Long-distance Travel Demand Research Articles

Integration of National Long-Distance Passenger Travel Demand Model with Tennessee Statewide Model and Calibration to Big Data

The Tennessee Department of Transportation replaced the quick-response-based long-distance component in its statewide model by integrating the new national long-distance passenger travel demand model in a new statewide model and calibrating it to long-distance trips observed in cell phone origin–destination data. The national long-distance model is a tour-based simulation model developed from FHWA research on long-distance travel behavior and patterns. The tool allows the evaluation of many policy scenarios, including fare or service changes for various modes, such as commercial air, intercity bus, Amtrak rail, and highway travel. The availability of this tool presents an opportunity for state departments of transportation in developing statewide models. Commercial big data from cell phones for long-distance trips also pre-sents an opportunity and a new data source for long-distance travel patterns, which previously have been the subject of limited data collection, in the form of surveys. This project is the first to seize on both of these opportunities by integrating the national long-distance model with the new Tennessee statewide model and by processing big data for use as a calibration target for long-distance travel in a statewide model. The paper demonstrates the feasibility of integrating the national model with statewide models, the ability of the national model to be calibrated to new data sources, the ability to combine multiple big data sources, and the value of big data on long-distance travel, as well as important lessons on its expansion.

Building Up Demand-Oriented Charging Infrastructure for Electric Vehicles in Germany

Mobility offerings have never been as abundant and varied as the present. While users welcome new and innovative mobility options, this current paradigm shift presents a challenge for authorities that plan, organize, and operate such services. In particular, integrating new mobility services into existing infrastructure systems can generate problems of acceptance, co-operability, and compatibility. This problem is especially relevant for electric vehicles. Limited range and battery capacity of battery electric vehicles make them dependent on charging infrastructure, which in turn hinders their acceptance. In light of the German government's goal of one million electric vehicles by 2020, establishing a demand-oriented charging infrastructure is of crucial importance. However, numerous questions remain unanswered regarding the quantity, type, and location of electric vehicle charging stations in Germany. This article presents the findings of the project “LADEN2020: Concept to build up a demand-oriented charging infrastructure in Germany between today and 2020.” The research project develops a systematically comprehensible and consistent strategy for electric vehicle charging infrastructure in Germany. The paper presents the methodological framework to estimate the charging demand for daily and long-distance travel, which is unique and innovative as similar comprehensive and consistent analytical tools do not exist to date.

Implementation of a Practical Model System to Predict Long-Distance Travel for the Entire U.S. Population

This paper describes a model system designed and implemented to simulate long-distance travel for all U.S. households. The model system was created in the final phase of FHWA research project Foundational Knowledge to Support a Long-Distance Passenger Travel Demand Modeling Framework. It is a tour-based system simulating individual tours for individual households from a synthetic population. The models are disaggregate models of auto ownership, tour generation, tour duration, travel party size, tour destination choice, and tour mode choice. The model system runs in 1 to 2 h on a standard PC, simulating a full year’s long-distance tours for the entire U.S. population. The paper describes the model structure, input data, and software implementation and provides some results from the initial model calibration, validation, highway assignment, and sensitivity tests.

High-Speed Rail Demand: Before-and-After Evidence from the Italian Market

Recent openings of new high-speed rail (HSR) lines in Italy, together with the April 2012 entrance of a new private operator in the Italian HSR market in competition with the national incumbent, have created the conditions for an investigation of long-distance travel demand. In this paper, we present some empirical evidences on the evolution of national passenger demand before and after HSR major openings, a unique case study based on source data, a retrospective survey and extensive counts on board trains, gathered between 2008 and 2011. Results show a significant increase of traffic volume by rail due to the introduction of HSR and we estimate short-term direct HSR demand elasticities with respect to travel time in the range of –1.5 and –2.1, and in the range – 0.7 to – 1.1 in terms of HSR modal shares. The different elasticity values confirm the presence of a strong “induced demand” effect.

Methodological Options and Data Sources for the Development of Long-Distance Passenger Travel Demand Models: A Comprehensive Review

Since the passage of the Intermodal Surface Transportation Efficiency Act in 1991, a significant number of state highway agencies have started to develop and implement statewide travel demand models to meet policy and legislative development needs. Currently, however, a lack of up-to-date multimodal and inter-regional passenger travel data hampers analysts’ ability to conduct quantitative assessments of long-distance travel infrastructure investment needs, at both the national and statewide levels. Despite these data limitations, but also largely shaped by them, long-distance travel modelling has become an increasingly popular topic in recent years. This paper reviews several methodologies for multimodal inter-regional travel demand estimation, drawing examples from both state-specific modelling within the USA and from fully national models being developed and applied in other parts of the world, notably in Europe.

Analysis of long-distance vacation travel demand in the United States: a multiple discrete–continuous choice framework

This study analyzes the annual vacation destination choices and related time allocation patterns of American households. More specifically, an annual vacation destination choice and time allocation model is formulated to simultaneously predict the different vacation destinations that a household visits in a year, and the time (no. of days) it allocates to each of the visited destinations. The model takes the form of a multiple discrete–continuous extreme value (MDCEV) structure. Further, a variant of the MDCEV model is proposed to reduce the prediction of unrealistically small amounts of vacation time allocation to the chosen destinations. To do so, the continuously non-linear utility functional form in the MDCEV framework is replaced with a combination of a linear and non-linear form. The empirical analysis was performed using the 1995 American Travel Survey data, with the United States divided into 210 alternative destinations. The model estimation results provide several insights into the determinants of households’ vacation destination choice and time allocation patterns. Results suggest that travel times and travel costs to the destinations, and lodging costs, leisure activity opportunities (measured by employment in the leisure industry), length of coastline, and weather conditions at the destinations influence households’ destination choices for vacations. The annual vacation destination choice model developed in this study can be incorporated into a larger national travel modeling framework for predicting the national-level, origin–destination flows for vacation travel.

A Long-Distance Travel Demand Model for Europe

In Europe, approximately 50% of all passenger kilometres come from trips beyond 100 km according to matrices developed in the TRANSTOOLS project. This accounts for an even larger share of CO2 emissions due to a higher modal share of air transport. Therefore long-distance trips are increasingly relevant from a political and environmental point of view. The paper presents the first tour-based long-distance travel demand model for passenger trips in and between 42 European countries. The model is part of a new European transport model developed for the European Commission, the TRANSTOOLS II model, and will serve as an important tool for transport policy analysis at a European level. The model is formulated as a nested logit model and estimated based on travel diary data with segmentation into business, private, and holiday trips. We analyse the estimation results and present elasticities for a number of different level-ofservice variables. The results suggest that the perception of both travel time and cost varies with journey length in a non-linear way. For car drivers and car passengers, elasticities increase with the length of the journey, whereas the opposite is true for rail, bus, and air passengers – a fact that reflects a change in substitutability. Moreover, elasticities differ significantly by trip purpose with private trips having the highest and holiday trips the lowest elasticities.

The effects of migration and personal effectiveness on long-distance travel

THE PROBLEM to which the research reported here is addressed is analysis of the demand for long-distance travel. Trips to places 100 miles away or more are considered. The analysis is based on a survey of a cross-section of the population of the United States conducted by the Survey Research Center of the University of Michigan in 1962. The novel element in this paper is the inclusion among the predictors of the volume of travel by individuals of measures of their prior geographic mobility and their personalities. Estimates will be presented of the relation between the geographic mobility of people and their subsequent frequency of travel, and of the relation between their sense of personal effectiveness and their frequency of travel. It is not possible using a single cross-section survey to develop a complete model of all the variables which are related to the freqeuncy with which people travel. The method is poorly adapted to the estimation of the effects of some variables, such as changes over time in the price of transportation. The method is useful for isolating characteristics of individuals associated with differences in how many trips they take. Apart from simple intellectual curiosity there are two basic reasons for interest in analysis of the demand for travel: forecasting and marketing. Most studies of forecasting rely upon projections into the future of the effects of the changing income distribution of the population and its changing demographic characteristics. The research presented here has some relevance to forecasting, especially to forecasting the volume of travel on particular routes, since it investigates the connections between migration and subsequent travel. The results are perhaps more directly concerned with problems of marketing, that is, problems of adapting the nature of the services supplied to the requirements of potential travellers. For the design and operation of transportation systems it is important to understand as thoroughly as possible who travels and why, as well as who does not travel, and the reasons why. HYPOTHESES