Here, we develop a dry eye syndrome (DES) incidence rate prediction model using air pollutants (PM₁₀, NO₂, SO₂, O₃, and CO), meteorological factors (temperature, humidity, and wind speed), population rate, and clinical data for South Korea. The prediction model is well fitted to the incidence rate (R² = 0.9443 and 0.9388, p < 2.2 × 10⁻¹⁶). To analyze regional deviations, we classify outpatient data, air pollutant, and meteorological factors in 16 administrative districts (seven metropolitan areas and nine states). Our results confirm NO₂ and relative humidity are the factors impacting regional deviations in the prediction model.

Time-stamped, media access control addresses, acquired from Bluetooth-enabled devices to collect travel time data, have received significant attention in the past few years. Research has focused mainly on the application of Bluetooth technology to obtain travel time data on freely flowing roads. A few studies have addressed the use of Bluetooth data collection systems on arterial roads, particularly with respect to travel times between signalized intersections, with questionable accuracy. The objective of this research was to develop a methodology to collect accurate and precise travel time data between signalized intersections, with the use of a data collection system that had a Bluetooth basis. The developed methodology utilized received signal strength indicator (RSSI) data to improve signifi-cantly the accuracy of intersection-to-intersection travel time samples. Test results on a busy arterial road showed that the travel time samples generated with the RSSI method were significantly better (i.e., had less error) than the travel time samples calculated with other methods reported in the literature. The proposed RSSI travel time data collection method could be implemented with any wireless technology that provided a unique identification number to distinguish between different mobile devices and an associated signal strength measurement during the wireless communication process.

Multiple applications of travel time data collection based on the reading of time-stamped media access control (MAC) addresses from Bluetooth-enabled devices have been reported in the literature, and commercial data collection units (DCUs) have been developed and deployed. One characteristic of Bluetooth-based travel time data obtained for travel time sample-generation purposes is that a MAC address is typically read multiple times as a vehicle containing the Bluetooth device travels within the length of road covered by the DCU’s antenna. Such data records of the same MAC address will have different timestamps and received signal strength levels. If the DCUs are installed at arterial intersections to collect travel time samples, this characteristic of MAC address data collection may also be used as the basis for estimating intersection performance. In this research, the use of the time/duration information present in the MAC address data to estimate intersection performance measures is investigate...

Ozone (O₃) is a commonly known air pollutant that causes adverse health effects. This study developed a multi-level prediction model for conjunctivitis in outpatients due to exposure to O₃ by using 3 years of ambient O₃ data, meteorological data, and hospital data in Seoul, South Korea. We confirmed that the rate of conjunctivitis in outpatients (conjunctivitis outpatient rate) was highly correlated with O₃ (R² = 0.49), temperature (R² = 0.72), and relative humidity (R² = 0.29). A multi-level regression model for the conjunctivitis outpatient rate was well-developed, on the basis of sex and age, by adding statistical factors. This model will contribute to the prediction of conjunctivitis outpatient rate for each sex and age, using O₃ and meteorological data.

A variety of automatic data collection technologies have been used to gather road and highway system data. The majority of these automatic data collection technologies are designed to collect vehicle-based data and either do not have the capability to collect other travel mode data (e.g., bicycles and pedestrians), or may need to be deployed differently to support this capability. One type of wireless-based data collection system that has been deployed recently is based on Bluetooth technology. A key feature of Bluetooth-based data collection systems that makes travel mode identification feasible is that the Bluetooth-enabled devices within vehicles are also present on bicyclists and pedestrians. This research explores the effectiveness of applying cluster analysis methods when processing data collected via Bluetooth technology from vehicles, bicyclists, and pedestrians to automatically identify the associated travel modes. The results of several experiments utilizing multiple Bluetooth-based data collection units arranged linearly and in relatively close proximity on a simulated intersection demonstrate the potential of cluster analysis to accurately differentiate transportation modes from the collected data.