Seung-Young Kho

Travel Time Prediction Using k Nearest Neighbor Method with Combined Data from Vehicle Detector System and Automatic Toll Collection System

Because of the development of scientific technology, drivers now have access to a variety of information to assist their decision making. In particular, an accurate prediction of travel time is valuable to drivers, who can use it to choose a route or decide on departure time. Although many researchers have sought to enhance their accuracy, such predictions are often limited by errors that result from the lagged pattern of predicted travel time, the use of nonrepresentative samples for making predictions, and the use of inefficient and nontransferable models. The proposed model predicts travel times on the basis of the k nearest neighbor method and uses data provided by the vehicle detector system and the automatic toll collection system. By combining these two sets of data, the model minimizes the limitations of each set and enhances the predictions accuracy. Criteria for traffic conditions allow the direct use of data acquired from the automatic toll collection system as predicted travel time. The proposed models predictions are compared with the predictions of other models by using actual data to show that the proposed model predicts travel times much more accurately. The proposed models predictions of travel time are expected to be free from the problems associated with an insufficient number of samples. Further, unlike the widely used artificial neural network and Kalman filter methods, the proposed model does not require long training programs, so the model is easily transferable.

Predicting crash risk and identifying crash precursors on Korean expressways using loop detector data

In order to improve traffic safety on expressways, it is important to develop proactive safety management strategies with consideration for segment types and traffic flow states because crash mechanisms have some differences by each condition. The primary objective of this study is to develop real-time crash risk prediction models for different segment types and traffic flow states on expressways. The mainline of expressways is divided into basic segment and ramp vicinity, and the traffic flow states are classified into uncongested and congested conditions. Also, Korean expressways have irregular intervals between loop detector stations. Therefore, we investigated on the effect and application of the detector stations at irregular intervals for the crash risk prediction on expressways. The most significant traffic variables were selected by conditional logistic regression analysis which could control confounding factors. Based on the selected traffic variables, separate models to predict crash risk were developed using genetic programming technique. The model estimation results showed that the traffic flow characteristics leading to crashes are differed by segment type and traffic flow state. Especially, the variables related to the intervals between detector stations had a significant influence on crash risk prediction under the uncongested condition. Finally, compared with the single model for all crashes and the logistic models used in previous studies, the proposed models showed higher prediction performance. The results of this study can be applied to develop more effective proactive safety management strategies for different segment types and traffic flow states on expressways with loop detector stations at irregular intervals.

User Equilibrium-Based Location Model of Rapid Charging Stations for Electric Vehicles with Batteries That Have Different States of Charge

A model was developed for the location of rapid charging stations for electric vehicles (EVs) in urban areas, taking into account the batteries’ state of charge and users’ charging and traveling behaviors. EVs are one means of preparing for the energy crisis and of reducing greenhouse gas emissions. To help relieve range anxiety, an adequate number of EV charging stations must be constructed. Rapid charging stations are needed in urban areas because there is inadequate space for slow-charging equipment. The objective function of the model is to minimize EVs’ travel fail distance and the total travel time of the entire network when the link flow is determined by a user equilibrium assignment. The remaining fuel range (RFR) at the origin node is assumed to follow a probabilistic distribution to reflect users’ charging behavior or technical development. The results indicate that the model described in this paper can identify locations for charging stations by using a probabilistic distribution function for the RFR. The location model, which was developed on the basis of user equilibrium assignment, is likely to consider the congested traffic conditions of urban areas, to avoid locating charging stations where they could cause additional traffic congestion. The proposed model can assist decision makers in developing policies that encourage the use of EVs, and it will be useful in developing an appropriate budget for implementing the plan.

Modeling and Simulation of Tandem Tollbooth Operations with Max-Algebra Approach

This study proposes a new model to simulate tandem tollbooth system in order to enhance planning and management of toll plaza facilities. A discrete-event stochastic microscopic simulation model is presented and developed to evaluate the operational performance of tandem tollbooth. Traffic behavior is represented using a set of mathematical and logical algorithms. Modified versions of Max-algebra approach are integrated into this new algorithm to simulate traffic operation at toll plazas. Computational results show that the benefit of tandem tollbooth depends on the number of serial tollbooth, service time and reaction time of drivers. The capacity of tandem tollbooth increases when service time follows a normal distribution rather than negative exponential distribution. Specifically, the lower variance of service time is, the better capacity tollbooth has. In addition, the ratio of drivers reaction time to service time affects the increasing ratio of the capacity extended by tollbooth.

Cross-classified multilevel models for severity of commercial motor vehicle crashes considering heterogeneity among companies and regions

This study analyzes 86,622 commercial motor vehicle (CMV) crashes (large truck, bus and taxi crashes) in South Korea from 2010 to 2014. The analysis recognizes the hierarchical structure of the factors affecting CMV crashes by examining eight factors related to individual crashes and six additional upper level factors organized in two non-nested groups (company level and regional level factors). The study considers four different crash severities (fatal, major, minor, and no injury). The company level factors reflect selected characteristics of 1,875 CMV companies, and the regional level factors reflect selected characteristics of 230 municipalities. The study develops a single-level ordinary ordered logit model, two conventional multilevel ordered logit models, and a cross-classified multilevel ordered logit model (CCMM). As the study develops each of these four models for large trucks, buses and taxis, 12 different statistical models are analyzed. The CCMM outperforms the other models in two important ways: 1) the CCMM avoids the type I statistical errors that tend to occur when analyzing hierarchical data with single-level models; and 2) the CCMM can analyze two non-nested groups simultaneously. Statistically significant factors include taxi companys type of vehicle ownership and municipalitys level of transportation infrastructure budget. An improved understanding of CMV related crashes should contribute to the development of safety countermeasures to reduce the number and severity of CMV related crashes.

Hierarchical Ordered Model for Injury Severity of Pedestrian Crashes in South Korea

INTRODUCTION The high percentage of fatalities in pedestrian-involved crashes is a critical social problem. The purpose of this study is to investigate factors influencing injury severity in pedestrian crashes by examining the demographic and socioeconomic characteristics of the regions where crashes occurred. METHOD To understand the correlation between the unobserved characteristics of pedestrian crashes in a defined region, we apply a hierarchical ordered model, in which we set crash characteristics as lower-level variables and municipality characteristics as upper-level. Pedestrian crash data were collected and analyzed for a three-year period from 2011 to 2013. The estimation results show the statistically significant factors that increase injury severity of pedestrian crashes. RESULTS At the crash level, the factors associated with increased severity of pedestrian injury include intoxicated drivers, road-crossing pedestrians, elderly pedestrians, heavy vehicles, wide roads, darkness, and fog. At the municipality level, municipalities with low population density, lower level of financial independence, fewer doctors, and a higher percentage of elderly residents experience more severe pedestrian crashes. Municipalities ranked as having the top 10% pedestrian fatality rate (fatalities per 100,000 residents) have rates 7.4 times higher than municipalities with the lowest 10% rate of fatalities. Their demographic and socioeconomic characteristics also have significant differences. The proposed model accounts for a 7% unexplained variation in injury severity outcomes between the municipalities where crashes occurred. CONCLUSION To enhance the safety of vulnerable pedestrians, considerable investments of time and effort in pedestrian safety facilities and zones should be made. More certain and severe punishments should be also given for the traffic violations that increase injury severity of pedestrian crashes. Furthermore, central and local governments should play a cooperative role to reduce pedestrian fatalities. Practical applications: Based on our study results, we suggest policy directions to enhance pedestrian safety.

Bayesian Network for Freeway Traffic State Prediction

Traffic state prediction is an important issue in traffic operations. One of the main purposes of traffic operations is to prevent a flow breakdown. Therefore, it is necessary to predict the traffic state in such a way as to reflect the stochastic process of traffic flow. To predict accurately the traffic state, machine learning-based models have been widely adopted, but they have difficulty in obtaining insights for traffic state prediction due to black-box procedures of the models. A Bayesian network (BN) is a methodology that is suitable for dealing with problems that involve uncertainty, and it can also improve the understanding of such problems. In this study, we develop a traffic state prediction model using a BN to reflect the dynamic and stochastic characteristics of traffic flow. To improve the BN, which has been used with a simple structure in transportation problems, we propose a modeling procedure using a mixture of Gaussians (MoGs). In the performance evaluation, the BN has better performance than a logistic regression, and it has the same level of performance as an artificial neural network based on machine learning. Also, by performing sensitivity analyses, we provide the understanding of traffic state prediction and the guidelines for improving the model. The BN developed in this study can be considered as a traffic state prediction model with good prediction accuracy and interpretability.

Effect of Regional Characteristics on Injury Severity in Local Bus Crashes: Use of Hierarchical Ordered Model

As the importance of public transportation increases, the management of bus-involved crashes has become a crucial issue for traffic safety. However, there are relatively few studies on crash severity for buses in South Korea. This study investigated factors that influence the severity of injuries that occur in local bus crashes. The study used commercial vehicle crash data from a 5-year period from 2010 through 2014 in South Korea. To determine unobserved regional effects on crash severity, a hierarchical ordered model was applied to the analysis. Individual crash characteristics were set to lower-level variables, and regional characteristics were adopted as upper-level variables. At the lower level, the factors affecting severity of injuries included vehicle speed, vehicle age, road alignment, surface status, road class, and traffic light installation, as found in previous studies. At the upper level, the factors included pavement, emergent medical environment, traffic rate of compliance, and ratio of elderly in the community. There was a 5.1% unobserved variation between regions from the intraclass correlation analysis. The validity of a hierarchical model for local bus crashes was verified by applying the model to other long-distance buses, and it appeared there were no regional effects. This study found a regional effect for local bus crash severity, and thus this factor is important when developing prevention plans to reduce local bus crashes. These results contribute to the study of traffic safety.

Identifying Roadway Sections Influenced by Speed Humps Using Survival Analysis

본 연구에서는 과속방지턱으로 인해 통과차량이 제한속도 이하로 주행하게 되는 구간을 영 향구간이라 정의하였다. 이를 과속방지턱 통과 전 구간 · 사이 구간 · 통과 후 구간으로 구분한 뒤, 단독 및 연속 설치 여부· 차종· 시간대 등 다양한 요인들로 인한 변화를 분석하였다. 특히, 사이 구간에서는 구간 내에서 제한속도 이하로 주행한 거리의 비율을 유효영향구간비율로 정 의하여 분석하였다. 스피드건으로 과속방지턱을 통과하는 차량들의 속도궤적을 수집하여 영향 구간의 길이를 산출하였고, 생존분석을 이용하여 추정한 영향구간의 생존함수를 비교하였다. 설치 형태에 따른 변 화 분석 결과, 50m 간격 연속형 과속방지턱의 통과 전 평균 영향구간 길이는 단일형보다 75.3% 길었으며, 통과 후 평균 영향구간은 18.9% 긴 것으로 나타났다. 연속형 과속방지턱의 유효영향구간비율은 30m와 50m 간격에서 각각 81.0%와 76.0%로 큰 차이가 없었으나, 제한속도 이하로 주행한 절대적 길이는 각각 24.3m와 38.0m로 50m 간격에 서 더 길었다. 차종별로 추정된 영향구간의 생존함수에 대해 로그순위검정을 수행한 결과 연속형 과속방지턱의 영 향구간이 단일형 과속방지턱보다 길다는 것이 통계적으로 유의하였다. 차종은 단일형 과속방지턱에서 유의한 차이 를 나타냈으나, 주야 시간대는 유효한 요인이 아닌 것으로 판명되었다. 본 연구의 결과는 과속방지턱의 적정 설치 위 치 또는 연속형 과속방지턱의 적정 간격 산정의 근거로 활용할 수 있을 것으로 판단된다.

Sustainability evaluation of rapid routes for buses with a network DEA model

ABSTRACT This study establishes a network data envelopment analysis (DEA) model to evaluate the sustainability of public transportation services targeting rapid routes for buses in the Seoul metropolitan area. A network DEA-based optimization model is formulated to evaluate the sustainability of the public transportation service. By considering public transportation services from both the operators’ and users’ perspectives, this model produces results that reflect the interaction of three sustainable transport service properties, i.e., efficiency, equity, and environmental impacts. It is identified that the expansion of median bus lanes and the conversion of conventional buses into compressed natural gas vehicles could improve the sustainability of the public transportation services in the Seoul metropolitan area. Some limitations and future research agenda also are presented.