Byoung-Jo Yoon

Injury severity in delivery-motorcycle to vehicle crashes in the Seoul metropolitan area.

More than 56% of motorcycles in Korea are used for the purpose of delivering parcels and food. Since such delivery requires quick service, most motorcyclists commit traffic violations while delivering, such as crossing the centerline, speeding, running a red light, and driving in the opposite direction down one-way streets. In addition, the fatality rate for motorcycle crashes is about 12% of the fatality rate for road traffic crashes, which is considered to be high, although motorcycle crashes account for only 5% of road traffic crashes in South Korea. Therefore, the objective of this study is to analyze the injury severity of vehicle-to-motorcycle crashes that have occurred during delivery. To examine the risk of different injury levels sustained under all crash types of vehicle-to-motorcycle, this study applied an ordered probit model. Based on the results, this study proposes policy implications to reduce the injury severity of vehicle-to-motorcycle crashes during delivery.

Potentialities of Data-Driven Nonparametric Regression in Urban Signalized Traffic Flow Forecasting

AbstractSingle-interval forecasting of traffic variables plays a key role in modern intelligent transportation systems (ITSs). Despite the achievements of advanced ITS forecasting in literature, forecast modeling of urban signalized traffic flow, which shows rapid-intensive fluctuations associated with the nonlinear and nonstationary behavior of temporal evolution, is still one of its big challenges. From the perspective of field experts, the mathematical complexity of an advanced model is also a renewal obstacle in practice. On the other hand, the accessibility of large volumes of historical data and the concurrent advanced data management systems used to access them provide data-driven nonparametric regression with a renewal opportunity in practice. In order to address these problems effectively, this paper proposes a k nearest neighbor nonparametric regression (KNN-NPR) forecasting methodology to be tested against vast quantities of real traffic volume data collected from urban signalized arterials. Th...

High-Speed Data-Driven Methodology for Real-Time Traffic Flow Predictions: Practical Applications of ITS

Despite the achievements of academic research on data-driven -nearest neighbour nonparametric regression (KNN-NPR), the low-speed computational capability of the KNN-NPR method, which can occur during searches involving enormous amounts of historical data, remains a major obstacle to improvements of real-system applications. To overcome this critical issue successfully, a high-speed KNN-NPR framework, capable of generating short-term traffic volume predictions, is proposed in this study. The proposed method is based on a two-step search algorithm, which has the two roles of building promising candidates for input data during nonprediction times and identifying decision-making input data for instantaneous predictions at the prediction point. To prove the efficacy of the proposed model, an experimental test was conducted with large-size traffic volume data. It was found that the performance of the model not only at least equals that of linear-search-based KNN-NPR in terms of prediction accuracy, but also shows a substantially reduced execution time in approximating real-time applications. This result suggests that the proposed algorithm can be also effectively employed as a preprocess to select useful past cases for advanced learning-based forecasting models.

Potentialities of Autonomous Vehicles for Online Monitoring of Motorway Traffic Volume

The fact that real-time autonomous vehicle (AV) traffic volume can be collected without a field detector by virtue of advanced global positioning system (GPS) and wireless communication technologies can render a promising solution to online monitoring of traffic volume in the upcoming AV era. To demonstrate this opportunity, this paper proposes a new method to monitor real-time motorway traffic volumes for road locations where no detector is installed using AV traffic volume. The modeling concept is based on the obvious fact that AV traffic volume is a direct portion of total traffic volume. The capabilities of the method are demonstrated through an experimental study using real-world GPS-enabled smartphone vehicle navigation data. The results show that online motorway traffic volume can be effectively monitored throughout the day with 5.69% average error at the 14.91% penetration rate of AVs during the daytime. Therefore, it is expected that AVs can at least be used as complementary means for the role of vehicle detectors in the near future due to the fact that the detection range of AVs is not spatially constrained.