Transportation Research Part A: General最新文献

This study develops a model that explains public transit ridership in Orange Country, California over quarterly periods during the 1974–1988 period. The model uses a Cobb-Douglas functional form and a Cochrane-Orcutt iterative procedure to measure the association between public transit ridership and the potential number of users, relative level of public transit service, relative price of public transit, seasonality, and external shocks. Relative measures of the explanatory variables are used to reduce the potential for multicollinearity and give greater confidence in the reliability of the estimated elasticities. The model is then used to prepare conditional quarterly forecasts for ridership in 1988 and unconditional quarterly forecasts during the 1989–1993 period.

This paper presents results of a study conducted to quantify the effect of fuel cost increases on household auto travel in Riyadh, the rapidly developing capital of Saudi Arabia. Responses of a stratified random sample of 1648 individual households provided the data base for the analysis. The auto trip measures of shrinkage ratio, arc and log-arc elasticities were calculated for households categorized by income and family size. The elasticity measures suggested the existence of significant relationship among the factors of fuel cost, the number of daily auto trips, and family size. It was found that as fuel prices increased, the number of daily trips decreased, and that this decrease in daily trips was greater with larger family size. A step-wise multiple regression analysis with three independent variables of car ownership, family size, and daily fuel expenditures was developed. The model was fairly accurate in predicting variations in daily household travel. The regression parameter of the variable fuel cost was also used to derive demand elasticity to fuel expenditures. Elasticity measures ranged between -0.30 and -0.37.

Over the years, an increasing interdependence of the world economy has led to the considerable growth of international trade. Due to the lengthy distribution channel, international trade is often characterized by intermodal shipment which moves products across national boundaries via more than one mode of transportation. Consequently, the intermodal choice is of vital importance to the success of international trade. The intermodal choice, however, has never been a simple matter for any distribution manager because it can be affected by the multitude of conflicting factors such as cost, on-time service, and risk. This article develops a chance-constrained goal programming model to aid the distribution manager in choosing the most effective intermodal mix that not only minimizes cost and risk, but also satisfies various on-time service requirements.

This article summarizes the evaluation results of six data sources in terms of their ability to estimate the number of commercial trucks operating in interstate commerce and their vehicle miles of travel by carrier type and by state. The six data sources were: (a) Truck Inventory and Use Survey of the U.S. Bureau of the Census; (b) Nationwide Truck Activity and Commodity Survey of the U.S. Bureau of the Census; (c) National Truck Trip Information Survey of the University of Michigan Transportation Research Institute; (d) Highway Performance Monitoring System of the Federal Highway Administration, U.S. Department of Transportation; (e) International Registration Plan of the American Association of Motor Vehicle Administrators; and (f) State fuel tax reports from each individual state and the International Fuel Tax Agreement. Evaluation results concluded that none of the data sources by themselves were capable of providing reliable estimates at the state level. Although several attempts were made to combine the strengths of different data sources so that reliable estimates could be generated, none of them were successful. Data inconsistency and incompatibility contributed primarily to the failures. Although several of the six data sources by themselves could provide estimates at the national level, each had limitations. As a result of these findings, two cost-effective methodologies were proposed to estimate the number of commercial trucks operating in interstate commerce and their vehicle miles of travel by carrier type. Neither method required collecting additional data.

Identification of the socioeconomic factors which affect the demand for buses, and the analysis of the use of the other transport modes by bus users are the two main objectives of this article. Work and school trips are highlighted as being very important trip purposes in Lagos metropolis by the multiple discriminant analysis model. It identifies mode of transport, distance, travel time, reliability, and the number of stops as significant mode choice variables. Multiple linear regression models for work and school trips identify mode of transport, transfort fare, travel time, annual income, and crew behaviour as significant variables in the choice of transport mode. These findings support the two alternative hypotheses of the study that the choice of bus is related to the individual perception of the quality of service of the different modes and that socioeconomic characteristics of the riders influence the patronage of buses. The attention of policy makers for the 22 transport corporations that operate inter-and intra-urban services in all the 21 states and the federal capital of Abuja in Nigeria is drawn to the importance of these variables for decisions.