Commuter Behavioral Model for the Pilot Program of an Electronic Toll System on Korea Express Highways

In recent years, congestion pricing has gained popularity as a method for managing peak-period congestion on major roads in urban areas. Intelligent Transportation Systems (ITS) technology has resulted in an electronic toll collection (ETC) system called “Hi-Pass,” which is operated by the Korea Highway Cooperation for the pilot program. The limited sections of highway involved in the program have gained good approval ratings from motorists. In this research, the Korean highway toll system was analyzed with respect to the brief legal history of the toll road, collection method and levels. In addition, the electronic toll collection policy for foreign countries including France, Norway, and Italy are investigated. The toll system of each country is clearly differentiated with regard to the institution, regulation and the country’s economy. In the review of the pilot ETC program “Hi-pass” system in Korea, several important factors were found. First, a more concrete marketing strategy for ETC users will needed to encourage continued usage. In particular, frequent users and higher-income users adopted the ETC system more easily than others did. Second, in order to encourage the non-user to try the ETC system, the costs of on-board units must be kept reasonably low (about $50), and aggressive discounts (about 3-5% compared to the current 1%) are needed

Socioeconomic Characteristics of Speeding Behavior

Many studies on drivers’ speeding behavior have been reported in the last decade. Most of the previous studies, however, have concentrated on the relationship between drivers\u27 speeding behavior and road/vehicle characteristics, without considering other important factors such as personal characteristics and drivers\u27 perception of the speed limit. This paper analyzes Korean drivers\u27 speeding behavior by taking into account such factors as trip characteristics in addition to personal, vehicular, and attitudinal factors. Speeding behavior is measured by a categorical measure over the speed limit, and an ordered probit model is used to econometrically estimate the speeding behavior equation. Results indicated that i) male drivers with higher income tend to drive faster, and experienced drivers drive at higher speeds than others ii) vehicles with more horsepower and vehicles with safety features go slower than vehicles with less safety features iii) trip distance and frequent use of the road are important factors for speed selection behavior, iv) perceived speed limit of the road and expectation of being caught for speeding are important factors for driving behavior

Posterior-Aided Regularization for Likelihood-Free Inference

The recent development of likelihood-free inference aims training a flexible density estimator for the target posterior with a set of input-output pairs from simulation. Given the diversity of simulation structures, it is difficult to find a single unified inference method for each simulation model. This paper proposes a universally applicable regularization technique, called Posterior-Aided Regularization (PAR), which is applicable to learning the density estimator, regardless of the model structure. Particularly, PAR solves the mode collapse problem that arises as the output dimension of the simulation increases. PAR resolves this posterior mode degeneracy through a mixture of 1) the reverse KL divergence with the mode seeking property; and 2) the mutual information for the high quality representation on likelihood. Because of the estimation intractability of PAR, we provide a unified estimation method of PAR to estimate both reverse KL term and mutual information term with a single neural network. Afterwards, we theoretically prove the asymptotic convergence of the regularized optimal solution to the unregularized optimal solution as the regularization magnitude converges to zero. Additionally, we empirically show that past sequential neural likelihood inferences in conjunction with PAR present the statistically significant gains on diverse simulation tasks

Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder

The problem of fair classification can be mollified if we develop a method to remove the embedded sensitive information from the classification features. This line of separating the sensitive information is developed through the causal inference, and the causal inference enables the counterfactual generations to contrast the what-if case of the opposite sensitive attribute. Along with this separation with the causality, a frequent assumption in the deep latent causal model defines a single latent variable to absorb the entire exogenous uncertainty of the causal graph. However, we claim that such structure cannot distinguish the 1) information caused by the intervention (i.e., sensitive variable) and 2) information correlated with the intervention from the data. Therefore, this paper proposes Disentangled Causal Effect Variational Autoencoder (DCEVAE) to resolve this limitation by disentangling the exogenous uncertainty into two latent variables: either 1) independent to interventions or 2) correlated to interventions without causality. Particularly, our disentangling approach preserves the latent variable correlated to interventions in generating counterfactual examples. We show that our method estimates the total effect and the counterfactual effect without a complete causal graph. By adding a fairness regularization, DCEVAE generates a counterfactual fair dataset while losing less original information. Also, DCEVAE generates natural counterfactual images by only flipping sensitive information. Additionally, we theoretically show the differences in the covariance structures of DCEVAE and prior works from the perspective of the latent disentanglement

ACTIVE DSRC APPLICATION FOR ITS AND ECONOMIC VALUATION IN KOREA

Providing real time traffic information is a key for effective implementation of Intelligent Transport Systems (ITS). The main purpose of this paper is to introduce the recent technological trends of Dedicated Short Range Communications (DSRC) applications for ITS and its economic evaluation, focusing on the City Bus Information System (CBIS) and Electronic Toll Collection System (ETC) in Korea. From a research perspective, it is necessary for the seamless development and maintenance of technical and competitive edges, and the proper budgetary allocation for research and development. Furthermore, the progressive participation of private companies that have the leading technologies on active DSRC is also required.

A Calculus of Virtual Community Knowledge Intentions: Anonymity and Perceived Network-Structure

This study investigates the underlying motivational factors of knowledge exchange intentions (intention to obtain and to provide knowledge) within virtual community contexts. Perceived virtual network structures, namely virtual network connectivity (CN) and virtual network closeness (CL), are suggested as the important antecedents of knowledge sharing intentions in the context of virtual knowledge exchange communities. Anonymity (AN), one of the unique characteristics of virtual communities, but controversial due to its multi-faceted effects, is considered in a structural model as a factor having an impact on a virtual network structure. Data collected from participants of virtual communities through online surveys are analyzed using Partial Least Squares (PLS) structural equation modeling (SEM) to empirically test the proposed hypotheses. The results reveal that both CN and CL have a significant impact on both of the knowledge exchange intentions although CL shows an opposite direction of the impact. The results also show that AN has a significant impact on CL as expected but not on CN. Implications of this study may shed some light on better understanding community participants’ intentions to obtain and provide knowledge, along with the impact of anonymity on the perceived network structure

Is effect of transcranial direct current stimulation on visuomotor coordination dependent on task difficulty?

Transcranial direct current stimulation (tDCS), an emerging technique for non-invasive brain stimulation, is increasingly used to induce changes in cortical excitability and modulate motor behavior, especially for upper limbs. The purpose of this study was to investigate the effects of tDCS of the primary motor cortex on visuomotor coordination based on three levels of task difficulty in healthy subjects. Thirty-eight healthy participants underwent real tDCS or sham tDCS. Using a single-blind, sham-controlled crossover design, tDCS was applied to the primary motor cortex. For real tDCS conditions, tDCS intensity was 1 mA while stimulation was applied for 15 minutes. For the sham tDCS, electrodes were placed in the same position, but the stimulator was turned off after 5 seconds. Visuomotor tracking task, consisting of three levels (levels 1, 2, 3) of difficulty with higher level indicating greater difficulty, was performed before and after tDCS application. At level 2, real tDCS of the primary motor cortex improved the accurate index compared to the sham tDCS. However, at levels 1 and 3, the accurate index was not significantly increased after real tDCS compared to the sham tDCS. These findings suggest that tasks of moderate difficulty may improve visuomotor coordination in healthy subjects when tDCS is applied compared with easier or more difficult tasks

Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation

Recent advances in diffusion models bring state-of-the-art performance on image generation tasks. However, empirical results from previous research in diffusion models imply an inverse correlation between density estimation and sample generation performances. This paper investigates with sufficient empirical evidence that such inverse correlation happens because density estimation is significantly contributed by small diffusion time, whereas sample generation mainly depends on large diffusion time. However, training a score network well across the entire diffusion time is demanding because the loss scale is significantly imbalanced at each diffusion time. For successful training, therefore, we introduce Soft Truncation, a universally applicable training technique for diffusion models, that softens the fixed and static truncation hyperparameter into a random variable. In experiments, Soft Truncation achieves state-of-the-art performance on CIFAR-10, CelebA, CelebA-HQ 256x256, and STL-10 datasets.Comment: 28 pages, 16 figures, 15 table

Evaluation of Heated Window System to Enhance Indoor Thermal Comfort and Reduce Heating Demands Based on Simulation Analysis in South Korea

Heated glass can be applied to improve windows’ condensation resistance and indoor thermal comfort in buildings. Although this applied technology has advantages, there are still some concerns in practical applications, such as additional energy consumption and control issues. This study evaluates the effectiveness of a heated window heating (HWH) system in terms of thermal comfort and heating energy performance (HEP). The simulation-based analysis is performed to evaluate the effectiveness of the HWH using a residential building model and to compare it with radiant floor heating (RFH) and hybrid heating (HH) systems (i.e., combined HWH and RFH). This study also investigates the peak and cumulative heating loads using HWH systems with various scenarios of control methods and setpoint temperature. The predicted mean vote (PMV) is used as an indoor thermal comfort index. The ratio of cumulative thermal comfort time to the entire heating period is calculated. The results show that HWH and HH can reduce the heating load by up to 65.60% and 50.95%, respectively, compared to RFH. In addition, the times of thermal comfort can be increased by 12.55% and 6.98% with HWH and HH, respectively. However, considering the social practices of South Korea, HH is more suitable than HWH. Further investigations for HH show that a surface setpoint of 26 °C is proper, considering both heating demands and thermal comfort. In addition, the setpoint temperature should be determined considering HEP and the thermal comfort for HWH, and the optimal setpoint temperature was suggested under specific conditions