Public transport demand estimation by frequency adjustments

Orlando, Victoria M.; Baquela, Enrique Gabriel; Bhouri, Neïla; Lotito, Pablo A.

doi:10.1016/j.trip.2023.100832

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…Qin et al [22] note that recurrent neural networks, convolutional neural networks, and other methodologies are used in road traffic demand management. Orlando et al [23] propose spatial models (a modern variation of the old gravity models) and the use of digital public transport data to predict the future frequency of public transport trips. However, all these methodological proposals face the problem of the difficulty of obtaining reliable passenger transport data.…”

Section: Introductionmentioning

confidence: 99%

Elasticities of Passenger Transport Demand on US Intercity Routes: Impact on Public Policies for Sustainability

Escañuela Romana,

Torres-Jiménez,

Carbonero-Ruz

2023

Sustainability

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Passenger transport is a key sector of the economy, and its sustainability depends on achieving the greatest possible efficiency, avoiding problems of congestion or underuse of infrastructures, and reducing the sector’s environmental impact. Knowing the elasticities of demand is critical to achieving these objectives, estimating the intensity of transport demand, and predicting the effect of different policies on reducing greenhouse gas emissions. This research proposes a relatively simple model for estimating and predicting the elasticity of demand for different modes of transport at the route level. This model could be used by companies and public management to obtain a vision of the different analysed routes and the pressure of their demand, as well as a relative perspective of each of them. Such a model is used to estimate the price and income demand elasticities of passenger transport modes in domestic routes in the United States (2003–2019), where there is competition between road, rail, and air transport. Series of passenger numbers, fares, and budget shares are reconstructed from the available data. A Rotterdam demand model (RDM) is estimated using a seemingly unrelated regression method (SUR). The estimated income elasticities imply that demand for road transport increases somewhat more proportionally than the increase in income, somewhat less than proportionally for air transport, and with very low proportionality for rail transport. This indicates the need to target investment and service improvement efforts, as well as technological solutions, according to this difference in demand pressures. Finally, the demand response of the three modes of transport to price increases is inelastic, and there is little or no pass-through from one mode to another. This implies that fiscal or carbon pricing actions could have a very limited impact and high social costs. Again, strategies based on investments in technological progress, infrastructure development, and normative interventions could be more effective.

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Section: Introductionmentioning

confidence: 99%