Defining domains for models of travel demand

Travel demand models implicitly assume that people respond to changes in a continuous way. This is in contrast to the physical sciences, where discontinuous response is a common phenomenon and is embodied in such concepts as sub-critical and supercritical states.Recent studies have shown that responses to transport policies differ in degree and kind according to the nature and severity of the stimulus and the types of people affected. Response patterns may be categorised by the extent to which they involve adjustments to spatio-temporal or inter-personal linkages. This paper identifies four response domains, with a further distinction between permissive and forced changes.Most travel demand models are designed to operate within an independent, forced (and to a less extent independent permissive) domain and their forecasts become unreliable when responses lie outside that domain. Conversely, a model designed for a more complex domain is unnecessarily cumbersome where simpler responses apply. This paper describes the types of model which are appropriate for each domain and discusses how the effects of a policy may be assigned to the correct domain(s).     

Testing probabilistic discrete choice models of travel demand by comparing predicted and observed aggregate choice shares

Probabilistic discrete choice models of travel demand often are tested for the presence of specification errors by comparing the models' predictions of aggregate choice shares in population strata with observed shares. A model is rejected as misspecified if the differences between its predictions and the observations are judged too large. This judgement usually is made on intuitive grounds without use of formal statistical methods and, therefore includes no systematic method for distinguishing the effects of specification errors on differences between predictions and observations from those of random sampling errors. This paper represents formal statistical tests for comparing predicted and observed aggregate chioce shares in population strata and reports the results of an investigation of the power of the tests. The test statistics are asymptotically χ² disturbed when the model being tested is correctly specified. The results of the power investigation suggests that greater power is obtained (i.e. there is ability to detect misspecified models) when all of the available data are used for both parameter estimation and specification testing than when the available data are divided into separate estimation and test data sets. Specification tests based on comparisons of predicted and observed aggregate choice shares appear to have less power than do likelihood ratio and likelihood ratio index specification tests when the alternative models required by the latter tests are correctly or approximately correctly specified. However, tests based on comparisons of predicted and observed shares ca have greater power than the other tests when the alternative models are seriouslymisspecified.     

Evaluation of transfer methods for spatial travel demand models

The main goal of the research was to compare alternative methods of spatial transfer as a function of sample size. The study was based on the mobility surveys conducted in the Helsinki Metropolitan Area in 1995 and in the Turku region in 1997. The Helsinki Metropolitan Area data base was used to estimate the models that were to be transferred. The data base in the Turku region represents the application context to which the estimated Helsinki Metropolitan Area models are transferred. The transfer procedures examined were Bayesian updating, combined transfer estimation, transfer scaling, and joint context estimation procedures. To explore the impact of sample size on transferring performance, model transferability was tested using six different sample sizes. The model transferability was examined by comparing the transferred models to the models estimated using the entire set of the data which can be regarded as the best estimate representing “the real situation”. The results indicated that joint context estimation gives the best prediction performance in almost all cases. In particular, the method is useful if the difference in the true parameters between the two contexts is large or only some of the model coefficients are precise. The applicability of joint context estimation can be improved by viewing the coefficients as variable-oriented and emphasizing precise and imprecise coefficients differently.     

Geographic information system design for network equilibrium-based travel demand models

Travel demand analyses are useful for transportation planning and policy development in a study area. However, travel demand modeling faces two obstacles. First, standard practice solves the four travel components (trip generation, trip distribution, modal split and network assignment) in a sequential manner. This can result in inconsistencies and non-convergence. Second, the data required are often complex and difficult to manage. Recent advances in formal methods for network equilibrium-based travel demand modeling and computational platforms for spatial data handling can overcome these obstacles. In this paper we report on the development of a prototype geographic information system (GIS) design to support network equilibrium-based travel demand models. The GIS design has several key features, including: (i) realistic representation of the multimodal transportation network, (ii) increased likelihood of database integrity after updates, (iii) effective user interfaces, and (iv) efficient implementation of network equilibrium solution algorithms.     

Estimating the functional form of travel demand models

This paper shows how many particular models found in the travel demand literature, such as the linear, multiplicative, logit forms and their formulation in terms of first differences, can de derived as special cases of the specification of the fixed and stochastic parts of more general models. Estimates of the appropriate functional form of the two equations of a cross-sectional inter-city travel demand model and of the two equations of a time-series urban travel demand model are presented. It is demonstrated, using current maximum likelihood techniques extended to take multiple-order auto-correlations into account, that estimating the functional form dominates more restricted procedures on grounds both of the statistical significance and of the economic reasonableness of the results. Notably, a standard logit market share equation and log-linear total demand equations appear to be clearly inferior to less specifications of these equations.     

Vehicle scheduling for on-demand vehicle fleets in macroscopic travel demand models

Transportation - The planning of on-demand services requires the formation of vehicle schedules consisting of service trips and empty trips. This paper presents an algorithm for building vehicle...     

Measuring uncertainty in discrete choice travel demand forecasting models

In transportation projects, uncertainty related to the difference between forecast and actual demand is of major interest for the decision-maker, as it can have a substantial influence on the viability of a project. This paper identifies and quantifies discrete choice model uncertainty, which is present in the model parameters and attributes, and determines its impact on risk taking for decision-making applied to a case study of the High-Speed Rail project in Portugal. The methodology includes bootstrapping for the parameter variation, a postulated triangular distribution for the mode-specific input and a probabilistic graphical model for the socio-economic input variation. In comparison to point estimates, the findings for mode shift results in a wider swing in the system, which constitutes valuable information for decision-makers. The methodology, findings and conclusions presented in this study can be generalized to projects involving similar models.     

A modeling approach for matching ridesharing trips within macroscopic travel demand models

State of the art travel demand models for urban areas typically distinguish four or five main modes: walking, cycling, public transport and car. The mode car can be further split into car-driver and car-passenger. As the importance of ridesharing may increase in the coming years, ridesharing should be addressed as an additional sub or main mode in travel demand modeling. This requires an algorithm for matching the trips of suppliers (typically car drivers) and demanders (travelers of non-car modes). The paper presents a matching algorithm, which can be integrated in existing travel demand models. The algorithm works likewise with integer demand, which is typical for agent-based microscopic models, and with non-integer demand occurring in travel demand matrices of a macroscopic model. The algorithm compares two path sets of suppliers and demanders. The representation of a path in the road network is reduced from a sequence of links to a sequence of zones. The zones act as a buffer along the path, where demanders can be picked up. The travel demand model of the Stuttgart Region serves as an application example. The study estimates that the entire travel demand of all motorized modes in the Stuttgart Region could be transported by 7% of the current car fleet with 65% of the current vehicle distance traveled, if all travelers were willing to either use ridesharing vehicles with 6 seats or traditional rail.     

Disaggregate simultaneous urban travel demand models: A brief introduction

This paper is intended to provide a general background to the two following papers, A Simultaneous Destination and Mode Choice Model for Shopping Trips and Some Estimation Results of a Simultaneous Model of Auto Ownership and Mode Choice to Work. Some of the deficiences of the conventional urban transport modelling system are reviewed and a case is made for the use of simultaneous models estimated with disaggregate data.     

The analysis of travel and emission impacts of travel demand management strategies using activity-based models

This paper demonstrates, tests and shows the value of activity-based travel demand models and household sample enumeration forecasting techniques in evaluating the transportation and air quality impacts of travel demand management strategies. Using data from the Portland, Oregon metropolitan area, three transportation policies were evaluated both individually and in combination: transit improvements, pricing, and telecommunications. The activity-based models used in this testing represents a significant improvement to today's "four-step" sequential model systems by providing a deeper insight into the individual decision making process in response to transportation policies. A wider range of impacts is predicted, and indirect effects as well as synergistic effects of such policies are taken into consideration. These models are capable of providing the information needed to improve the linkage of transportation models with emissions and air quality analysis methodologies by improving the prediction of variables that are important to accurately estimating emissions and air quality impacts of transportation actions.     

A demand model for recreational travel

A model is developed to describe and to predict the patterns of regional recreational travel. The model is designed in such a manner to allow its calibration and use without the need to conduct extensive travel surveys in a large region. To allow its use for prediction, the model is based on a causal structure and attempts to derive recreational travel demand from behavioural variables. The main hypothesis of the model is that the amount of recreational travel a recreation area attracts is affected by the accessibility of this area to points of demand potential and by its attractiveness relative to the recreation areas.

The calibration is founded on actual data on recreational travel to national forests in California, U.S.A. It is found in the calibration that accessibility to demand potential is the single most important determinant of recreational travel attraction. A simple relationship is derived to relate travel to each national forest to the relative accessibility of the forest. The model is calibrated and statistically validated.

It is suggested that when constructing travel demand models simplicity be sought, even at the risk of the loss of some explanatory power. In the calibration of such models statistical significant is more important than the ability to reproduce observed patterns.     

Further evidence on the temporal stability of disaggregate travel demand models

Current evidence on the transferability of disaggregate travel demand models is inconclusive. Adding to this body of research, the present analysis focuses upon the temporal characteristics of work trip behavior in the San Francisco Bay Area. Using before and after data sets associated with the BART Impact Travel Study, multinomial logit models of work trip modal choice are estimated. The results indicate that the general form and the coefficient estimates of a pre BART model are transferable in time. Moreover, when updated to reflect BART's presence, the model's predictive success and its implied elasticity measures are generally accurate, relative to those implied by reestimating the entire model on post BART data. Finally, as economic theory would predict, elasticity measures of the service related variables were found to increase over time.     

A review of the impact of parking policy measures on travel demand

This paper reviews the empirical evidence relating to the impact of parking policy measures on the demand for parking and for travel. Disaggregate modal choice models, disaggregate parking location models and site‐specific studies of parking behaviour are examined. With regard to modal choice models, it is concluded that few studies deal adequately with parking factors, but that there is some support for the view that parking policy measures are a relatively important influence on modal choice. When parking location models are examined parking policy variables are shown to have a substantial impact on choice of parking location. With regard to site‐specific studies, the paper concludes that there is a great variation in the parking price elasticities quoted, which reflects partly the methodological problems associated with such studies. Suggestions to improve model specification are made.     

Telecommunications and travel demand and supply: Aggregate structural equation models for the US

Disaggregate studies of the impacts of telecommunications applications (e.g. telecommuting) on travel have generally found a net substitution effect. However, such studies have all been short-term and small-scale, and there is reason to believe that when more indirect and longer-term effects are accounted for, complementarity is the likely outcome. At least two aggregate studies have focused on the relationships between telecommunications and travel from economic perspectives (consumer and industry). However, both use the monetary value of consumption or transactions rather than actual activity measures (e.g. miles, number of calls), and neither fully explains the direct and indirect causal relationships between the two. The purpose of this study is to develop a conceptual model in a comprehensive framework, considering causal relationships among travel, telecommunications, land use, economic activity, and socio-demographics, and to explore the aggregate relationships between telecommunications and travel, using structural equation modeling of national time series data spanning 1950–2000 in the US. In this paper we focus on number of telephone calls as the measure of telecommunications, and passenger vehicle–miles traveled as the measure of transportation. Future research will investigate additional measures of these two constructs. Our empirical results strongly support the hypothesis that telecommunications and travel are complementary. That is, as telecommunications demand increases, travel demand increases, and vice versa. These results offer a more realistic picture to policy makers and transportation planners than has been available till now, and suggest useful directions for them to develop transportation or telecommunications strategies designed to reduce traffic congestion, air pollution, and energy consumption.     

Life-style and travel demand

(Added to the original text) A fundamental solution to the issue of congestion cannot be reached without addressing the question of life-style. This paper reviews the definitions of life-style found in the literature, and identifies variables that have been commonly associated with life-style. Using US consumer expenditure data, life-styles are analyzed longitudinally (examining trends during the 1953–1983 time frame) and cross-sectionally (comparing segments of the population stratified by income, life-cycle stage, and age), and likely relationships to travel behavior are noted. The usefulness of existing empirical findings to long-range forecasting is explored by speculating on shifts of behavioral units across life-style segments, and on shifts in the behavioral patterns within each life-style segment.     

Hysteresis in travel demand

This paper presents evidence that there is a hysteresis effect in the demand for transport usage with respect to price. Consequently the neoclassical concept of a smooth continuous demand curve is questioned. The author presents a hysteresis model whereby changes in demand and of demand are embraced in a single theory related to generalized cost.     

Investigation and analysis of evidence of asymmetric churn in travel demand models

There is a large amount of research work that has been devoted to the understanding of travel behaviour and for the prediction of travel demand and its management. Different types of data including stated preference and revealed preference, as well as different modelling approaches have been used to predict this. Essential to most travel demand forecasting models are the concepts of utility maximisation and equilibrium, although there have been alternative approaches for modelling travel behaviour. In this paper, the concept of asymmetric churn is discussed. That is travel behaviour should be considered as a two way process which changes over time. For example over time some travellers change their mode of travel from car to bus, but more travellers change their mode from bus to car. These changes are not equal and result in a net change in aggregate travel behaviour. Transport planners often aim at producing this effect in the opposite direction. It is important therefore to recognise the existence of churns in travel behaviour and to attempt to develop appropriate policies to target different groups of travellers with the relevant transport policies in order to improve the transport system. A data set collected from a recent large survey, which was carried out in Edinburgh is investigated to analyse the variations in departure time choice behaviour. The paper reports on the results of the investigation.     

Estimating the demand for urban bus travel

The disadvantages of conventional transportation study models, in particular their large data requirements and their weaknesses in dealing with changes in trip generation rates have led to a need for a simple model that can quickly and at low cost examine alternative public transport strategies.This paper investigates simple economic models of bus demand, examines alternative variables that can be used and discusses some alternative model forms. It demonstrates the results of a model using data from twelve urban bus operators in Britain and compares the results with those from other types of study. The model utilises fare and service quality elasticities to explain the decline in passengers on urban bus services, and derives an average elasticity with respect to fare changes of –0.31 and with respect to service quality changes of +0.62. It is estimated that fare rises accounted for 13% of the 43% decline in passengers over the last fifteen years, vehicle mileage reductions for 14.3% and that only 15.7% was due to such factors as rising car ownership which are often given as the cause of declining bus patronage.The results, by showing that passengers are far more sensitive to changes in service than they are to fare rises, are a useful guide to the broader public transport policy issues, and the paper concludes that the model does provide a useful method of forecasting public transport demand at a strategic level. Further work is needed, however, to establish more accurate forecasts for different types of passenger and studies are now being undertaken to establish these and to construct an operational forecasting model that can be applied with only limited data requirements