A mixed Bayesian network for two-dimensional decision modeling of departure time and mode choice

2017 ◽  
Vol 45 (5) ◽  
pp. 1499-1522 ◽  
Author(s):  
Zheng Zhu ◽  
Xiqun Chen ◽  
Chenfeng Xiong ◽  
Lei Zhang
Keyword(s):  
Bayesian Network ◽  
Mode Choice ◽  
Decision Modeling ◽  
Two Dimensional ◽  
Departure Time

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 Joint Analysis of the Commuting Departure Time and Travel Mode Choice: Role of the Built Environment

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Xiaolei Ma ◽  
Jie Yang ◽  
Chuan Ding ◽  
Jianfeng Liu ◽  
Quan Zhu
Keyword(s):  
Built Environment ◽  
Socioeconomic Factors ◽  
Public Transit ◽  
Mode Choice ◽  
Joint Analysis ◽  
Mobile Source ◽  
Travel Mode ◽  
Study Region ◽  
Travel Mode Choice ◽  
Departure Time

This paper aims to conduct an empirical study to evaluate the influence of built environment features and socioeconomic factors on commuters’ simultaneous choice of departure time and travel mode. Using Kunming, China, as the study region, the 2015 Regional Household Travel Survey and 2016 Point of Interest data are used in the analysis. The results show that, in addition to socioeconomic factors, built environment, such as the density of residential building, employment, and service facility are correlated with joint choice behavior. Moreover, there exist differences regarding the influence of built environment and socioeconomic factors on departure time and travel mode choice. The dissimilarity parameters show that commuters prefer to shift travel mode than departure time generally when travel condition alters. In order to examine the policy measures’ potential performance, the paper conducts simulation tests based on the Monte Carlo method. The simulation results show that congestion pricing of car travel during peak hours can reduce the number of commuting trips, and reducing travel time of public transit would be a better strategy to attract more passengers during peak hours. Moreover, reasonable land use planning, such as building more bus stops around commuters’ home location, would be a long term and fundamental approach to reduce mobile-source emissions and attract more public transit passengers.

scholarly journals A Bayesian Network Model on the Public Bicycle Choice Behavior of Residents: A Case Study of Xi’an

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Qiuping Wang ◽  
Hao Sun ◽  
Qi Zhang
Keyword(s):  
Bayesian Network ◽  
Network Model ◽  
Expert Knowledge ◽  
Choice Model ◽  
Mode Choice ◽  
Bayesian Network Model ◽  
Travel Mode ◽  
The Public ◽  
Proposed Model ◽  
Travel Mode Choice

In order to study the main factors affecting the behaviors that city residents make regarding public bicycle choice and to further study the public bicycle user’s personal characteristics and travel characteristics, a travel mode choice model based on a Bayesian network was established. Taking residents of Xi’an as the research object, a K2 algorithm combined with mutual information and expert knowledge was proposed for Bayesian network structure learning. The Bayesian estimation method was used to estimate the parameters of the network, and a Bayesian network model was established to reflect the interactions among the public bicycle choice behaviors along with other major factors. The K-fold cross-validation method was used to validate the model performance, and the hit rate of each travel mode was more than 80%, indicating the precision of the proposed model. Experimental results also present the higher classification accuracy of the proposed model. Therefore, it may be concluded that the resident travel mode choice may be accurately predicted according to the Bayesian network model proposed in our study. Additionally, this model may be employed to analyze and discuss changes in the resident public bicycle choice and to note that they may possibly be influenced by different travelers’ characteristics and trip characteristics.

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