Evolution over time of heavy vehicle volume in toll roads: A dynamic panel data to identify key explanatory variables in Spain

Abstract

a b s t r a c tImproving the knowledge of demand evolution over time is a key aspect in the evaluation of transport policies and in forecasting future investment needs. It becomes even more critical for the case of toll roads, which in recent decades has become an increasingly common device to fund road projects. However, literature regarding demand elasticity estimates in toll roads is sparse and leaves some important aspects to be analyzed in greater detail. In particular, previous research on traffic analysis does not often disaggregate heavy vehicle demand from the total volume, so that the specific behavioral patterns of this traffic segment are not taken into account. Furthermore, GDP is the main socioeconomic variable most commonly chosen to explain road freight traffic growth over time. This paper seeks to determine the variables that better explain the evolution of heavy vehicle demand in toll roads over time. To that end, we present a dynamic panel data methodology aimed at identifying the key socioeconomic variables that explain the behavior of road freight traffic throughout the years. The results show that, despite the usual practice, GDP may not constitute a suitable explanatory variable for heavy vehicle demand. Rather, considering only the GDP of those sectors with a high impact on transport demand, such as construction or industry, leads to more consistent results. The methodology is applied to Spanish toll roads for the 1990-2011 period. This is an interesting case in the international context, as road freight demand has experienced an even greater reduction in Spain than elsewhere, since the beginning of the economic crisis in 2008.

Ex-post evaluation of government loans provided to shadow toll motorways in Spain

“…For its calculation, the evolution of the main variables affecting the PPP cash flow in the period 2017-2026 is estimated by assuming the elasticities to socioeconomic variables calculated by previous research studies for the case of Spain (Gomez et al, 2015;Gomez and Vassallo, 2016). According to the aforementioned authors, there is a marked and stable correlation between the variation in GDP per capita and the evolution of light vehicle traffic; and between the variation of industrial GDP and the evolution of heavy vehicle traffic.…”

Towards Deriving Freight Traffic Measures from Truck Movement Data for State Road Planning: A Proposed System Framework

“…Freight e-corridors Travel data supports deployment of e-corridor for trucks [32] Parking area planning Freight data allows for justification of parking zones and rationalisation of areas where new parking may be suggested [33][34][35] Toll road planning Data can be used to identify potentials for toll roads, as well as spreading out freight transport through the day by using time differentiated toll charges [36,37] New road planning Trip data can support development of new roads, by considering route choices, allowing for shorter trips [38,39] Freight transport regulations Emergency lane running Allowing hard shoulder running, based on analyses of data from former congestions, etc., could reduce travel time during peak hours [40] Air quality regulation Combining traffic flow data with air quality sensors allows for the regulation of air quality by traffic policy [41] Evaluating traffic policies A data-based approach allows for the objective and effective proposal of programmes and policies to decision makers, and makes it possible to simulate effects of policy suggestions [18,32,33,36,39,[42][43][44][45][46] Road maintenance regulation Maintenance schemes and prediction is possible through analyses of road use by freight data and Weigh-in-Motion (WIM) data [11,47,48] Priority policies Priority measures for freight vehicles can reduce driving time. Use freight data to analyse the impact of priority policies.…”

Is Ex-Post Fiscal Support to PPPs Sustainable? Analysis of Government Loans Granted to Shadow-Toll Roads in Spain: A Case Study

“…For its calculation, the evolution of the main variables affecting the PPP cash flow in the period 2017-2026 is estimated by assuming the elasticities to socioeconomic variables calculated by previous research studies for the case of Spain [59,60]. According to the aforementioned authors, there is a marked and stable correlation between the variation in GDP per capita and the evolution of light vehicle traffic; and between the variation of industrial GDP and the evolution of heavy vehicle traffic.…”