A Heteroscedastic Polarized Logit Model to Investigate the Competition of Bicycle for the Bikeable Trips with the Other Modes

2018 ◽  
Vol 2672 (49) ◽  
pp. 58-68
Author(s):  
Khandker Nurul Habib
Keyword(s):  
Choice Model ◽  
National Capital Region ◽  
Travel Mode ◽  
Household Travel Survey ◽  
Market Shares ◽  
Proposed Model ◽  
National Capital ◽  
Mode Share ◽  
Household Travel ◽  
Low Sensitivity

The paper proposes a new discrete choice model, named the Heteroscedastic Polarized Logit (HPL) to investigate choice contexts with one or more alternatives with remarkably low market shares. The proposed model is used to investigate the factors influencing the choice of a bicycle as a travel mode in the National Capital Region (NCR) of Canada. Data from the latest household travel survey of the NCR are used to investigate the mode choices of bikeable trips. Bikeable trips are defined as trips with lengths shorter than 16 km as this is the observed maximum limit of a bicycle trip in the dataset. A large dataset with over 40,000 trip records is used for empirical investigation where the bicycle has the lowest mode share of 3%. The HPL model clearly shows its appropriateness and superiority over comparable models in such a context. The choice to walk is found to be more sensitive to trip length than the choice to cycle, yet walking is found to have three times larger market share than that of cycling. Similarly, motorized modes are found to have low sensitivity to travel time and other impedances and have larger market shares. Women and students are found not to prefer the bicycle as a travel mode. Cycling infrastructure is seen to be effective in increasing the choice of the bicycle as a travel mode, but it also becomes clear that additional soft policy initiatives would be necessary to increase the popularity of cycling among young people, students, and women.

scholarly journals Myopic choice or rational decision making? An investigation into mode choice preference structures in competitive modal arrangements in a multimodal urban area, the City of Toronto

2016 ◽  
Vol 43 (5) ◽  
pp. 420-428 ◽  
Author(s):  
Mohamed Salah Mahmoud ◽  
Adam Weiss ◽  
Khandker Nurul Habib
Keyword(s):  
Urban Areas ◽  
Choice Model ◽  
Mode Choice ◽  
Model Development ◽  
Choice Preference ◽  
Proposed Model ◽  
Mode Share ◽  
Household Travel ◽  
The City ◽  
Dependent Behaviour

This paper presents an investigation into the preference structure of commuting mode choice in dense urban areas. The paper aims to investigate the phenomenon of myopic choice and extends the phenomenon to the concept of modal culture. Using a household travel diary survey from the greater Toronto and Hamilton Area, an empirical discrete choice model was estimated. This model was used to provide general comments on the commuting and dependent behaviour of the sample, with a particular focus on the factors that influence bicycling captivation and culture. The model was then used for a hypothetical policy scenario analysis, which found that an investment in biking infrastructure had the capacity to increase bicycling mode share by nearly 50%. Based on this result, this paper recommends further investigation into both data collection for more comprehensive empirical model development and investigation into the policy applicability of the proposed model structure.

scholarly journals Inferring proxy response in household travel surveys with unknown completer using a group-based choice model

2019 ◽  
Author(s):  
Takuya Maruyama ◽  
Kenta Hosotani ◽  
Tomoki Kawano
Keyword(s):  
Response Bias ◽  
Choice Model ◽  
Middle Aged ◽  
Social Surveys ◽  
Travel Survey ◽  
Household Travel Survey ◽  
Proposed Model ◽  
Household Travel ◽  
Household Travel Surveys ◽  
Response Biases

Abstract A proxy response is often accepted for household travel surveys to reduce the survey cost and increase the sample size, but proxy-response biases may be introduced into the sample data. To investigate and correct the bias, completer information for the survey is important, but such information is not always available in practice. This study proposes a novel model that can be applicable in situations where completer information is unavailable. The method introduces group-decision modeling in analyzing the response choices of the household travel survey, where the survey response is considered to be a task allocation among household members. The proposed model can infer the probability of proxy response and the proxy-response bias of trip-related records without completer information. The potential of the proposed model was confirmed by application to a household travel survey in Japan. The inferred probability of the proxy response and the inferred bias without completer data demonstrated surprisingly similar results to the existing study with actual proxy-response data. Specifically, the model inferred a high probability of proxy response in young adults and a low proxy probability in middle-aged females, and the model inferred the proxy-response bias that female proxy respondents in the middle-aged group report lower trip rates than self-respondents. This method will be valuable not only in travel surveys, but also in the general research and practice of social surveys.

scholarly journals Modelling the Effects of Parking Charge and Supply Policy Using System Dynamics Method

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Zhenyu Mei ◽  
Qifeng Lou ◽  
Wei Zhang ◽  
Lihui Zhang ◽  
Fei Shi
Keyword(s):  
System Dynamics ◽  
Choice Model ◽  
Evaluation Model ◽  
Travel Cost ◽  
Travel Mode ◽  
Travel Choice ◽  
Supply Policy ◽  
Proposed Model ◽  
Traffic Speed ◽  
Business District

Reasonable parking charge and supply policy are essential for the regular operation of the traffic in city center. This paper develops an evaluation model for parking policies using system dynamics. A quantitative study is conducted to examine the effects of parking charge and supply policy on traffic speed. The model, which is composed of three interrelated subsystems, first summarizes the travel cost of each travel mode and then calibrates the travel choice model through the travel mode subsystem. Finally, the subsystem that evaluates the state of traffic forecasts future car speed based on bureau of public roads (BPR) function and generates new travel cost until the entire model reaches a steady state. The accuracy of the model is verified in Hangzhou Wulin business district. The related error of predicted speed is only 2.2%. The results indicate that the regular pattern of traffic speed and parking charge can be illustrated using the proposed model based on system dynamics, and the model infers that reducing the parking supply in core area will increase its congestion level and, under certain parking supply conditions, there exists an interval of possible pricing at which the service reaches a level that is fairly stable.

Incorporating Product Choice into Transit Fare Policy Scenario Models

2019 ◽  
Vol 2673 (8) ◽  
pp. 209-220
Author(s):  
Andrew Stuntz ◽  
John Attanucci ◽  
Frederick P. Salvucci
Keyword(s):  
Choice Model ◽  
Revealed Preference ◽  
Product Choice ◽  
Large Shift ◽  
Policy Scenario ◽  
Spreadsheet Model ◽  
Market Shares ◽  
Proposed Model ◽  
Product Choices ◽  
Transit Agencies

Customer fare product choices can affect both ridership and revenue, so they are strategically important for transit agencies. Nearly all major agencies offer choices between pay-per-use and pass products, and with each potential fare change, agencies face decisions about whether to modify pass “multiples”—the number of rides needed to “break even” on a pass purchase. However, the simple elasticity spreadsheet models often used to analyze the potential ridership and revenue impacts of fare changes make little or no adjustment for shifts in fare product choices. This paper reviews different options for incorporating product choice into fare policy scenario models, and it presents a ridership and revenue prediction procedure that combines a multinomial logit fare product choice model with the logic of an elasticity spreadsheet model. This combination facilitates evaluation of complex fare changes that are likely to alter fare product market shares while maintaining much of the flexibility and simplicity of a traditional spreadsheet model. Additionally, the proposed model uses only preexisting, revealed-preference automated fare collection data rather than requiring customer surveys. The proposed model is demonstrated using examples at the Chicago Transit Authority (CTA). The CTA experienced a large shift from passes to pay-per-use following a fare change in 2013, illustrating the potential value of accounting for fare product choices in fare scenario evaluation.

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.

scholarly journals Spatial variation in bicycling: a retrospective review of travel survey data from Greater Melbourne, Australia

2021 ◽  
Author(s):  
Ben Beck ◽  
Meghan Winters ◽  
Jason Thompson ◽  
Mark Stevenson ◽  
Christopher Pettit
Keyword(s):  
Linear Regression ◽  
Spatial Variation ◽  
Survey Data ◽  
Descriptive Analysis ◽  
Percentage Increase ◽  
Travel Survey ◽  
Household Travel Survey ◽  
Trip Distance ◽  
Mode Share ◽  
Household Travel

Understanding spatial variation in bicycling within cities is necessary to identify and address inequities. We aimed to explore spatial variation in bicycling and explore how bicycling rates vary across population sub-groups. We conducted a retrospective analysis of household travel survey data in Greater Melbourne, Australia. We present a descriptive analysis of bicycling behaviour across local government areas (LGAs; n=31), with a focus on quantifying spatial variation in the number and proportion of trips made by bike, and by age, sex and trip distance. Associations between the proportion of infrastructure that had provision for biking and the proportion of all trips made by bike were analysed using linear regression. Overall, 1.7% of all trips were made by bike. While more than half (53.2%) of all trips were less than 5km, only 2% of these trips were by bike. Across LGAs, there was considerable variation in the proportion of trips made by bike (range: 0.1% to 5.7%). Mode share by females was 35.0%, and this varied across LGAs from 0% to 49%. Tor each percentage increase in the proportion of infrastructure that had provision for biking, there was an associated 0.2% increase in the proportion of trips made by bike (coefficient = 0.20; SE = 0.05; adjusted R2 = 0.38). While we observed a low bicycle mode share, more than half of all trips were less than 5 km, demonstrating substantial opportunity to increase the number of trips taken by bike.

Impact of bicycle highways on commuter mode choice: A scenario analysis

2018 ◽  
Vol 47 (4) ◽  
pp. 662-677 ◽  
Author(s):  
Hema S Rayaprolu ◽  
Carlos Llorca ◽  
Rolf Moeckel
Keyword(s):  
Discrete Choice ◽  
Choice Model ◽  
Mode Choice ◽  
Nested Logit ◽  
Household Travel Survey ◽  
Health Consciousness ◽  
Modelling Framework ◽  
Potential Impact ◽  
Household Travel ◽  
National Household Travel Survey

The Dutch concept of ‘bicycle highways’ is increasingly being adopted by urban planners owing to rising environmental and health consciousness, and the growing popularity of electric bicycles. Bicycle highways differ from other types of cycling infrastructure in that they avoid intersections with motorised traffic, and are wide enough to allow for safe overtaking, thereby increasing cycling speeds. While many studies investigate the feasibility of constructing bicycle highways, few explore their effect on users’ travel preferences. In this context, our study aims to assess the potential impact of bicycle highways on commuter mode choice. We built a discrete choice model based on individual commute data from a national household travel survey, Mobilität in Deutschland 2008. The model was estimated in a logit modelling framework using Biogeme. We estimated multinomial logit and nested logit models and found nested logit to be more appropriate. The model estimates were then applied to forecast mode shares in scenarios with the pilot bicycle highway proposed in the Munich region. The variation in mode shares across scenarios with increasing average cycling speeds was analysed in areas with varying proximity to the infrastructure. The results suggest that bicycle highways reduce motorised travel and increase cycling. The effect is stronger as proximity to the corridor increases. The analysis helps to quantify the potential impact of bicycle highways on commuter mode choice even without considering further benefits beyond travel time reductions, such as increased safety, convenience, comfort, and reduced risks due to fewer interactions with motorised traffic.

Does Brand Awareness Impact Purchase Decision: A Case of Organic Grocery

2018 ◽  
Vol 6 (7) ◽  
pp. 45
Author(s):  
Gunjan Gumber ◽  
Jyoti Rana
Keyword(s):  
Organic Food ◽  
Brand Awareness ◽  
National Capital Region ◽  
Promising Alternative ◽  
Brand Recall ◽  
Brand Building ◽  
Brand Recognition ◽  
National Capital ◽  
High Level ◽  
Structured Questionnaire

In India, the concept of organic food is gaining widespread acceptability. Consumers are becoming more conscious about their health and are looking for food that serves as a promising alternative. Corporates, NGOs, Spiritual leaders and Government are also promoting this food, as it is free from irradiation, chemicals and artificial additives. A number of organic food brands are available in the market. The main objective of this study is to find out the level of brand awareness and its influence on purchase of organic grocery. The data was collected from 150 organic consumers in National Capital Region (Delhi, Gurgaon, Faridabad and Noida) through a structured questionnaire. Questions related to brand recall, brand recognition and purchase of organic grocery were asked. It was found that in general, there is a low level of brand awareness among consumers, and those who have high level of awareness; they consume organic grocery more often. The study will help corporates to make effective communication and brand-building strategies.

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