Choul-Ki Lee

Effectiveness of Speed-Monitoring Displays in Speed Reduction in School Zones

Speeding is one of the major causes of the frequent and severe traffic accidents that occur in school zones. Two field studies were conducted to assess the short-term and long-term effectiveness of speed-monitoring displays (SMDs) for speed reduction in school zones. The performance difference is discussed according to several dependent variables, including the average speed, the 85th percentile speed, and the distribution of speeds. The short-term study results showed that the speed of vehicles began to be reduced when the driver recognized the presence of an SMD, and the average speed was reduced by about 17.5% (8.2 km/h) at the SMD location. This speed reduction was observed throughout the day, regardless of the time of day. A similar performance trend was identified from the long-term study results, but the average speed reduction was slightly reduced to 12.4% (5.8 km/h) at the SMD location. However, statistical analyses showed that the speed differences were statistically significant. In addition, an...

Study on Applicability of the Vehicle Detection Using a Coil Sensor

This study was intended to evaluate the feasibility of the vehicle detector using a coil sensor. For the evaluation, the research team built a test environment for the detector consisting of a oscillation circuit, data collecting circuit, data monitoring and saving circuit, etc. As the result of the frequency analysis of the detector from the test environment, it was verified for the detector using a coil sensor to generate stable frequencies. In addition, the ease of construction and management was tested by comparing the size of cutting areas, consumption of installation materials, and installation time for a traditional loop detector and the detector using a coil sensor. As a result, the installation of the detector using a coil sensor requires less size of cutting areas, consumption of installation materials, and installation time.

Study on Map Matching Using Characteristics of Vehicular Movements

PURPOSES : In link matching using vehicular trajectory in a car navigation system, it is technically difficult to match the location of the subject vehicle with a link on an electronic map in the vehicle on a grade separation highway segment consisting of an elevated highway and atgrade highway, because of the overlap of geometric lines of the two highways. This study was initiated to propose a link matching algorithm using the characteristics of vehicular movement of the subject vehicle. METHODS : The selected test site is the highway segment between Jeong-reung IC and Gil-eum IC where the Inner Circulation Road and Jeong-reung-ro run together. To evaluate the proposed algorithm, this study collected the raw packet data of vehicles that drove on the test site. In a simulation environment, link matching was performed using an existing algorithm as well as the proposed algorithm. RESULTS: It was clearly found that the characteristics of vehicular movements are different on the two highways. CONCLUSIONS: The proposed algorithm outperformed the existing algorithm.

Analysis Period of Input Data for Improving the Prediction Accuracy of Express-Bus Travel Times

PURPOSES : The travel times of expressway buses have been estimated using the travel time data between entrance tollgates and exit tollgates, which are produced by the Toll Collections System (TCS). However, the travel time data from TCS has a few critical problems. For example, the travel time data include the travel times of trucks as well as those of buses. Therefore, the travel time estimation of expressway buses using TCS data may be implicitly and explicitly incorrect. The goal of this study is to improve the accuracy of the expressway bus travel time estimation using DSRC-based travel time by identifying the appropriate analysis period of input data. METHODS : All expressway buses are equipped with the Hi-Pass transponders so that the travel times of only expressway buses can be extracted now using DSRC. Thus, this study analyzed the operational characteristics as well as travel time patterns of the expressway buses operating between Seoul and Dajeon. And then, this study determined the most appropriate analysis period of input data for the expressway bus travel time estimation model in order to improve the accuracy of the model. RESULTS : As a result of feasibility analysis according to the analysis period, overall MAPE values were found to be similar. However, the MAPE values of the cases using similar volume patterns outperformed other cases. CONCLUSIONS : The best input period was that of the case which uses the travel time pattern of the days whose total expressway traffic volumes are similar to that of one day before the day during which the travel times of expressway buses must be estimated.

Traffic Crash Prediction Models for Expressway Ramps

PURPOSES: Using the collected data for crash, traffic volume, and design elements on ramps between 2007 and 2009, this research effort was initiated to develop traffic crash prediction models for expressway ramps. METHODS: Three negative binomial regression models and three zero-inflated negative binomial regression models were developed for individual ramp types, including direct, semi-direct and loop, respectively. For validating the developed models, authors compared the estimated crash frequencies with actual crash frequencies of twelve randomly selected interchanges, the ramps of which have not been used for model developing. RESULTS: The results show that the negative binomial regression models for direct, semi-direct and loop ramps showed 60.3%, 63.8% and 48.7% error rates on average whereas the zero-inflated negative binomial regression models showed 82.1%, 120.4% and 57.3%, respectively. CONCLUSIONS: Conclusively, the negative binomial regression models worked better in traffic crash prediction than the zero-inflated negative binomial regression models for estimating the frequency of traffic accidents on expressway ramps.

Green-intelli travel service for public transit based on context awareness

This paper demonstrates a framework of context awareness based multimodal connectivity passenger information system for green-intelli travel services for public transit. A critical issue is developing context awareness services to provide a specific user with his/her context demand along the circumstances related to the complex information of transportation infrastructure. Recent technologies including location tracking of users and transportation modes, real time traffic management, and geographic information are integrated as the primary determinants of the likelihood of making the user oriented services on demand. Field tests of the framework on the test bed yield the proposed system applicable to the personalized service on demand for public transport users along the technologies and infrastructure in wireless communication networks, where a variety of service contents and business models has been considered to be a green intelli-travel services in the near future.