Analyzing Road Transport (Passenger and Freight) Demand in Pakistan with Auto-Regressive Distributed Lag Co-Integration Approach

Abstract

Understanding the determinants of transport demand is crucial in making effective transport and environmental policies. In that context, the present study provides an empirical analysis of both road passenger and freight transport demand in Pakistan, using annual time series data from 1980 to 2016. The auto-regressive distributed lag bounds testing approach of co-integration is employed to estimate the short- and long-run elasticities. The empirical results show that fuel price, per-capita income, urbanization and road density are important determinants of road passenger transport demand in Pakistan. Similarly, fuel price, industrial production and international trade are the main drivers of road freight transport demand. In general, long-run elasticities are greater than short-run elasticities. Moreover, the long-run fuel price elasticities of passenger and freight transport demand are –0.044 and –0.784, respectively, implying that policy instruments (raising fuel taxes) are relatively less effective in controlling the future road transport demand and associated environment problems. The results based on short-run error correction models indicate that passenger transport demand adjusts about 75% in the first year to achieve its long-run equilibrium, while that of freight demand adjusts toward long-run equilibrium at a relatively slower rate, with about 16% of error correction taking place in the following year to reach long-run equilibrium.

Estimating the Lag Time Between Flight Arrivals and Parking Exit Volumes at a Major Airport: A Practical Approach

“…In recent years, it has been widely applied in fields such as monetary economics [22], housing prices [23], energies [24], air pollution [25], etc. Moreover, it has gained popularity in transportation research for investigating the relationship between transport and socioeconomic variables (see [26], [27], [28], [29], [30]). Our methodology adopts distributed lag models to depict the dynamic causal relationship between the number of arriving flights and exiting vehicles.…”