Modeling and estimation of travel behaviors using bayesian network

A tour-based travel demand model using person trip data and its application to advanced policies

Urban density not only represents the intensity of travel demand, but also serves as a useful tool for quantifying the elements that influence travel behavior and demand. In this study, Khulna City of Bangladesh has been selected as a case study to explore the impacts of urban density on travel behavior in a city of a developing country. The average characteristics of urban density, as well as their impacts on travel behavior and demand, have been studied using geospatial analysis techniques. Urban densities have been categorized into Single Density (Population Density, Employment Density, and Building Density) and Composite Density (Household to Work trip Density, Household to School trip Density and Household to Shop trip Density) based on characteristics of travel behavior and demand. Descriptive statistics have been used to explore the impacts of urban densities on the various trip characteristics such as trip production, trip distance, trip cost, trip time, and trip mode. Diversity of trips and overall trip attraction areas have been identified using the Shannon diversity index and other spatial analyses. Trip production, trip distance, trip time, trip cost, and mode choice all show a positive linear relationship with urban single-density, according to this study. The everyday activities of residents are closely related to urban composite densities, and trip production increase in tandem with composite densities. It has been concluded that different urban densities have significant impacts on travel demand and travel behavior.

AN INTEGRATED MODELING FRAMEWORK FOR ENVIRONMENTALLY EFFICIENT CAR OWNERSHIP AND TRIP BALANCE

Understanding the demand for access information

Predicting coastal hazards for sandy coasts with a Bayesian Network

Prelims

Most existing studies using household travel surveys have focused on travel behavior; however, this study demonstrates another use: examining the temporal profile of households with every member out-of-home (HEMO). Because a household travel survey records all trips by each member of a household, the calculation of HEMO is not a difficult task; however, few studies have empirically shown the temporal profile of HEMO rates. This study calculated the HEMO and individual out-of-home (IO) rates using a household travel survey, also known as the Person Trip (PT) survey in Japan. Specifically, we compared these rates using the Kumamoto PT surveys in 1984, 1997, and 2012 and discussed the factors for change using a novel method to decompose the change in HEMO. The results showed that the HEMO rate has increased considerably, although there was no major change in the IO rate over 28 years. Decomposition analysis revealed that the increase in HEMO is mainly attributed to an increase in single-person households and active seniors, and a decrease in household size.

During the last decades, concepts of Intelligent Transportation Systems (ITS) were continuously adapted and improved based on new insights into human travel behavior. Drivers for improvements are the quantity and quality of available mobility data, which increased significantly in recent years. Based on travel behavior, literature proposes a large number of different solutions for next step or future location prediction. However a holistic spatio-temporal prediction, which could further improve the quality of ITS, creates a more complex task. The prediction of medium-term mobility for one to seven days is challenging in particular for atypical travel behavior, since the weekdays’ order delivers no reliable indication for the next day’s travel behavior. With our contribution, we explore the benefits of various prediction approaches for medium-term mobility prediction and combine them dynamically to predict individual mobility behavior for a period of one week. The derived framework utilizes an exhaustive search approach to benefit from a machine learning based clustering method on location data. In conjunction with an Artificial Neural Network, the prediction framework is robust against prediction errors created by atypical behavior. With two data sets consisting of smartphone and vehicle data, we demonstrate the framework’s real-world applicability. We show that clustering an individual’s historical movement data can improve the prediction accuracy of different prediction methods that will be explained in detail and illustrate the interrelation of entropy and prediction accuracy.

Bayesian Network is a knowledge representation formalism that has been proven to be valuable in gene regulatory network reconstruction. However, it is showed that the structure learning of Bayesian networks is an NP-hard problem. Several heuristic searching techniques have been used to find better network structures. Among these algorithms, K2 algorithm has been found to be the most successful. However, the performance of K2 algorithm is greatly affected by a prior ordering of input nodes. If using inappropriate orderings, the accuracy of the learned structures will be relatively low. This paper represents a new structure learning method to reconstruct gene networks from the observational gene expression data. The proposed method is based on Binary Particle Swarm Optimization and K2 algorithm. It firstly identifies nodes ordering from the experimental data by using Binary Particle Swarm Optimization algorithm. Then, the nodes ordering information is used as an input of K2 algorithm so as to improve the performance and efficiency of the K2 algorithm in determining the network structure. Our method is evaluated using two benchmark network datasets with known structures. Experimental studies show that the proposed method can identify networks more efficiently and accurately than comparable methods reported in the literature.

Learning the structure of Bayesian networks (BNs) from high dimensional discrete data is common nowadays but a challenging task, due to the large parameter space, the acyclicity constraint placed on the graphical structures and the difficulty in searching for a sparse structure. In this article, we propose a sparse structure learning algorithm (SSLA) to solve this problem. The algorithm uses the negative log-likelihood function of multi-logit regression as a loss function, adding the adaptive group lasso as a penalty term for sparsity, with a new penalty term to ensure that the learned graph is a directed acyclic graph. A block coordinate descent algorithm (BCD) combining with the alternating direction multiplier method (ADMM) algorithm is developed to solve the proposed model. The learned graph is proved theoretically to be a Bayesian network. In order to evaluate the proposed SSLA and compare with its competitors, we conducted intensive simulation studies and applied them to the benchmark Bayesian networks. The results indicate that the SSLA is superior to the hill climbing (HC) algorithm, the CD algorithm and the BFO-B algorithm respectively, and is competitive with K2 algorithm when the order of the nodes is given.

For several years, the United Kingdom (UK) transport policy has discouraged the construction of additional road space to meet the rising demand for road travel. Increasingly, Local Traffic Authorities (LTAs) in England are looking towards technology and Intelligent Transport Systems (ITS) for maximizing the efficiency with which they use their road networks. The aim of this paper is twofold. First, it provides a perspective on how English LTAs are using and planning to use ITS. Primarily, the recent UK Traffic Management Act influences these new strategies, which include the types of technology they are choosing. Findings are presented from some research carried out with a variety of English LTAs examining their use of and opinions concerning technology and ITS. Second, a case study is then provided on how one English LTA (Surrey County Council) is deploying ITS to help meet its new obligations. Conclusions are then drawn on the efficacy of setting central policy to influence LTAs in this manner.

An efficient node ordering method using the conditional frequency for the K2 algorithm

A multilevel structural equation model (SEM) is applied to handle the hierarchical nature of the data and explain complex relationships among socioeconomic factors for individuals and households, activity participation, and travel behavior by using data from Wave 3 of the Puget Sound Transportation Panel. On the basis of the model structure, variations in individual activity participation and travel behavior can be divided into two categories: within-household variation and between-household variation. Empirical results show that the interdependency index of leisure activity duration for household members within a household has a value of 0.33, indicating high interdependency. Total travel time duration likewise has a value of 0.26, indicating high similarity. These results suggest that the multilevel SEM approach is an appropriate modeling methodology and can provide additional information on activity and travel behavior. The effects from exogenous variables on endogenous variables developed in this study also provide meaningful and interpretable results, which are consistent with results of previous studies. Most personal and household characteristics influence activity participation and travel behavior within a household as well as between households.

Travel mode detection based on GPS track data and Bayesian networks

Bayesian Networks is one of simple Probabilistic Graphical Models are built from theory of bayes probability and graph theory. Probability theory Is directly related to data while graph theory directly related to the form representation to be obtained. Multinomial Bayesian Network method is one method that involves the influence of spatial linkages suggest a link between rainfall observation stations. The objective of this study was seek the result of the model probabilistic a graph Multinomial Bayesian Network and apply it in forecasting with Oldeman classification based on one or two rainfall stations are known. This research uses simulated data for 14 stations respectively each 300 sets of data. The data generated is normal distribution of data based on parameters that have been determined and classified using the classification Oldeman. Bayesian Network structure constructed using the K2 algorithm. Markov chain transition matrix is formed based on the Bayesian of the nodes are directional. Model of Multinomial Bayesian Network was established based on Markov transition matrices. The result of probability model can predict the probability of rainfall in some stations based on one or two rainfall stations are known, which is a model graph with 14 nodes and 13 arcs.