Passenger Travel Demand Research Articles

A nested logit model of travel mode to work and auto ownership

Estimating the demand for urban passenger travel is very useful in transport planning. This study estimates a joint model of choice for both auto ownership and travel mode to work in Karachi, Pakistan. The model used for analysis is based on stochastic utility maximization and is estimated utilizing nested logit techniques. The purpose of the study is to measure the impact of various policy changes on welfare. The model reveals that the decision to own a car is made simultaneously with the decision regarding mode of travel to work. However, the study shows that had this simultaneity been ignored, the loss in terms of bias in the estimated coefficients would not have been large. The model has several advantages over conventional transport demand models. It is estimated using a small household sample; it is behavioral and responsive to more important policy instruments available to a planner; it allows one to make welfare comparisons between various policy regimes; and it enables one to calculate the effect of a policy change or change in the attributes of various modes on the demand for each mode and auto ownership.

A service quality model of air travel demand: An empirical study

The demand for air passenger travel represents a simultaneous system in which traffic, fare and service quality, in the form of expected delay times, are jointly determined. An econometric model of air passenger travel based on the concept of service quality competition is estimated for domestic American city-pairs during the 1973–1977 regulatory period. Service quality is shown to be a significant determinant of air traffic, and the results are used to provide some indication of the relative effects deregulation is likely to have on fare and service quality across markets.

Transferabilities of Urban Travel Demand Models

This paper presents an analysis of recent investigations on the transferability of passenger travel demand models under different empirical and policy conditions. The results of this study generally indicate that with proper selection of urban areas the transferability of demand models is possible under similar empirical and policy conditions. Specific findings of this study include: (1) “Universal” models accounted only for partial urban character and thus met only partial success; (2) the change of transportation policy variable at a small scale has no significant impact on the model transferability; (3) the change in base conditions of socio‐economic variables has profound influence on model transferability; (4) demand models are temporally stable over a period equal to or less than 12 years; and (5) the model transferability can be ranked in order of household models, zonal average models and zonal models. It is hoped that these study findings would make a significant contribution to travel demand estimation, especially when a quick‐response technique is required for transportation policy‐oriented decision‐making.

Simulation and analysis of energy demand for passenger automobiles in Australia

The demand for gasoline for auto passenger travel has been identified to be the most crucial aspect of energy policy in Australia. Qualitative as well as quantitative analysis has been presented to explore the energy saving potential of various measures in reducing travel demand and in making travel more energy efficient. A computer simulation model has also been developed to assess future fuel demand for automobiles in Australia in response to a variety of assumptions about social, economic, technological and policy variables. On the basis of the present analysis it is concluded that the passenger travel demand is less likely to be contained but a combination of conservation and efficiency measures can significantly reduce the energy intensity, thereby controlling the gasoline demand in the next 25 years to present consumption levels.

Forecasting urban travel demand for quick policy analysis with disaggregate choice models: A Monte Carlo simulation approach

A spatial aggregation methodology based on continuous mathematical functions is employed in urban passenger travel demand prediction. The approach derives aggregate travel d models in the form of multi-dimensional integrals which are solved by Monte Carlo simulation. Approximate empirical relationships are developed in the paper to examine the statistical properties of biases and random errors in Monte Carlo prediction. The methodology has been used in developing a comprehensive urban travel demand prediction model suitable for policy-sensitive sketch planning.

Airline Simulation for Analysis of Commercial Airplane Markets

The Lockheed Airline System Simulation is discussed as a step-by-step evaluation of the economics of different ways of routing passenger travel demand. Interacting items that are discussed include forecast passenger traffic demand, persistence of demand, purpose of travel, passenger preferences toward fare and speed, aircraft characteristics, flight frequency, route interaction, and competitive and regulatory influences. Also discussed are specific uses of the simulation program during the design of the supersonic transport. The first use is in determining the optimum SST size, speed, and sonic boom characteristics. The impact of a refined SST on the air transport environment is then discussed. This latter subject is presented in terms of airplane requirements, load factors, airline earnings, and airline return on investment.