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Lingtao Wu
Center for Transportation Safety, Texas A&M Transportation Institute, Bryan, USA

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Article
Published: 01 August 2020 in Journal of Central South University
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Before-after study with the empirical Bayes (EB) method is the state-of-the-art approach for estimating crash modification factors (CMFs). The EB method not only addresses the regression-to-the-mean bias, but also improves accuracy. However, the performance of the CMFs derived from the EB method has never been fully investigated. This study aims to examine the accuracy of CMFs estimated with the EB method. Artificial realistic data (ARD) and real crash data are used to evaluate the CMFs. The results indicate that: 1) The CMFs derived from the EB before-after method are nearly the same as the true values. 2) The estimated CMF standard errors do not reflect the true values. The estimation remains at the same level regardless of the pre-assumed CMF standard error. The EB before-after study is not sensitive to the variation of CMF among sites. 3) The analyses with real-world traffic and crash data with a dummy treatment indicate that the EB method tends to underestimate the standard error of the CMF. Safety researchers should recognize that the CMF variance may be biased when evaluating safety effectiveness by the EB method. It is necessary to revisit the algorithm for estimating CMF variance with the EB method.

ACS Style

Ying Chen; Ling-Tao Wu; Zhong-Xiang Huang. Assessing quality of crash modification factors estimated by empirical Bayes before-after methods. Journal of Central South University 2020, 27, 2259 -2268.

AMA Style

Ying Chen, Ling-Tao Wu, Zhong-Xiang Huang. Assessing quality of crash modification factors estimated by empirical Bayes before-after methods. Journal of Central South University. 2020; 27 (8):2259-2268.

Chicago/Turabian Style

Ying Chen; Ling-Tao Wu; Zhong-Xiang Huang. 2020. "Assessing quality of crash modification factors estimated by empirical Bayes before-after methods." Journal of Central South University 27, no. 8: 2259-2268.

Research article
Published: 16 May 2019 in Transportation Research Record: Journal of the Transportation Research Board
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Human factors studies have shown that route familiarity affects driver behavior in various ways. Specifically, when drivers become more familiar with a roadway, they pay less attention to signs, adopt higher speeds, cut curves more noticeably, and exhibit slower reaction times to stimuli in their peripheral vision. Numerous curve speed models have been developed for purposes such as predicting driver behavior, evaluating roadway design consistency, and setting curve advisory speeds. These models are typically calibrated using field data, which gives information about driver behavior in relation to speed and sometimes lane placement, but does not provide insights into the drivers themselves. The objective of this paper is to examine the differences between the speeds of familiar and unfamiliar drivers as they traverse curves. The authors identified four two-lane rural highway sections in the State of Indiana which include multiple horizontal curves, and queried the Second Strategic Highway Research Program (SHRP2) database to obtain roadway inventory and naturalistic driving data for traversals through these curves. The authors applied a curve speed prediction model from the literature to predict the speed at the curve midpoints and compared the predicted speeds with observed speeds. The results of the analysis confirm earlier findings that familiar drivers choose higher speeds through curves. The successful use of the SHRP2 database for this analysis of route familiarity shows that the database can facilitate similar efforts for a wider range of driver behavior and human factors issues.

ACS Style

Michael P. Pratt; Srinivas R. Geedipally; Bahar Dadashova; Lingtao Wu; Mohammadali Shirazi. Familiar versus Unfamiliar Drivers on Curves: Naturalistic Data Study. Transportation Research Record: Journal of the Transportation Research Board 2019, 2673, 225 -235.

AMA Style

Michael P. Pratt, Srinivas R. Geedipally, Bahar Dadashova, Lingtao Wu, Mohammadali Shirazi. Familiar versus Unfamiliar Drivers on Curves: Naturalistic Data Study. Transportation Research Record: Journal of the Transportation Research Board. 2019; 2673 (6):225-235.

Chicago/Turabian Style

Michael P. Pratt; Srinivas R. Geedipally; Bahar Dadashova; Lingtao Wu; Mohammadali Shirazi. 2019. "Familiar versus Unfamiliar Drivers on Curves: Naturalistic Data Study." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 6: 225-235.

Research article
Published: 17 April 2019 in Transportation Research Record: Journal of the Transportation Research Board
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Hotspot identification is an important step in the highway safety management process. Errors in hotspot identification (HSID) may result in an inefficient use of limited resources for safety improvements. The empirical Bayesian (EB) HSID has been widely applied as an effective approach in identifying hotspots. However, there are some limitations with the EB approach. It assumes that the parameter estimates of the safety performance function (SPF) are correct without any uncertainty, and does not consider temporal instability in crashes, which has been reported in recent studies. The Bayesian hierarchical model is an emerging technique that addresses the limitations of the EB method. Thus, the objective of this study is to compare the performance of the standard EB method and the Bayesian hierarchical model in identifying hotspots. Three methods (crash rate, EB, and the Bayesian hierarchical model) were applied to identify risky intersections with different significance levels. Four evaluation tests (site consistency, method consistency, total rank differences, and Poisson mean differences tests) were conducted to assess the performance of these three methods. The testing results suggest that: (1) the Bayesian hierarchical model outperforms the crash rate and the EB methods in most cases, and the Bayesian hierarchical model improves the accuracy of HSID significantly; and (2) hotspots identified with crash rates are generally unreliable. This is significant for roadway agencies and practitioners trying to accurately rank sites in the roadway network to effectively manage safety investments. Roadway agencies and practitioners are encouraged to consider the Bayesian hierarchical model in identifying hotspots.

ACS Style

Xiaoyu Guo; Lingtao Wu; Yajie Zou; Lee Fawcett. Comparative Analysis of Empirical Bayes and Bayesian Hierarchical Models in Hotspot Identification. Transportation Research Record: Journal of the Transportation Research Board 2019, 2673, 111 -121.

AMA Style

Xiaoyu Guo, Lingtao Wu, Yajie Zou, Lee Fawcett. Comparative Analysis of Empirical Bayes and Bayesian Hierarchical Models in Hotspot Identification. Transportation Research Record: Journal of the Transportation Research Board. 2019; 2673 (7):111-121.

Chicago/Turabian Style

Xiaoyu Guo; Lingtao Wu; Yajie Zou; Lee Fawcett. 2019. "Comparative Analysis of Empirical Bayes and Bayesian Hierarchical Models in Hotspot Identification." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 7: 111-121.

Research article
Published: 01 April 2019 in Journal of Advanced Transportation
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Two common types of animal-vehicle collision data (reported animal-vehicle collision (AVC) data and carcass removal data) are usually recorded by transportation management agencies. Previous studies have found that these two datasets often demonstrate different characteristics. To accurately identify the higher-risk animal-vehicle collision sites, this study compared the differences in hotspot identification and the effect of explanation variables between carcass removal and reported AVCs. To complete the objective, both the Negative Binomial (NB) model and the generalized Negative Binomial (GNB) are applied in calculating the Empirical Bayesian (EB) estimates using the animal collision data collected on ten highways in Washington State. The important findings can be summarized as follows. (1) The explanatory variables have different effects on the occurrence of carcass removal data and reported AVC data. (2) The ranking results from EB estimates when using carcass removal data and reported AVC data differ significantly. (3) The results of hotspot identification are different between carcass removal data and reported AVC data. However, the ranking results of GNB models are better than those of NB models in terms of consistency. Thus, transportation management agencies should be cautious when using either carcass removal data or reported AVC data to identify hotspots.

ACS Style

Xiaoxue Yang; Yajie Zou; Lingtao Wu; Xinzhi Zhong; Yinhai Wang; Muhammad Ijaz; Yichuan Peng. Comparative Analysis of the Reported Animal-Vehicle Collisions Data and Carcass Removal Data for Hotspot Identification. Journal of Advanced Transportation 2019, 2019, 1 -13.

AMA Style

Xiaoxue Yang, Yajie Zou, Lingtao Wu, Xinzhi Zhong, Yinhai Wang, Muhammad Ijaz, Yichuan Peng. Comparative Analysis of the Reported Animal-Vehicle Collisions Data and Carcass Removal Data for Hotspot Identification. Journal of Advanced Transportation. 2019; 2019 ():1-13.

Chicago/Turabian Style

Xiaoxue Yang; Yajie Zou; Lingtao Wu; Xinzhi Zhong; Yinhai Wang; Muhammad Ijaz; Yichuan Peng. 2019. "Comparative Analysis of the Reported Animal-Vehicle Collisions Data and Carcass Removal Data for Hotspot Identification." Journal of Advanced Transportation 2019, no. : 1-13.

Journal article
Published: 15 January 2019 in Sustainability
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Wildlife‒vehicle collision (WVC) data usually contain two types: the reported WVC data and carcass removal data. Previous studies often found a discrepancy between the number of reported WVC and carcass removal data, and the quality of both datasets is affected by underreporting. Underreporting means the number of WVCs is not fully recorded in the database; neglecting the underreporting in WVC data may result in biased parameter estimation results. In this study, a copula regression model linking wildlife‒vehicle collisions and the underreporting outcome was proposed to consider the underreporting in WVC data. The WVC data collected from 10 highways in Washington State were analyzed using the copula regression model and the Negative Binomial (NB) model. The main findings from this study are as follows: (1) the Gaussian copula model can provide different modeling results when compared with the conventional modeling approach; (2) the hotspot identification results indicate that the Gaussian copula-based Empirical Bayes (EB) method can more accurately identify hotspots than the NB-based EB method. Thus, the proposed copula model may be a better alternative to the conventional NB model for modeling underreported WVC data.

ACS Style

Yajie Zou; Xinzhi Zhong; Jinjun Tang; Xin Ye; Lingtao Wu; Muhammad Ijaz; Yinhai Wang. A Copula-Based Approach for Accommodating the Underreporting Effect in Wildlife‒Vehicle Crash Analysis. Sustainability 2019, 11, 418 .

AMA Style

Yajie Zou, Xinzhi Zhong, Jinjun Tang, Xin Ye, Lingtao Wu, Muhammad Ijaz, Yinhai Wang. A Copula-Based Approach for Accommodating the Underreporting Effect in Wildlife‒Vehicle Crash Analysis. Sustainability. 2019; 11 (2):418.

Chicago/Turabian Style

Yajie Zou; Xinzhi Zhong; Jinjun Tang; Xin Ye; Lingtao Wu; Muhammad Ijaz; Yinhai Wang. 2019. "A Copula-Based Approach for Accommodating the Underreporting Effect in Wildlife‒Vehicle Crash Analysis." Sustainability 11, no. 2: 418.

Journal article
Published: 01 September 2018 in Transportation Research Record: Journal of the Transportation Research Board
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Rumble strips are known to be one of the most cost-effective treatments for preventing roadway departure crashes. However, in recent years some studies have found controversial results indicating that rumble strips may in fact increase the number of more severe crashes. Although these effects are estimated to be very small, highway safety agencies deploy these treatments with an expectation that they will reduce all crash types. In this paper, the authors have conducted a statistical evaluation to determine the impact of rumble strip presence on fatal and injury crashes at freeway locations. For this purpose, the authors acquired an existing database used for one of the aforementioned studies and evaluated the variables using alternative assessment methods. The results of the current study suggest that rumble strips do in fact improve the safety outcomes in rural freeways. These findings are observed to also apply to urban freeways, but the effects are not statistically significant for the study database.

ACS Style

Bahar Dadashova; Lingtao Wu; Karen Dixon. Evaluating the Impact of Rumble Strips on Fatal and Injury Freeway Crashes. Transportation Research Record: Journal of the Transportation Research Board 2018, 2672, 131 -141.

AMA Style

Bahar Dadashova, Lingtao Wu, Karen Dixon. Evaluating the Impact of Rumble Strips on Fatal and Injury Freeway Crashes. Transportation Research Record: Journal of the Transportation Research Board. 2018; 2672 (30):131-141.

Chicago/Turabian Style

Bahar Dadashova; Lingtao Wu; Karen Dixon. 2018. "Evaluating the Impact of Rumble Strips on Fatal and Injury Freeway Crashes." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 30: 131-141.

Research article
Published: 05 August 2018 in Discrete Dynamics in Nature and Society
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This study aims to examine the impact of traffic incidents occurring on general purpose lanes (GPLs) on the travel time reliability of high-occupancy vehicle (HOV) lanes on freeways and to evaluate the differences of travel time reliability on GPLs and HOV lanes under the same incident conditions. In this paper, an empirical travel time reliability analysis is conducted using the travel time and incident data collected between 2009 and 2012 on Interstate 5 and Interstate 405 of the Seattle metropolitan area. Three incident types (i.e., shoulder incident, single lane incident, and multiple lane incident) are considered. Two measures, percentile-based indicator and inflow–percentile travel time function, are used. The results suggest that incidents result in lower values of travel time reliability for all the measures. The results also show that multiple lane incident type has the most significant impact on the freeway route travel time reliability, while shoulder incident type has the least impact. Generally, HOV lanes have higher travel time reliability than GPLs under the same incident types. The findings in this study provide useful decision support for transportation agencies to improve travel time reliability on freeways.

ACS Style

Xianzhe Chen; Yajie Zou; Jinjun Tang; Yichuan Peng; Lingtao Wu; Yuming Jiang. Analysing the Impact of Traffic Incidents on the Travel Time Reliability of Freeway High-Occupancy Vehicle Lanes. Discrete Dynamics in Nature and Society 2018, 2018, 1 -12.

AMA Style

Xianzhe Chen, Yajie Zou, Jinjun Tang, Yichuan Peng, Lingtao Wu, Yuming Jiang. Analysing the Impact of Traffic Incidents on the Travel Time Reliability of Freeway High-Occupancy Vehicle Lanes. Discrete Dynamics in Nature and Society. 2018; 2018 ():1-12.

Chicago/Turabian Style

Xianzhe Chen; Yajie Zou; Jinjun Tang; Yichuan Peng; Lingtao Wu; Yuming Jiang. 2018. "Analysing the Impact of Traffic Incidents on the Travel Time Reliability of Freeway High-Occupancy Vehicle Lanes." Discrete Dynamics in Nature and Society 2018, no. : 1-12.

Research article
Published: 11 June 2018 in Transportation Research Record: Journal of the Transportation Research Board
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Roadway departure crashes are a major contributor to traffic fatalities and injury. Rumble strips have been shown to be an effective countermeasure in reducing roadway departure crashes. However, some roadway situations, for instance, inadequate shoulder width or roadway surface depth, have limited the application of conventional milled or rolled in rumble strips. Alternative audible lane departure warning systems, including profile (audible) pavement markings and preformed rumble bars, are increasingly used to overcome the limitations that exist with the milled rumble strips. So far, the safety effectiveness of these alternative audible lane departure warning systems has not been extensively assessed. The main purpose of this paper is to examine the safety effect of installing profile pavement markings and preformed rumble bars. Specifically, this study developed crash modification factors for these treatments that quantify the effectiveness in reducing single-vehicle-run-off-road (SVROR) and opposite-direction (OD) crashes. Traffic, roadway, and crash data at the treated sites on 189 miles of rural two-lane highways in Texas were analyzed using an empirical Bayes (EB) before–after analysis method. Safety performance functions from the Highway Safety Manual and Texas Highway Safety Design Workbook were used in the EB analysis. The results revealed a 21.3% reduction in all SVROR and OD crashes, and 32.5% to 39.9% reduction in fatal and injury SVROR and OD crashes after installing profile pavement marking and preformed rumble bars.

ACS Style

Lingtao Wu; Srinivas R. Geedipally; Adam M. Pike. Safety Evaluation of Alternative Audible Lane Departure Warning Treatments in Reducing Traffic Crashes: An Empirical Bayes Observational Before–After Study. Transportation Research Record: Journal of the Transportation Research Board 2018, 2672, 30 -40.

AMA Style

Lingtao Wu, Srinivas R. Geedipally, Adam M. Pike. Safety Evaluation of Alternative Audible Lane Departure Warning Treatments in Reducing Traffic Crashes: An Empirical Bayes Observational Before–After Study. Transportation Research Record: Journal of the Transportation Research Board. 2018; 2672 (21):30-40.

Chicago/Turabian Style

Lingtao Wu; Srinivas R. Geedipally; Adam M. Pike. 2018. "Safety Evaluation of Alternative Audible Lane Departure Warning Treatments in Reducing Traffic Crashes: An Empirical Bayes Observational Before–After Study." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 21: 30-40.

Original articles
Published: 24 October 2017 in Journal of Applied Statistics
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The empirical Bayes (EB) method is commonly used by transportation safety analysts for conducting different types of safety analyses, such as before–after studies and hotspot analyses. To date, most implementations of the EB method have been applied using a negative binomial (NB) model, as it can easily accommodate the overdispersion commonly observed in crash data. Recent studies have shown that a generalized finite mixture of NB models with K mixture components (GFMNB-K) can also be used to model crash data subjected to overdispersion and generally offers better statistical performance than the traditional NB model. So far, nobody has developed how the EB method could be used with finite mixtures of NB models. The main objective of this study is therefore to use a GFMNB-K model in the calculation of EB estimates. Specifically, GFMNB-K models with varying weight parameters are developed to analyze crash data from Indiana and Texas. The main finding shows that the rankings produced by the NB and GFMNB-2 models for hotspot identification are often quite different, and this was especially noticeable with the Texas dataset. Finally, a simulation study designed to examine which model formulation can better identify the hotspot is recommended as our future research.

ACS Style

Yajie Zou; John E. Ash; Byung-Jung Park; Dominique Lord; Lingtao Wu. Empirical Bayes estimates of finite mixture of negative binomial regression models and its application to highway safety. Journal of Applied Statistics 2017, 45, 1652 -1669.

AMA Style

Yajie Zou, John E. Ash, Byung-Jung Park, Dominique Lord, Lingtao Wu. Empirical Bayes estimates of finite mixture of negative binomial regression models and its application to highway safety. Journal of Applied Statistics. 2017; 45 (9):1652-1669.

Chicago/Turabian Style

Yajie Zou; John E. Ash; Byung-Jung Park; Dominique Lord; Lingtao Wu. 2017. "Empirical Bayes estimates of finite mixture of negative binomial regression models and its application to highway safety." Journal of Applied Statistics 45, no. 9: 1652-1669.

Journal article
Published: 01 May 2017 in Accident Analysis & Prevention
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This study further examined the use of regression models for developing crash modification factors (CMFs), specifically focusing on the misspecification in the link function. The primary objectives were to validate the accuracy of CMFs derived from the commonly used regression models (i.e., generalized linear models or GLMs with additive linear link functions) when some of the variables have nonlinear relationships and quantify the amount of bias as a function of the nonlinearity. Using the concept of artificial realistic data, various linear and nonlinear crash modification functions (CM-Functions) were assumed for three variables. Crash counts were randomly generated based on these CM-Functions. CMFs were then derived from regression models for three different scenarios. The results were compared with the assumed true values. The main findings are summarized as follows: (1) when some variables have nonlinear relationships with crash risk, the CMFs for these variables derived from the commonly used GLMs are all biased, especially around areas away from the baseline conditions (e.g., boundary areas); (2) with the increase in nonlinearity (i.e., nonlinear relationship becomes stronger), the bias becomes more significant; (3) the quality of CMFs for other variables having linear relationships can be influenced when mixed with those having nonlinear relationships, but the accuracy may still be acceptable; and (4) the misuse of the link function for one or more variables can also lead to biased estimates for other parameters. This study raised the importance of the link function when using regression models for developing CMFs.

ACS Style

Lingtao Wu; Dominique Lord. Examining the influence of link function misspecification in conventional regression models for developing crash modification factors. Accident Analysis & Prevention 2017, 102, 123 -135.

AMA Style

Lingtao Wu, Dominique Lord. Examining the influence of link function misspecification in conventional regression models for developing crash modification factors. Accident Analysis & Prevention. 2017; 102 ():123-135.

Chicago/Turabian Style

Lingtao Wu; Dominique Lord. 2017. "Examining the influence of link function misspecification in conventional regression models for developing crash modification factors." Accident Analysis & Prevention 102, no. : 123-135.

Conference paper
Published: 15 February 2017 in Proceedings of the Institution of Civil Engineers - Transport
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ACS Style

Yajie Zou; Jinjun Tang; Lingtao Wu; Kristian Henrickson; Yinhai Wang. Quantile analysis of factors influencing the time taken to clear road traffic incidents. Proceedings of the Institution of Civil Engineers - Transport 2017, 1 -9.

AMA Style

Yajie Zou, Jinjun Tang, Lingtao Wu, Kristian Henrickson, Yinhai Wang. Quantile analysis of factors influencing the time taken to clear road traffic incidents. Proceedings of the Institution of Civil Engineers - Transport. 2017; ():1-9.

Chicago/Turabian Style

Yajie Zou; Jinjun Tang; Lingtao Wu; Kristian Henrickson; Yinhai Wang. 2017. "Quantile analysis of factors influencing the time taken to clear road traffic incidents." Proceedings of the Institution of Civil Engineers - Transport , no. : 1-9.

Journal article
Published: 01 December 2016 in Accident Analysis & Prevention
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This study aimed to investigate the relative performance of two models (negative binomial (NB) model and two-component finite mixture of negative binomial models (FMNB-2)) in terms of developing crash modification factors (CMFs). Crash data on rural multilane divided highways in California and Texas were modeled with the two models, and crash modification functions (CMFunctions) were derived. The resultant CMFunction estimated from the FMNB-2 model showed several good properties over that from the NB model. First, the safety effect of a covariate was better reflected by the CMFunction developed using the FMNB-2 model, since the model takes into account the differential responsiveness of crash frequency to the covariate. Second, the CMFunction derived from the FMNB-2 model is able to capture nonlinear relationships between covariate and safety. Finally, following the same concept as those for NB models, the combined CMFs of multiple treatments were estimated using the FMNB-2 model. The results indicated that they are not the simple multiplicative of single ones (i.e., their safety effects are not independent under FMNB-2 models). Adjustment Factors (AFs) were then developed. It is revealed that current Highway Safety Manual's method could over- or under-estimate the combined CMFs under particular combination of covariates. Safety analysts are encouraged to consider using the FMNB-2 models for developing CMFs and AFs.

ACS Style

Byung-Jung Park; Dominique Lord; Lingtao Wu. Finite mixture modeling approach for developing crash modification factors in highway safety analysis. Accident Analysis & Prevention 2016, 97, 274 -287.

AMA Style

Byung-Jung Park, Dominique Lord, Lingtao Wu. Finite mixture modeling approach for developing crash modification factors in highway safety analysis. Accident Analysis & Prevention. 2016; 97 ():274-287.

Chicago/Turabian Style

Byung-Jung Park; Dominique Lord; Lingtao Wu. 2016. "Finite mixture modeling approach for developing crash modification factors in highway safety analysis." Accident Analysis & Prevention 97, no. : 274-287.

Research article
Published: 07 September 2015 in Mathematical Problems in Engineering
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Hotspot identification (HSID) is an important component of the highway safety management process. A number of methods have been proposed to identify hotspots. Among these methods, previous studies have indicated that the empirical Bayes (EB) method can outperform other methods for identifying hotspots, since the EB method combines the historical crash records of the site and expected number of crashes obtained from a safety performance function (SPF) for similar sites. However, the SPFs are usually developed based on a large number of sites, which may contain heterogeneity in traffic characteristic. As a result, the hotspot identification accuracy of EB methods can possibly be affected by SPFs, when heterogeneity is present in crash data. Thus, it is necessary to consider the heterogeneity and homogeneity of roadway segments when using EB methods. To address this problem, this paper proposed three different classification-based EB methods to identify hotspots. Rural highway crash data collected in Texas were analyzed and classified into different groups using the proposed methods. Based on the modeling results for Texas crash dataset, it is found that one proposed classification-based EB method performs better than the standard EB method as well as other HSID methods.

ACS Style

Yajie Zou; Kristian Henrickson; Lingtao Wu; Yinhai Wang; Zhaoru Zhang. Application of the Empirical Bayes Method with the Finite Mixture Model for Identifying Accident-Prone Spots. Mathematical Problems in Engineering 2015, 2015, 1 -10.

AMA Style

Yajie Zou, Kristian Henrickson, Lingtao Wu, Yinhai Wang, Zhaoru Zhang. Application of the Empirical Bayes Method with the Finite Mixture Model for Identifying Accident-Prone Spots. Mathematical Problems in Engineering. 2015; 2015 ():1-10.

Chicago/Turabian Style

Yajie Zou; Kristian Henrickson; Lingtao Wu; Yinhai Wang; Zhaoru Zhang. 2015. "Application of the Empirical Bayes Method with the Finite Mixture Model for Identifying Accident-Prone Spots." Mathematical Problems in Engineering 2015, no. : 1-10.

Journal article
Published: 01 January 2015 in Analytic Methods in Accident Research
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ACS Style

Yajie Zou; Lingtao Wu; Dominique Lord. Modeling over-dispersed crash data with a long tail: Examining the accuracy of the dispersion parameter in Negative Binomial models. Analytic Methods in Accident Research 2015, 5-6, 1 -16.

AMA Style

Yajie Zou, Lingtao Wu, Dominique Lord. Modeling over-dispersed crash data with a long tail: Examining the accuracy of the dispersion parameter in Negative Binomial models. Analytic Methods in Accident Research. 2015; 5-6 ():1-16.

Chicago/Turabian Style

Yajie Zou; Lingtao Wu; Dominique Lord. 2015. "Modeling over-dispersed crash data with a long tail: Examining the accuracy of the dispersion parameter in Negative Binomial models." Analytic Methods in Accident Research 5-6, no. : 1-16.

Research article
Published: 01 January 2015 in Transportation Research Record: Journal of the Transportation Research Board
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Crash modification factors (CMFs) can be used to capture the safety effects of countermeasures and play a significant role in traffic safety management. As an alternative to the before-and-after study, the regression model method has been widely used for estimating CMFs. Although before-and-after studies are considered to be superior, the use of regression models for estimating CMFs has never been fully investigated. Consequently, the conditions in which regression models could be used for such a purpose were examined. CMFs for three variables—lane width, curve density, and pavement friction—were assumed and used for generating random crash counts. Then CMFs were derived from regression models by using the simulated crash data for three different scenarios. The results were then compared with the assumed true values. The study results showed that (a) when all factors affecting traffic safety are identical in all segments except those of interest, CMFs derived from regression models should be unbiased; (b) if some factors having minor safety effects are omitted from the models, the accuracy of estimated CMFs can still be acceptable; and (c) if some factors already known to have significant effects on crash risk are omitted, CMFs derived from the regression models are generally unreliable. Thus, depending on missing variables not included in the model, the transportation safety analyst can decide whether CMFs developed from regression models should be used for highway safety applications.

ACS Style

Lingtao Wu; Dominique Lord; Yajie Zou. Validation of Crash Modification Factors Derived from Cross-Sectional Studies with Regression Models. Transportation Research Record: Journal of the Transportation Research Board 2015, 2514, 88 -96.

AMA Style

Lingtao Wu, Dominique Lord, Yajie Zou. Validation of Crash Modification Factors Derived from Cross-Sectional Studies with Regression Models. Transportation Research Record: Journal of the Transportation Research Board. 2015; 2514 (1):88-96.

Chicago/Turabian Style

Lingtao Wu; Dominique Lord; Yajie Zou. 2015. "Validation of Crash Modification Factors Derived from Cross-Sectional Studies with Regression Models." Transportation Research Record: Journal of the Transportation Research Board 2514, no. 1: 88-96.

Research article
Published: 14 May 2014 in Discrete Dynamics in Nature and Society
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Risk assessment of roads is an effective approach for road agencies to determine safety improvement investments. It can increases the cost-effective returns in crash and injury reductions. To get a powerful Chinese risk assessment model, Research Institute of Highway (RIOH) is developing China Road Assessment Programme (ChinaRAP) model to show the traffic crashes in China in partnership with International Road Assessment Programme (iRAP). The ChinaRAP model is based upon RIOH’s achievements and iRAP models. This paper documents part of ChinaRAP’s research work, mainly including the RIOH model and its pilot application in a province in China.

ACS Style

Tiejun Zhang; Chengcheng Tang; Greg Smith; Lingtao Wu. Road Assessment Model and Pilot Application in China. Discrete Dynamics in Nature and Society 2014, 2014, 1 -7.

AMA Style

Tiejun Zhang, Chengcheng Tang, Greg Smith, Lingtao Wu. Road Assessment Model and Pilot Application in China. Discrete Dynamics in Nature and Society. 2014; 2014 ():1-7.

Chicago/Turabian Style

Tiejun Zhang; Chengcheng Tang; Greg Smith; Lingtao Wu. 2014. "Road Assessment Model and Pilot Application in China." Discrete Dynamics in Nature and Society 2014, no. : 1-7.

Journal article
Published: 01 January 2014 in Transportation Research Record: Journal of the Transportation Research Board
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Identification of crash hot spots is the critical component of the highway safety management process. Errors in hot spot identification (HSID) may result in the inefficient use of resources for safety improvements. One HSID method that is based on the empirical Bayesian (EB) method has been widely used as an effective approach for identifying crash-prone sites. For the EB method, the negative binomial (NB) model is usually needed to obtain the EB estimates. Recently, some studies have shown that the Sichel (SI) model can be easily used in the EB modeling framework and potentially yield better EB estimates. The objective of this study was to compare the performance of the two crash prediction (SI and NB) models in identifying hot spots with the EB method. To accomplish the objective of this study, empirical crash data collected at highway segments in Texas were used to generate simulated crash counts. Three commonly used HSID methods (simple ranking, confidence interval, and EB) were applied with the use of simulated data. False positives, false negatives, and false identifications were calculated and compared across the methods. The simulation results in this study suggested that the SI-based EB method could consistently provide a better HSID result than the NB-based EB method. Moreover, EB methods yielded the lowest error percentage of the three HSID methods. This study confirmed that the EB technique was an effective method for identifying hazardous sites. On the basis of the findings in this study, it is recommended that transportation safety researchers consider the SI model as an alternative crash prediction model when the EB approach is used.

ACS Style

Lingtao Wu; Yajie Zou; Dominique Lord. Comparison of Sichel and Negative Binomial Models in Hot Spot Identification. Transportation Research Record: Journal of the Transportation Research Board 2014, 2460, 107 -116.

AMA Style

Lingtao Wu, Yajie Zou, Dominique Lord. Comparison of Sichel and Negative Binomial Models in Hot Spot Identification. Transportation Research Record: Journal of the Transportation Research Board. 2014; 2460 (1):107-116.

Chicago/Turabian Style

Lingtao Wu; Yajie Zou; Dominique Lord. 2014. "Comparison of Sichel and Negative Binomial Models in Hot Spot Identification." Transportation Research Record: Journal of the Transportation Research Board 2460, no. 1: 107-116.

Journal article
Published: 01 June 2012 in Journal of Highway and Transportation Research and Development (English Edition)
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ACS Style

Manjuan Yang; Lingtao Wu; Chengcheng Tang. Study of Influence of Foreign Characters in Guide Signs on Legibility. Journal of Highway and Transportation Research and Development (English Edition) 2012, 6, 91 -95.

AMA Style

Manjuan Yang, Lingtao Wu, Chengcheng Tang. Study of Influence of Foreign Characters in Guide Signs on Legibility. Journal of Highway and Transportation Research and Development (English Edition). 2012; 6 (2):91-95.

Chicago/Turabian Style

Manjuan Yang; Lingtao Wu; Chengcheng Tang. 2012. "Study of Influence of Foreign Characters in Guide Signs on Legibility." Journal of Highway and Transportation Research and Development (English Edition) 6, no. 2: 91-95.

Journal article
Published: 01 July 2011 in Applied Mechanics and Materials
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This study introduces the transport situation and polices of the highway agency for the M25. Then the current implementation of the Managed Motorway on M25 between junction 5 and junction 7 is reviewed, and some examples are given in this paper. Then a technical state-of-the-art of application is also reviewed. After that, the requirements for integration with complementary systems and polices are presented. Finally, this paper discusses the arguments for and against taking this application forward.

ACS Style

Sui Chao; Ling Tao Wu; Jian Yun Chen. The Impact Assessment of the Managed Motorway on M25 Junction 5 to 7. Applied Mechanics and Materials 2011, 66-68, 692 -696.

AMA Style

Sui Chao, Ling Tao Wu, Jian Yun Chen. The Impact Assessment of the Managed Motorway on M25 Junction 5 to 7. Applied Mechanics and Materials. 2011; 66-68 ():692-696.

Chicago/Turabian Style

Sui Chao; Ling Tao Wu; Jian Yun Chen. 2011. "The Impact Assessment of the Managed Motorway on M25 Junction 5 to 7." Applied Mechanics and Materials 66-68, no. : 692-696.

Proceedings article
Published: 16 June 2011 in ICTIS 2011
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A comprehensive road traffic Accident Index (AI) of two-lane highway intersection is established, using statistical value of traffic accidents. About 170 highway intersections are surveyed, accidents happened in recent four years at these intersections are collected, and AI of each intersection is calculated. The AI of the intersections is analyzed by the clustering statistical method and the critical value of each grade is generated. The results will provide reference to improving highway intersection safety, and help to optimize the allocation of resources.

ACS Style

Lingtao Wu; Feng Li; Daoyue Peng; Guohua Shen; Chengcheng Tang. Study on Traffic Safety Classification of Two-Lane Highway Intersection Based on Traffic Accident. ICTIS 2011 2011, 1 .

AMA Style

Lingtao Wu, Feng Li, Daoyue Peng, Guohua Shen, Chengcheng Tang. Study on Traffic Safety Classification of Two-Lane Highway Intersection Based on Traffic Accident. ICTIS 2011. 2011; ():1.

Chicago/Turabian Style

Lingtao Wu; Feng Li; Daoyue Peng; Guohua Shen; Chengcheng Tang. 2011. "Study on Traffic Safety Classification of Two-Lane Highway Intersection Based on Traffic Accident." ICTIS 2011 , no. : 1.