Development of disaggregate mode choice models of intercity travel in Canada

1992 ◽  
Vol 19 (6) ◽  
pp. 965-974 ◽  
Author(s):  
Walid M. Abdelwahab ◽  
J. David Innes ◽  
Albert M. Stevens
Keyword(s):  
Travel Behavior ◽  
Mode Choice ◽  
Geographical Location ◽  
Predictive Ability ◽  
Multinomial Logit ◽  
Choice Models ◽  
Trip Purpose ◽  
Market Shares ◽  
Intercity Travel ◽  
Statistics Canada

This paper reports and discusses the results of an effort to develop disaggregate behavioral mode choice models of intercity travel in Canada. Currently available data bases of intercity travel in Canada are reviewed. The feasibility of using data from national travel surveys to develop statistically reliable intercity mode choice models is examined, and directions for future disaggregate data collection efforts are offered. The models developed are of the multinomial logit (MNL) type which included all intercity passenger travel modes: auto, air, bus, and rail. For purposes of estimation, the travel market was segmented by trip length (short, long); trip purpose (business, recreational); and geographical location of the trip (east, west). Then, a separate model was estimated in each sector. The models were estimated using the data collected by Statistics Canada as a part of the Labor Force Survey (The Canadian Travel Survey, CTS). The quality of the calibrated models varied from one region to another and from one travel sector to another. Overall, the models were reasonably accurate in predicting modal shares of the most frequently used modes (auto and air). The underrepresentation of the bus and rail modes in the data sets led to a deterioration in the performance of the models in predicting market shares of these two modes. More specifically, the predictive ability of the models measured by the likelihood ratio index varied from a low of 0.58 in the short travel sector to a high of 0.94 in the long travel sector. The transferability of the models described in this study was recently examined by Abdelwahab (1991). Key words: mode choice, disaggregate, travel behavior, multinomial logit, intercity, data base.

Transferability of intercity disaggregate mode choice models in Canada

1991 ◽  
Vol 18 (1) ◽  
pp. 20-26 ◽  
Author(s):  
Walid M. Abdelwahab
Keyword(s):  
Travel Behavior ◽  
Travel Demand ◽  
Mode Choice ◽  
Model Development ◽  
Predictive Ability ◽  
Multinomial Logit ◽  
Choice Models ◽  
Data Sets ◽  
Policy Analysts ◽  
Transportation Studies

In many transportation studies, the time span of data collection, model development, and analysis is often too long to be responsive to the needs of policy analysts and decision makers. This problem is often exacerbated in situations with severely constrained analysis resources. Therefore, it is often useful to transfer a model from one area to another. Model transfer is defined as the application of a model developed in one area to describe the corresponding behavior in another area. This paper examines the transferability of a class of models used in intercity travel demand analysis. Specifically, disaggregate mode choice models of the multinomial logit type are developed for two regions in Canada, and some established measures of transferability are applied to assess the potential of calibrating these models in one region and applying them in the other. Comparison of mode choice models estimated on data sets from the two regions yielded inconclusive results regarding model transferability. In general, transferred models were found to be 18–23% less accurate than local models in predicting modal shares. Adjusting models' parameters to reflect observed modal shares in the application context improved the predictive ability of the models by about 10%. Key words: transferability, mode choice, disaggregate, travel behavior, multinomial logit, intercity.

Could a New Mode Alternative Modify Psycho-Attitudinal Factors and Travel Behavior?

2019 ◽  
Vol 2673 (9) ◽  
pp. 94-106
Author(s):  
Eleonora Sottile ◽  
Francesco Piras ◽  
Italo Meloni
Keyword(s):  
Travel Behavior ◽  
Latent Variables ◽  
Mode Choice ◽  
Choice Models ◽  
Individual Characteristics ◽  
Policy Implications ◽  
Light Rail ◽  
Attitudinal Factors ◽  
The Impact ◽  
Over Time

There is ample consensus that, besides objective characteristics, psycho-attitudinal factors play a key role in influencing people’s mode choice. Hybrid choice models use these theoretical frameworks so as to include latent constructs for capturing the impact of subjective factors on mode choice. But recent work in transportation research raised the question about the ability of hybrid choice models to derive policy implications that aim to change travel behavior, given the focus on cross-sectional data. To address this problem we designed a survey for collecting longitudinal data (socio-economic and psycho-attitudinal) to evaluate, on the one hand, the long-term effects on travel mode choice of the implementation of a new light rail line in the metropolitan area of Cagliari (Italy), on the other to detect any changes in the psycho-attitudinal factors and socio-economic characteristics after implementation of those measures. In particular, the objective of the study is to analyze whether these changes in individual characteristics are able to affect mode choice from a modeling perspective, through the specification and estimation of hybrid models. Our results show that latent variables were not significantly different over waves, showing that the impact of the psychological construct remained stable over time, even after the introduction of the new light rail. Additionally, we found some evidence that the variables that explain the latent variables could change over time.

scholarly journals Shopping trip mode choice of older adults: an application of activity space and hybrid choice models in understanding the effects of built environment and personal goals

2019 ◽  
Author(s):  
Samira Ramezani ◽  
Tiina Laatikainen ◽  
Kamyar Hasanzadeh ◽  
Marketta Kyttä
Keyword(s):  
Older Adults ◽  
Travel Behavior ◽  
Mode Choice ◽  
Choice Models ◽  
Activity Space ◽  
Exposure Model ◽  
Personal Goals ◽  
Age Group ◽  
Physically Active ◽  
Shopping Trip

Abstract Rapid growth of the older population worldwide, coupled with their overreliance on automobile and its negative consequences for the environment and for their wellbeing, has encouraged research on travel behavior of this age group. This study contributes to the literature by providing an integrated analysis of the effects of sociodemographic, built environmental, psycho-social, trip, and activity space attributes on shopping trip mode choice of older adults in Helsinki Metropolitan Area. Data was collected using an online map-based survey. Two person-based activity space models were developed, in addition to the commonly used 500-m buffer, to measure activity space and built environmental attributes. Integrated Choice and Latent Variable (ICLV) models were utilized to explore modal choice. Although the use of activity space models did not significantly increase the fit of ICLV models, it provided different information. Walkability index showed a positive significant effect on walking trips in individualized residential exposure model. A positive effect on transit use or biking was found in individual home range and 500-m buffer. The shape and dispersion of activity spaces affected mode choice as well. Green space influenced the goal of being physically active which in turn affected mode choice. Three personal goals of being physically active, having cultural and social affairs, and caring for others influenced mode choice. Results indicate the priority of the use of activity space and hybrid choice models in understanding travel behavior. Findings of this study can guide policies aiming to increase the use of more sustainable modes among this age group.

scholarly journals Sustainable Mobility Policy Analysis Using Hybrid Choice Models: Is It the Right Choice?

2021 ◽  
Vol 13 (5) ◽  
pp. 2993
Author(s):  
Gustavo García-Melero ◽  
Rubén Sainz-González ◽  
Pablo Coto-Millán ◽  
Alejandra Valencia-Vásquez
Keyword(s):  
Policy Analysis ◽  
Public Transportation ◽  
Travel Behavior ◽  
Latent Variables ◽  
Mode Choice ◽  
Choice Models ◽  
Sensitivity Study ◽  
Sustainable Mobility ◽  
Explanatory Variables ◽  
The Impact

In recent years, sustainable mobility policy analysis has used Hybrid Choice Models (HCM) by incorporating latent variables in the mode choice models. However, the impact on policy analysis outcomes has not yet been determined with certainty. This paper aims to measure the effect of HCM on sustainable mobility policy analysis compared to traditional models without latent variables. To this end, we performed mode choice research in the city of Santander, Spain. We identified two latent variables—Safety and Comfort—and incorporated them as explanatory variables in the HCM. Later, we conducted a sensitivity study for sustainable mobility policy analysis by simulating different policy scenarios. We found that the HCM amplified the impact of sustainable mobility policies on the modal shares, and provided an excessive reaction in the individuals’ travel behavior. Thus, the HCM overrated the impact of sustainable mobility policies on the modal switch. Likewise, for all of the mode choice models, policies that promoted public transportation were more effective in increasing bus modal shares than those that penalized private vehicles. In short, we concluded that sustainable mobility policy analysis should use HCM prudently, and should not set them as the best models beforehand.

scholarly journals Exploring Influencing Factors of Intercity Mode Choice from View of Entire Travel Chain

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xiaowei Li ◽  
Siyu Zhang ◽  
Yao Wu ◽  
Yuting Wang ◽  
Wenbo Wang
Keyword(s):  
Logit Model ◽  
Unobserved Heterogeneity ◽  
Mode Choice ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
Travel Mode ◽  
Intercity Travel ◽  
Travel Mode Choice ◽  
Mixed Multinomial Logit Model ◽  
Significant Factors

Exploring the influencing factors of intercity travel mode choice can reveal passengers’ travel decision mechanisms and help traffic departments to develop an effective demand management policy. To investigate these factors, a survey was conducted in Xi’an, China, to collect data about passengers’ travel chains, including airplane, high-speed railway (HSR), train, and express bus. A Bayesian mixed multinomial logit model is developed to identify significant factors and explicate unobserved heterogeneity across observations. The effect of significant factors on intercity travel mode choice is quantitatively assessed by the odds ratio (OR) technique. The results show that the Bayesian mixed multinomial logit model outperforms the traditional Bayesian multinomial logit model, indicating that accommodating the unobserved heterogeneity across observations can improve the model fit. The model estimation results show that ticket purchasing method, comfort, punctuality, and access time are random parameters that have heterogeneous effects on intercity travel mode choice.

scholarly journals Mode Choice Modeling to Shift Car Travelers towards Park and Ride Service in the City Centre of Karachi

2021 ◽  
Vol 13 (10) ◽  
pp. 5638
Author(s):  
Irfan Ahmed Memon ◽  
Saima Kalwar ◽  
Noman Sahito ◽  
Mir Aftab Hussain Talpur ◽  
Imtiaz Ahmed Chandio ◽  
...  
Keyword(s):  
Public Transportation ◽  
Travel Behavior ◽  
Traffic Congestion ◽  
Travel Demand ◽  
Mode Choice ◽  
Central Business District ◽  
Choice Models ◽  
City Centre ◽  
Travel Demand Management ◽  
Park And Ride

Currently, congestion in Karachi’s central business district (CBD) is the result of people driving their cars to work. Consequently, a park and ride (P&R) service has proved successful in decreasing traffic congestion and the difficulty of finding parking spaces from urban centers. The travelers cannot be convinced to shift towards the P&R service without an understanding of their travel behavior. Therefore, a travel behavior survey needs to be conducted to reduce the imbalance between public and private transport. Hence, mode choice models were developed to determine the factors that influence single-occupant vehicle (SOV) travelers’ decision to adopt the P&R service. Data were collected by an adapted self-administered questionnaire. Mode choice models were developed through logistic regression modeling by using the Statistical Package for the Social Sciences version 22. The findings concluded that more than 70%, specifically motorbike users, to avoid mental stress, and to protect the environment are willing to adopt the P&R service. Moreover, to validate the mode choice models, logit model training and a testing approach were used. In conclusion, by overcoming these influencing factors and balancing push and pull measures of travel demand management (TDM), SOV users can be encouraged to shift towards P&R services. Thus, research outcomes can support policymakers in implementing sustainable modes of public transportation.

The Impact of Zoning and Built Environment Characteristics on Transit, Biking, and Walking

2019 ◽  
Vol 11 (1) ◽  
pp. 108-129
Author(s):  
Andrew G. Mueller ◽  
Daniel J. Trujillo
Keyword(s):  
Land Use ◽  
Built Environment ◽  
Discrete Choice ◽  
Travel Behavior ◽  
Choice Model ◽  
Mode Choice ◽  
Land Use Regulation ◽  
Policy Action ◽  
Development Goals ◽  
The Impact

This study furthers existing research on the link between the built environment and travel behavior, particularly mode choice (auto, transit, biking, walking). While researchers have studied built environment characteristics and their impact on mode choice, none have attempted to measure the impact of zoning on travel behavior. By testing the impact of land use regulation in the form of zoning restrictions on travel behavior, this study expands the literature by incorporating an additional variable that can be changed through public policy action and may help cities promote sustainable real estate development goals. Using a unique, high-resolution travel survey dataset from Denver, Colorado, we develop a multinomial discrete choice model that addresses unobserved travel preferences by incorporating sociodemographic, built environment, and land use restriction variables. The results suggest that zoning can be tailored by cities to encourage reductions in auto usage, furthering sustainability goals in transportation.

Sign in / Sign up

Export Citation Format

Share Document