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There have been plenty of studies intended to use different methods, for example, empirical Bayes before after methods, to get accurate estimation of CMFs. All of them have different assumptions toward crash count if there was no ...
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There have been plenty of studies intended to use different methods, for example, empirical Bayes before after methods, to get accurate estimation of CMFs. All of them have different assumptions toward crash count if there was no treatment. Additionally, another major assumption is that multiple sites share the same true CMF. Under this assumption, the CMF at an individual intersection is randomly drawn from a normally distributed population of CMFs at all intersections. Since CMFs are non-zero values, the population of all CMFs might not follow normal distributions, and even if it does, the true mean of CMFs at some intersections may be different from that at others. Therefore, a bootstrap method based on before after empirical Bayes theory was proposed to estimate CMFs, but it did not make distributional assumptions. This bootstrap procedure has the added benefit of producing a measure of CMF stability. Furthermore, based on the bootstrapped CMF, a new CMF precision rating method was proposed to evaluate the reliability of CMFs. This study chose 29 urban four-legged intersections as treated sites, and their controls were changed from stop-controlled to signal-controlled. Meanwhile, 124 urban four-legged stop-controlled intersections were selected as reference sites. At first, different safety performance functions (SPFs) were applied to five crash categories, and it was found that each crash category had different optimal SPF form. Then, the CMFs of these five crash categories were estimated using the bootstrap empirical Bayes method. The results of the bootstrapped method showed that signalization significantly decreased Angle + Left-Turn crashes, and its CMF had the highest precision. While, the CMF for Rear-End crashes was unreliable. For KABCO, KABC, and KAB crashes, their CMFs were proved to be reliable for the majority of intersections, but the estimated effect of signalization may be not accurate at some sites.
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Stormwater wet detention ponds hold a permanent pool of water and offer many beneficial uses including flood mitigation, pollution prevention, downstream erosion control, increased aesthetics, and recreational uses. Although the r...
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Stormwater wet detention ponds hold a permanent pool of water and offer many beneficial uses including flood mitigation, pollution prevention, downstream erosion control, increased aesthetics, and recreational uses. Although the removal of nutrients is generally low for stormwater wet detention ponds in urban areas, floating treatment wetlands (FTWs) can be installed to offer an innovative solution toward naturally removing excess nutrients and aiding in stormwater management. To improve the stormwater reuse potential, this study assessed nutrient, microcystin, and chlorophyll-a interactions in three Florida stormwater wet detention ponds with recently implemented FTWs. Both episodic (storm events) and routine (non-storm events) sampling campaigns were carried out at the three ponds located in Ruskin, Gainesville, and Orlando. The results showed a salient negative correlation between total phosphorus and microcystin concentrations for both storm and non-storm events across all three ponds. The dominant nutrient species in correlation seemed to be total phosphorus, which correlated positively with chlorophyll-a concentrations at all ponds and sampling conditions, with the exception of Orlando non-storm events. These results showed a correlation conditional to the candidate pond and sampling conditions for microcystin and chlorophyll-a concentrations. (C) 2015 Elsevier Ltd. All rights reserved.
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Safety Performance Functions (SPFs) have been widely used by researchers and practitioners to conduct roadway safety evaluation. Traditional SPFs are usually developed by using annual average daily traffic (AADT) along with geomet...
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Safety Performance Functions (SPFs) have been widely used by researchers and practitioners to conduct roadway safety evaluation. Traditional SPFs are usually developed by using annual average daily traffic (AADT) along with geometric characteristics. However, the high level of aggregation may lead to a failure to capture the temporal variation in traffic characteristics (e.g., traffic volume and speed) and crash frequencies. In this study, SPFs at different aggregation levels were developed based on microscopic traffic detector data from California, Florida, and Virginia. More specifically, five aggregation levels were considered: (1) annual average weekday hourly traffic (AAWDHT), (2) annual average weekend hourly traffic (AAWEHT), (3) annual average weekday peak/off-peak traffic (AAWDPT), (4) annual average day of the week traffic (AADOWT), and (5) annual average daily traffic (AADT). Model estimation results showed that the segment length and volume, as exposure variables, are significant across all the aggregation levels. Average speed is significant with a negative coefficient, and the standard deviation of speed was found to be positively associated with the crash frequency. It is noteworthy that the operation of the high occupancy vehicle (HOV) lanes was found to have a positive effect on crash frequency across all the aggregation levels. The model results also showed that the AAWDPT and AADOWT models consistently performed better (the improvements range from 3.14%-16.20%) than the AADT-based SPF, which implies that the differences between the day of the week and peak/off-peak periods should be considered in the development of crash prediction models. The model transferability results indicated that the SPFs between Florida and Virginia are transferrable, while the models between California and the other two states are not transferrable.
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In our study, we examine the joint choice of freight transportation mode and shipment size. While shipment size could be considered as an explanatory variable in modeling mode choice (or vice-versa), it is more likely that the dec...
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In our study, we examine the joint choice of freight transportation mode and shipment size. While shipment size could be considered as an explanatory variable in modeling mode choice (or vice-versa), it is more likely that the decision of mode and shipment choice is a simultaneous process. A joint model system is developed in the form of an unordered choice model for mode and an ordered choice model for shipment size. We adopt a closed form copula-based model structure for capturing the impact of common unobserved factors affecting these two choices. Further, we explore alternatives to the traditional random utility structure in modeling mode choice. Specifically, we explore both the random utility (RU) based multinomial logit and the random regret (RR) minimization based multinomial logit (MNL) within a copula-based model. The shipment size is analyzed using ordered logit (OL) model within the copula structure. The RU and RR MNL structures are explored for several copula-based structures including Gaussian, Farlie-Gumbel-Morgenstern (FGM), Clayton, Gumbel, Frank and Joe. The proposed approach considers copula models with multiple copula-based dependencies within a single model. The copula-based model dependency is also allowed to vary across the data by parameterizing the dependency as a function of observed attributes. The models are estimated based on the data from 2012 U.S. Commodity Flow Survey data. The copula RRM based MNL-OL copula with Frank and Joe copula dependencies offered the best data fit indicating the strong interconnectedness between shipment mode and shipment size choice decisions. A validation exercise provides further evidence of the joint model superiority for overall sample level and freight characteristics variables specific sub-samples.
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Recycled materials were used in three types of green sorption media for nutrient removal and possible recovery in high nutrient-laden agricultural discharge and stormwater runoff. The three types of green sorption media included i...
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Recycled materials were used in three types of green sorption media for nutrient removal and possible recovery in high nutrient-laden agricultural discharge and stormwater runoff. The three types of green sorption media included in this comparative study were two new aluminum-based green environmental media (AGEM) and one existing iron-filings based green environmental media (IFGEM). The corresponding adsorption isotherm, thermodynamics, and kinetics models were simulated based on isotherm studies to determine their removal efficiency and potential for recovery of nitrate, phosphate, and ammonia when used as a soil amendment in crop fields or in a filter for water treatment. AGEM-2 exhibited the shortest contact time required to achieve nutrient removal above 80% with an average of 7 h, followed by AGEM-1 and IFGEM with 10.6 and 28 h, respectively. Natural soil was included as a control and exhibited minimal nutrient removal. Ammonia, which may be recovered as fertilizer for drop fields in a soil-water-waste nexus, was generated by all three green sorption media mixes, therefore indicating their potential for use as soil amendments in agricultural and forested land after engineering filter applications. The kinetics analysis indicated that nitrate adsorption follows pseudo-first-order kinetics, while phosphate adsorption follows pseudo-second-order kinetics. The Gibbs free energy indicated that most of the adsorption reactions proceeded as exothermic. Lastly, a few equilibrium models, including the Langmuir, Freundlich, First Modified Langmuir, Temkin, Jovanovic, and Elovich models, were ranked and three were selected for use with IFGEM, AGEM-1, and AGEM-2, respectively, as below: (1) Langmuir, (2) Freundlich, and (3) First Modified Langmuir, according to three indices. (C) 2020 Elsevier Ltd. All rights reserved.
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The forward collision warning (FCW) system is expected to reduce rear-end crashes; however, its effects on driving behavior and safety have not been thoroughly investigated, specifically the effect variations between different pre...
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The forward collision warning (FCW) system is expected to reduce rear-end crashes; however, its effects on driving behavior and safety have not been thoroughly investigated, specifically the effect variations between different pre-crash scenarios. To identify these variations, this study conducted a driving simulator experiment and compared the FCW's effects between three pre-crash scenarios: the freeway scenario, the arterial scenario and the intersection dilemma zone scenario. Thirty-nine participants were involved in the experiment. The results showed that the adaptation of driver behavior in impending rear-end collision events resulted from both the FCW and the scenario. The intersection dilemma zone scenario has indications of slowing down, which encouraged drivers to take a more aggressive response strategy under the FCW; the arterial scenario might be regarded as an "easy-to-handle" situation in which a significant portion of drivers adopted moderate level of response strategy under the FCW; both the intersection dilemma zone scenario and freeway scenario have burdened driving tasks, and this might deteriorate a driver's ability to adapt to the FCW. In addition, different types of drivers experienced varied benefits from the FCW in each scenario. The FCW would be particularly recommended for non-experienced drivers in the freeway scenario and for female drivers in the arterial scenario; moreover, in the scenario of the intersection dilemma zone, the FCW would be particularly recommended for drivers who have a crash/citation before. The results also support specific FCW designs which are able to highlight the collision risk. This study demonstrated that it would be better to indicate the effects of the FCW under the restriction of specific scenario features and develop the FCW based on that. (C) 2020 Elsevier Ltd. All rights reserved.
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Pedestrian-to-vehicle (P2V) technology may offer a promising approach to reducing pedestrian crashes. However, its influences on both driver response and safety benefits have been little studied in previous research, particularly ...
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Pedestrian-to-vehicle (P2V) technology may offer a promising approach to reducing pedestrian crashes. However, its influences on both driver response and safety benefits have been little studied in previous research, particularly in regard to the variation of influences between different pre-crash scenarios. To investigate these influences, this study designed three pre-crash scenarios based on pedestrian crash contributing factors identified from crash reports, and collected 44 drivers' driving simulator experiments' data. The results clarified how using P2V technology to warn drivers of an impending collision improves safety by causing a series of changes for both brake operation and braking profile. These series of changes were further demonstrated to vary between scenarios. The study showed that P2V technology may be particularly useful in scenarios in which a pedestrian's crossing intention is unclear; specifically, in this type of scenario, the P2V warning had changed the braking process from a panic brake of "slow reaction-hard brake" to a comfortable brake of "quick reaction-gentle brake." In addition, the P2V warning may be less effective in "low-risk" level scenarios where a driver is confident that he/she can handle the situation through a more conservative evasive action and don't need to react strongly to a warning. Moreover, depending on the pre-crash scenario, the P2V warning may be mostly beneficial for drivers who had a crash/citation in the past five years and working-aged drivers. (C) 2020 Elsevier Ltd. All rights reserved.
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Real-time crash prediction is essential for proactive traffic safety management. However, developing an accurate prediction model is challenging as the traffic data of crash and non-crash cases are extremely imbalanced. Most of th...
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Real-time crash prediction is essential for proactive traffic safety management. However, developing an accurate prediction model is challenging as the traffic data of crash and non-crash cases are extremely imbalanced. Most of the previous studies undersampled non-crash cases to balance the data, which may not capture the heterogeneity of the full non-crash data. This study aims to use the emerging deep learning method called deep convolutional generative adversarial network (DCGAN) model to fully understand the traffic data leading to crashes. With the full understanding of the traffic data of crashes, the DCGAN model could generate more synthetic data related to crashes to balance the dataset. All non-crash data could be used for developing the prediction models. To capture the correlations between different variables, the data are augmented to 2-D matrix as the input for the DCGAN model. The suggested model is evaluated based on data from expressways and compared to two counterparts: (1) synthetic minority over-sampling technique (SMOTE); (2) random undersampling technique. The results suggest that the DCGAN could better understand the crash data characteristics by generating data with better fit of the real data distribution. Four different crash prediction algorithms (i.e., logistic regression model, support vector machine, artificial neural network, and convolutional neural network) are developed based on each balanced data and totally twelve models were estimated. The results indicate that the convolutional neural network model based on the DCGAN balanced data could provide the best prediction accuracy, validating that the proposed oversampling method could be used for the data balance. Besides, compared to other two models, only the DCGAN-based model could identify the significant effects of speed difference between the upstream and downstream locations which could help guide traffic management strategies. With the prediction model developed based on the balanced data by DCGAN, it is expected that more crashes could be predicted and prevented with more appropriate proactive traffic safety management strategies such as Variable Speed Limits (VSL) and Dynamic Message Signs (DMS).
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Introduction: A pedestrian crash occurs due to a series of contributing factors taking effect in an antecedent-consequent order. One specific type of antecedent-consequent order is called a crash causation pattern. Understanding c...
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Introduction: A pedestrian crash occurs due to a series of contributing factors taking effect in an antecedent-consequent order. One specific type of antecedent-consequent order is called a crash causation pattern. Understanding crash causation patterns is important for clarifying the complicated growth of a pedestrian crash, which ultimately helps recommend corresponding countermeasures. However, previous studies lack an in-depth investigation of pedestrian crash cases, and are insufficient to propose a representative picture of causation patterns. Method: In this study, pedestrian crash causation patterns were discerned by using the Driving Reliability and Error Analysis Method (DREAM). One hundred and forty-two pedestrian crashes were investigated, and five pedestrian pre-crash scenarios were extracted. Then, the crash causation patterns in each pre-crash scenario were analyzed; and finally, six distinct patterns were identified. Accordingly, 17 typical situations corresponding to these causation patterns were specified as well. Results: Among these patterns, the pattern related to distracted driving and the pattern related to an unexpected change of pedestrian trajectory contributed to a large portion of the total crashes (i.e., 27% and 24%, respectively). Other patterns also played an important role in inducing a pedestrian crash; these patterns include the pattern related to an obstructed line of sight caused by outside objects (9%), the pattern that involves reduced visibility (13%), and the pattern related to an improper estimation of the gap distance between the vehicle and the pedestrian (10%). The results further demonstrated the inter-heterogeneity of a crash causation pattern, as well as the intra-heterogeneity of pattern features between different pedestrian pre-crash scenarios. Conclusions and practical applications: Essentially, a crash causation pattern might involve different contributing factors by nature or dependent on specific scenarios. Finally, this study proposed suggestions for roadway facility design, roadway safety education and pedestrian crash prevention system development. (C) 2020 National Safety Council and Elsevier Ltd. All rights reserved.
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Reflective Cracking (RC) has been a daunting challenge in pavement maintenance and rehabilitation (M&R), yet, still, after several decades of research, no exclusive solution prevails. Moreover, RC mitigation methods have shown significant variation in in situ performance. Therefore, a technique tailored to select an effective RC mitigation method is essential for the success of pavement M&R. In this study, a life cycle cost (LCC) and multi-criteria decision-making (MCDM) analyses were conducted to evaluate the effectiveness of currently available RC mitigation methods and to select the optimal method for an asphalt concrete overlay above flexible pavements. The MCDM includes three components: LCC, performance, and materials (recyclability). These criteria determine the selection ranking of each RC mitigation method. In addition, the effects of the priority level including cost, performance, and recyclability on the final decision were evaluated by conducting a series of sensitivity analysis under multiple scenarios; therefore, weight combination of the three criteria were recorded to define the measurements affecting the final decision....
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Reflective Cracking (RC) has been a daunting challenge in pavement maintenance and rehabilitation (M&R), yet, still, after several decades of research, no exclusive solution prevails. Moreover, RC mitigation methods have shown significant variation in in situ performance. Therefore, a technique tailored to select an effective RC mitigation method is essential for the success of pavement M&R. In this study, a life cycle cost (LCC) and multi-criteria decision-making (MCDM) analyses were conducted to evaluate the effectiveness of currently available RC mitigation methods and to select the optimal method for an asphalt concrete overlay above flexible pavements. The MCDM includes three components: LCC, performance, and materials (recyclability). These criteria determine the selection ranking of each RC mitigation method. In addition, the effects of the priority level including cost, performance, and recyclability on the final decision were evaluated by conducting a series of sensitivity analysis under multiple scenarios; therefore, weight combination of the three criteria were recorded to define the measurements affecting the final decision.
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