Discrete Choice Elasticities for Elderly and Disabled Travelers Between Fixed-Route Transit and Paratransit

1998 ◽  
Vol 1623 (1) ◽  
pp. 31-36 ◽  
Author(s):  
Joel P. Franklin ◽  
Debbie A. Niemeier
Keyword(s):  
Discrete Choice ◽  
Current Practice ◽  
Public Transit ◽  
Choice Model ◽  
Mode Choice ◽  
The Elderly ◽  
The Public ◽  
The Difference ◽  
Mode Choice Modeling ◽  
Significant Factors

In the current practice of mode-choice modeling, models typically focus on the more traditional choices, such as those between automobile, transit, and nonmotorized transportation. For most travelers these are, indeed, the most relevant modes. However, for some segments of the population, particularly the elderly, the choice is more limited. This study investigates the factors that affect the elderly and disabled travelers’ choice between public transit and paratransit. Data collected from the public transit service, Sacramento Regional Transit, and the paratransit service, Paratransit, Inc., in Sacramento, California, were used to develop a mode-choice model and to calculate elasticities of significant factors. Age was found to have an elastic effect, whereas the difference in fare had an inelastic effect.

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.

scholarly journals Mode choice analysis using discrete choice model from transport user (Case study: Jakarta LRT, Indonesia)

2018 ◽  
Vol 181 ◽  
pp. 03001
Author(s):  
Dwi Novi Wulansari ◽  
Milla Dwi Astari
Keyword(s):  
Discrete Choice ◽  
Public Transportation ◽  
Choice Model ◽  
Stated Preference ◽  
Mode Choice ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Light Rail ◽  
Choice Analysis ◽  
Mode Choice Model

Jakarta Light Rail Transit (Jakarta LRT) has been planned to be built as one of mass rail-based public transportation system in DKI Jakarta. The objective of this paper is to obtain a mode choice models that can explain the probability of choosing Jakarta LRT, and to estimate the sensitivity of mode choice if the attribute changes. Analysis of the research conducted by using discrete choice models approach to the behavior of individuals. Choice modes were observed between 1) Jakarta LRT and TransJakarta Bus, 2) Jakarta LRT and KRL-Commuter Jabodetabek. Mode choice model used is the Binomial Logit Model. The research data obtained through Stated Preference (SP) techniques. The model using the attribute influences such as tariff, travel time, headway and walking time. The models obtained are reliable and validated. Based on the results of the analysis shows that the most sensitive attributes affect the mode choice model is the tariff.

Forecasting Paratransit Ridership Using Discrete Choice Models with Explicit Consideration of Availability

1998 ◽  
Vol 1618 (1) ◽  
pp. 60-65 ◽  
Author(s):  
Julian Benjamin ◽  
Shinya Kurauchi ◽  
Takayuki Morikawa ◽  
Amalia Polydoropoulou ◽  
Kuniaki Sasaki ◽  
...  
Keyword(s):  
Choice Model ◽  
Mode Choice ◽  
Cost Effective ◽  
The Elderly ◽  
Developed Countries ◽  
Discrete Choice Models ◽  
Mode Share ◽  
Bus Service ◽  
Reservation System ◽  
The Impact

In most developed countries, the population of the elderly and disabled is growing rapidly. These individuals require transportation service suited to their needs. Such service may be provided by applying emerging technologies to dial-a-ride transit. This research develops a methodology to quantitatively evaluate the impact of paratransit services on a traveler’s mode choice behavior. The mode choice model explicitly considers availability of alternative modes and includes latent factors to account for taste heterogeneity. Stated preferences are also used to elicit preferences for new paratransit services. The methodology is empirically tested with data collected in Winston-Salem, North Carolina. The model system developed is applied to evaluate the effect of improving service attributes and the impact of the introduction of new cost-effective modes on modal shares. Results of the policy analysis indicate that ( a) transit policy changes, such as fare reduction, would have little effect on automobile driver and automobile passenger shares; ( b) an improved reservation system for dial-a-ride services would produce shifts in mode share; ( c) the proposed new bus deviation service was favored; ( d) free bus service reduces dial-a-ride share; and ( e) an increase in awareness of a dial-a-ride system would significantly increase its share.

scholarly journals Assessing effective mask use by the public in two countries: an observational study

BMJ Open ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. e049389
Author(s):  
Clare L Atzema ◽  
Ivona Mostarac ◽  
Dana Button ◽  
Peter C Austin ◽  
Arshia P Javidan ◽  
...  
Keyword(s):  
Observational Study ◽  
Disease Transmission ◽  
Public Transit ◽  
The Elderly ◽  
Large Reduction ◽  
Cross Sectional ◽  
The Public ◽  
Younger Age ◽  
Improper Use ◽  
The Mean

ObjectivesDuring the COVID-19 pandemic wearing a mask in public has been recommended in some settings and mandated in others. How often this advice is followed, how well, and whether it inadvertently leads to more disease transmission opportunities due to a combination of improper use and physical distancing lapses is unknown.DesignCross-sectional observational study performed in June–August 2020.SettingEleven outdoor and indoor public settings (some with mandated mask use, some without) each in Toronto, Ontario, and in Portland, Oregon.ParticipantsAll passers-by in the study settings.Outcome measuresMask use, incorrect mask use, and number of breaches (ie, coming within 2 m of someone else where both parties were not properly masked).ResultsWe observed 36 808 persons, the majority of whom were estimated to be aged 31–65 years (49%). Two-thirds (66.7%) were wearing a mask and 13.6% of mask-wearers wore them incorrectly. Mandatory mask-use settings were overwhelmingly associated with mask use (adjusted OR 79.2; 95% CI 47.4 to 135.1). Younger age, male sex, Torontonians, and public transit or airport settings (vs in a store) were associated with lower adjusted odds of wearing a mask. Mandatory mask-use settings were associated with lower adjusted odds of mask error (OR 0.30; 95% CI 0.14 to 0.73), along with female sex and Portland subjects. Subjects aged 81+ years (vs 31–65 years) and those on public transit and at the airport (vs stores) had higher odds of mask errors. Mask-wearers had a large reduction in adjusted mean number of breaches (rate ratio (RR) 0.19; 95% CI 0.17 to 0.20). The 81+ age group had the largest association with breaches (RR 7.77; 95% CI 5.32 to 11.34).ConclusionsMandatory mask use was associated with a large increase in mask-wearing. Despite 14% of them wearing their masks incorrectly, mask users had a large reduction in the mean number of breaches (disease transmission opportunities). The elderly and transit users may warrant public health interventions aimed at improving mask use.

scholarly journals The Implication of Road Toll Discount for Mode Choice: Intercity Travel during the Chinese Spring Festival Holiday

2018 ◽  
Vol 10 (8) ◽  
pp. 2700 ◽  
Author(s):  
Xiaomei Lin ◽  
Yusak Susilo ◽  
Chunfu Shao ◽  
Chengxi Liu
Keyword(s):  
Chinese Spring ◽  
High Speed ◽  
Public Transit ◽  
Mode Choice ◽  
Mixed Logit ◽  
Negative Effects ◽  
High Speed Rail ◽  
The Public ◽  
Spring Festival ◽  
Intercity Travel

Intercity travel congestion during the main national holidays takes place every year at different places around the world. Charge reduction measurements on existing toll roads have been implemented to promote an efficient use of the expressways and to reduce congestion on the public transit networks. However, some of these policies have had negative effects. A more comprehensive understanding of the determinants of holiday intercity travel patterns is critical for better policymaking. This paper aims to investigate the effectiveness of the road toll discount policy on mode choice behavior for intercity travel. A mixed logit model is developed to model the mode choices of intercity travelers, which is estimated based on survey data about intercity journeys from Beijing during the 2017 Chinese Spring Festival holiday. The policy impact is further discussed by elasticity and scenario simulations. The results indicate that the expressway toll discount does increase the car use and decrease the public transit usage. Given the decreased toll on expressways, the demand tends to shift from car to public transit, in an order of coach, high-speed rail, conventional rail, and airplane. When it comes to its effect on socio-demographic groups, men and lower-income travelers are identified to be more likely to change mode in response to variation of road toll. Finally, policy effectiveness is found to vary for travelers in different travel distance groups. Conclusions provide useful insights on road pricing management.

Travel Mode Choice Modeling: Predictive Efficacy between Machine Learning Models and Discrete Choice Model

2021 ◽  
Vol 15 (1) ◽  
pp. 241-255
Author(s):  
Nur Fahriza Mohd. Ali ◽  
Ahmad Farhan Mohd. Sadullah ◽  
Anwar PP Abdul Majeed ◽  
Mohd Azraai Mohd. Razman ◽  
Muhammad Aizzat Zakaria ◽  
...  
Keyword(s):  
Machine Learning ◽  
Discrete Choice ◽  
Choice Model ◽  
Mode Choice ◽  
Binary Logistic Regression ◽  
Discrete Choice Model ◽  
Learning Models ◽  
Travel Mode ◽  
Travel Mode Choice ◽  
Machine Learning Models

Background: A complex travel behaviour among users is intertwined with many factors. Traditionally, the exploration in travel mode choice modeling has been dominated by the Discrete Choice model, nonetheless, owing to the advancement in computational techniques, machine learning has gained traction in understanding travel behavior. Aim: This study aims at predicting users’ travel model choice by means of machine learning models against a conventional Discrete Choice Model, i.e., Binary Logistic Regression. Objective: To investigate the comparison between machine learning models, namely Neural Network, Random Forest, Decision Tree, and Support Vector Machine against the Discrete Choice Model (Binary Logistic Regression) in the prediction of travel mode choice amongst Kuantan City. Methodology: The dataset was collected in Kuantan City, Malaysia, through the Revealed/Stated Preferences (RP/SP) Survey. The data collected was split into a ratio of 80:20 for training and testing before evaluating them between the aforesaid models. The hyperparameters of the models were set to default. The performance of the models is evaluated based on classification accuracy. Results: It was shown in the present study that the Neural Network Model is able to attain a higher prediction accuracy as compared to Binary Logistic Regression (Discrete Choice Model) in classifying mode choice of Kuantan users either to choose public transport or private vehicles as daily transportation. Feature importance technique is crucial for identifying the significant features in modelling travel mode choice. It is demonstrated that the Neural Network Model can yield exceptional classification of mode choice up to 73.4% and 72.4% of training and testing data, respectively, by considering the features identified via the feature importance technique, suggesting the viability of the proposed technique in supporting an informed decision. Conclusion: The findings highlight the strengths and limitations of the Machine Learning Technique as well as the Discrete Choice Model in modeling travel mode choice. It was shown that Machine Learning models have the capability to provide better prediction that could assist the urban transportation planning among policymakers. Meanwhile, it could be also demonstrated that the Discrete Choice Model (Binary Logistic Regression) is helpful in getting a better understanding in expressing the inference relationship between variables for improvising the future transportation system.

scholarly journals A Bayesian Network Model on the Public Bicycle Choice Behavior of Residents: A Case Study of Xi’an

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Qiuping Wang ◽  
Hao Sun ◽  
Qi Zhang
Keyword(s):  
Bayesian Network ◽  
Network Model ◽  
Expert Knowledge ◽  
Choice Model ◽  
Mode Choice ◽  
Bayesian Network Model ◽  
Travel Mode ◽  
The Public ◽  
Proposed Model ◽  
Travel Mode Choice

In order to study the main factors affecting the behaviors that city residents make regarding public bicycle choice and to further study the public bicycle user’s personal characteristics and travel characteristics, a travel mode choice model based on a Bayesian network was established. Taking residents of Xi’an as the research object, a K2 algorithm combined with mutual information and expert knowledge was proposed for Bayesian network structure learning. The Bayesian estimation method was used to estimate the parameters of the network, and a Bayesian network model was established to reflect the interactions among the public bicycle choice behaviors along with other major factors. The K-fold cross-validation method was used to validate the model performance, and the hit rate of each travel mode was more than 80%, indicating the precision of the proposed model. Experimental results also present the higher classification accuracy of the proposed model. Therefore, it may be concluded that the resident travel mode choice may be accurately predicted according to the Bayesian network model proposed in our study. Additionally, this model may be employed to analyze and discuss changes in the resident public bicycle choice and to note that they may possibly be influenced by different travelers’ characteristics and trip characteristics.

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