Development of Decision Support System for Active Traffic Management Systems Considering Travel Time Reliability

As traffic problems on roadways have been increasing, active traffic management systems (ATM) using proactive traffic management concept have been deployed on freeways and arterials. The ATM aims to integrate and automate various traffic control strategies such as variable speed limits, queue warning, and ramp metering through a decision support system (DSS). Over the past decade, there have been many efforts to integrate freeways and arterials for the efficient operation of roadway networks. It has been required that these systems should prove their effectiveness in terms of travel time reliability. Therefore, this study aims to develop a new concept of a decision support system integrating variable speed limits, queue warning, and ramp metering on the basis of travel time reliability of freeways and arterials. Regarding the data preparation, in addition to collecting multiple data sources such as traffic data, crash data and so on, the types of traffic data sources that can be applied for the analysis of travel time reliability were investigated. Although there are many kinds of real-time traffic data from third-party traffic data providers, it was confirmed that these data cannot represent true travel time reliability through the comparative analysis of measures of travel time reliability. Related to weather data, it was proven that nationwide land-based weather stations could be applicable. Since travel time reliability can be measured by using long-term periods for more than six months, it is necessary to develop models to estimate travel time reliability through real-time traffic data and event-related data. Among various matrix to measure travel time reliability, the standard deviation of travel time rate [minute/mile] representing travel time variability was chosen because it can represent travel time variability of both link and network level. Several models were developed to estimate the standard deviation of travel time rate through average travel time rate, the number of lanes, speed limits, and the amount of rainfall. Finally, a DSS using a model predictive control method to integrate multiple traffic control measures was developed and evaluated. As a representative model predictive control, METANET model was chosen, which can include variable speed limit, queue warning, and ramp metering, separately or combined. The developed DSS identified a proper response plan by comparing travel time reliability among multiple combinations of current and new response values of strategies. In the end, it was found that the DSS provided the reduction of travel time and improvement of its reliability for travelers through the recommended response plans

Spatial Analysis Of The Effective Coverage Of Land-Based Weather Stations For Traffic Crashes

This paper investigates the effective spatial coverage of nationwide land-based weather stations of the National Oceanic Atmospheric Administration (NOAA) for traffic crash analysis. The weather data were collected from the Quality Controlled Local Climatological Data (QCLCD) and the fatal crashes were obtained from the Fatality Analysis Reporting Systems (FARS) during the year of 2007–2014. Both QCLCD and FARS contain geographic coordinates for locations and weather condition information as a categorical variable. The spatial coverage of weather stations for the analysis was made by geoprocessing, which uses multiple buffers (i.e. radii 5, 10, 15, and 20 miles), and then was evaluated via Cohen\u27s κ statistics, which is used to determine an agreement of weather between QCLCD and FARS within the buffer. The applicability of the weather station\u27s data by nine climate regions was assessed by developing a series of negative binomial models. According to the estimated Cohen\u27s κ statistics, the rain and snow weather conditions have a moderate agreement up to 20 miles. However, in the case of fog weather condition, it has a slight agreement. The statistical modeling results showed that weather stations data can be a good exposure measure for weather-related fatal crashes along with the vehicle-miles-traveled. Considering one geographical feature that approximately more than 75% of all fatal crashes are located within 20-miles radius of the weather stations in the USA, it is evident that the data from the existing weather stations can be cost-effective to develop geospatial crash risk analysis model

A Comparative Study Between Private- Sector And Automated Vehicle Identification System Data Through Various Travel Time Reliability Measures

Traffic data from private-sector sources is increasingly used to estimate the travel time reliability of major road infrastructure. However, there is as yet no study evaluating the difference in estimating travel time reliability between the private-sector data and automated vehicle identification (AVI) based on radio frequency identification. As ground truth data, the AVI data were collected from an AVI system using toll tags and aggregated into five-minute intervals. As one of the representative traffic information providers, data from HERE was obtained through the Regional Integrated Traffic Information System, calculated in five-minute intervals. For the comparison, four kinds of measures were selected and estimated on the basis of the day of the week, specific time periods, and time of day in five-minute, 15-minute, and one-hour intervals. The statistical difference in travel time reliability was assessed through paired t-tests. According to the results, AVI and HERE data are comparable based on day of the week, specific time periods, and time of day at one-hour intervals, whereas at five-minute and 15-minute intervals, HERE and AVI data are not generally comparable. Thus, when estimating travel time reliability in real time, travel time reliability derived from HERE data may be different from the true travel time reliability. Considering that private-sector traffic data are currently used to estimate travel time reliability measures, the measures should be harmonized on the basis of robust statistics to provide more consistent measures related to the true travel time reliability

Application of nationwide weather data for traffic safety analysis in the United States : a spatial analysis and crash prediction modeling

There has been a wide range of scientific research to investigate the effects of adverse weather on traffic safety. In particular, some researchers have analyzed the relationship between traffic crash occurrence and weather condition in the United States using the data from land-based weather stations. Using traffic crash and weather data throughout the United States, Eisenberg (2004) analyzed the mixed effects of precipitation, and Eisenberg and Warner (2005) investigated the impact of snowfall. In addition, Ashley et al. (2015) dealt with a nationwide analysis of visibility-related fatal crashes in the United States and Black and Mote (2015) conducted a spatial and temporal analysis of winter-precipitation-related fatal crashes. Different from the previous research, this paper investigates the effective spatial coverage of nationwide land-based weather stations of the National Oceanic Atmospheric Administration (NOAA) for traffic crash analysis. Based on the effective spatial coverage, statistical models by the United States climate regions were developed to confirm whether the weather data within the coverage could be a good exposure measure for traffic crash analysis

Spatial analysis of the effective coverage of land-based weather stations for traffic crashes

This paper investigates the effective spatial coverage of nationwide land-based weather stations of the National Oceanic Atmospheric Administration (NOAA) for traffic crash analysis. The weather data were collected from the Quality Controlled Local Climatological Data (QCLCD) and the fatal crashes were obtained from the Fatality Analysis Reporting Systems (FARS) during the year of 2007–2014. Both QCLCD and FARS contain geographic coordinates for locations and weather condition information as a categorical variable. The spatial coverage of weather stations for the analysis was made by geoprocessing, which uses multiple buffers (i.e. radii 5, 10, 15, and 20 miles), and then was evaluated via Cohen\u27s κ statistics, which is used to determine an agreement of weather between QCLCD and FARS within the buffer. The applicability of the weather station\u27s data by nine climate regions was assessed by developing a series of negative binomial models. According to the estimated Cohen\u27s κ statistics, the rain and snow weather conditions have a moderate agreement up to 20 miles. However, in the case of fog weather condition, it has a slight agreement. The statistical modeling results showed that weather stations data can be a good exposure measure for weather-related fatal crashes along with the vehicle-miles-traveled. Considering one geographical feature that approximately more than 75% of all fatal crashes are located within 20-miles radius of the weather stations in the USA, it is evident that the data from the existing weather stations can be cost-effective to develop geospatial crash risk analysis model