Aggregate road passenger travel demand in New Zealand: A seemingly unrelated regression approach. (June 2019)

• Record Type: Journal Article
• Authors: Sheng, Mingyue; Sharp, Basil
• Is Part Of: Transportation research. Volume 124(2019)
• Page Start: 55
• Page End: 68
• Publication Date: 2019-06
• Subjects: Road passenger transport demand -- Vehicle kilometres travelled -- Public transport -- Seemingly unrelated regression -- Correlated disturbance; Transportation -- Research -- Periodicals; 388.011
• DOI: 10.1016/j.tra.2019.03.005
• Languages: English
• ISSNs: 0965-8564

Abstract

Abstract: Road passenger transportation, which includes private vehicles, public transport, and motorcycles, is regarded a vital link that connects people and economic activities across New Zealand. Given the fact that road passenger transport modes are considered substitutes/complements to one another, there is a strong possibility that an interrelationship exists between the travel demand functions, primarily due to the correlation between their disturbances. This research gap is addressed in this study using a seemingly unrelated regression (SUR) method. The objective of this paper is twofold. First, the study examines whether the error terms of the demands for the four main road passenger transport choices: petrol cars, diesel cars, buses and motorcycles, respectively, are correlated. Second, the study identifies factors that have significant impact on the demand for each available road passenger transport choice. Empirical result from the Breusch-Pagan test of independence confirms the existence of correlated error terms in the demand equations. Moreover, estimated results from the SUR model also highlight various policy implications, including: implementing a fuel tax in the short-run to reduce the travel demand by both petrol and diesel car users, subsidising public transport providers, and several opportunities to alleviate the first-/last-mile problem associated with public transit. Recommendations for further research include developing regional analysis to compare the dynamics of different cities and constructing a forecasting model for private and public transport, and motorcycles, given detailed assumptions about energy and economic conditions.