Bayesian Network Inference on Departure Time Choice Behavior

2018 ◽  
pp. 599-607
Author(s):  
Xian Li ◽  
Haiying Li ◽  
Linqiao Qin ◽  
Xinyue Xu
Keyword(s):  
Bayesian Network ◽  
Choice Behavior ◽  
Network Inference ◽  
Departure Time ◽  
Departure Time Choice ◽  
Bayesian Network Inference

scholarly journals Bayesian network inference using marginal trees

2016 ◽  
Vol 68 ◽  
pp. 127-152 ◽  
Author(s):  
Cory J. Butz ◽  
Jhonatan S. Oliveira ◽  
Anders L. Madsen
Keyword(s):  
Bayesian Network ◽  
Network Inference ◽  
Bayesian Network Inference

scholarly journals Bayesian Network Inference Using Marginal Trees

2014 ◽  
pp. 81-96 ◽  
Author(s):  
Cory J. Butz ◽  
Jhonatan de S. Oliveira ◽  
Anders L. Madsen
Keyword(s):  
Bayesian Network ◽  
Network Inference ◽  
Bayesian Network Inference

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.

Estimating Travel Time Distributions by Bayesian Network Inference

2020 ◽  
Vol 21 (5) ◽  
pp. 1867-1876 ◽  
Author(s):  
Anatolii Prokhorchuk ◽  
Justin Dauwels ◽  
Patrick Jaillet
Keyword(s):  
Bayesian Network ◽  
Travel Time ◽  
Network Inference ◽  
Bayesian Network Inference

FPGA implementation of Bayesian network inference for an embedded diagnosis

2015 ◽  
Author(s):  
Sara Zermani ◽  
Catherine Dezan ◽  
Hanen Chenini ◽  
Jean-Philippe Diguet ◽  
Reinhardt Euler
Keyword(s):  
Bayesian Network ◽  
Network Inference ◽  
Fpga Implementation ◽  
Bayesian Network Inference
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