Browsing by Author "Wu, Kun-Feng"

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A stochastic dairy transportation problem considering collection and delivery phases
(2019-09-01)
Huang, Kuancheng
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Wu, Kun-Feng
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Ardiansyah, Muhammad Nashir
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運輸與物流管理系註：原交通所+運管所
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Department of Transportation and Logistics Management
Based on the dairy cooperatives in Indonesia, this study formulates a stochastic programming model to handle the milk collection and delivery process with the maximum route duration limitation, the external cooling facility option, and travel time uncertainty. Besides, the local practice of sharing the fleet for both collection and delivery further complicates the decision. A set covering-based solution approach is used, due to its flexibility in handling complicated operational requirements and alignment with the decision environment. According to the numerical experiment, the proposed solution algorithm can provide quality solutions with a reasonable computational time for the cooperative operators.
An evaluation scheme for assessing the effectiveness of intersection movement assist (IMA) on improving traffic safety
(2018-01-01)
Wu, Kun-Feng
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Ardiansyah, Muhammad Nashir
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Ye, Wei-Jyun
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運輸與物流管理系註：原交通所+運管所
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Department of Transportation and Logistics Management
Objective: Intersection movement assist (IMA) has been recognized as one of the prominent countermeasures to reduce angle crashes at intersections, which constitute 22% of total crashes in the United States. Utilizing vehicle-based sensors, vehicle-to-vehicle (V2V), and vehicle-to-infrastructure (V2I) communications, IMA offers extended vision to provide early warning for an imminent crash. However, most of IMA-related research implements their methods and strategies only in simulations, test tracks, or driving simulator studies that have quite a few assumptions and limitations and hence the effectiveness evaluations reported may not be transferable or comparable.Methods: This study seeks to develop a generalized evaluation scheme that can be used not only to assess the effectiveness of IMA on improving traffic safety at intersections but to facilitate comparisons across similar studies. The proposed evaluation scheme utilizes the concepts of traffic conflict in terms of time-to-collision (TTC) as a crash surrogate. This approach avoids the issue of having insufficient crash frequency data for system evaluation. To measure the effectiveness of IMA on reducing traffic conflicts, a relative risk is calculated for comparing the risk of with/without using the IMA. As a proof-of-concept study, this study applied the proposed evaluation scheme and reported the effectiveness of IMA on improving traffic safety in a field operation test (FOT). Seven test scenarios were conducted at 4 intersections, and a total of 40 participants were recruited to use the IMA for 6months.Results: It was estimated that IMA users have 26% fewer conflicts with TTC less than 5s and have 15% fewer conflicts with TTC less than 4s. However, the results vary across different sites and different definitions of conflicts in terms of TTC.Conclusions: Overall, IMA is promising to effectively reduce angle crashes related to sight obstruction and has potential to reduce not only crash frequency but crash severity.
Cohort-Based Analysis Structure for Modeling Driver Behavior with an In-Vehicle Data Recorder
(2016)
Wu, Kun-Feng
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Jovanis, Paul P.
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管理學院
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College of Management
In recent years, the increasing availability of in-vehicle data recorder technologies has resulted in more safety studies that have unobtrusively observed the behavior of drivers during actual driving. The research in this paper proposes and applies a flexible analysis structure that can be applied to data that reflect events of interest including crash data and safety critical event data from naturalistic driving studies and alert warnings from onboard safety warning systems. The fundamental requirements for the data include a need for GPS and a geographic information system to allow positioning the vehicle on a network and the occurrence and recording of events of interest with attributes, which may include those reflecting the roadway, environment, driver, and event. A distinguishing feature of the formulation is the explicit inclusion of exposure to risk. The analysis approach responds to four challenges facing researchers using in-vehicle data recorder data to assess driving behavior with surrogate safety performance measures: inclusion of exposure to risk; allowance for inclusion of driver, event, environment, and roadway attributes in a structured formulation; modeling of driving exposure in different contexts, including both roadway and environment; and allowance for comparison to a baseline hazard where no events occur. A hierarchical mixed-effects Poisson regression model is used with data from University of Michigan Transportation Institute\'s curve speed warning system field operation test in the application of the method. The model identifies drivers who have significantly higher alert frequency than similar drivers, both for all driving and for driving on major and minor arterials. The paper concludes with a discussion of potential applications beyond field operational test settings, which include analysis of crash and other naturalistic driving study observed events.
Crash sequence based risk matrix for motorcycle crashes
(2018-08-01)
Wu, Kun-Feng
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Sasidharan, Lekshmi
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Thor, Craig P.
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Chen, Sheng-Yin
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運輸與物流管理系註：原交通所+運管所
Considerable research has been conducted related to motorcycle and other powered-two-wheeler (PTW) crashes; however, it always has been controversial among practitioners concerning with types of crashes should be first targeted and how to prioritize resources for the implementation of mitigating actions. Therefore, there is a need to identify types of motorcycle crashes that constitute the greatest safety risk to riders - most frequent and most severe crashes. This pilot study seeks exhibit the efficacy of a new approach for prioritizing PTW crash causation sequences as they relate to injury severity to better inform the application of mitigating countermeasures. To accomplish this, the present study constructed a crash sequence-based risk matrix to identify most frequent and most severe motorcycle crashes in an attempt to better connect causes and countermeasures of PTW crashes. Although the frequency of each crash sequence can be computed from crash data, a crash severity model is needed to compare the levels of crash severity among different crash sequences, while controlling for other factors that also have effects on crash severity such drivers' age, use of helmet, etc. The construction of risk matrix based on crash sequences involve two tasks: formulation of crash sequence and the estimation of a mixed-effects (ME) model to adjust the levels of severities for each crash sequence to account for other crash contributing factors that would have an effect on the maximum level of crash severity in a crash. Three data elements from the National Automotive Sampling System - General Estimating System (NASS-GES) data were utilized to form a crash sequence: critical event, crash types, and sequence of events. A mixed-effects model was constructed to model the severity levels for each crash sequence while accounting for the effects of those crash contributing factors on crash severity. A total of 8039 crashes involving 8208 motorcycles occurred during 2011 and 2013 were included in this study, weighted to represent 338,655 motorcyclists involved in traffic crashes in three years (2011-2013)(NHTSA, 2013). The top five most frequent and severe types of crash sequences were identified, accounting for 23 percent of all the motorcycle crashes included in the study, and they are (1) run-offroad crashes on the right, and hitting roadside objects, (2) cross-median crashes, and rollover, (3) left-turn oncoming crashes, and head-on, (4) crossing over (passing through) or turning into opposite direction at intersections, and (5) side-impacted. In addition to crash sequences, several other factors were also identified to have effects on crash severity: use of helmet, presence of horizontal curves, alcohol consumption, road surface condition, roadway functional class, and nighttime condition.
Exploring the application of latent class cluster analysis for investigating pedestrian crash injury severities in Switzerland
(2015-12-01)
Sasidharan, Lekshmi
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Wu, Kun-Feng
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Menendez, Monica
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土木工程學系
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Department of Civil Engineering
One of the major challenges in traffic safety analyses is the heterogeneous nature of safety data, due to the sundry factors involved in it. This heterogeneity often leads to difficulties in interpreting results and conclusions due to unrevealed relationships. Understanding the underlying relationship between injury seventies and influential factors is critical for the selection of appropriate safety countermeasures. A method commonly employed to address systematic heterogeneity is to focus on any subgroup of data based on the research purpose. However, this need not ensure homogeneity in the data. In this paper, latent class cluster analysis is applied to identify homogenous subgroups for a specific crash type-pedestrian crashes. The manuscript employs data from police reported pedestrian (2009-2012) crashes in Switzerland. The analyses demonstrate that dividing pedestrian severity data into seven clusters helps in reducing the systematic heterogeneity of the data and to understand the hidden relationships between crash severity levels and socio-demographic, environmental, vehicle, temporal, traffic factors, and main reason for the crash. The pedestrian crash injury severity models were developed for the whole data and individual clusters, and were compared using receiver operating characteristics curve, for which results favored clustering. Overall, the study suggests that latent class clustered regression approach is suitable for reducing heterogeneity and revealing important hidden relationships in traffic safety analyses. (C) 2015 Elsevier Ltd. All rights reserved.
Exploring the application of latent class cluster analysis for investigating pedestrian crash injury severities in Switzerland (vol 85, pg 219, 2015)
(2016-09)
Sasidharan, Lekshmi
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Wu, Kun-Feng
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Menendez, Monica
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運輸與物流管理系註：原交通所+運管所
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Department of Transportation and Logistics Management
Exploring the effects of critical driving situations on driver perception time (PT) using SHRP2 naturalistic driving study data
(2019-07-01)
Wu, Kun-Feng
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Lin, Ya-Jin
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運輸與物流管理系註：原交通所+運管所
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Department of Transportation and Logistics Management
Driver PT is critical when a driver faces an imminent crash risk and needs to determine what evasive maneuvers to execute. Therefore, it is of utmost importance to study how PT varies across different critical driving situations. PT refers to the time drivers need to recognize the nature and significance of external stimuli. Driver PT is critical when he or she faces a potentially hazardous driving situation, and must determine what action(s) or evasive maneuver(s) to execute. Although past research has identified many factors associated with PT, little research has been done on the effects of critical driving situations on PT, let alone in a real-world driving environment. Naturalistic driving study (NDS) data provides an unprecedented opportunity to look into PT prior to the occurrence of safety-related events. This study seeks to shed light on how critical driving situations influence driver PT, as well as how the driving environment and driver behavior affect PT during real-world driving by utilizing the Second Strategic Highway Research Program (SHRP2) NDS data. An NDS consists of two primary features that distinguish it from retrospective approaches: vehicles are equipped with video camera technologies that observe the driver and the road ahead of the vehicle continuously while driving, and drivers are asked to drive as they normally would. To best study PT while minimizing the effects of confounding factors, this study focused on a total of 1417 rear-end crashes and near crashes. It was found that critical driving situations, the driving environment, and driver behavior are all influential factors in explaining the variation of PT among different drivers. The longest PTs are during critical driving situations where the vehicle ahead is stop-and-go, which can be as long as 2.84 s while controlling for the effects of driving environment and driver behavior factors, compared to other types of driving situations such as a vehicle ahead decelerating or lane changing.
Identify sequence of events likely to result in severe crash outcomes
(2016-11-01)
Wu, Kun-Feng
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Thor, Craig P.
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Ardiansyah, Muhammad Nashir
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運輸與物流管理系註：原交通所+運管所
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Department of Transportation and Logistics Management
The current practice of crash characterization in highway engineering reduces multiple dimensions of crash contributing factors and their relative sequential connections, crash sequences, into broad definitions, resulting in crash categories such as head-on, sideswipe, rear-end, angle, and fixed-object. As a result, crashes that are classified in the same category may contain many different crash sequences. This makes it difficult to develop effective countermeasures because these crash categorizations are based on the outcomes rather than the preceding events. Consequently, the efficacy of a countermeasure designed for a specific type of crash may not be appropriate due to different pre-crash sequences. This research seeks to explore the use of event sequence to characterize crashes. Additionally, this research seeks to identify crash sequences that are likely to result in severe crash outcomes so that researchers can develop effective countermeasures to reduce severe crashes. This study utilizes the sequence of events from roadway departure crashes in the Fatality Analysis Reporting System (FARS), and converts the information to form a new categorization called "crash sequences." The similarity distance between each pair of crash sequences were calculated using the Optimal Matching approach. Cluster analysis was applied to group crash sequences that are etiologically similar in terms of the similarity distance. A hybrid model was constructed to mitigate the potential sample selection bias of FARS data, which is biased toward more severe crashes. The major findings include: (1) in terms of a roadway departure crash, the crash sequences that are most likely to result in high crash severity include a vehicle that first crosses the median or centerline, runs-off-road on the left, and then collides with a roadside fixed-object; (2) seat-belt and airbag usage reduces the probability of dying in a roadway departure crash by 90%; and (3) occupants who are seated on the side of the vehicle that experience a direct impact are 2.6 times more likely to die in a roadway departure crash than those not seated on the same side of the vehicle where the impact occurs. (C) 2016 Elsevier Ltd. All rights reserved.
Method for the Use Naturalistic Driving Study Data to Analyze Rear-End Crash Sequences
(2015)
Wu, Kun-Feng
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Thor, Craig P.
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運輸與物流管理系註：原交通所+運管所
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Department of Transportation and Logistics Management
Many factors are associated with crash risk, but their contribution to the progression of a crash is often unclear. Research on and understanding of the crash sequence are needed. One of the advantages of studying crash sequence is to identify the causative chain of crashes and, subsequently, the identification of effective countermeasures that may feasibly mitigate crash risk. An analogy to epidemiological studies is particularly useful: although researchers confirm that many factors are associated with an increase in the probability of myocardial infarction, there is still a need to research the disease process, the physiological process that promotes disease growth, and the possible interventions to treat or even prevent the disease. This study used data from the Integrated Vehicle-Based Safety System program. Two freeway rear-end events, including one crash and one near crash, with similar crash sequences were studied. The crash sequences of the two events were as follows: (a) the drivers maintained a temporal headway of less than 1 s at the beginning; (b) the traffic flow conditions were likely to cause misjudgments by the drivers and result in glances in inappropriate directions; (c) when the drivers were distracted, the leading vehicle decelerated; (d) the drivers did not decelerate accordingly and had their feet on the accelerator pedal; and (e) one event resulted in a crash and the other in a near crash. This study highlighted an approach for comparing and dissecting the differences in the crash sequence that led to the different outcomes.
Reducing intercity bus crashes through driver rescheduling
(2019-01-01)
Wang, Shuo-Yen
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Wu, Kun-Feng
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運輸與物流管理系註：原交通所+運管所
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Department of Transportation and Logistics Management
Intercity bus crashes often involve driver fatigue, which itself is usually the result of sleep deprivation, long driving hours, a maladjusted circadian rhythm, or some combination of the above. And driver scheduling has long been suspected as the root cause affecting sleepiness and fatigue. As such, a fundamental question for intercity bus carriers is how to reduce crashes associated with driver schedules, while maintaining a nonstop service? This research seeks to develop a paradigm to minimize overall fleet crash risk by rescheduling. In this study, we first identified those driving schedules associated with the highest crash risks, and a rescheduling scheme is then proposed to reduce fleet crashes overall. A case-study approach was employed to identify driver scheduling associated with higher crash risk, and a mathematical program was then formulated to minimize fleet crash risk. Our results showed that several types of driver schedules would lead to higher crash risk; for example: (1) working in the afternoon or early hours in the morning for two consecutive days; and (2) commencing a driving shift in the mornings, the afternoon or the early hours of the morning after being off-duty for more than 24 h. To meet the challenge of maintaining a nonstop service while simultaneously minimizing the crash risk associated with these risk patterns, a mathematical program was developed, and it was found that rescheduling based on our algorithm could reduce the incidence of crashes by approximately 30 percent.
Relating crash frequency and severity: Evaluating the effectiveness of shoulder rumble strips on reducing fatal and major injury crashes
(2014-06-01)
Wu, Kun-Feng
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Donnell, Eric T.
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Aguero-Valverde, Jonathan
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運輸與物流管理系註：原交通所+運管所
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Department of Transportation and Logistics Management
To approach the goal of "Toward Zero Deaths," there is a need to develop an analysis paradigm to better understand the effects of a countermeasure on reducing the number of severe crashes. One of the goals in traffic safety research is to search for an effective treatment to reduce fatal and major injury crashes, referred to as severe crashes. To achieve this goal, the selection of promising countermeasures is of utmost importance, and relies on the effectiveness of candidate countermeasures in reducing severe crashes. Although it is important to precisely evaluate the effectiveness of candidate countermeasures in reducing the number of severe crashes at a site, the current state-of-the-practice often leads to biased estimates. While there have been a few advanced statistical models developed to mitigate the problem in practice, these models are computationally difficult to estimate because severe crashes are dispersed spatially and temporally, and cannot be integrated into the Highway Safety Manual framework, which develops a series of safety performance functions and crash modification factors to predict the number of crashes. Crash severity outcomes are generally integrated into the Highway Safety Manual using deterministic distributions rather than statistical models. Accounting for the variability in crash severity as a function geometric design, traffic flow, and other roadway and roadside features is afforded by estimating statistical models. Therefore, there is a need to develop a new analysis paradigm to resolve the limitations in the current Highway Safety Manual methods. We propose an approach which decomposes the severe crash frequency into a function of the change in the total number of crashes and the probability of a crash becoming a severe crash before and after a countermeasure is implemented. We tested this approach by evaluating the effectiveness of shoulder rumble strips on reducing the number of severe crashes. A total of 310 segments that have had shoulder rumble strips installed during 2002-2009 are included in the analysis. It was found that shoulder rumble strips reduce the total number of crashes, but have no statistically significant effect on reducing the probability of a severe crash outcome. (C) 2014 Elsevier Ltd. All rights reserved.
Using naturalistic driving data to explore the association between traffic safety-related events and crash risk at driver level
(2014-11-01)
Wu, Kun-Feng
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Aguero-Valverde, Jonathan
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Jovanis, Paul P.
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運輸與物流管理系註：原交通所+運管所
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Department of Transportation and Logistics Management
There has been considerable research conducted over the last 40 years using traffic safety-related events to support road safety analyses. Dating back to traffic conflict studies from the 1960s these observational studies of driver behavior have been criticized due to: poor quality data; lack of available and useful exposure measures linked to the observations; the incomparability of self-reported safety-related events; and, the difficulty in assessing culpability for safety-related events. This study seeks to explore the relationships between driver characteristics and traffic safety-related events, and between traffic safety-related events and crash involvement while mitigating some of those limitations. The Virginia Tech Transportation Institute 100-Car Naturalistic Driving Study dataset, in which the participants\' vehicles were instrumented with various cameras and sensors during the study period, was used for this study. The study data set includes 90 drivers observed for 12-13 months driving. This study focuses on single vehicle run-off-road safety-related events only, including 14 crashes and 182 safety-related events (30 near crashes, and 152 crash-relevant incidents). Among the findings are: (1) drivers under age 25 are significantly more likely to be involved in safety-related events and crashes; and (2) significantly positive correlations exist between crashes, near crashes, and crash-relevant incidents. Although there is still much to learn about the factors affecting the positive correlation between safety-related events and crashes, a Bayesian multivariate Poisson log-normal model is shown to be useful to quantify the associations between safety-related events and crash risk while controlling for driver characteristics. (C) 2014 Elsevier Ltd. All rights reserved.