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Curved segments account for a disproportionately high proportion of fatal and serious injury crashes, with most of these crashes occurring on rural two-lane (R2L) highways. During the 10-year period from 2008 to 2017, a total of 1 234 fatal single-vehicle roadway departure (SV-RwD) crashes occurred on R2L roads in Louisiana, out of which 635 (51.5 %) crashes occurred on curved segments. Therefore, it is critical to investigate the causes of SV-RwD crashes, specifically those that occur on curved segments. This study aimed to investigate the ‘association knowledge’ of the factors contributing to SV-RwD crashes on R2L curved segments in Louisiana using fatal and injury crash data collected from 2008 to 2017. The study utilized Cluster Correspondence Analysis (CCA), a robust joint dimension reduction and clustering method for handling high-dimensionality and multicollinearity of crash data, to achieve this objective. Based on the cluster validation measures, the study identified five clusters with specific traits, including alcohol-impaired male drivers with no seatbelt usage, young (15–24 years old) female drivers’ crash involvement in cloudy weather conditions, animal-involved crashes in rainy weather conditions, crashes occurring on hillcrest locations under cloudy weather conditions, and crashes in the dark with the presence of streetlights and higher traffic volume. Furthermore, young (15–24 years) female drivers were identified in most clusters, implying that this specific age group of female drivers requires special consideration when dealing with SV-RwD collisions on R2L curved segments. To improve safety on R2L curved segments, policymakers can use the findings of this study to develop targeted countermeasures.

The first edition of the Highway Safety Manual (HSM) contains a simplistic version of the crash prediction model for two-way stop-controlled intersections (TWSC) on rural two-lane highways. This model considers AADT on major and minor roads, with the base conditions defined as no intersection skewness, no turning lanes, and no lighting. A crash modification factor (CMF) will be applied if an intersection has conditions different from the base condition. However, the HSM model does not take account of curvature. It is well known that curved TWSC intersections are less safe than non-curved ones, particularly on rural two-lane roadways. This paper presents the development of crash prediction models incorporating intersection geometrics for TWSC intersections on rural two-lane highways in Louisiana. Then, it compares the results from the developed model with the calibrated HSM model. The negative binomial model was used with 5126 TWSC intersections verified one by one, including both three- and four-leg intersections from all parishes (counties). The estimation results indicate that AADT, curve radius, and intersection skewness angle significantly impact expected crash frequency for both three- and four-leg intersections. This research utilizes cumulative residual plots, mean absolute error, and root mean square error for comparative analysis of HSM models, HSM models with calibration, and Louisiana-specific models. The results show that Louisiana-specific SPFs outperformed the calibrated SPFs with superior reliability. Calibration factors of 0.58 for three-leg intersections and 0.46 for four-leg intersections are estimated, suggesting that the original HSM model overpredicts crashes in Louisiana.

Pedestrian crashes at high-speed locations are a persistent road safety concern. Driving at high speeds means that the driver has less time to react and make evasive maneuvers to avoid a pedestrian crash. On top of this, other crash-contributing factors such as humans (pedestrians or drivers), vehicles, roadways, and surrounding environmental factors actively interact together to cause a crash at high-speed locations. The pattern of pedestrian crashes also differs significantly according to the high-speed intersection and segment locations which require further investigation. This study applied association rules mining (ARM), an unsupervised learning algorithm, to reveal the hidden association of pedestrian crash risk factors according to the high-speed intersection and segments separately. The study used Louisiana pedestrian fatal and injury crash data (2010 to 2019). Any crash location with a posted speed limit of 45 mph or above is classified as a high-speed location. Based on the generated association rules, the results show that pedestrian crashes at a high-speed intersection are associated with the intersection geometry (3-leg) and control (1 stop, no traffic control device), driver characteristics (careless operation, failure to yield, inattentive-distracted, older, and younger driver), pedestrian-related factors (violations, alcohol/drug involvement), settings (open country, residential, business, industrial), dark lighting conditions and so on. Most pedestrian crashes at high-speed segments are associated with roadways with no physical separation, dark-no-streetlight conditions, open country locations, interstates and so on. The findings of the study may help to select appropriate countermeasures to reduce pedestrian crashes at high-speed locations.

Introduction: Icaritin (ICT), a promising anti-hepatocellular carcinoma (HCC) prenylated flavonoid, is hindered from being applied due to its low water solubility and high lipophilicity in poorly differentiated HCC which is associated with upregulation of CD44 isoforms. Thus, hyaluronic acid (HA), a natural polysaccharide with high binding ability to CD44 receptors, was used to formulate a modified liposome as a novel targeted ICT-delivery system for HCC treatment.Methods: The ICT-Liposomes (Lip-ICT) with and without HA were prepared by a combined method of thin-film dispersion and post-insertion. The particle size, polydispersity (PDI), zeta potential, encapsulation efficacy (%EE), drug loading content (%DLC), and in vitro drug release profiles were investigated for physicochemical properties, whereas MTT assay was used to assess cytotoxic effects on HCC cells, HepG2, and Huh7 cells. Tumor bearing nude mice were used to evaluate the inhibitory effect of HA-Lip-ICT and Lip-ICT in vivo.Results: Lip-ICT and HA-Lip-ICT had an average particle size of 171.2 ± 1.2 nm and 208.0 ± 3.2 nm, with a zeta potential of −13.9 ± 0.83 and −24.8 ± 0.36, respectively. The PDI resulted from Lip-ICT and HA-Lip-ICT was 0.28 ± 0.02 and 0.26 ± 0.02, respectively. HA-Lip-ICT demonstrated higher in vitro drug release when pH was dropped from 7.4 to 5.5, The 12-h release rate of ICT from liposomes increased from 30% at pH7.4 to more than 60% at pH5.5. HA-Lip-ICT displayed higher toxicity than Lip-ICT in both HCC cells, especially Huh7with an IC50 of 34.15 ± 2.11 μM. The in vivo tissue distribution and anti-tumor experiments carried on tumor bearing nude mice indicated that HA-Lip- ICT exhibited higher tumor accumulation and achieved a tumor growth inhibition rate of 63.4%.Discussion: The nano-sized Lip-ICT was able to prolong the drug release time and showed long-term killing HCC cells ability. Following conjugation with HA, HA-Lip-ICT exhibited higher cytotoxicity, stronger tumor targeting, and tumor suppression abilities than Lip-ICT attributed to HA-CD44 ligand-receptor interaction, increasing the potential of ICT to treat HCC.

Centerline rumble strips are low-cost effective countermeasures installed on the center of the highway segments to reduce crashes, especially roadway departure crashes. For safety evaluation of centerline rumble strips, methodologies such as naïve before-after analysis and cross-sectional study with Empirical Bayes have been widely utilized. The implementation of these methodologies may be limited due to the lack of relevant control groups, and/or other temporal variations in crashes such as seasonality and serial autocorrelation. This study aims to explore Intervention Time Series Analysis approach as an alternative method for the safety evaluation of centerline rumble strips on rural-two-lane undivided highways in Louisiana. Two different methodologies are explored in the intervention time series approach including the Forecast modeling technique and the Auto-regressive Integrated Moving Average intervention model. The forecast models are based on the exponential smoothing technique, state-space framework, and neural network model. The database consists of monthly observations of total and target crashes on 312 highway segments of 1274 miles in length in which centerline rumble strips were installed during the 2010–2012 period. The time frame 2005–2012 is defined as the pre-intervention period whereas the time frame 2013–2017 is defined as the post-intervention period. The analysis revealed that the Auto-regressive Integrated Moving Average intervention model performed better in terms of error estimates including root means square error, mean absolute error, and mean absolute percentage error. The proposed Auto-regressive Integrated Moving Average intervention model reveals a 17.75% total and 40.54% target crash reduction on the selected rural-two-lane undivided highway segments during the post-intervention period. All the findings are found statistically significant at a 95% confidence level.

The pedestrian crash pattern in the dark-no-streetlight condition is a noteworthy ongoing traffic safety concern. The persistently high percentage of pedestrian fatalities at night in the U.S., as well as in the state of Louisiana, necessitates new research to supplement existing studies. This study utilized 10 years (2010–2019) of pedestrian fatal and severe injury crashes in Louisiana that occurred in the dark without streetlights to identify the associated crash patterns. The methodology is based on Multiple Correspondence Analysis (MCA), an exploratory approach used to discover the association of multiple categorical variables from a crash dataset. The findings suggest that driver characteristics (age, gender, and physical condition), pedestrian action, pedestrian alcohol impairment, and physical settings (posted speed limit, location, and roadway type) have a substantial impact on pedestrian collisions at night without streetlights. Moreover, the obtained combination clouds of MCA reveal associations such as elderly pedestrian (>64 years) alcohol impairment resulting in fatalities, crashes in an open country location with a high posted speed limit, crashes involving pedestrians in dark clothing on high-speed (50–55 mph) roadways, alcohol-impaired driver involvement in crashes on two-way roads without physical separation, severe injury crashes at intersections, male pedestrian crashes on midblock locations during weekends, and young (15–24 years) female driver's involvement in crashes while pedestrians were walking against the traffic. Based on the findings, this research also suggests safety recommendations that can assist highway safety practitioners in determining appropriate countermeasures to reduce pedestrian crashes in the dark-no-streetlight condition.

Expressways in urban agglomerations are important in connecting cities, thus attracting great attention from researchers in the expressways risk assessment. However, there is a lack of safety assessment models suitable for the characteristics of expressways in Chinese urban agglomerations, and the nature and mode of dynamic risks on Chinese highways are still unclear. Therefore, this study adopts the Adaptive Neural Fuzzy Inference System (ANFIS) and the method of decision tree, combined with data from the Beijing section of the Beijing Harbin Expressway, to model the risk of accident-prone highways in urban agglomerations. To determine the optimal model, we evaluated the model’s bias at different time intervals. In addition, key factors affecting highway safety were analyzed, providing scientific support for the risk prevention of highways in urban agglomerations in China.

Teenagers have been emphasized as a critical driver population class because of their overrepresentation in fatal and injury crashes. The conventional parametric approaches rest on few predefined assumptions, which might not always be valid considering the complicated nature of teen drivers' crash characteristics that are reflected by multidimensional crash datasets. Also, individual attributes may be more speculative when combined with other factors. This research employed joint correspondence analysis (JCA) and association rule mining (ARM) to investigate the fatal and injury crash patterns of at-fault teen drivers (aged 15 to 19 years) in Louisiana. The unsupervised learning algorithms can explore meaningful associations among crash categories without restricting the nature of variables. The analyses discover intriguing associations to understand the potential causes and effects of crashes. For example, alcohol impairment results in fatal crashes with passengers, daytimes severe collisions occur to unrestrained drivers who have exceeded the posted speed limits, and adverse weather conditions are associated with moderate injury crashes. The findings also reveal how the behavior patterns connected with teen driver crashes, such as distracted driving in the morning hours, alcohol intoxication or using cellphone in pickup trucks, and so on. The research results can lead to effectively targeted teen driver education programs to mitigate risky driving maneuvers. Also, prioritizing crash attributes of key interconnections can help to develop practical safety countermeasures. Strategy that covers multiple interventions could be more effective in curtailing teenagers' crash risk.

Intersection safety continues to be a crucial issue throughout the United States. In 2016, 27% of the 37,461 traffic fatalities on U.S. roadways occurred at or near intersections. Nearly 70% of intersection-related fatalities occurred at unsignalized intersections. At such intersections, vehicles stopping or slowing to turn create speed differentials between vehicles traveling in the same direction. This is particularly problematic on two-lane highways. Research was performed to analyze safety performance for intersections on rural, two-lane roadways, with stop control on the minor roadway. Roadway, traffic, and crash data were collected from 4148 stop-controlled intersections of all 64 Parishes (counties) statewide in Louisiana, for the period of 2013 to 2017. Four count approaches, Poisson, Negative Binomial (NB), Zero-inflated Poisson (ZIP) and Zero-inflated Negative Binomial (ZINB) were used to model the number of intersection crashes for different severity levels. The results indicate that ZIP models provide a better fit than all other models. In addition to traffic volume, larger curve radii of major and minor roads and wider lane widths of major roads led to significantly smaller crash occurrences. However, higher speed limits of major roads led to significantly greater crash occurrences. Four-leg stop-controlled intersections have 35% greater total crashes, 49% greater fatal and injury crashes, and 25% greater property damage only (PDO) crashes, relative to three-leg intersections.

During 2015–2017, the yearly average of fatalities caused by roadway departure (RwD) crashes was 19,233 in the U.S. Roadway departure crashes, or crashes in which a vehicle crosses an edge line, centerline, or otherwise leaves the traveled way, account for 52 percent of all traffic fatalities in the U.S. during this time. A majority of RwD crashes are run-off-road (ROR) crashes; these are crashes that result in a vehicle crossing the edge line on either side of the roadway. In this study, the research team analyzed seven years (2010–2016) of rural two-lane ROR crash data from Louisiana to better comprehend ROR crashes in refining safety predictions for rural two-lane highways. Statistical model (negative binomial model) and three separate machine learning models (random forest, support vector machine, and Cubist) were applied to determine the best fit models. Overall, Cubist is characterized by a better performance in estimating ROR crashes on rural two-lane roadways. The Cubist approach introduced rules-based safety performance functions (SPFs) for total and fatal and injury crashes. This approach will be beneficial for the safety practitioners in tackling localized issues in crash data with an emphasis on prediction accuracy.

Due to the escalating usage of cellphones, cellphone distracted driving remains one of the fastest-growing traffic safety problems. Two basic types of cellphone distractions are – talking (handheld or hands-free) and manipulating (texting, internet browsing, using apps for social networking, navigation, entertainment, etc.). To explore the association between the cellphone distraction type and characteristics of a driver, roadway, and vehicle, we used Fisher's exact test and association rule mining to analyze the data from ten different hour-long observation sessions performed both at intersections and on straight continuous segments. Among 3727 drivers observed, 22.1% were found distracted by cellphones. More drivers were involved in manipulating (12.8%) than talking on the phone (9.3%). The Fisher's exact test results show that intersections and the presence of passengers may be significantly associated with driver distraction type. This study makes a novel attempt to utilize association rule mining in cellphone distraction observation. A set of generated rules and their network visualization demonstrated how a specific group of characteristics can be related to cellphone conversation or manipulation through the measures of support, confidence, and lift. 'Peak hour', 'Absence of passengers', and 'Urban setting' are more likely to be associated with distracted driving than other characteristics. The results from association rules revealed that male drivers were found to be predominantly involved in cellphone manipulation during peak hours on urban segments than female drivers, and they were also largely involved in cellphone conversation during peak hours on the rural segment. Female drivers engaged in manipulating cellphones at the urban intersection during both peak and off-peak hours. In the rural area during peak hours, female drivers were manipulating at the intersection and were talking while driving on the continuous segment. Drivers aged '30–60 years' were found to be more associated with cellphone use compared to '60 years' age group. 'Passenger car drivers during peak hours' were more associated with manipulating cellphones than expected compared to any other collective group, whereas 'female passenger car drivers driving on continuous segment' were strongly associated with cellphone conversation. The detailed discussion of the findings from association rule mining will be useful for targeting a specific group of relevant characteristics in potential cellphone distraction reduction strategies.

Roadway departure (RwD) crashes are a major contributor of rural two-lane (R2L) highway crashes and fatalities. For targeted reduction of crashes and fatalities due to roadway departure, a thorough understanding of factors associated with RwD crashes is necessary. This study quantitatively assessed the available pre-crash factors that might influence the RwD crashes by developing a logit model comparing roadway, crash environment, and the vehicle and driver-related characteristics of 122,978 crashes that occurred in Louisiana over thirteen years. With a high prediction accuracy (81.9% area under the receiver operating characteristics curve), the model presented significant individual associations across crash characteristics with RwD crashes on R2L highways, for example – animals on roadways, snow/sleet/hail, 50–55 mph speed limit, AADT of 1,001–5,000 vehicles per day, drug intoxication, motorcycles, driving during 12 am to 6 am, curve radius of 501–1,000 ft., absence of streetlight, alcohol intoxication. Investigation on these top factors using association rules mining reveals findings such as – a higher likelihood of RwD crashes can be strongly associated animal presence coupled with the absence of streetlights, male drivers during the early morning (12 am to 6 am), male drivers driving with no passengers, drivers being intoxicated by both drugs and alcohol, etc. Findings from this study are expected to help highway safety specialists not only in identifying and predicting RwD crashes but also in an improved understanding of associated contributing factors leading to the application of proper countermeasures for the strategic reduction of RwD crashes.

Despite the adoption of the Graduated Driver Licensing (GDL) in 1998, the disproportionately high rate of young driver (15–24 years) crashes is still prevalent on Louisiana roadways. This paper utilizes the five years (2014–2018) crash data in Louisiana aiming to compare the crash profiles of three age groups (15–16 years, 17–19 years, and 20–24 years) to determine the contributing factors, crash characteristics, and severity outcomes. An extensive review of previous literature along with engineering judgments facilitated the understanding of the crash variables that were eventually used in the multinomial logit model with additional consideration of the current framework of the statewide GDL program. Results revealed that factors such as driving violation, distraction, passenger presence, time of crash, and driver protection system have been highlighted as the top ten predominant factors in multiple young driver groups. This study shows there is a ground for data-driven improvement for young drivers in Louisiana by presenting a broader view of a variety of issues based on the findings. Nighttime driving outside passenger restricted hours in GDL program, violations especially ‘failure to yield’, driving behaviors such as cellphone distraction especially in drivers aged 17–19 years and non-use of driver protection system across all young driver age groups are among the addressable key issues that may be influential in reducing young driver crashes. The study concludes that implementation of the GDL program would be more effective through continuous improvement and reevaluation of its components and strategic application of complementary young driver safety programs.

Hit and run crash is a punishable offense. In many cases, it involves higher severity levels of the associated roadway users due to the delay of emergency help. For vulnerable road users like pedestrians and bicyclists, this issue is more gruesome. We present an analysis of the effect of crash, geometric, and environmental characteristics on the bicycle-involved hit and run crashes by using six years of Louisiana crash data with an application of fast and frugal tree (FFT) heuristics algorithm. Over 1000 bicycle-involved hit and run crashes occurred in Louisiana out of around 108,000 hit and run crashes during 2010–2015. The fatal bicycle crashes represent 10% of the total fatal hit and run crashes. Additionally, hit and run bicycle crashes represent 22% of total bicycle crashes in Louisiana. In the preliminary analysis, we provided statistical significance test of the key contributing factors for two major groups (bicycle-involved hit and run crashes, and not bicycle-involved hit and run crashes). We divided the complete dataset into two separate datasets: training data for model development, and testing data for performance evaluation. FFT identifies five major cues or variable threshold attributes that contribute significantly in predicting bicycle-involved crashes. These cues include fatal and injury crashes, right angle/turning/head on collisions, city streets/others, intersection, and residential/mixed localities. The balanced accuracy is around 76% for both training and testing data. The current model shows higher sensitivity than other complex and black box machine learning models (e.g., support vector machine, random forest). Findings of our study will provide valuable insights for hit and run bicycle crash reduction in both planning and operation levels. Keywords: Hit and run crashes, Bicycle, Fast and frugal algorithm, Heuristics, Decision tree

Abstract Background Osteoporosis is a bone-incapacitating malady and it is characterized by obvious bone mass loss and bone microarchitecture deterioration. Current treatments for osteoporosis have many limitations, including the non-obvious therapeutic effect and long-term safety issues. Icariin is a pharmacologically active flavonoid glycoside, which shows potential application in treatment of osteoporosis. But its clinical application is limited by the inherent disadvantages such as poor water solubility, first pass effect after oral administration, and low bioavailability. Moreover, due to lack of targeting ability, icariin cannot accumulate at the local diseased region to provide early protection from fractures. To solve the application problems of icariin and enhance its therapeutic effects on osteoporosis, this work aimed to design a targeting drug delivery system of biomineral-binding liposomes (BBL) mediated by pyrophosphate ions. Results Biomineral-binding liposomes enhanced the binding ability of liposomes with hydroxyapatite particles. It increased the serum level of alkaline phosphatase and reduced that of tartrate-resistant acid phosphatase 5b. Meanwhile, BBL increased the mechanical strength of femoral midshaft, preserving the trabecular bone microarchitecture. Moreover, BBL could initiate bone turnover/remodeling of rats with osteoporosis. Conclusions This drug targeting delivery system of BBL loading with icariin showed more therapeutic advantages than the free icariin for the treatment of osteoporosis, which may be a kind of valid candidate in future osteoporosis therapy.

In urban or suburban areas with a large number of access points, four-lane undivided highways are prone to crashes due to left-turning and through movements in a single lane. Many studies recommended expensive countermeasures like conversion from undivided to divided road with physical separation. One inexpensive alternative is reconfiguring the existing roadways by either increasing or decreasing the number of lanes. This study investigated the safety impact of converting four lane undivided roadways (4U) to five lane undivided roadways (5T) with a two way left turn lane (TWLTL). This study used Empirical Bayes method to determine the safety impact of this inexpensive countermeasure. In this study, data from eight sites from Louisiana were collected for investigation, and site-specific crash modification factor (CMF) values were calculated. Although 5T is usually not preferable due to its exposure of higher number of crashes in the existing literature, the findings of the current study indicated a positive safety impact. The benefit-cost ratio of this conversion ranges from 97 to 379. The current findings indicate that conversion of 4U to 5T is a feasible inexpensive solution for urban roadways with lower volume and a limited number of driveways. Keywords: Lane restriping, Two way left turn lane, Empirical Bayes, Crash modification factor, Benefit cost ratio

In order to strengthen the safety of highway roadsides, it is necessary to take targeted measures according to the roadside hazards, so there is an urgent need to develop research on risk evaluation of highway roadside accidents. Based on accident simulation analysis and the form of accident after vehicles run to the roadside, the rollover risk of roadside accident is classed into four grades, namely, no departure from the ground, slight departure from the ground, one or two turnovers, and more than two turnovers. The factors involved in the causes of roadside accidents of different rollover risks are studied, and the thresholds of driving speed, sideslope gradient, and sideslope height are given. A Bayesian network for risk evaluation of roadside accidents is constructed. Based on the factor thresholds for the causes of roadside accidents, the calculation methods of the probability of different rollover risks of roadside accidents are carried out according to a single factor, two factors, and three factors. The typical cases of roadside accidents are analyzed and the calculation results of the Bayesian network show that the probability of one or two turnover accidents is 0.939, which is consistent with the results of roadside accident simulation tests, proving the accuracy of the risk evaluation methods for highway roadside accidents.

In 2013, 346 out of 616 fatal crashes in Louisiana were single vehicle crashes with Run-Off-Road (ROR) crashes being the most common type of single vehicle crash. In order to create effective countermeasures for reducing the number of fatal single vehicle ROR crashes, it is important to identify any associated key factors that can quantitatively assess the performance of roads, vehicles and humans. This research uses Multiple Correspondence Analysis (MCA), a multidimensional descriptive data analysis method that associates a combination of factors based on their relative distance in a two dimensional plane, to analyze eight years (2004–2011) of fatal ROR crashes in Louisiana. This method measures important contributing factors and their degree of association. The results revealed that drivers of lightweight trucks, drivers on undivided state highways, male drivers in passenger-vehicles at dawn, older female (65–74) drivers in non-passenger vehicles, older drivers facing hardship to yield in partial access control zones, and drivers with poor reaction time due to impaired driving were closely associated with fatal ROR crashes. Results of the MCA method can help researchers select the most effective crash countermeasures. Further work on the degree of association between the identified crash contributing factors can help safety management systems develop the most efficient crash reduction strategies.

The objective of this research was to investigate the impact of roadway geometrics and speed on lateral stability of tractor-semitrailer on combined alignments of freeways since the current design guidelines of combined alignments are not available in China. A closed-loop vehicle dynamic simulation model was established using TruckSim multibody dynamics software. The maximum wheel side friction demand and lateral load transfer ratio were used to measure the skidding and rollover risks, and the variation laws of each indicator were determined by developing the controlled variable simulation scenarios. Based on the theory of statistical analysis, an orthogonal test was designed, and the effects of various impact factors on the lateral stability of the tractor-semitrailer were analyzed. The models for lateral stability indicators were developed by multiple linear regression analysis and applied to evaluate the driving risk of tractor-semitrailer on a wet road surface. The results showed that the radius and speed have significant effects on the lateral stability while the effect of the downgrade is of general significance. In addition, lower safe speed should be adopted on wet road surfaces of curved downgrade. This paper proposed a surrogate approach to road safety analysis and the models can be used for building the freeway driving security system.

At-fault crash-prone drivers are usually considered as the high risk group for possible future incidents or crashes. In Louisiana, 34% of crashes are repeatedly committed by the at-fault crash-prone drivers who represent only 5% of the total licensed drivers in the state. This research has conducted an exploratory data analysis based on the driver faultiness and proneness. The objective of this study is to develop a crash prediction model to estimate the likelihood of future crashes for the at-fault drivers. The logistic regression method is used by employing eight years' traffic crash data (2004–2011) in Louisiana. Crash predictors such as the driver's crash involvement, crash and road characteristics, human factors, collision type, and environmental factors are considered in the model. The at-fault and not-at-fault status of the crashes are used as the response variable. The developed model has identified a few important variables, and is used to correctly classify at-fault crashes up to 62.40% with a specificity of 77.25%. This model can identify as many as 62.40% of the crash incidence of at-fault drivers in the upcoming year. Traffic agencies can use the model for monitoring the performance of an at-fault crash-prone drivers and making roadway improvements meant to reduce crash proneness. From the findings, it is recommended that crash-prone drivers should be targeted for special safety programs regularly through education and regulations.

To accurately assess the safety of freeway work zones, this paper investigates the safety of vehicle lane change maneuvers with improved cellular automata model. Taking the traffic conflict and standard deviation of operating speed as the evaluation indexes, the study evaluates the freeway work zone safety. With improved deceleration probability in car-following raies and the addition of lanechanging rules under critical state, the lane-changing behavior under critical state is defined as a conflict count. Through 72 schemes of simulation runs, the possible states of the traffic flow are carefully studied. The results show that under the condition of constant saturation traffic conflict count and vehicle speed standard deviation reach their maximums when the mixed rate of heave vehicles is 40%. Meanwhile, in the case of constant heavy vehicles mix, traffic conflict count and vehicle speed standard deviation reach maximum values when saturation rate is 0. 75. Integrating ail simulation results, it is known the traffic safety in freeway work zones is classified into four levels : safe, relatively safe, relatively dangerous, and dangerous.

Utilizing the data obtained from both scanning and counting sensors is critical for efficiently managing traffic flow on roadways. Past studies mainly focused on the optimal layout of one type of sensor, and how to optimize the arrangement of more than one type of sensor has not been fully researched. This paper develops a methodology that optimizes the deployment of different types of sensors to solve the well-recognized network sensors location problem (NSLP). To answer the questions of how many, where and what types of sensors should be deployed on each particular link of the network, a novel bi-level programming model for full route observability is presented to strategically locate scanning and counting sensors in a network. The methodology works in two steps. First, a mathematical program is formulated to determine the minimum number of scanning sensors. To solve this program, a new ‘differentiating matrix’ is introduced and the corresponding greedy algorithm of ‘differentiating first’ is put forward. In the second step, a scanning map and an incidence matrix are incorporated into the program, which extends the theoretical model for multiple sensors’ deployment and provides the replacement method to reduce total cost of sensors without loss of observability. The algorithm developed at the second step involved in two coefficient matrixes from scanning map and incidence parameter enumerate all possibilities of replacement schemes so that cost of different combination schemes can be compared. Finally, the proposed approach is demonstrated by comparison of Nguyen-Dupuis network and real network, which indicates the proposed method is capable to evaluate the trade-off between cost and all routes observability.