Younshik Chung

Development of an accident duration prediction model on the Korean Freeway Systems

Since duration prediction is one of the most important steps in an accident management process, there have been several approaches developed for modeling accident duration. This paper presents a model for the purpose of accident duration prediction based on accurately recorded and large accident dataset from the Korean Freeway Systems. To develop the duration prediction model, this study utilizes the log-logistic accelerated failure time (AFT) metric model and a 2-year accident duration dataset from 2006 to 2007. Specifically, the 2006 dataset is utilized to develop the prediction model and then, the 2007 dataset was employed to test the temporal transferability of the 2006 model. Although the duration prediction model has limitations such as large prediction error due to the individual differences of the accident treatment teams in terms of clearing similar accidents, the results from the 2006 model yielded a reasonable prediction based on the mean absolute percentage error (MAPE) scale. Additionally, the results of the statistical test for temporal transferability indicated that the estimated parameters in the duration prediction model are stable over time. Thus, this temporal stability suggests that the model may have potential to be used as a basis for making rational diversion and dispatching decisions in the event of an accident. Ultimately, such information will beneficially help in mitigating traffic congestion due to accidents.

A DATA FUSION ALGORITHM FOR ESTIMATING LINK TRAVEL TIME

The growing demand for real-time traffic information brought about various types of traffic collection mechanisms in the area of Intelligent Transport Systems (ITS). There are, however, two procedures in making various traffic data into information. First, a robust information-making process of utilizing data into the representative information for each traffic collection mechanism is required. Second, the integration process of fusing the “estimated” information into the “representative information” for each link out of each source is also required. That is, both data reduction and/or data-to-information process and a higher-level information fusion are required. This article focuses on the development of an information fusion algorithm based on a voting technique, fuzzy regression, and Bayesian pooling technique for estimating dynamic link travel time in congested urban road networks. The algorithm has been proposed and validated using field experimental data—GPS probes and detector data collected over various roadway segments. It has been found that the estimated link travel time from the proposed algorithm is more accurate than the mere arithmetic mean counterpart from each traffic source. The limitations of the algorithm and future research agenda have also been discussed.

A Methodological Approach for Estimating Temporal and Spatial Extent of Delays Caused by Freeway Accidents

Given that reliable prediction of such relatively rare-and random-events as accident occurrence will remain elusive, the most important potentially soluble factor in the development of accident management strategies is to identify and quantify the conditions affecting the nonrecurrent congestion caused by accidents once they have been known to have occurred. The objective of the research reported in this paper is to develop a method of quantifying the delay due to accidents on urban freeways, as well as to identify the causal factors affecting the total delay caused by such accidents. Binary integer programming (BIP) is applied in estimating the temporal and spatial extent of delay caused by freeway accidents, based solely on commonly available inductance loop detector data. The basic idea behind the method is to estimate the most likely temporal and spatial extent of the region of congestion caused by an accident by solving a BIP problem that is consistent with the topology of the spatio-temporal region that defines candidate speed differences between normal flow conditions and accident conditions. The procedures developed in this paper will be useful for the performance evaluation of accident management systems by quantifying accident congestion in terms of the total delay to evaluate the benefit of accident management systems accrued from efficient traffic operations. The procedures are demonstrated by a case study using accident data collected from six major freeways in Orange County, CA.

Calculation of Travel Time Variability from Loop Detector Data

Travel time variability has recently become an important measure in transportation system management and has begun to be treated as a major factor influencing travel decisions. The main purpose of this study is to develop a database to help understand traffic variability of point measures and route-level measures by incorporating existing traffic surveillance systems. This study develops an integrated geographic information system database for traffic analysis in Orange County, California, and reports temporal and spatial traffic variability as measures of travel reliability. The database is to provide the analysis framework to study travel time variability at both the section level and the route level. In many cases, traffic data are used for traffic operations without the data being stored. The archived traffic data can play an important role as a way of measuring freeway performance. Such historic data make it possible to analyze such travel time variability from a long-term perspective. The database developed in this study is expected to help researchers and practitioners understand travel time variability as a measure of transportation system reliability by providing the analysis framework.

Modeling Accident Duration and Its Mitigation Strategies on South Korean Freeway Systems

Understanding the relationship between the characteristics of an accident and its duration is crucial for efficient response to and timely clearance of an accident, which can minimize traffic delays and congestion on the freeway. Although about 3,000 traffic accidents occur annually on South Korean freeways, only limited studies have been conducted that comprehensively model traffic accidents, including characteristic attributes such as accident duration. The objective of this study is to analyze the critical factors that affect accident duration by means of an accelerated failure time (AFT) metric model and to develop strategic plans and mitigation measures for reducing accident duration on South Korean freeway systems. In total, 2,940 accident data sets, spanning one year (2004), are used. On the basis of the log logistic AFT metric model and survival analysis, the paper suggests some strategic plans and mitigation measures to reduce accident duration on freeway systems in South Korea. In particular, it is hypothesized that, for accidents that occur at night or in a work zone area or are taxi related, the accident duration can be reduced by, among other measures, adding more roadside facilities, employing better freeway management strategies, changing taxi-related policies, or using any combination of those measures. Overall, analytical modeling of traffic accidents and their characteristic attributes, as demonstrated in this paper, should be used routinely as an aid in the strategic planning and formulation of mitigation measures for reducing accident duration on Korean freeway systems.

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.

Quantification of Nonrecurrent Congestion Delay Caused by Freeway Accidents and Analysis of Causal Factors

The objectives of this study are twofold: to quantify nonrecurrent congestion delay caused by traffic accidents that occur on freeways and to analyze the causal factors by different types of accidents. Unlike previous studies, this study proposes an analytical procedure to estimate congestion impact by using the difference between speed reduced by a traffic accident and accident-free normal flow speed. The proposed method is demonstrated by a case study using accident data derived from 27 freeways in South Korea in 2008. Ultimately, the method can develop a performance measure for evaluating transportation policies and planning level analyses associated with the design of transportation systems and preparation of operating plans for safety, as well as for quantitatively evaluating deployed transportation projects or technologies. Moreover, the results from statistical analyses can potentially be useful in making strategic plans and formulating mitigation measures for reducing nonrecurrent congestion delay caused by accidents on the South Korean freeway system.

Identifying Primary and Secondary Crashes from Spatiotemporal Crash Impact Analysis

The identification of secondary crashes is accompanied by the definition of the primary crash impact area. Although the crash impact area varies with the geometric characteristics of roads and periodic characteristics of traffic flow as well as with crash type, most previous studies have used a fixed boundary to identify secondary crashes and primary crashes. Thus, the objective of this research is to develop a method to define the spatiotemporally different boundaries varying with different types of crash. On the basis of the developed boundaries, the secondary crash is identified in the primary crash location as well as in its opposite direction. Secondary crashes in the same and opposite directions are identified to be 7.4% and 3.8% of total primary crashes, respectively. Also, only 0.3% of total primary crashes are connected with the secondary crash in both the same and the opposite directions. Although the proposed method seems to be complicated, the results will be useful in understanding secondary crash characteristics in a more realistic analysis through the spatiotemporal crash impact area in the crash direction as well as in the opposite direction. Specifically, the results can be used by public-sector transportation agencies in making operational strategies for reducing secondary crashes on freeways.

Spatiotemporal Analysis of Traffic Congestion Caused by Rubbernecking at Freeway Accidents

In this paper, we present a well-specified analytical methodology for estimating capacity reduction that is attributable to accidents in the opposite direction of accident-the condition whereby drivers in the opposite direction of an accident, by virtue of their curiosity, tend to be distracted by the accident. The methodology is based on a binary integer programming formulation that is used to identify the spatiotemporal region that is affected by the influence of the accident. Thresholds measured against control sample readings from inductance loop detectors are used to determine the patterns and magnitudes of the delay. A key feature of the methodology is its ability to separate nonrecurrent delay from any recurrent delay that is present on the road at the time and place of a reported accident, to estimate the contribution of nonrecurrent delay caused by the specific accident. A case study that is based on historical inductance loop detector data from six major freeways in Orange County, California, is presented. Potential factors contributing to delay, including accident characteristics, geometric characteristics, environmental condition, traffic characteristics, and congestion characteristics, are analyzed for their effects by using the semiparametric Cox proportional-hazards model.

Frailty Models for the Estimation of Spatiotemporally Maximum Congested Impact Information on Freeway Accidents

The objective of this paper is to develop models for the estimation of the temporal and spatial extent of congestion impact caused by accidents. Although there have been various approaches based on the deterministic queuing diagrams and kinematic wave (or shockwave) theory, only a few studies have been able to estimate the spatiotemporal congested region based on field data, such as ubiquitous loop detector data. Accordingly, this paper applies a previously developed procedure to capture the spatiotemporal accident impacts based on binary integer programming (BIP). The procedure provides a foundation for models of the following: 1) maximum spatial distance to the end of the congestion region affected by each accident and 2) maximum time affected by congestion resulting from each accident. Based on these models, the objective of this paper is to estimate two statistical models for providing maximum congested distance and time information due to freeway accidents. Since various observations from BIP were censored with respect to time and space, survival analysis - specifically, frailty models to account for unobserved heterogeneity - is applied to identify factors critical to spatiotemporal congestion impacts of freeway accidents.