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 Can Road Toll Convince Car Travelers to Adjust Their Departure Times? Accounting for the Effect of Choice Behavior under Long and Short Holidays

2020 ◽  
Vol 12 (24) ◽  
pp. 10470
Author(s):  
Haiyan Zhu ◽  
Hongzhi Guan ◽  
Yan Han ◽  
Wanying Li
Keyword(s):  
Traffic Congestion ◽  
Choice Behavior ◽  
Road Traffic ◽  
Stated Preference ◽  
Revealed Preference ◽  
The Road ◽  
Departure Time ◽  
Departure Time Choice ◽  
Mnl Model ◽  
Peak Travel

The adjustment of road toll is an important measure that can alleviate road traffic congestion by convincing car travelers to travel during off-peak times. In order to reduce congestion on the expressway on the first day of a holiday, factors that affect the departure times of holiday travelers must be comprehensively understood to determine the best strategy to persuade car travelers to avoid peak travel times. This paper takes holiday car travelers as the research object and explores the characteristics and rules of departure time choice behavior for different holiday lengths. Based on Utility Maximization Theory, a multinomial logit (MNL) model of departure time choice for a three-day short holiday and a seven-day long holiday was established. Model calibration and elastic analysis were carried out using Revealed Preference/Stated Preference (RP/SP) survey data. Additionally, the influence of the highway toll policy on departure times for long and short holidays was analyzed. The results show that the rate of first-day departures is much higher than that of other departure times for both short and long vacations under the current policy of free holiday passage on highways. Factors such as trip duration, size of the tourist group, the number of visits, travel range, travel time, monthly income, occupation, age and road toll have a significant influence on the departure time decisions of holiday car travelers, and the effect and degree of influence are markedly different for different holiday lengths. The effects of tolls for each departure time and different pricing scenarios on the choice behavior of travelers are different between long and short holidays. Furthermore, the effectiveness of the road toll policy also varies for travelers with different travel distances. This study can provide useful information for the guidance of holiday travelers, the management of holiday tolls on expressways and the formulation of holiday leave time.

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.

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 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%.

Departure and travel time model for the temporal distribution of morning rush-hour traffic congestion

2019 ◽  
Vol 31 (02) ◽  
pp. 2050023
Author(s):  
Sida Luo
Keyword(s):  
Travel Time ◽  
Traffic Congestion ◽  
Temporal Distribution ◽  
Time Estimation ◽  
Wave Model ◽  
Model Parameters ◽  
Travel Time Estimation ◽  
Time Model ◽  
Departure Time ◽  
Departure Time Choice

The chronic traffic congestion undermines the level of satisfaction within a society. This study proposes a departure time model for estimating the temporal distribution of morning rush-hour traffic congestion over urban road networks. The departure time model is developed based on the point queue model that is used for estimating travel time. First, we prove the effectiveness of the travel time model (i.e. point queue), showing that it gives the same travel time estimation as the kinematic wave model does for a road with successive bottlenecks. Then, a variant of the bottleneck model is developed accordingly, aiming to capture travelers’ departure time choice for commute trips. The proposed departure time model relaxes a traditional assumption that the last commuter experiences the free flow travel time and considers travelers’ unwillingness of late arrivals for work. Numerical experiments show that the morning rush-hour generally starts at 7:29 am and ends at 8:46 am with a traffic congestion delay index (TCDI) of 2.164 for Beijing, China. Furthermore, the estimation of rush-hour start and end time is insensitive to most model parameters including the proportion of travelers who tend to arrive at work earlier than their schedules.

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.

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