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.

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.

Product Choice with Large Assortments: A Scalable Deep-Learning Model

2021 ◽  
Author(s):  
Sebastian Gabel ◽  
Artem Timoshenko
Keyword(s):  
Choice Model ◽  
Predictive Performance ◽  
Cross Product ◽  
Product Choice ◽  
Transaction Data ◽  
Price Discounts ◽  
Experimental Variation ◽  
Product Choices ◽  
Large Product ◽  
Marketing Actions

Personalized marketing in retail requires a model to predict how different marketing actions affect product choices by individual customers. Large retailers often handle millions of transactions daily, involving thousands of products in hundreds of categories. Product choice models thus need to scale to large product assortments and customer bases, without extensive product attribute information. To address these challenges, we propose a custom deep neural network model. The model incorporates bottleneck layers to encode cross-product relationships, calibrates time-series filters to capture purchase dynamics for products with different interpurchase times, and relies on weight sharing between the products to improve convergence and scale to large assortments. The model applies to loyalty card transaction data without predefined categories or product attributes to predict customer-specific purchase probabilities in response to marketing actions. In a simulation, the proposed product choice model predicts purchase decisions better than baseline methods by adjusting the predicted probabilities for the effects of recent purchases and price discounts. The improved predictions lead to substantially higher revenue gains in a simulated coupon personalization problem. We verify predictive performance using transaction data from a large retailer with experimental variation in price discounts. This paper was accepted by Gui Liberali, Management Science Special Issue on Data-Driven Prescriptive Analytics.

scholarly journals Do millennials value travel time differently because of productive multitasking? A revealed-preference study of Northern California commuters

2021 ◽  
Author(s):  
Aliaksandr Malokin ◽  
Giovanni Circella ◽  
Patricia L. Mokhtarian
Keyword(s):  
Older Adults ◽  
Young Adults ◽  
Willingness To Pay ◽  
Travel Time ◽  
Choice Model ◽  
Mode Choice ◽  
Revealed Preference ◽  
Northern California ◽  
Factors Affecting ◽  
The Impact

AbstractMillennials, the demographic cohort born in the last two decades of the twentieth century, are reported to adopt information and communication technologies (ICTs) in their everyday lives, including travel, to a greater extent than older generations. As ICT-driven travel-based multitasking influences travelers’ experience and satisfaction in various ways, millennials are expected to be affected at a greater scale. Still, to our knowledge, no previous studies have specifically focused on the impact of travel multitasking on travel behavior and the value of travel time (VOTT) of young adults. To address this gap, we use an original dataset collected among Northern California commuters (N = 2216) to analyze the magnitude and significance of individual and household-level factors affecting commute mode choice. We estimate a revealed-preference mode choice model and investigate the differences between millennials and older adults in the sample. Additionally, we conduct a sensitivity analysis to explore how incorporation of explanatory factors such as attitudes and propensity to multitask while traveling in mode choice models affects coefficient estimates, VOTT, and willingness to pay to use a laptop on the commute. Compared to non-millennials, the mode choice of millennials is found to be less affected by socio-economic characteristics and more strongly influenced by the activities performed while traveling. Young adults are found to have lower VOTT than older adults for both in-vehicle (15.0% less) and out-of-vehicle travel time (15.7% less), and higher willingness to pay (in time or money) to use a laptop, even after controlling for demographic traits, personal attitudes, and the propensity to multitask. This study contributes to better understanding the commuting behavior of millennials, and the factors affecting it, a topic of interest to transportation researchers, planners, and practitioners.

scholarly journals Railway and road discrete choice model for foreign trade freight between Antioquia and the Port of Cartagena

2016 ◽  
Vol 36 (3) ◽  
pp. 22 ◽  
Author(s):  
Juan Diego Pineda Jaramillo ◽  
Iván Reinaldo Sarmiento Ordosgoitia ◽  
Jorge Eliécer Córdoba Maquilón
Keyword(s):  
Qualitative Research ◽  
Discrete Choice ◽  
Logit Model ◽  
Choice Model ◽  
Stated Preference ◽  
Revealed Preference ◽  
Freight Transportation ◽  
Multinomial Logit Model ◽  
Discrete Choice Model ◽  
The Cost

Most Colombian freight is transported on roads with barely acceptable conditions, and although there is a speculation about the need for a railway for freight transportation, there is not a study in Colombia showing the variables that influence the modal choice by the companies that generate freight transportation. This article presents the calculation of demand for a hypothetical railway through a discrete choice model. It begins with a qualitative research through focus group techniques to identify the variables that influence the choice of persons responsible for the transportation of large commercial companies in Antioquia (Colombia). The influential variables in the election were the cost and service frequency, and these variables were used to apply a Stated Preference (SP) and Revealed Preference (RP) survey, then to calibrate a Multinomial Logit Model (MNL), and to estimate the influence of each of them. We show that the probability of railway choice by the studied companies varies between 67% and 93%, depending on differences in these variables.

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.

scholarly journals Understanding the Transit Market: A Persona-Based Approach for Preferences Quantification

2020 ◽  
Vol 12 (9) ◽  
pp. 3863 ◽  
Author(s):  
Gamal Eldeeb ◽  
Moataz Mohamed
Keyword(s):  
Real Time ◽  
Choice Model ◽  
Information Provision ◽  
Travel Behaviour ◽  
Error Component ◽  
Transit Market ◽  
Bus Service ◽  
Transit Agencies ◽  
Real Time Information ◽  
Time Information

The study aims at utilizing a persona-based approach in understanding, and further quantifying, the preferences of the key transit market groups and estimating their willingness to pay (WTP) for service improvements. The study adopted an Error Component (EC) interaction choice model to investigate personas’ preferences in a bus service desired quality choice experiment. Seven personas were developed based on four primary characteristics: travel behaviour, employment status, geographical distribution, and Perceived Behavioural Control (PBC). The study utilized a dataset of 5238 participants elicited from the Hamilton Street Railway Public Engagement Survey, Ontario, Canada. The results show that all personas, albeit significantly different in magnitude, are negatively affected by longer journey times, higher trip fares, longer service headways, while positively affected by reducing the number of transfers per trip and real-time information provision. The WTP estimates show that, in general, potential users are more likely to have higher WTP values compared to current users except for at-stop real-time information provision. Also, there is no consensus within current users nor potential users on the WTP estimates for service improvements. Finally, shared and unique preferences for service attributes among personas were identified to help transit agencies tailor their marketing/improvement plans based on the targeted segments.

A Proposal Model for Measuring the Impact of Viral Marketing Through Social Networks on Purchasing Decision

2018 ◽  
Vol 9 (3) ◽  
pp. 13-33 ◽  
Author(s):  
Hussein Moselhy Sayed Ahmed
Keyword(s):  
Social Networks ◽  
Knowledge Sharing ◽  
Social Networking Sites ◽  
Viral Marketing ◽  
Purchasing Decision ◽  
Market Shares ◽  
Online Word Of Mouth ◽  
Proposed Model ◽  
Social Network Influence ◽  
The Impact

Viral marketing has become a conduit for today's organizations and an important pillar for managing the organization and a source that enhances its competitiveness and creates new opportunities for organizations through which they are trying to achieve competitive advantages to obtain new market shares. So, this study provides insight into how social network influence on purchasing decision through viral marketing and knowledge sharing on social networking sites (SNSs). By using the sample from 650 Egyptian college students - who spend more time on SNSs, this study investigates the relationship among the use of SNSs, users' social relationships, online word-of-mouth, and knowledge sharing. Therefore, this paper is working on the study of the impact of viral marketing through social networks on consumer buying decisions, and working on the development of a proposed model to measure this effect.

Labour-Supply Behaviour

2021 ◽  
pp. 115-149
Author(s):  
Cathal O'Donoghue
Keyword(s):  
Fiscal Policy ◽  
Labour Supply ◽  
Choice Model ◽  
Policy Instruments ◽  
Revealed Preference ◽  
Behavioural Response ◽  
Trade Off ◽  
Measurement Issues ◽  
Labour Participation ◽  
Microsimulation Models

In the preceding chapters, the focus was on simulating policies that aim to reduce poverty, generate revenue, or redistribute resources. However, many public policies also try to incentivize behaviour, such as those to improve labour participation or supply, or to change behaviours in relation to savings or pollution. Social- and fiscal-policy instruments face a fundamental trade-off. An instrument that performs well from an income-maintenance perspective may have unintended behavioural consequences. This chapter considers the structure of instruments that have an explicit goal to improve behavioural response, particularly focusing on in-work benefits. The chapter also describes how to use a microsimulation mode to simulate the inputs required for the estimation of a behavioural-econometric model, and then estimates a revealed-preference-choice model. The chapter then describes a method often used in microsimulation models to calibrate choice models for simulation purposes. In terms of measurement issues related to the behavioural analysis, we describe the design and use of replacement rates. The chapter concludes by undertaking a simulation of the introduction of a change in in-work benefits.

Accounting for Voter Heterogeneity within and across Districts with a Factor-Analytic Voter-Choice Model

2007 ◽  
Vol 15 (1) ◽  
pp. 67-84 ◽  
Author(s):  
Wagner A. Kamakura ◽  
José Afonso Mazzon
Keyword(s):  
Logit Model ◽  
Choice Model ◽  
Random Coefficients ◽  
Nested Logit ◽  
Second Stage ◽  
Voter Preferences ◽  
Political Preferences ◽  
Factor Analytic ◽  
Proposed Model ◽  
Voter Choice

In this study, we propose a model of individual voter behavior that can be applied to aggregate data at the district (or precinct) levels while accounting for differences in political preferences across districts and across voters within each district. Our model produces a mapping of the competing candidates and electoral districts on a latent “issues” space that describes how political preferences in each district deviate from the average voter and how each candidate caters to average voter preferences within each district. We formulate our model as a random-coefficients nested logit model in which the voter first evaluates the candidates to decide whether or not to cast his or her vote, and then chooses the candidate who provides him or her with the highest value. Because we allow the random coefficient to vary not only across districts but also across unobservable voters within each district, the model avoids the Independence of Irrelevant Alternatives Assumption both across districts and within each district, thereby accounting for the cannibalization of votes among similar candidates within and across voting districts. We illustrate our proposed model by calibrating it to the actual voting data from the first stage of a two-stage state governor election in the Brazilian state of Santa Catarina, and then using the estimates to predict the final outcome of the second stage.

scholarly journals Competition and Product Choice in Option Demand Markets

2016 ◽  
Vol 16 (2) ◽  
pp. 785-805 ◽  
Author(s):  
Gilad Sorek
Keyword(s):  
Spot Markets ◽  
Product Choice ◽  
Medical Providers ◽  
Insurance Policy ◽  
Public Insurance ◽  
Market Structures ◽  
The Public ◽  
Medical Products ◽  
Equilibrium Prices ◽  
Product Choices

Abstract This work presents the first analysis of competition through horizontal and vertical differentiation in option demand markets, which are common in the health-care sector. I studied two alternative market structures: (a) a “pure” option demand market where medical providers sell insurance directly to consumers and (b) a public insurance regime where the public insurer bargains over prices with providers before bundling both products under a single insurance policy. I show that (a) product choices in option demand markets differ greatly from those in respective spot markets and (b) bundling medical products under a single insurance policy alters product choices and equilibrium prices in a way that does not benefit consumers.

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