Car Travel Demand: Spillovers and Asymmetric Price Effects in a Spatial Setting

Abstract

A novel analysis framework for the spatial aspects of car travel, measured by vehicle miles travelled (VMT), is introduced in this paper. The specification of a dynamic Spatial Durbin Model (SDM) enables the analysis of VMT spatial spillovers and diffusion between neighboring areas in the short and long-run. The framework is further developed to capture and introduce to a spatial setting potential asymmetry and hysteresis that can reflect reference dependence and habits. A panel dataset is compiled at sub-regional level, based on official car mileage recordings in England and Wales. In addition to the inelastic long-run responses of VMT to fuel price (-0.124) and income (0.116) changes, the results illustrate asymmetries and hysteresis in price elasticities with a significant spatial component. The impact magnitude on VMT from a number of factors, such as alternative fuel use, fuel deserts in rural areas, road network and car fleet characteristics, is also estimated. The results are consistent with the car use saturation hypothesis through the positive impact of motorization rate to VMT. The negative effect of public transport infrastructure on car travel is only significant in the spatial models. The paper demonstrates the applicability and importance of spatial econometrics in transport research.

Car traffic, habit persistence, cross-sectional dependence, and spatial heterogeneity: New insights using French departmental data

“…One might conclude that this is in line with the outcome of Moran's I test statistic and the argument of cross-border car traffic among departments set out at the end of the previous section. A similar result has been found by Pirotte and Madre (2011) for the coefficient δ of the spatial lag of y it using a static spatial panel data model, and by Thanos et al (2018) using a dynamic spatial panel data model. However, there are four reasons why this model and the results it produced need to be rejected.…”

“…Although this regularity does not have to be met, empirical evidence in favor of this constraint has been found in many studies. Thanos et al (2018), studying the same topic as in this paper, find a negative but insignificant value for η of -0.0252, as a result of which the constraint η = τ * δ (−0.0252 ≈ −0.1279 * 0.5670) holds. In the short empirical application on housing prices accompanying the work of Shi and Lee (2017, Table 4), the authors find a positive and this time significant value for η of 0.05405, while the constraint η = τ * δ (0.05405 ≈ −[(−0.05527) * 0.68981] holds as well.…”

“…The explanation for this finding is the near perfect multicollinearity between the time fixed effects and the gasoline price, caused by the fact that the latter varies only a little across space (see Table 1). Since the gasoline price elasticity is the main focus of this type of research, many empirical studies decide not to control for time fixed effects, perhaps to get rid of this multicollinearity problem, among which Bastian and Börjesson (2015) and Thanos et al (2018). However, if one or more variables are omitted from the model, when relevant, the coefficient estimates of the remaining variables may become biased and inconsistent (Greene, 2008, pp.…”

“…In the literature, several measures have been used, among which vehicle kilometers (miles) traveled on the road network (by private cars), the total volume of gasoline delivered, the total number of vehicles owned, either in total or averaged per capita, and the vehicle stock. Since 1973, a vast literature has emerged on this topic, among which Griffin (1983, 1997), Dahl (1986), Dahl andSterner (1991a, 1991b), Johansson and Schipper (1997), Graham and Glaister (2002), Baltagi, Bresson, Griffin and Pirotte (2003), Basso and Oum (2007), Pirotte and Madre (2011) and Thanos, Kamargianni and Schäfer (2018). The main body of this literature has sought to identify and estimate the determinants of gasoline demand or car traffic.…”

“…Finally, Pirotte and Madre (2011) find that by estimating a static spatial panel data model using regional panel data over the period 1973-1999, traffic per light vehicle exhibits significant spatial dependence; traffic inside a region may also be caused by transit and exchange traffic generated by the inhabitants of neighboring regions. Thanos et al (2018) adopt a dynamic spatial Durbin model explaining vehicle milestraveled using data of England and Wales over the period 2005-2010. They find a spatial autoregressive coefficient for W Y ranging from 0.45 to 0.57, but a major objection to this study concerns the omission of the most simple form of common factors, i.e., time-specific effects.…”