scholarly journals Unconstrained Estimation of Multitype Car Rental Demand

2021 ◽  
Vol 11 (10) ◽  
pp. 4506
Author(s):  
Yazao Yang ◽  
Avishai (Avi) Ceder ◽  
Weiyong Zhang ◽  
Haodong Tang
Keyword(s):  
Stated Preference ◽  
Revealed Preference ◽  
Repair Process ◽  
Discrete Choice Models ◽  
Difficult Problem ◽  
Average Error ◽  
Demand Forecast ◽  
Car Rental ◽  
Future Demand

The unconstrained demand forecast for car rentals has become a difficult problem for revenue management due to the need to cope with a variety of rental vehicles, the strong subjective desires and requests of customers, and the high probability of upgrading and downgrading circumstances. The unconstrained demand forecast mainly includes repairing of constrained historical demand and forecasting of future demand. In this work, a new methodology is developed based on multiple discrete choice models to obtain customer choice preference probabilities and improve a known spill model, including a repair process of the unconstrained demand. In addition, the linear Holt–Winters model and the nonlinear backpropagation neural network are combined to predict future demand and avoid excessive errors caused by a single method. In a case study, we take advantage of a stated preference and a revealed preference survey and use the variable precision rough set to obtain factors and weights that affect customer choices. In this case study and based on a numerical example, three forecasting methods are compared to determine the car rental demand of the next time cycle. The comparison with real demand verifies the feasibility and effectiveness of the hybrid forecasting model with a resulting average error of only 3.06%.

scholarly journals A Bayesian Network Modeling for Departure Time Choice: A Case Study of Beijing Subway

2018 ◽  
Vol 30 (5) ◽  
pp. 579-587 ◽  
Author(s):  
Xian Li ◽  
Haiying Li ◽  
Xinyue Xu
Keyword(s):  
Bayesian Network ◽  
Stated Preference ◽  
Discrete Choice Models ◽  
Model Parameters ◽  
Time Saving ◽  
Morning Peak ◽  
Departure Time ◽  
Departure Time Choice ◽  
Beijing Subway

Departure time choice is critical for subway passengers to avoid congestion during morning peak hours. In this study, we propose a Bayesian network (BN) model to capture departure time choice based on data learning. Factors such as travel time saving, crowding, subway fare, and departure time change are considered in this model. K2 algorithm is then employed to learn the BN structure, and maximum likelihood estimation (MLE) is adopted to estimate model parameters, according to the data obtained by a stated preference (SP) survey. A real-world case study of Beijing subway is illustrated, which proves that the proposed model has higher prediction accuracy than typical discrete choice models. Another key finding indicates that subway fare discount higher than 20% will motivate some passengers to depart 15 to 20 minutes earlier and release the pressure of crowding during morning peak hours.

scholarly journals Is Urbanisation Rate a Feasible Supplemental Parameter in Forecasting Electricity Consumption in China?

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Biao Yang ◽  
Yingcheng Li ◽  
Haokun Wei ◽  
Huan Lu
Keyword(s):  
Regression Models ◽  
Electricity Consumption ◽  
Forecast Model ◽  
Electricity Demand ◽  
Shaanxi Province ◽  
Average Error ◽  
Demand Forecast ◽  
Valid Predictor ◽  
The Government

Traditional method of forecasting electricity consumption based only on GDP was sometimes ineffective. In this paper, urbanisation rate (UR) was introduced as an additional predictor to improve the electricity demand forecast in China at provincial scale, which was previously based only on GDP. Historical data of Shaanxi province from 2000 to 2013 was collected and used as case study. Four regression models were proposed and GDP, UR, and electricity consumption (EC) were used to establish the parameters in each model. The model with least average error of hypothetical forecast results in the latest three years was selected as the optimal forecast model. This optimal model divides total EC into four parts, of which forecasts can be made separately. It was found that GDP was only better correlated than UR on household EC, whilst UR was better on the three sectors of industries. It was concluded that UR is a valid predictor to forecast electricity demand at provincial level in China nowadays. Being provided the planned value of GDP and UR from the government, EC in 2015 were forecasted as 131.3 GWh.

Survey of Cross-Regional Intermodal Passenger Travel

2015 ◽  
Vol 2526 (1) ◽  
pp. 108-118 ◽  
Author(s):  
Mohamed S. Mahmoud ◽  
Khandker M. Nurul Habib ◽  
Amer Shalaby
Keyword(s):  
Choice Behavior ◽  
Choice Model ◽  
Stated Preference ◽  
Mode Choice ◽  
Revealed Preference ◽  
Discrete Choice Models ◽  
Efficient Design ◽  
Start Time ◽  
Passenger Travel ◽  
Preference Experiment

This paper presents an investigation of the mode choice behavior of cross-regional commuters in the greater Toronto and Hamilton area of Ontario, Canada. A survey of cross-regional intermodal passenger travel (called SCRIPT) was developed and conducted during the spring and the fall of 2014. SCRIPT collects data on respondents' revealed preference in daily commuting trips to pivot each respondent's mode choice stated preference experiment separately. An innovative multimodal trip planner tool was developed to generate feasible travel options for each stated preference experiment with information on household auto ownership level, proximity to transit, work start time, and total travel time from home to work, as well as predeveloped discrete choice models to identify access station locations of intermodal travel modes. The stated preference experiments were based on the D-efficient design technique. The survey used 1,203 randomly selected cross-regional commuters. The paper reports on a mode choice model estimated by the revealed preference data portion of the survey to verify the validity of the survey design, sampling procedure, and data quality. An empirical model provides insight into cross-regional commuters' mode choice behavior.

scholarly journals Approaches for Identifying Consumer Preferences for the Design of Technology Products: A Case Study of Residential Solar Panels

2013 ◽  
Vol 135 (6) ◽  
Author(s):  
Heidi Q. Chen ◽  
Tomonori Honda ◽  
Maria C. Yang
Keyword(s):  
Consumer Preferences ◽  
Stated Preference ◽  
Revealed Preference ◽  
Time Span ◽  
Solar Panels ◽  
Photovoltaic Panels ◽  
Machine Learning Methods ◽  
Technology Products ◽  
Technical Specifications

This paper investigates ways to obtain consumer preferences for technology products to help designers identify the key attributes that contribute to a product's market success. A case study of residential photovoltaic panels is performed in the context of the California, USA, market within the 2007–2011 time span. First, interviews are conducted with solar panel installers to gain a better understanding of the solar industry. Second, a revealed preference method is implemented using actual market data and technical specifications to extract preferences. The approach is explored with three machine learning methods: Artificial neural networks (ANN), Random Forest decision trees, and Gradient Boosted regression. Finally, a stated preference self-explicated survey is conducted, and the results using the two methods compared. Three common critical attributes are identified from a pool of 34 technical attributes: power warranty, panel efficiency, and time on market. From the survey, additional nontechnical attributes are identified: panel manufacturer's reputation, name recognition, and aesthetics. The work shows that a combination of revealed and stated preference methods may be valuable for identifying both technical and nontechnical attributes to guide design priorities.

scholarly journals Market value of the view restriction

2019 ◽  
Vol 11 (1) ◽  
pp. 1925-1932
Author(s):  
István Hajnal
Keyword(s):  
Stated Preference ◽  
Revealed Preference ◽  
Market Value ◽  
Evaluation Study ◽  
Building Construction ◽  
Delphi Approach ◽  
Negative Effect ◽  
The Impact ◽  
Creative Construction

Abstract Based on the international literature, the effect of an existing panoramic view on the market value of properties is positive and significant. This value-adding factor varies by location and by type of view. In Central Europe, no such evaluation study has been elaborated until now. New building construction may restrict the existing panorama, and this is the other side of the same phenomenon. View restriction may result in stigmatization, which is a negative effect on the property. There are two major methodologies to observe the effect: revealed preference method (RPM) and stated preference method (SPM). One SPM approach is contingent valuation (CV), wherein well-informed stakeholders give their opinion about the impact caused by the investigated effect. The CV methodology, using the Delphi approach, was employed to observe the market value decrease in the cases of several restricted panorama situations in Budapest. Based on the research, this effect in Budapest is in line with the published western results. The result of the study can be used to support real estate developers and architects in their development decisions. This is an extended version of the article titled “The impact of view-restriction: a Delphi case study from Budapest”, presented at Creative Construction Conference 2018, CCC 2017, 30 June to 3 July 2018, Ljubljana, Slovenia.

scholarly journals Mobility surveys beyond stated preference: introducing MyTrips, an SP-off-RP survey tool, and results of two case studies

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Christian Rudloff ◽  
Markus Straub
Keyword(s):  
Case Studies ◽  
Latent Class ◽  
Stated Preference ◽  
Revealed Preference ◽  
Hypothetical Bias ◽  
Rural Setting ◽  
Daily Routine ◽  
Survey Tool ◽  
First Case

AbstractWhen introducing new mobility offers or measures to influence traffic, stated preference (SP) surveys are often used to assess their impact. In SP surveys, respondents do not answer questions about their actual behaviour, but about hypothetical settings. Therefore, answers are often biased. To minimise this hypothetical bias, so-called stated preference-off-revealed preference (SP-off-RP) surveys were developed. They base SP questions on respondents’ revealed behaviour and place unknown scenarios in a familiar context. Until now, this method was applied mostly to scenarios investigating the willingness to pay. The application to more complex mode or route choice problems, which require the calculation of routes, has not yet been done. In this paper, the MyTrips survey tool for the collection of SP-off-RP data based on respondents’ actual mobility behaviour is presented. SP questions are based on alternatives to typical routes of respondents, which are calculated on the fly with an intermodal router. MyTrips includes a larger survey and collects mobility diaries for one day representing respondents’ daily routine, calculates alternative routes and creates SP questions based on a Bayesian optimal design. Results from two case studies investigating behaviour changes are presented. The first case study investigated the extension of a subway line in Vienna, Austria. The second case study focused on the introduction of micro transit vehicles in a rural setting, replacing infrequent bus services. Results of the two case studies show a difference in response behaviour between SP and RP settings and suggest a reduction of hypothetical bias. For the latter study, a Latent Class SP-off-RP model was estimated. It shows that availability and accessibility of public transport are the main influences on the willingness to use it, independent of other household characteristics.

scholarly journals Values of Travel Time for Recreational Trips under Different Behavioural Rules

2021 ◽  
Vol 13 (12) ◽  
pp. 6831
Author(s):  
Rosa Marina González ◽  
Concepción Román ◽  
Ángel Simón Marrero
Keyword(s):  
Travel Time ◽  
National Parks ◽  
Traffic Congestion ◽  
National Park ◽  
Revealed Preference ◽  
Discrete Choice Models ◽  
Model Specification ◽  
Regret Minimization ◽  
Random Regret Minimization

In this study, discrete choice models that combine different behavioural rules are estimated to study the visitors’ preferences in relation to their travel mode choices to access a national park. Using a revealed preference survey conducted on visitors of Teide National Park (Tenerife, Spain), we present a hybrid model specification—with random parameters—in which we assume that some attributes are evaluated by the individuals under conventional random utility maximization (RUM) rules, whereas others are evaluated under random regret minimization (RRM) rules. We then compare the results obtained using exclusively a conventional RUM approach to those obtained using both RUM and RRM approaches, derive monetary valuations of the different components of travel time and calculate direct elasticity measures. Our results provide useful instruments to evaluate policies that promote the use of more sustainable modes of transport in natural sites. Such policies should be considered as priorities in many national parks, where negative transport externalities such as traffic congestion, pollution, noise and accidents are causing problems that jeopardize not only the sustainability of the sites, but also the quality of the visit.

scholarly journals Gender Inequality in Safety and Security Perceptions in Railway Stations

2021 ◽  
Vol 13 (7) ◽  
pp. 4007
Author(s):  
Pierluigi Coppola ◽  
Fulvio Silvestri
Keyword(s):  
Gender Gap ◽  
Public Transport ◽  
Stated Preference ◽  
Revealed Preference ◽  
Behavioral Models ◽  
Mobility Patterns ◽  
Railway Stations ◽  
Commercial Activities ◽  
Transport Services ◽  
Disaggregate Data

Recent studies have shown that gender is the personal aspect that mostly affects mobility patterns and travel behaviors. It has been observed, for instance, that female perception of unsafety and insecurity when traveling using public transport forces them to make unwanted travel choices, such as avoiding traveling at certain times of day and to specific destinations. In order to improve the attractiveness of public transport services, this gender gap must not be overlooked. This paper aims at contributing to research in gendered mobility by investigating differences in safety and security perceptions in railway stations, and by identifying which policies could be effective in bridging any existing gap. The methodology includes the collection of disaggregate data through a mixed Revealed Preference/Stated Preference survey, and the estimation of fixed and random parameters behavioral models. Results from a medium-sized Italian railway station show that female travelers feel safer in the presence of other people; they prefer intermodal infrastructures close to the entrance of the station and commercial activities in the internal premises. Moreover, unlike male travelers, they do not appreciate the presence of hedges and greenery outside stations.

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