A behavioral housing search model: Two-stage hazard-based and multinomial logit approach to choice-set formation and location selection

Residential location search has become an important topic to both practitioners and researchers as more detailed and disaggregate land-use and transportation demand models are developed which require information on individual household location decisions. The housing search process starts with an alternative formation and screening stage. At this level households evaluate all potential alternatives based on their lifestyle, preferences, and utilities to form a manageable choice set with a limited number of plausible alternatives. Then the final residential location is selected among these alternatives. This two-stage decision making process can be used for both aggregate zone-level selection as well as searching disaggregate parcel or building-based housing markets for potential dwellings. In this paper a zonal level household housing search model is developed. Initially, a household specific choice set is drawn from the entire possible alternatives in the area based on the average household work distance to each alternative. Following the choice set formation step, a discrete choice model is utilized for modeling the final residential zone selection of the household. A hazard-based model is used for the choice set formation module while the final choice selection is modeled using a multinomial logit formulation with a deterministic sample correction factor. The approach presented in the paper provides a remedy for the large choice set problem typically faced in housing search models.     

A note on the consistent aggregation of nested logit demand functions

The present paper derives a set of rules allowing for the consistent aggregation of nested logit travel demand functions across origin and destination zones. Presented aggregation rules are derived for the case when the mode choice is performed conditional on destination choice. The derivation is based on the principles of consistency between aggregate and disaggregate travel demand models introduced by Sweet as well as upon the sampling theory.     

Nested logit models for mixed-mode travel in urban corridors

The nested logit (NL) model is a generalisation of the well-known multinomial logit (MNL) model which copes with its “independence from irrelevant alternatives” problem, at the expense of more difficult calibration and use. Mixed-mode movements (i.e. park-and-ride) are by nature not independent of competing single-mode options and have, therefore, traditionally been inadequately modelled in most empirical applications. This paper reports on the specification, estimation, testing and comparison of MNL and NL models using disaggregate data of work trips in an urban corridor, where choice was among several alternatives including mixed-mode options. It was found that the more general NL model was more adequate, not only in theory but in practice. The paper concludes by comparing the disaggregate NL model with previously calibrated aggregate NL models for the same corridor using a different data set.     

A comparative study of aggregate and disaggregate gravity models using Seoul metropolitan subway trip data

Abstract

Existing origin constrained and doubly constrained gravity models have not been compared, theoretically or empirically, in terms of their forecasting power. Due to the newly advanced technology of intelligent transport systems, the expanded data presently available have made various models more comparable in terms of forecasting power. This paper uses archived automatic passenger counting (APC) data for urban rail in the Seoul metropolitan area. The APC data contains information about each trip's origin, destination, ticket type, fare, and distance on a daily basis. The objective of this paper is to compare the goodness-of-fit of aggregate and disaggregate gravity modeling using these data. A Hyman aggregate gravity model is used as the aggregate model without the spatial effect. The disaggregate model adopts a multinomial logit as the destination choice model with the spatial effect. In general, while the formulation of aggregate and disaggregate gravity model models are similar, the calibration and parameter estimation methods of the two models are different. As a result, this empirical study demonstrates that the variation in goodness-of-fit and forecasting power largely depends on the estimation method and selected variables. The forecasting power of the disaggregate modeling approach outperforms that of the aggregate model. This paper further confirms that spatial arrangement plays important roles in gravity modeling.     

Air itinerary shares estimation using multinomial logit models

ABSTRACT

The main goal of this study is the development of an aggregate air itinerary market share model. In order to achieve this, multinomial logit models are applied to distribute the city-pair passenger demand across the available itineraries. The models are developed at an aggregate level using open-source booking data for a large group of city-pairs within the US air transport system. Although there is a growing trend in the use of discrete choice models in the aviation industry, existing air itinerary share models are mostly focused on supporting carrier decision-making. Consequently, those studies define itineraries at a more disaggregate level using variables describing airlines and time preferences. In this study, we define itineraries at a more aggregate level, i.e. as a combination of flight segments between an origin and destination, without further insight into service preferences. Although results show some potential for this approach, there are challenges associated with prediction performance and computational intensity.     

A simultaneous destination and mode choice model for shopping trips

This paper describes a disaggregate simultaneous destination and mode choice model for shopping trips. Following an introduction to the model structure and a review of the data, the results of five different model specifications are discussed. The models were estimated using data from two communities adjacent to Eindhoven, the Netherlands and utilise the multinomial logit model.     

Incorporating Logistics in Freight Transport Demand Models: State-of-the-Art and Research Opportunities

Freight transport demand is a demand derived from all the activities needed to move goods between locations of production to locations of consumption, including trade, logistics and transportation. A good representation of logistics in freight transport demand models allows us to predict the effects of changes in logistics systems on future transport flows. As such it provides better estimations of the costs of interaction and allows to predict changes in spatial patterns of freight transport flows more accurately. In recent years, the attention for freight modelling has been growing and new research work has appeared aimed at incorporating logistics in freight models. In this paper we review the state of the art in the representation of logistics considerations in freight transport demand models. Our focus is on the service and cost drivers of changes in logistics networks and how these affect freight transport. Our review proceeds along a conceptual framework for modelling that goes beyond the conventional 4-step modelling approach. We identify promising areas for freight modelling that have an integrative function within this framework, such as spatial computable general equilibrium models, supply chain choice models and hypernetwork models.     

Home-based trip end models — A comparison between category analysis and regression analysis procedures

The trip end models which have been used in past transportation studies are briefly summarised. Problems associated with the use of zone-based models are outlined and reasons are given to support the development of models at the household rather than zonal level.It is suggested that recent developments which have taken place in household-based models have not been entirely logical. In particular, arguments between regression models and category analysis models have been confused with the use of aggregate (zonal) as against disaggregate (household) data — regression models being associated with the use of zonal data and category analysis models with household data. Misunderstood arguments and false notions regarding sample sizes have directed attention from the regression analysis approach.A detailed comparison of the category analysis and regression analysis methods for developing household-based trip end models is given. Both methods have been applied using data from the Monmouthshire Land Use Transportation Study. The regression results reported are from a very preliminary analysis and contain a number of anomalies, although it is thought that sufficient work has been done to provide an objective evaluation of the two methods.It is recommended that the household regression approach should be further investigated since it has advantages as a modelbuilding procedure and makes better use of sample data. A certain amount of categorisation of household types is necessary and the investigations would attempt to determine the best balance between categorisation and regression fitting. Further development will be restricted if the trend towards minimum sample sizes of about 1000 households is continued. Larger samples should be taken in certain circumstances to pursue development work.     

Operational route choice methodologies for practical applications

This paper focuses on the application of tractable route choice models and presents a set of methods for deriving relevant disaggregate and aggregate route choice indicators, namely link and route flows. Tractability is achieved at the disaggregate level by the recursive logit model and at the aggregate level by the mental representation item (\(\mathrm {MRI}\)) approach. These two approaches are analyzed here, and extensions of the \({\mathrm {MRI}}\) approach are presented. The analysis elaborates on the features of each model and allows to draw insights into the use of a specific model, depending on the needs of the application and the data availability. The performance of the two models is tested on real data. The results demonstrate the validity of the \({\mathrm {MRI}}\) model that is intended for aggregate analysis.

     

State-of-the art of freight forecast modeling: lessons learned and the road ahead

In this paper we review freight forecasting models and current advances and needs with respect to data and model development. We then present a case study to suggest which models should be developed for the State of California in the US. We suggest several alternatives including an aggregate commodity flow model, a disaggregate regional logistics model and a hybrid regional logistics model with a truck touring model. We point out however, that the data requirements for the latter model would be extensive. In addition, the development of hybrid models, for example progress in the integration of regional logistics models with urban truck touring models, will introduce new problems such as reconciling the outputs of multiple models for consistency.     

Long-term trends in domestic US passenger travel: the past 110 years and the next 90

Based upon a long-term historical data set of US passenger travel, a model is estimated to project aggregate transportation trends through 2100. One of the two model components projects total mobility (passenger-km traveled) per capita based on per person GDP and the expected utility of travel mode choices (logsum). The second model component has the functional form of a logit model, which assigns the projected travel demand to competing transportation modes. An iterative procedure ensures the average amount of travel time per person to remain at a pre-specified level through modifying the estimated value of time. The outputs from this model can be used as a first-order estimate of a future benchmark against which the effectiveness of various transportation policy measures or the impact of autonomous behavioral change can be assessed.     

A nested logit model of automobile holdings for one vehicle households

This paper presents a model of automobile choice by single vehicle households. This effort is distinguished from previous disaggregate automobile holdings models primarily by the use of the nested logit model rather than the more restrictive multinomial logit model. We present a 2-step estimation technique that provides consistent and asymptotically efficient parameter estimates, yet is tractable for very large choice sets. Using disaggregate data on 237 one-vehicle households we estimate the unknown parameters on an automobile choice model containing 785 individual makes, models and vintages of passenger vehicles.     

Evolution over time of heavy vehicle volume in toll roads: A dynamic panel data to identify key explanatory variables in Spain

Improving the knowledge of demand evolution over time is a key aspect in the evaluation of transport policies and in forecasting future investment needs. It becomes even more critical for the case of toll roads, which in recent decades has become an increasingly common device to fund road projects. However, literature regarding demand elasticity estimates in toll roads is sparse and leaves some important aspects to be analyzed in greater detail. In particular, previous research on traffic analysis does not often disaggregate heavy vehicle demand from the total volume, so that the specific behavioral patterns of this traffic segment are not taken into account. Furthermore, GDP is the main socioeconomic variable most commonly chosen to explain road freight traffic growth over time. This paper seeks to determine the variables that better explain the evolution of heavy vehicle demand in toll roads over time. To that end, we present a dynamic panel data methodology aimed at identifying the key socioeconomic variables that explain the behavior of road freight traffic throughout the years. The results show that, despite the usual practice, GDP may not constitute a suitable explanatory variable for heavy vehicle demand. Rather, considering only the GDP of those sectors with a high impact on transport demand, such as construction or industry, leads to more consistent results. The methodology is applied to Spanish toll roads for the 1990–2011 period. This is an interesting case in the international context, as road freight demand has experienced an even greater reduction in Spain than elsewhere, since the beginning of the economic crisis in 2008.     

A multivariate cointegrating vector auto regressive model of freight transport demand: evidence from Indian railways

In this paper long run structural relationship for freight transport demand is derived for railways in India using annual time series data for 1960–1995. Some of the recent developments in multivariate dynamic econometric time series modelling have been employed such as estimation of long-run structural cointegrating relationship, short-run dynamics and measurement of the effects of shocks and their persistence during the evolution of dynamic freight transport demand system. The models are estimated using a cointegrating vector autoregressive (VAR) modelling framework, which allows for endogeneity of regressors. Results indicate high GDP elasticity and low price elasticity, with real freight rate, i.e. the price variable behaving exogenously with respect to the system. Any disequilibrium in the short-run is likely to be corrected in the long run via adjustments in freight transport demand and GDP. Further, the demand system seems to be stable in the long run and converges to equilibrium in a period of around 3 years after a typical system-wide shock.     

Aggregation with multinomial probit and estimation of disaggregate models with aggregate data: A new methodological approach

This paper describes an analytic aggregation procedure for disaggregate demand models similar to the one proposed in earlier publications by Westin (1974) and McFadden and Reid (1975). The technique, which uses a multivariate normal approximation for the distribution of the vector of attributes, is based on the multinomial profit algorithm proposed by Daganzo, Bouthelier and Sheffi (1977) and can be applied to an arbitrary number of alternatives. The procedure is computationally so efficient that it enables us to calibrate disaggregate models with aggregate data by maximum likelihood using the same or slightly modified codes developed for disaggregated data. The paper also contains a small scale numerical example intended to illustrate the important highlights of the aggregation-estimation problem.     

Profit-based maritime container assignment models for liner shipping networks

We propose the problem of profit-based container assignment (P-CA), in which the container shipment demand is dependent on the freight rate, similar to the “elastic demand” in the literature on urban transportation networks. The problem involves determining the optimal freight rates, the number of containers to transport and how to transport the containers in a liner shipping network to maximize the total profit. We first consider a tactical-level P-CA with known demand functions that are estimated based on historical data and formulate it as a nonlinear optimization model. The tactical-level P-CA can be used for evaluating and improving the container liner shipping network. We then address the operational-level P-CA with unknown demand functions, which aims to design a mechanism that adjusts the freight rates to maximize the profit. A theoretically convergent trial-and-error approach, and a practical trial-and-error approach, are developed. A numerical example is reported to illustrate the application of the models and approaches.     

An aggregate demand model for intercity passenger travel in Sri Lanka

Inspite of the inherent weaknesses in aggregate demand models, they continue to be used in everyday applications, especially in developing countries. The largely data intensive disaggregate model preclude its application in many cases. This paper attempts the formulation and calibration of an aggregate total demand model for estimating inter-district passenger travel by public transport in Sri Lanka. In its process, an investigation is made of the common problems in the aggregate approach while examining possible remedial measures to improve the accuracy and (hence) the usability of the aggregate model. It is argued that commonly used variables and functional forms are inappropriate for making accurate estimates in developing countries. Consequently, the model calibration is shown to incorporate variables representing urbanisation, under-development, transfers, a mode-abstract cost function and intrinsic features. The necessity for functional form for each variable to be based on behavioral assumptions that are tested using the Box-Cox transformation for ensuring the best fit of the data is also observed. Although, the model form was calibrated for Sri Lanka, the model is generalised in order for its applications to other countries as well as, both, inter-district and intercity travel demand estimation.     

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.     

Travel demand forecasts improved by using cross-sectional data from multiple time points

Forecasts of travel demand are often based on data from the most recent time point, even when cross-sectional data is available from multiple time points. This is because forecasting models with similar contexts have higher transferability, and the context of the most recent time point is believed to be the most similar to the context of a future time point. In this paper, the author proposes a method for improving the forecasting performance of disaggregate travel demand models by utilising not only the most recent dataset but also an older dataset. The author assumes that the parameters are functions of time, which means that future parameter values can be forecast. These forecast parameters are then used for travel demand forecasting. This paper describes a case study of journeys to work mode choice analysis in Nagoya, Japan, using data collected in 1971, 1981, 1991, and 2001. Behaviours in 2001 are forecast using a model with only the most recent 1991 dataset and models that combine the 1971, 1981, and 1991 datasets. The models proposed by the author using data from three time points can provide better forecasts. This paper also discusses the functional forms for expressing parameter changes and questions the temporal transferability of not only alternative-specific constants but also level-of-service and socio-economic parameters.     

Freight Data Assembling and Modeling: Methodologies and Practice

Abstract

In comparison to personal travel, freight movements within large metropolitan areas are much less studied. Most conventional transportation models and planning analysis that disregarded freight flows have been criticized on the plausibility of their results and conclusions. To alleviate these problems, this study proposes a non-survey based approach to assemble and process freight data in a systematic way. A freight origin–destination (OD) matrix of freight flows can be developed using secondary data sources. The estimated freight flows can be loaded together with conventional passenger flows onto the regional highway network of a large metropolitan area. As a case study, this non-survey based approach was applied to build a freight OD and study the traffic flows in Los Angeles. It concluded that this approach can be used to analyze urban freight movement in a low-cost way in which planning agencies can overcome the common omission of freight flow information in their transportation plans.