK Mode Choice Model Multinomial Logit Model Vehicle Ownership Model (Trip Level) Bivariate Ordered Probit Model (Household Level) Accessibility Measures Household members’ SE Trip makers’ LOS SE 11

MODELLING FRAMEWORK Comparing Vehicle Ownership Models and Evaluating their Transferability NGO81 NGO91 NGO01 BKK95 Inter-temporal comparison and temporal transferability KL97 Inter-regional comparison and spatial transferability MNL96 12

CASE STUDY CITIES AND THE DATA Nagoya, Japan (1981, 1991, 2001) Bangkok, Thailand (1995/96) Manila, Philippines (1996) Kuala Lumpur, Malaysia (1997) 13

Chukyo Metropolitan Area (Nagoya and Surrounding Areas) 1991 Area: 5656, 5173, 6696km 2 (1981, 1991, 2001) Population: 7.8, 8.1, 9.0 million (1981, 1991, 2001) 14

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Bangkok Metropolitan Region (BMR) Nakorn Pathumthani Pathom Nonthaburi BMA Samut Samut N Sakorn Prakarn Area: 7758 km 2 Population: 13 million Data Source: UTDM survey in 1995/96 . 16

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Kuala Lumpur Metropolitan (KLMP) Klang Vally 500 km 2 Area: 500 km 2 Population: 4.1 million 243 km 2 Data source: JICA survey in 1997. (JICA: Japan International Cooperation Agency) 18

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Metro Manila Area: 636 km 2 Population: 14.4 million Data source: JICA survey in 1996. 20

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Modal Splits in Case Study Cities NGO81 NGO91 NGO01 BKK KL MNL 0% 20% 40% 60% 80% 100% Rail Bus Car Motorcycle 23

Vehicle Ownership Characteristics in Case Study Cities NGO01 NGO91 NGO81 40% 40% 40% 30% 30% 30% 20% 20% 20% 10% 10% 10% 0 0 0 0% 1 0% 0% 1 1 2+ 0 MC MC 1 0 0 2+ 2+ MC 2 1 1 3+ 2 2 Car 3+ 3+ Car Car BKK95 KL97 MNL96 90% 40% 40% 40% 30% 30% 30% 20% 20% 20% 10% 10% 10% 0 0 0 0% 1 0% 1 0% 1 MC 0 0 0 2+ 2+ 2+ 1 1 1 MC 2 2 2 Car MC 3+ Car 3+ 3+ Car In NGO, household without car (-) and with 2+ cars (+) 24

LOS DATA Survey area is divided into zones Travel time: Average travel time reported by respondents (if no trip is made, larger zones are considered) Cost: Not available in all case study cities, thus not included in the model SOCIO-ECONOMIC DATA Driving license holding: Difficult to forecast and highly endogenous, thus not included in the model 25

MODELLING FRAMEWORK Mode Choice Model Multinomial Logit Model Vehicle Ownership Model (Trip Level) Bivariate Ordered Probit Model (Household Level) Accessibility Measures Household members’ SE Trip makers’ LOS SE 26

Estimation Results (Summary statistics) NGO81 NGO91 NGO01 BKK KL MNL N 15,000 15,000 15,000 13,882 12,667 15,000 β L ( ) -10,834.2 -9,254.1 -8,223.8 -9,433.7 -9,212.4 -9,513.2 L ( 0 ) -15,702.5 -15,140.8 -14,787.2 -12,249.1 -13,434.0 -12,948.8 ρ 2 0.309 0.388 0.443 0.229 0.313 0.265 •15,000 samples are drawn randomly in NGO and MNL •Goodness of fit indexes are satisfactory 27

Estimation Results (alternative-specific constants and LOS) Variable NGO81 NGO91 NGO01 BKK KL MNL Constant (R) 0 0 0 0 -- 0 Constant (B) -1.30 -1.54 -1.69 0.04 0 1.03 Constant (C) -1.95 -1.27 -0.66 -1.54 -0.72 -0.52 Constant (MC) -4.46 -4.15 -3.90 -1.75 -1.62 -0.82 Time (60 min.) -1.92 -1.95 -2.53 -0.17 -0.14* -0.30 *Not significant at 5% level •Four alternatives except for KL (Rail, Bus, Car, MotorCycle) •Travel time is negatively estimated (not significant in KL) 28

Estimation Results (SE: Socio-Economic variables) Variable NGO81 NGO91 NGO01 BKK KL MNL Male (C, MC) 1.74 1.49 1.02 0.72 0.95 0.40 Age ≥ 20 (C, MC) 1.36 1.23 1.02 1.17 4.30 0.79 In City (C) -0.75 -0.81 -1.02 -0.01* -0.27 -0.91 Age ≥ 65 (B) 1.78 1.83 1.29 -- -- -- Female (R) -0.75 -0.77 -0.54 -0.57 -- -0.43 Student (R) 0.64 0.97 1.04 -0.35 -- -0.64 *Not significant at 5% level •Three SE variables have effects on car and motorcycle usage • Male and age ≥ 20 (+) •In City ( − ), not significant in BKK •Three SE variables have effects on transit usage • Age ≥ 65 (+, bus) •Female ( − , rail) •Student (+, in NGO; − , in BKK and MNL, rail) 29

MODELLING FRAMEWORK Mode Choice Model Multinomial Logit Model Vehicle Ownership Model (Trip Level) Bivariate Ordered Probit Model (Household Level) Accessibility Measures Household members’ SE Trip makers’ LOS SE 30

ACCESSIBILITY = z z For individual residing in zone ( 1, …, ) n Z n n Systematic component of the utility when individual uses rail n z and bus from zone to zone 1 respectively n ( ( ) ( ) ) + ln exp V exp V Zone 1 R 1 n B 1 n … z Zone n Zone Z Accessibility to Transit z (Convenience of transit for those reside in zone ) n Z ( ( ) ( ) ) ∑ = + AT ln exp V exp V z n Rzn Bzn n = ≠ z 1 , z z n 31

ACCESSIBILITY = z z For individual residing in zone ( 1, …, ) n Z n n ( ( ) ( ) ( ) ) + + ln exp V exp V exp V R 1 n B 1 n C 1 n ( ( ) ( ) ) − + ln exp V exp V Zone 1 R 1 n B 1 n ( ( ) ( ) ( ) ) + + … ln exp V exp V exp V z Zone 1 1 1 R n B n MC n n ( ( ) ( ) ) − + ln exp V exp V Zone Z R 1 n B 1 n Additional Accessibility of Car and Motorcycle Availability (Convenience of car and motorcycle if the individual can use these alternatives in addition to transit which is usually available to all citizens) Z [ ] ( ( ) ( ) ( ) ) ( ( ) ( ) ) ∑ = + + − + AAC ln exp V exp V exp V ln exp V exp V z n Rzn Bzn Czn Rzn Bzn n = ≠ z 1 , z z n Z [ ] ( ( ) ( ) ( ) ) ( ( ) ( ) ) ∑ = + + − + AAMC ln exp V exp V exp V ln exp V exp V z n Rzn Bzn MCzn Rzn Bzn n = ≠ z 1 , z z n 32

ACCESSIBILITY A potential drawback of “ accessibility to transit ” and “ Additional accessibility of car and motorcycle availability ” When the survey area is large, considering accessibility to all zones is questionable Weighted accessibility measures based on # of trips are considered. 33

ACCESSIBILITY = z z For individual residing in zone ( 1, …, ) n Z n n ( ( ) ( ) ) + w ln exp V exp V 1 1 1 Zone 1 RB R n B n … z Zone n Zone Z z Traffic volume from zone to n z zone by rail and bus Z ( ) ( ) ∑ = + + w Q Q Q Q respectively RBz Rz Bz Rz Bz = ≠ z 1 , z z n z : importance of zone z for those reside in zone n Weighted Accessibility to Transit Z ( ( ) ( ) ) ∑ = + WAT w ln exp V exp V z n RBz Rzn Bzn n = ≠ 1 z , z z n 34