Agent-based en-route diversion: Dynamic behavioral responses and network performance represented by Macroscopic Fundamental Diagrams

Xiong, Chenfeng; Chen, Xiqun; He, Xiang; Lin, Xi; Zhang, Lei

doi:10.1016/j.trc.2015.04.008

Cited by 33 publications

(10 citation statements)

References 48 publications

(62 reference statements)

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“…Instead, previous studies have found that a Naive Bayes classifier has a relatively higher accuracy and adaptability if dealing with small data samples ( 15 , 34 ). An NB model utilizes a reasonable behavior function rather than the maximum utility theory for analyzing data ( 35 ).…”

Section: Model Formulationsmentioning

confidence: 99%

Frequentist and Bayesian Approaches for Understanding Route Choice of Drivers under Stop-and-Go Traffic

Saxena¹,

Wang

Dixit³

et al. 2020

Transportation Research Record: Journal of the Transportation R

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Driving in congested traffic is a nuisance that not only results in longer travel times, but also triggers frustration and impatience among drivers. A few studies have modeled the effects of congested traffic in the resulting route choice behavior of car drivers. The studies used frequentist models such as discrete choice models to analyze large samples. However, these studies did not compare the inferences obtained from the frequentist and Bayesian approaches, particularly for datasets which are not sufficiently large. It has been shown by researchers that Bayesian models perform well, especially when the sample size is small. Thus, this paper develops and compares a multinomial logit (frequentist) and a Naïve Bayes (Bayesian) model on a mid-sized dataset of size around 100 participants which was obtained from a driving simulator experiment to understand driver’s route choice under stop-and-go traffic. The results show that the prediction power of the Naïve Bayes model is much higher than the multinomial logit model (MNL). The Naïve Bayes model is also found to perform better than machine learning algorithms like the decision tree model. The findings from this study will be useful to researchers and practitioners as they should test both the approaches and select the appropriate model, particularly in the case of seemingly large datasets.

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Section: Model Formulationsmentioning

confidence: 99%

Frequentist and Bayesian Approaches for Understanding Route Choice of Drivers under Stop-and-Go Traffic

Saxena¹,

Wang

Dixit³

et al. 2020

Transportation Research Record: Journal of the Transportation R

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show abstract

“…The authors see a potential integration of the HMM and a one-day traffic simulation model to simulate day-to-day behavior changes. One example of integrated modeling is provided in Xiong et al (2015b). Interesting results on multimodal behavior responses can be captured.…”

Section: Closing Remarksmentioning

confidence: 99%

Dynamic travel mode searching and switching analysis considering hidden model preference and behavioral decision processes

Xiong

Zhang

2015

Transportation

Self Cite

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This paper proposes a conceptual framework to model the travel mode searching and switching dynamics. The proposed approach is structurally different from existing mode choice models in the way that a non-homogeneous hidden Markov model (HMM) has been constructed and estimated to model the dynamic mode srching process. In the proposed model, each hidden state represents the latent modal preference of each traveler. The empirical application suggests that the states can be interpreted as car loving and carpool/transit loving, respectively. At each time period, transitions between the states are functions of time-varying covariates such as travel time and travel cost of the habitual modes. The level-of-service (LOS) changes are believed to have an enduring impact by shifting travelers to a different state. While longitudinal data is not readily available, the paper develops an easy-to-implement memory-recall survey to collect required process data for the empirical estimation. Bayesian estimation and Markov chain Monte Carlo method have been applied to implement full Bayesian inference. As demonstrated in the paper, the estimated HMM is reasonably sensitive to mode-specific LOS changes and can capture individual and system dynamics. Once applied with travel demand and/or traffic simulation models, the proposed model can describe time-dependent multimodal behavior responses to various planning/policy stimuli.

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“…In response to the empirical MFD literature, a theoretical literature is emerging that explores the implications of the existence of an MFD for the regulation of urban congestion through, e.g., metering and gating (Keyvan-Ekbatani et al, 2012;Ramezani et al, 2015), pricing (Fosgerau, 2015;Simoni et al, 2015;Daganzo and Lehe, 2015), route guidance (Hajiahmadi et al, 2013;Xiong et al, 2016), and provision of road and transit capacity (Geroliminis et al, 2014;Loder et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Emergence of an Urban Traffic Macroscopic Fundamental Diagram

2019

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Agent-based en-route diversion: Dynamic behavioral responses and network performance represented by Macroscopic Fundamental Diagrams

Cited by 33 publications

References 48 publications

Frequentist and Bayesian Approaches for Understanding Route Choice of Drivers under Stop-and-Go Traffic

Frequentist and Bayesian Approaches for Understanding Route Choice of Drivers under Stop-and-Go Traffic

Dynamic travel mode searching and switching analysis considering hidden model preference and behavioral decision processes

Emergence of an Urban Traffic Macroscopic Fundamental Diagram

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