scholarly journals Using Clustering Methods in Multinomial Logit Model for Departure Time Choice

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shahriar Afandizadeh Zargari ◽  
Farshid Safari
Keyword(s):  
Test Group ◽  
Time Discretization ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
Discrete Choice Models ◽  
Clustering Methods ◽  
Clustering Method ◽  
Choice Sets ◽  
Departure Time ◽  
Departure Time Choice

Travellers have to make some decisions for each trip, and one of them is the choice of departure time. Discrete choice models have been employed as an approach to departure time modelling by many researchers. In this method, preparing choice set is a primary challenge which involves the definition of some departure periods to be selected by the traveller. In this research, choice sets were formed by applying the clustering methods on departure times. Afterwards, we developed Multinomial Logit (MNL) models on different choice sets and compared the models. The data used throughout this research belonged to Mashhad City. Research results indicated that Ward’s hierarchical clustering method is improper for time discretization; furthermore, the K-means clustering method is more efficient than the expectation maximization and K-medoids methods in the time discretization for MNL modelling. The developed model (based on K-means clustering method) accurately predicts departure time for 58% of persons within the test group, which reflects the effectiveness of the resulting model compared to the 36% which is obtained without the model.

scholarly journals Modeling Departure Time Choice of Car Commuters in Dhaka, Bangladesh

2021 ◽  
pp. 036119812110398
Author(s):  
Khatun Zannat ◽  
Charisma Farheen Choudhury ◽  
Stephane Hess
Keyword(s):  
Travel Time ◽  
Traffic Congestion ◽  
Logit Model ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
Data Sources ◽  
Similar Data ◽  
Schedule Delay ◽  
Departure Time ◽  
Departure Time Choice

Dhaka, one of the fastest-growing megacities in the world, faces severe traffic congestion leading to a loss of 3.2 million business hours per day. While peak-spreading policies hold the promise to reduce the traffic congestion levels, the absence of comprehensive data sources makes it extremely challenging to develop econometric models of departure time choices for Dhaka. This motivates this paper, which develops advanced discrete choice models of departure time choice of car commuters using secondary data sources and quantifies how level-of-service attributes (e.g., travel time), socio-demographic characteristics (e.g., type of job, income, etc.), and situational constraints (e.g., schedule delay) affect their choices. The trip diary data of commuters making home-to-work and work-to-home trips by personal car/ride-hailing services (957 and 934 respectively) have been used in this regard. Given the discrepancy between the stated travel times and those extracted using the Google Directions API, a sub-model is developed first to derive more reliable estimates of travel time throughout the day. A mixed multinomial logit model and a simple multinomial logit model are developed for outbound and return trip, respectively, to capture the heterogeneity associated with different departure time choice of car commuters. Estimation results indicate that the choices are significantly affected by travel time, schedule delay, and socio-demographic factors. The influence of type of job on preferred departure time (PDT) has been estimated using two different distributions of PDT for office employees and self-employed people (Johnson’s SB distribution and truncated normal respectively). The proposed framework could be useful in other developing countries with similar data issues.

Pricing Competition Under Specific Discrete Choice Models

2020 ◽  
Vol 37 (02) ◽  
pp. 2050008
Author(s):  
Farhad Etebari
Keyword(s):  
Discrete Choice ◽  
Logit Model ◽  
Choice Model ◽  
Public Information ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
Discrete Choice Models ◽  
Nested Logit ◽  
Nested Logit Model ◽  
Substitution Pattern

Recent developments of information technology have increased market’s competitive pressure and products’ prices turned to be paramount factor for customers’ choices. These challenges influence traditional revenue management models and force them to shift from quantity-based to price-based techniques and incorporate individuals’ decisions within optimization models during pricing process. Multinomial logit model is the simplest and most popular discrete choice model, which suffers from an independence of irrelevant alternatives limitation. Empirical results demonstrate inadequacy of this model for capturing choice probability in the itinerary share models. The nested logit model, which appeared a few years after the multinomial logit, incorporates more realistic substitution pattern by relaxing this limitation. In this paper, a model of game theory is developed for two firms which customers choose according to the nested logit model. It is assumed that the real-time inventory levels of all firms are public information and the existence of Nash equilibrium is demonstrated. The firms adapt their prices by market conditions in this competition. The numerical experiments indicate decreasing firm’s price level simultaneously with increasing correlation among alternatives’ utilities error terms in the nests.

scholarly journals On Modeling Departure-Time Choice for Home-Based Social/Recreational and Shopping Trips

2000 ◽  
Vol 1706 (1) ◽  
pp. 152-159 ◽  
Author(s):  
Jennifer L. Steed ◽  
Chandra R. Bhat
Keyword(s):  
Discrete Choice ◽  
Discrete Choice Models ◽  
Fort Worth ◽  
Service Characteristics ◽  
Home Based ◽  
Departure Time ◽  
Departure Time Choice ◽  
Shopping Trips ◽  
Activity Survey ◽  
Employment Characteristics

The existing literature on departure-time choice has primarily focused on work trips. Departure-time choice for nonwork trips, which constitute an increasingly large proportion of urban trips, is examined. Discrete choice models are estimated for home-based social/recreational and home-based shopping trips using the 1996 activity survey data collected in the Dallas—Fort Worth metropolitan area. The effects of individual and household sociodemographics, employment attributes, and trip characteristics on departure-time choice are presented and discussed. The results indicate that departure-time choice for social/recreational trips and shopping trips is determined for the most part by individual or household sociodemographics and employment characteristics, and to a lesser extent by trip level-of-service characteristics. This suggests that departure times for social/recreational and shopping trips are not as flexible as one might expect and are confined to certain times of day because of overall scheduling constraints. Future methodological and empirical extensions of the current research are identified.

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.

Analysis of Time Allocation, Departure Time, and Route Choice Behavior Under Congestion Pricing

2000 ◽  
Vol 1725 (1) ◽  
pp. 95-101 ◽  
Author(s):  
Toshiyuki Yamamoto ◽  
Satoshi Fujii ◽  
Ryuichi Kitamura ◽  
Hiroshi Yoshida
Keyword(s):  
Time Allocation ◽  
Choice Model ◽  
Stated Preference ◽  
Congestion Pricing ◽  
Route Choice ◽  
Multinomial Logit Model ◽  
Allocation Model ◽  
Departure Time ◽  
Departure Time Choice ◽  
Pricing Schemes

Driver behavior under congestion pricing is analyzed to evaluate the effects of alternative congestion pricing schemes. The analysis, which is based on stated preference survey results, focuses on time allocation, departure time choice, and route choice when a congestion pricing scheme is implemented on toll roads in Japan. A unique feature of the model system of this study is that departure time choice and route choice are analyzed in conjunction with the activities before and after the trip. A time allocation model is developed to describe departure time choice, and a route and departure time choice model is developed as a multinomial logit model with alternatives representing the choice between freeways and surface streets and, for departure time, the choice from among before, during, or after the period when congestion pricing is in effect. The results of the empirical analysis suggest that departing during the congestion pricing period tends to have higher utilities and that a worker and a nonworker have quite different utility functions. The comparative analysis of different congestion pricing schemes is carried out based on the estimated parameters. The results suggest that the probability of choosing each alternative is stable even if the length of the congestion pricing period changes, but a higher congestion price causes more drivers to change the departure time to before the congestion pricing period.

scholarly journals Departure Time Choice (DTC) Behavior for Intercity Travel during a Long-Holiday in Bangkok, Thailand

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Bhawat Chaichannawatik ◽  
Kunnawee Kanitpong ◽  
Thirayoot Limanond
Keyword(s):  
Travel Behavior ◽  
Traffic Congestion ◽  
Travel Demand ◽  
Multinomial Logit Model ◽  
Time Of Day ◽  
Travel Patterns ◽  
Peak Period ◽  
Intercity Travel ◽  
Departure Time ◽  
Departure Time Choice

Time-of-day (TOD) or departure time choice (DTC) has become an interesting issue over two decades. Many researches have intensely focused on time-of-day or departure time choice study, especially workday departures. However, the travel behavior during long-holiday/intercity travel has received relatively little attention in previous studies. This paper shows the characteristics of long-holiday intercity travel patterns based on 2012 New Year data collected in Thailand with a specific focus on departure time choice of car commuters due to traffic congestion occurring during the beginning of festivals. 590 interview data were analyzed to provide more understanding of general characteristics of DTC behavior for intercity travel at the beginning of a Bangkok long-holiday. Moreover, the Multinomial Logit Model (MNL) was used to find the car-based DTC model. The results showed that travelers tend to travel at the peak period when the parameters of personal and household are not so significant, in contrast to the trip-related characteristics and holiday variables that play important roles in traveler decision on departure time choice. Finally, some policies to distribute travel demand and reduce the repeatable traffic congestion at the beginning of festivals are recommended.

Exploiting Rank Ordered Choice Set Data within the Stochastic Utility Model

1982 ◽  
Vol 19 (3) ◽  
pp. 288-301 ◽  
Author(s):  
Randall G. Chapaaan ◽  
Richard Staelin
Keyword(s):  
Logit Model ◽  
Estimation Procedure ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
Small Sample ◽  
Utility Model ◽  
Model Formulation ◽  
Decomposition Procedure ◽  
Choice Sets ◽  
Choice Set

The authors report on a procedure for exploiting the information content of rank ordered choice sets to estimate efficiently the parameters of the multinomial logit model formulation of the stochastic utility model of choice behavior. The availability of rank ordered choice set data leads to an “explosion” or decomposition procedure for exploiting such extra information. This “explosion” process involves the decomposition of a ranked choice set into a series of unranked and statistically independent choice sets. In relation to explosion strategies, several heuristics and an analytical procedure for determining the “optimal” explosion depth are discussed in detail. The results of a Monté Carlo study of the small sample properties of the conditional logit estimation procedure (the maximum likelihood estimation procedure used to develop parameter estimates of the multinomial logit model formulation of the stochastic utility model) are reported and interpreted. A college choice empirical application illustrates the procedures developed.

scholarly journals Factors Affecting Pharmaceutical Care Implementation by Community Pharmacist: A Discrete Choice Experiment

2018 ◽  
Vol 8 (3) ◽  
pp. 136
Author(s):  
Nia Kurnia Sholihat ◽  
Masita Wulandari Suryoputri ◽  
Ade Martinus
Keyword(s):  
Pharmaceutical Care ◽  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Choice Experiment ◽  
Multinomial Logit Model ◽  
Simple Random Sampling ◽  
Multinomial Logit ◽  
Cross Sectional ◽  
Factors Affecting ◽  
Choice Sets

Even though pharmaceutical care has been proven increasing patients’ quality of life, pharmacists still have barriers to implement it. Our study aims to examine factors affecting pharmacists in the community to implement pharmaceutical care using a Discrete Choice Experiment (DCE). The study was a cross-sectional study. A structured DCE questionnaire was administered to 90 community pharmacists in Banyumas district, Indonesia. Respondents were chosen using a simple random sampling method. According to the literature review and expert opinions, the following six attributes were selected: pharmacists’ confidence; willingness to implement pharmaceutical care; communication skill; knowledge and professional skill; availability of time; and availability of space in pharmacy. Eighteen choice sets were developed. Each choice sets comprised of two scenarios. Respondents were asked to choose the scenario they preferred the most. Data were analyzed using multinomial logit model. Of 90 questionnaires distributed, 67 were analyzed. Based on multinomial logit, all attributes had a significant effect on pharmacists’ preferences to implement pharmaceutical care. The findings suggested that pharmacist association should train their member to increase professional skills, as well as the management of pharmacy should provide enough space to perform pharmaceutical care.

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