Connected population synthesis for transportation simulation

Zhang, Danqing; Cao, Junyu; Feygin, Sidney; Tang, Dounan; Shen, Zuo‐Jun Max; Pozdnoukhov, Alexei

doi:10.1016/j.trc.2018.12.014

Cited by 23 publications

(20 citation statements)

References 43 publications

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“…Some SL methods are hindered by the scalability problem when the number of attributes is large. For Bayesian Networks and Hierarchical Mixtures, it is preferable for the number of attributes to be small, as pointed out by Borysov et al (51) and Zhang et al (50). In fact, a Bayesian network with six nodes (corresponding to attributes) contains some 3 million possible DAGs, and this number becomes 1.1 billion with a network of seven nodes (21).…”

Section: Comparison Of Methodsmentioning

confidence: 99%

“…According to the DAG, nodes represent random variables, while arrows show the probabilistic dependencies between these variables. From the definition given by Zhang et al (50), BN consists of two main parts: (1) structural learning to define the network structure that describes the conditional independence of the random variables through a scoring approach, and (2) parameter estimation to learn a conditional distribution of random variables given this DAG structure. The synthetic population can then be generated accordingly, by sampling from the obtained Bayesian Network.…”

Section: Statistical Learningmentioning

confidence: 99%

“…Another SL-based method applied to population synthesis is the Bayesian Network (BN) ( 21, 50 ). A BN is a probabilistic graphical representation of the factorization of the joint probability distribution into conditional distributions, presented by means of a directed acyclic graph (DAG) ( 51 ).…”

Section: Statistical Learningmentioning

confidence: 99%

“…As pointed out by Sun and Erath ( 21 ), a network with six nodes (where each node represents an attribute) contains some 3 million possible DAGs. Zhang et al ( 50 ) suggested keeping the BN as simple as possible and creating a “whitelist” defining a set of relationships in the structural learning procedure that are to be preserved and guaranteed present in the final graph. This procedure involves building the network based on expert knowledge; yet despite this, mistakes can still be made.…”

Section: Statistical Learningmentioning

confidence: 99%

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Comparing Methods for Generating a Two-Layered Synthetic Population

Yameogo

Gastineau

Hankach

et al. 2020

Transportation Research Record: Journal of the Transportation R

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Synthetic population is used in many transport models ranging from trip-based, hybrid trip, tour-based, and activity-based models. As mobility decisions depend on both individuals’ characteristics and family situation, generating a two-layered population that takes into account not only the individual level but also household level is essential. In the literature, three main categories of methods for two-layered population generation have been proposed. These categories are synthetic reconstruction (SR), combinatorial optimization (CO), and statistical learning (SL). SR and CO methods produce synthetic populations by means of replicating individuals, whereas SL methods generate a population following a joint probability estimation. However, selecting a generation process is not straightforward as it depends on input data and synthetic population characteristics. To the best of our knowledge, no clear methodology for selecting between these methods exists. The main objectives of this paper are to provide (1) a detailed description of the available methods, (2) a comparison between these methods, and (3) a decision-making procedure for selecting between these methods. The description and comparison of the methods relies on different criteria: marginals availability, sample size, number of potential attributes that can be handled, population size to generate, possibility of zero-cell problem, and so forth. The advantages and shortcomings of each method are illustrated, and method performance is assessed. The decision-making procedure is carried out through the proposal of a decision tree. Researchers and practitioners have now access to a comprehensive and unified framework to select the appropriate method depending on available data and features of their modeling purposes.

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Section: Comparison Of Methodsmentioning

confidence: 99%

Section: Statistical Learningmentioning

confidence: 99%

Section: Statistical Learningmentioning

confidence: 99%

Section: Statistical Learningmentioning

confidence: 99%

See 2 more Smart Citations

Comparing Methods for Generating a Two-Layered Synthetic Population

Yameogo

Gastineau

Hankach

et al. 2020

Transportation Research Record: Journal of the Transportation R

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“…Delivering insight into the housing markets plays a significant role in the establishment of real estate policies and mastering real estate knowledge [ 1 , 2 , 3 ]. Thus, the advancement of accurate models for predicting real estate prices is of utmost importance for several essential economic key functions, for example, banking, insurance, and urban development [ 4 , 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence for Modeling Real Estate Price Using Call Detail Records and Hybrid Machine Learning Approach

Pintér

Mosavi

Felde

2020

Entropy

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Advancement of accurate models for predicting real estate price is of utmost importance for urban development and several critical economic functions. Due to the significant uncertainties and dynamic variables, modeling real estate has been studied as complex systems. In this study, a novel machine learning method is proposed to tackle real estate modeling complexity. Call detail records (CDR) provides excellent opportunities for in-depth investigation of the mobility characterization. This study explores the CDR potential for predicting the real estate price with the aid of artificial intelligence (AI). Several essential mobility entropy factors, including dweller entropy, dweller gyration, workers’ entropy, worker gyration, dwellers’ work distance, and workers’ home distance, are used as input variables. The prediction model is developed using the machine learning method of multi-layered perceptron (MLP) trained with the evolutionary algorithm of particle swarm optimization (PSO). Model performance is evaluated using mean square error (MSE), sustainability index (SI), and Willmott’s index (WI). The proposed model showed promising results revealing that the workers’ entropy and the dwellers’ work distances directly influence the real estate price. However, the dweller gyration, dweller entropy, workers’ gyration, and the workers’ home had a minimum effect on the price. Furthermore, it is shown that the flow of activities and entropy of mobility are often associated with the regions with lower real estate prices.

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