A disaggregate logit model of intercity common carrier passenger modal choice

The use of the logit functional form is common in traditional, aggregate intercity passenger demand models. The use of disaggregate logit models (that is, models calibrated on data pertaining to individual travellers' behaviour and derivable from explicit assumptions concerning travellers' decision-making processes) in the intercity context is surprisingly rare. The major obstacle to such usage would appear to be a general lack of the disaggregate data required to calibrate these models. The major rationale for their use, on the other hand, is the potential that they possess for achieving a more “behavioural” representation of travellers' decision-making and actions.The paper presents a primarily empirical study of the use of disaggregate choice models to represent one component of intercity travel demand, choice of mode. Using 1969 Canadian Transport Commission (CTC) survey data for the Windsor–Quebec City corridor, a disaggregate logit model of common carrier modal choice is presented and discussed. Despite its age and the lack of the auto mode, the CTC data set was used in this study because it appears to be the best disaggregate data set currently available in Canada. The paper also discusses several key issues involved in the development of this model and in its use in intercity passenger demand forecasting, including model data requirements and comparison of the model's predictive performance with traditional aggregate models. Key words: intercity passenger travel, mode choice, disaggregate logit model.

Download Full-text

A copula-based continuous cross-nested logit model for tour scheduling in activity-based travel demand models

Transportation Research Part B Methodological ◽

10.1016/j.trb.2021.01.001 ◽

2021 ◽

Vol 145 ◽

pp. 324-341

Author(s):

Sepehr Ghader ◽

Carlos Carrion ◽

Liang Tang ◽

Arash Asadabadi ◽

Lei Zhang

Keyword(s):

Logit Model ◽

Travel Demand ◽

Nested Logit ◽

Nested Logit Model ◽

Demand Models ◽

Tour Scheduling

Download Full-text

Addressing Household Income Distribution in Air Travel Demand Models: Case Study of the Baltimore–Washington Region

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198118823197 ◽

2019 ◽

Vol 2673 (1) ◽

pp. 491-502 ◽

Cited By ~ 1

Author(s):

Geoffrey D. Gosling ◽

David Ballard

Keyword(s):

Income Distribution ◽

Household Income ◽

Travel Demand ◽

Metropolitan Region ◽

Annual Data ◽

Demand Model ◽

Passenger Demand ◽

Average Household Income ◽

Demand Models ◽

The Future

The paper describes the development of an air passenger demand model for the Baltimore–Washington metropolitan region that was undertaken as part of a recently concluded ACRP project that explored the use of disaggregated socioeconomic data in air passenger demand studies. The model incorporated a variable reflecting the change in household income distribution, together with more traditional aggregate causal variables: population, employment, average household income, and airfares as measured by the average U.S. airline yield, as well as several year-specific dummy variables. The model was estimated on annual data for the period 1990 to 2010 and obtained statistically significant estimated coefficients for all variables, including both the average household income and the household income distribution variable. Including household income distribution in the model resulted in a significant change to the estimated coefficient for average household income, giving a much higher estimated elasticity of demand with respect to average household income compared with a model that does not consider changes in household income distribution. This has important implications for the use of such demand models for forecasting, as household income distribution and average household income may change in the future in quite different ways, which would affect the future levels of air passenger travel projected by the models.

Download Full-text

Estimating Costs and Benefits of Transportation Corridor Alternatives

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1606-14 ◽

1997 ◽

Vol 1606 (1) ◽

pp. 115-123

Author(s):

Patrick Decorla-Souza ◽

Brian Gardner ◽

Michael Culp ◽

Jerry Everett ◽

Chimai Ngo ◽

...

Keyword(s):

Decision Making ◽

Travel Demand ◽

Salt Lake ◽

Salt Lake City ◽

Costs And Benefits ◽

Benefit Cost Analysis ◽

Demand Models ◽

Data Output ◽

Lake City ◽

Benefit Cost

Although benefit-cost assessment is a useful tool in structuring the decision making process, it has not generally been used to assist in multi-modal decision making in metropolitan areas. Also, although detailed zone-to-zone trip information can be obtained from metropolitan travel-demand models, this information is not currently used by planners in developing detailed information on cross-modal comparisons of costs and benefits. A real-world application of benefit-cost analysis for multi-modal decision making using detailed zone-to-zone trip data output from travel-demand models for the I-15 corridor in Salt Lake City is presented. The analysis was conducted at two levels: corridor and region-wide. The research suggests that, when major investments are to be evaluated, the analyst should be very cautious in performing corridor-level analyses when such a trip-based approach is used, because of significant effects on the evaluation caused by traffic diverted into (or out of) the corridor.

Download Full-text

Household Travel, Household Characteristics, and Land Use: An Empirical Study from the 1994 Portland Activity-Based Travel Survey

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1617-02 ◽

1998 ◽

Vol 1617 (1) ◽

pp. 10-17 ◽

Cited By ~ 45

Author(s):

Xiaoduan Sun ◽

Chester G. Wilmot ◽

Tejonath Kasturi

Keyword(s):

Land Use ◽

Travel Demand ◽

Land Development ◽

Data Set ◽

Travel Survey ◽

Forecasting Models ◽

Demand Models ◽

Residence Location ◽

Household Travel ◽

Use Factors

How a household’s travel behavior is influenced by its socioeconomic and land use factors has been a subject of interest for the development of travel demand forecasting models. This study investigates the relative importance of these factors based on the number of household daily trips and vehicle miles traveled (VMT). The travel data used in the study come from the 1994 Portland Activity-Based Travel Survey. In addition to income, vehicle ownership, and household size, other significant factors in household travel have been identified, such as the presence of car phones, dwelling type, home ownership, and even the length of resident’s time in the current home. Most important, this study has qualitatively revealed that land use makes a big difference in household VMT, whereas its impact on the number of daily trips is rather limited. After controlling for the land use variables, such as density and land development balance, it appears that there is little difference in household income distribution among three different land use areas. The household life stage/lifestyle appears to be more relevant to the residence location. And the land use development of the residence location imposes the greatest impact on the household daily VMT. The results from this study provide some empirical evidence to the development of travel forecasting models. Especially by examining the relationship between land use and household travel, the results shed light on how to incorporate land use factors into comprehensive travel demand models that can be used by policy makers in evaluation of alternative land use policies. This study serves as a step toward more comprehensive studies on transportation and land use. The results presented represent a preliminary analysis of an extensive data set; considerable additional analysis is already in process.

Download Full-text

Identifying Decision Structures Underlying Activity Patterns: An Exploration of Data Mining Algorithms

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1718-01 ◽

2000 ◽

Vol 1718 (1) ◽

pp. 1-9 ◽

Cited By ~ 39

Author(s):

Geert Wets ◽

Koen Vanhoof ◽

Theo Arentze ◽

Harry Timmermans

Keyword(s):

Data Mining ◽

Decision Tree ◽

Logit Model ◽

Goodness Of Fit ◽

Travel Demand ◽

Activity Patterns ◽

Future Research ◽

Data Set ◽

Data Mining Algorithms ◽

Mining Algorithms

The utility-maximizing framework—in particular, the logit model—is the dominantly used framework in transportation demand modeling. Computational process modeling has been introduced as an alternative approach to deal with the complexity of activity-based models of travel demand. Current rule-based systems, however, lack a methodology to derive rules from data. The relevance and performance of data-mining algorithms that potentially can provide the required methodology are explored. In particular, the C4 algorithm is applied to derive a decision tree for transport mode choice in the context of activity scheduling from a large activity diary data set. The algorithm is compared with both an alternative method of inducing decision trees (CHAID) and a logit model on the basis of goodness-of-fit on the same data set. The ratio of correctly predicted cases of a holdout sample is almost identical for the three methods. This suggests that for data sets of comparable complexity, the accuracy of predictions does not provide grounds for either rejecting or choosing the C4 method. However, the method may have advantages related to robustness. Future research is required to determine the ability of decision tree-based models in predicting behavioral change.

Download Full-text

Creating a Synthetic Household Travel and Activity Survey: Rationale and Feasibility Analysis

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1706-10 ◽

2000 ◽

Vol 1706 (1) ◽

pp. 82-91 ◽

Cited By ~ 17

Author(s):

S. P. Greaves ◽

P. R. Stopher

Keyword(s):

Survey Data ◽

Travel Demand ◽

Model Development ◽

Synthetic Data ◽

Data Set ◽

Travel Survey ◽

Simulation Procedure ◽

Demand Models ◽

Study Results ◽

Household Travel

Proposed is a new approach for developing the travel survey data required for use in local travel-demand models. Using readily available local sociodemographic information in conjunction with a freely available national travel survey, a simulation procedure is described to create, in effect, a synthetic household travel survey. The reasons for interest in such a procedure are outlined, including the costs and difficulties associated with gathering high-quality travel data. Consideration is then given to alternatives for local model development, such as the use of national data averages and borrowed models. The simulation procedure is then described and tested in a region that has recently completed a travel survey; this provides a direct source of comparison of the merit of the approach. Trip production models are then built using the synthetic data set. The case study results show that the synthetic data ( a) offer significant improvements over the use of borrowed models and ( b) estimate new models that are similar to those same models estimated using the local travel survey data. It is concluded that these results show that the approach has considerable promise. Finally, some future directions are described, including the planned extension of the approach to other regions.

Download Full-text

Updating National Air Passenger Demand from Traffic Counts: The Case of a Secondary Airport in an Underdeveloped Region

Sustainability ◽

10.3390/su13158372 ◽

2021 ◽

Vol 13 (15) ◽

pp. 8372

Author(s):

Francesco Russo ◽

Giuseppe Fortugno ◽

Marco Merante ◽

Domenica Savia Pellicanò ◽

Maria Rosaria Trecozzi

Keyword(s):

Public Service ◽

Environmental Sustainability ◽

Logit Model ◽

Air Transport ◽

Transport Networks ◽

Traffic Counts ◽

Aggregated Data ◽

Passenger Demand ◽

Demand Models ◽

Underdeveloped Region

Demand models allow to estimate the choices made by users on different alternatives. Demand models depend on the characteristic attributes of the users and transport networks, as well as on parameters. Their significance translates into the reliability of the model in reproducing users’ choices as demand values. Traffic counts are aggregated data that can be used to update demand values of O/D matrix and/or for re-calibrating parameters from sets of parameters obtained in different situations or at different times in the same scenario using a reverse assignment modal. This paper provides the use of passenger counts to update national air transport demand by calibrating a hierarchical logit model. The application focuses on estimating the demand values for a secondary airport of an underdeveloped European region with the calibration of the logsum parameter working between distribution and modal choice. The updated model can be used to test new conditions for the supply of a new service or to increase the frequency or to modify the ticket level by means of public service obligations. The results show that the introduction of public obligations in the secondary airport in an underdeveloped region is crucial for future sustainability. Considering the decline in the economic, social and environmental sustainability in the region, the airport is central to economic and social development at the same time as being important for environmental sustainability, as it limits the impacts on the territory related to the construction of large transport infrastructures.

Download Full-text

PENGEMBANGAN ”REAL TIME TRAFFIC INFORMATION SYSTEM” BAGI PENGGUNA JALAN

Jurnal Sains dan Teknologi Indonesia ◽

10.29122/jsti.v12i3.861 ◽

2013 ◽

Vol 12 (3) ◽

Author(s):

Rusmadi Suyuti

Keyword(s):

Information System ◽

Real Time ◽

Travel Demand ◽

Urban Traffic ◽

Road User ◽

Traffic Information ◽

Estimation Methods ◽

Traffic Counts ◽

Real Time Traffic ◽

Demand Models

Traffic information condition is a very useful information for road user because road user can choose his best route for each trip from his origin to his destination. The final goal for this research is to develop real time traffic information system for road user using real time traffic volume. Main input for developing real time traffic information system is an origin-destination (O-D) matrix to represent the travel pattern. However, O-D matrices obtained through a large scale survey such as home or road side interviews, tend to be costly, labour intensive and time disruptive to trip makers. Therefore, the alternative of using traffic counts to estimate O-D matrices is particularly attractive. Models of transport demand have been used for many years to synthesize O-D matrices in study areas. A typical example of the approach is the gravity model; its functional form, plus the appropriate values for the parameters involved, is employed to produce acceptable matrices representing trip making behaviour for many trip purposes and time periods. The work reported in this paper has combined the advantages of acceptable travel demand models with the low cost and availability of traffic counts. Two types of demand models have been used: gravity (GR) and gravity-opportunity (GO) models. Four estimation methods have been analysed and tested to calibrate the transport demand models from traffic counts, namely: Non-Linear-Least-Squares (NLLS), Maximum-Likelihood (ML), Maximum-Entropy (ME) and Bayes-Inference (BI). The Bandung’s Urban Traffic Movement survey has been used to test the developed method. Based on several statistical tests, the estimation methods are found to perform satisfactorily since each calibrated model reproduced the observed matrix fairly closely. The tests were carried out using two assignment techniques, all-or-nothing and equilibrium assignment.

Download Full-text