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Purpose: To investigate the effects of stellate ganglion block (SGB) on postoperative cognition dysfunction (POCD) in elderly patients undergoing radical resection for malignant tumor. Methods: Herein, 40 patients undergoing radical reaction of gastrointestinal malignant tumor were randomly divided into two groups: Group S (preoperative SGB implementation) and Group C (control group). General anesthesia combined with epidural anesthesia was performed in both groups. HR, MAP and SPO2 were recorded before general anesthesia induction (T1), immediately after tracheal intubation (T2), at surgery beginning (T3), 1 h after surgery beginning (T4), 2 h after surgery beginning (T5), at the end of surgery (T6), immediately after extubation (T7) and 30 min after transferring into the PACU (T8), respectively. Changes of electrolytes and blood glucose were recorded at T1, T4, T5 and T6. Plasma IL-1, IL-6, IL-10 and S100-β concentrations were recorded at 1 day before surgery (T0), 1 day postoperatively (T9) and 3 day postoperatively (T10). Cognitive function was assessed at T0, T9, T10, 5 day postoperatively (T11) and 7 day postoperatively (T12) using the Mini-mental State Examination (MMSE) scale. Results: Compared with Group C, Group S had significantly lower blood glucose levels at T5 and T6 (P < 0.05), lower IL-6 at T9 and S100-β at T9 and T10 (P < 0.05), lower IL-1 while higher IL-10 at T10 (P < 0.05), and higher MMSE scores at T9 and T10, with lower incidence of POCD (P < 0.05). Conclusion: SGB improves postoperative cognitive function and decreases the incidence of POCD in elderly patients undergoing radical resection for gastrointestinal malignant tumor.

To develop a scientific and practicable guideline for implementing warning piles on Chinese low-grade highways, it is necessary to study the effect of warning piles on driving performance in different road alignments and environments. Based on a driving simulator, this paper evaluates the effect of unilateral and bilateral warning piles on vehicle speed and lateral position on a two-lane rural highway curve with different road geometries. The results show a significant effect of bilateral warning piles on speed control, which becomes more obvious as the radius of the curve decreases and the superelevation increases. In sharp curves, vehicle speed increases rapidly in the second half of the curve, and bilateral warning piles could significantly control speed increase to prevent danger. Meanwhile, the effect of bilateral warning piles on keeping vehicles in a safer lane position is also statistically significant in the second half of the curve. With a decreasing radius and an increasing superelevation, the value of the maximum lateral position will increase. Bilateral warning piles could reduce the lateral position to keep the vehicle on a stable track. Moreover, bilateral warning piles could also perform better at night. This paper studies both unilateral and bilateral warning piles’ effects on driving behavior in different road geometries, thus providing a theoretical basis for the engineering application of warning piles.

In China, F-type-5 m intersections are not uncommon. One approach of these intersections usually includes a driveway closely followed by an intersecting street, and the driveway and the intersecting street are parallel and approximately 5 m apart. Nowadays, drivers often rely on the navigation systems for directions. However, it is found that the navigation systems sometimes mislead or confuse drivers to make wrong turns or miss their turns at such F-type-5 m intersections. This study proposed to employ driving simulation to identify the appropriate prompt message delivered at the right prompt timing to help drivers navigate through such F-type-5 m intersections. First, a within-subjects two-factor experiment was designed. One factor was the Prompt Timing Mode (PTM), representing a set of three sequential messages broadcast by the navigation system at varying distances to the intended intersection; the other factor was the Prompt Message Type (PMT), representing various sets of three sequential messages broadcast by the navigation system. Three Prompt Timing Modes were used: PTM1 = {− 400 m, -200 m, − 30 m}, PTM2 = {− 300 m, − 150 m, − 30 m}, and PTM3 = {− 200 m, − 100 m, − 30 m}. Three Prompt Message Types were defined: PMT-A = {Turn right at the traffic light XXm ahead; Turn right at the traffic light XXm ahead; Turn right}, PMT-B = {Turn right at the traffic light XXm ahead, enter YY street; Turn right at the traffic light XXm ahead, enter YY street; Turn right}, PMT-C = {Turn right at the traffic light XXm ahead, enter YY street, and please use the second right turn lane; Turn right at the traffic light XXm ahead, enter YY street, and please use the second right turn lane; Turn right}. The combinations of the two factors generated nine experimental intersections which were randomly assigned to three experimental routes. Then, a total of 37 drivers were recruited, and participated in the driving simulation experiment from which vehicle operation data were collected under different prompt timing modes and message types. Next, the repeated Analysis of Variance (rANOVA) was performed to examine the effects of different prompt timing modes and prompt message types on vehicle operation indicators, such as Driving Time, Standard Deviation of Speed, Absolute Value of Acceleration, and Standard Deviation of Acceleration. Finally, the grey near-optimal method was adopted to evaluate the effectiveness of three prompt message types under each prompt timing mode. The rANOVA results showed the vehicle operation in the F-type-5 m intersection was affected by prompt timing modes and prompt message types; the evaluation results indicated that PMT-C made drivers perform better in PTM1 and PTM3, while PMT-B made drivers perform better inPTM2. However, the effectiveness of PMT-A was the lowest in each prompt timing mode. The research results provide valuable guidance to design the human machine interface of navigation systems, which can help drivers safely navigate through F-type-5 m intersections. This research also has laid solid foundations for establishing navigation messaging design guidelines.

The accidents in the freeway are frequent and more serious on foggy days. Connected vehicle (CV) technology as a new technology can inform drivers of fog conditions in advance so the drivers can adjust their driving behaviors through Human Machine Interface (HMI) and Dynamic Message Sign (DMS) in the fog warning system. The level of driver's compliance with a fog warning system is the key to assessing the effectiveness of the fog warning system. To evaluate the compliance level of fog warning system, the study established a CV system testing platform based on driving simulator, and analyzed the changes in driving behavior and influencing factors for drivers under three different visibility conditions (No fog, light fog and heavy fog) based on the platform, finally, comprehensive evaluation of the optimal fog warning system under different foggy conditions based on the compliance level were made. Research indicators are divided into three aspects: 1) the response degree, including the mean speed, the minimum speed in the fog zone, the difference of the speeds when entering and leaving the fog zone, the proportion of speed following, and the speed reduction proportion prompt; 2) the response start time, which is the time to start slowing down to the speed limit; and 3) the response time duration, namely, the time in which the speed limit is followed. The results show that the response degree of the driver to the fog warning system is high, the fog warning system can effectively reduce the driving speed of the drivers and improve the speed following proportion of the drivers, the deceleration ratio of the driver at each warning point is relatively high, and the influencing factors of various indicators are complex but are mostly related to visibility and technical level. Comprehensive evaluation results show that the warning mode of the combined HMI and DMS has the highest level of compliance under light fog conditions, and when fog concentration increases, compliance level of fog warning system with HMI only is higher than others. The study establishes a reference platform for CV system and provides methods and index system for the compliance level of CV research.

In this study, a driving simulator experiment was conducted to investigate the influence of tunnel retro-reflective arch (TA) on driving behavior, through the employment and analysis of TAs with di...

Connected vehicle technology relying on Human Machine Interface (HMI) achieve a dominant position in the overall safety improvement. However, the impact of HMI on the driver’s visual attention cann...

The geometric design of airport curbsides is critical for both airport planners and passengers as it affects airport ground traffic capacity and creates airline delays. Rational design of curbsides can yield increased efficiency in the use of road space and time resources. Yet a limited number of studies have reported the impact of traffic features on curbside’s geometric design. This paper provides a two-lane curbside measurement with a simulation model to calculate the length of curbsides, which solves the curbside berth supply and curbside length calculation problems using a refined curbside parking model (RCPM) with constraints of a car-following (CF) model, as well as uniformly distributed speed, dwell time, and parking demand; it thereby unveils the detailed microscopic mechanisms of interaction of traffic features and curbsides’ length. The required lengths of curbsides under various parking demands are obtained after a set of numerical simulations. The result further reveals the significant impacts of car-following and braking behaviors, particularly under the huge parking demands on curbside traffic flow. It also demonstrates that a certain delay is created and a suboptimum level of service (LOS) occurs even though satisfying all parking demands, which indicates that lengthening the curbside and increasing parking supply do not necessarily improve the LOS.

Traffic congestion induces significant economic loss each year, and identifying traffic congestion patterns is necessary for better traffic control and management. Floating car data (FCD) provides ...

This study uses driving behavior and speed variation data from navigation software to propose a traffic order index (TOI) for evaluating the order of traffic on urban roads. Based on the significance analysis of driving behaviors and speed variation under different road types and congestion levels, the TOI calculation method is proposed by using the order of preference by similarity to ideal solution (TOPSIS) method. Through a case study of an urban area in Beijing, the distribution of TOI under different road types and congestion levels is described, and the hourly and daily TOI heat maps are generated to show changes in TOI for urban roads during different periods. The relationships between TOI, congestion index (CI) and crash data were explored. A nonlinear relationship of TOI and CI was discovered, and roads with more crashes or longer crash durations were associated with a lower level of traffic order. The TOI could help traffic management departments better understand the order of traffic on roads, reveal causes of the poor level of traffic order in some roads, and more reasonably dispatch traffic police to handle traffic crashes.

Background In this study, we investigated whether G9a related DNA methylation and histone modification is involved in the transcription alteration of NR2B following peripheral nerve injury and subsequently contributes to pain facilitation of G9a inhibition. Methods After approval by the institutional ethical committee on pain research in conscious animals, C57BL/6 mice were used to induce neuropathic pain with spared nerve injury (SNI). G9a/Glp expression, GRIN2B gene 5’-regulatory region CpG sites methylation profile, as well as NR2B expression in the spinal dorsal horn following SNI was detected with immunofluorescence, bisulfite sequencing, and western blot, respectively. For mechanism study, threshold doses of G9a/Glp inhibitors BIX01294/UNC0638 or direct DNA demethylation agent 5-Aza was intrathecal injected through the pre-buried catheter daily in bolus for 3 days, G9a/Glp and its enzymatic substrate H3K9me2/H3K9me3 expression, GRIN2B gene methylation alteration, as well as NR2B expression were observed. Nociceptive behavior was depicted in response to von Frey filaments following SNI with or without intrathecal BIX/UNC and 5-Aza treatments. Results Ipsilateral mechanical withdrawal threshold rather than thermal withdrawal latency prominently decreased and peaked at day 7 to 14 post SNI. Besides, nerve injury consistently increased G9a/Glp, H3K9me2 expression, GRIN2B gene 5’-regulatory region CpG sites methylation, as well as NR2B expression in the spinal dorsal horn at day 7 post SNI. Furthermore, either G9a/Glp inhibition by BIX/UNC or H3K9me2 blockade by 5-Aza independently reversed GRIN2B gene high methylation, followed with disinhibition of NR2B transcription inhibition. Consistent with molecular changes, either BIX/UNC or 5-Aza further worsens nerve injury-induced allodynia. Conclusion G9a/Glp contributes to the pathogenesis of neuropathic pain via methylating GRIN2B gene affecting NR2B transcription. G9a/Glp at an elevated setpoint may prevent the over-sensitization following peripheral nerve injury.

This research sought to establish an eco-driving training system based on a driving simulator. The eco-driving training system contained five modules: human machine interface, data management, scene management, mode management, and evaluation algorithm management. It was proposed to base the new eco-driving training system on drivers’ individual characteristics. This system first asked drivers to conduct a diagnostic drive on a stretch of roadway in a driving simulator. The data on each driver’s non-ecological driving behaviors under different events were collected. Then each driver was given a customized training course based on an evaluation of his/her driving behaviors during the diagnostic drive. This training process is called eco-driving training based on individual characteristics (EDTIC). Eighty taxi drivers were recruited and divided into two groups for eco-driving training. One group was trained by watching videos, and the other was trained by the EDTIC training. An analysis of results shows that the EDTIC training was significantly more effective than traditional video training. Under the EDTIC training, all driving behaviors improved and emissions and fuel consumption were greatly reduced; the reduction was as great as 8.3–8.4%. The EDTIC training was proven effective at improving the eco-driving behavior of taxi drivers (i.e., professional drivers), and it could also be employed to train other professional drivers (bus and truck drivers) and non-professional drivers.

Traffic crashes pose a serious threat to global health and have negative impacts on social and economic development. Driving safety could be significantly improved if drivers obtained effective training based on practical suggestions from studies of the relationship between driver characteristics and driving safety. In this study, driver characteristics are analyzed with a focus on illegal driving behaviors, and a new method is developed to reduce driver error. However, driver characteristics are complex, and the associated interrelationships among variables are not easily identified. A structural equation model (SEM) is adopted in this paper to capture the complex relationships among related variables. This model can simultaneously address the complex relationships among endogenous and exogenous variables. We investigate five illegal driving behaviors, including illegal passing, illegal lane changes, speeding, running red lights and distracted driving, based on a driving simulator. Distracted driving is chosen as an example to construct the SEM. We group 13 variables into four latent variables in the model. In this paper, 44 participants (average age ± S.D. = 33 ± 12.8 years, range: 19–55 years; average driving experience ± S.D. = 10 ± 8.8 years, range: 2–30 years) were recruited for the experiment, driving behavior data were collected via a driving simulator and driving attitude data were collected using a questionnaire. Our findings reveal that the main factor that influences illegal driving actions is driving attitude and the main factor that influences distracted driver performance is basic driver characteristics. Moreover, the driver training level is the most significant factor that negatively affects the basic driver characteristics (factor load = −0.91), and anger with slow driving is the main factor that influences the driver attitude (factor load = 0.90). According to the SEM results, the relationship between driver characteristics and driving safety is analyzed. The model results suggest that driver training lessons must be implemented and traffic issues must be urgently solved. In addition, our results suggest that extra training sessions should be required of young females and drivers with bad attitudes and serious road rage to improve driving safety. Although the results are based on distracted driving, the method presented in this paper is suitable for other analyses of driving behavior.

High-energy Li-rich layered cathode materials (≈900 Wh kg

Cerebral vessel events have long been considered a leading cause in postmenopausal women with physiological changes in expressing and secreting of sex hormones. Hormone replacement therapy emerged as a supplementary therapeutic strategy under the risk of cerebrovascular accidents and bone loss. Epidemiological and genetic data showed that an interrelationship among hormone and cerebrovascular disorders exist. A battery of animal experiments and clinical observations received different results both positive and negative. Recent studies still cannot give a once-for-all answer to whichever hormone, estrogen or progesterone overweighs the other in benefits. Here we review and analyze the two hormones’ effects on cerebrovascular diseases and that of associated epidemiological and genetic evidences.

Raised pavement markers (RPMs) are among the common safety features of roads, playing an important role in preventing and reducing traffic crashes. RPMs are regarded as an effective measure for reducing the high crash rate and mortality in freeway tunnels in China. In this study, a driving simulator experiment was conducted to investigate the safety of RPMs in a freeway tunnel. Two different RPM layouts were designed and compared to a control with no RPMs, and 32 drivers participated in the driving simulator experiments. The speed, relative speed difference, lateral position, accelerator power, acceleration, and pupil area were used as indicators of the response characteristics of drivers to RPMs, and the interaction of tunnel length, tunnel zone, and RPM alternatives was discussed. The results indicate that a significant interaction effect exists between tunnel length, tunnel zone, and RPM alternatives. RPMs could help reduce driver anxiety, boredom, and fatigue caused by the dark and monotonous tunnel driving environment, and improve driver alertness and consciousness of speed. Also, the driving risk increases with increasing tunnel length (1800 m to 3500 m).

Traffic crashes have posed a serious threat to global health and have had a negative impact on social and economic development. In 2015, 1.25 million people died in traffic crashes around the world, over 90% of which were at least partially caused by human error. This research intends to develop a new driver training tool which can help reduce human error by correcting drivers’ risky driving behaviors. On the basis of the theory of planned behavior (TPB), this research proposed a “perception–norm–execution” (PNE) driving simulator-based training model. To evaluate the effectiveness of this PNE training model, five risky driving behaviors (i.e. improper lane changes, unsafe overtaking, red-light running, speeding, and distracted driving) were selected as training scenarios. A total of 44 participants were recruited to participate in the trainings. Twenty-two participants received the PNE model (PM) training, and the remaining participants received the normal (video-based) model (NM) training. This research used the empirical data collected from a driving simulator to evaluate participants’ driving performance before and after they received the training. It is found that both training models significantly improved driving performance, but the PM training was demonstrated to be more effective than the NM training in terms of correcting drivers’ risky driving behaviors such as improper lane changes, unsafe passing, and running red lights. The research findings can be used to change policies and develop more effective driver education and training programs.

Due to noise interference and signal aliasing, the existing methods are difficult to effectively extract the characteristics of the individual characterization of the radiation source, and achieve high-precision identification of the radiation source. So we propose a method which is based on the Multivariate Empirical Mode Decomposition (MEMD) technique, and it can be used to separate wireless frame stray signals, then identify individual devices. Firstly, MEMD method is used to extract the stray signal from the wireless frame signal; then the subtle features in the wireless frame stray signal can be extracted by a series of feature extraction methods; finally, many wireless network device individuals of the same type are classified and identified by a Support Vector Machine (SVM) classifier. The experimental results show that in the identification of five wireless network devices of the same type, the MEMD method is used to extract the stray features and obtain an average recognition rate of 94.59%. At the same time, the recognition rate of the MEMD method is significantly higher than the EMD method.

Longitudinal speed reduction markings (LSRMs) are designed to alert drivers to an upcoming change in roadway geometry (e.g. direct connectors with smaller radii). In Beijing, LSRMs are usually installed on direct connectors of urban expressways. The objective of this paper is to examine the influence of LSRMs on vehicle operation and driver behavior, and evaluate the decelerating effectiveness of LSRMs on direct connectors with different radii. Empirical data were collected in a driving simulator, and indicators representing vehicle operation status and driving behavior were proposed. To examine the influence of LSRMs, an analysis segment was defined, which begins 500 m prior to the entering point of the connector and ends at the exiting point of the connector. Furthermore, the analysis segment was evenly divided into a series of subsections; the length of each subsection is 50 m. This definition is introduced based on the assumption that drivers would decelerate smoothly in advance of the connector. The analysis results show that drivers tend to decelerate earlier when the radii were 200 m or 300 m. When approaching the connector, drivers tend to decelerate at 500 m thru 250 m in advance of the connector with a 200 m radius; deceleration happens at 300 m–0 m in advance of the connector with a 300 m radius. On the connector, drivers controlled the throttle pedal use at 100 thru 300 m after the entering point when the radius was 200 m; deceleration occurred in two regions when the radius was 300 m: 0 m–900 m from the entering point, and the last 1,000 m of the connector. The analytical results further revealed that LSRMs would be effective at reducing speeds when the radius of the direct connector was 300 m.

Integrated hydrologic and hydrodynamic modeling is useful in evaluating hydrodynamic characteristics (e.g. water exchange processes) in data-scarce water bodies, however, most studies lack verification of the hydrologic model. Here, water exchange (represented by water age) was investigated through integrated hydrologic and hydrodynamic modeling of the Hongfeng Reservoir, a poorly gauged reservoir in southwest China. The performance of the hydrologic model and parameter replacement among sub-basins with hydrological similarity was verified by historical data. Results showed that hydrological similarity based on the hierarchical cluster analysis and topographic index probability density distribution was reliable with satisfactory performance of parameter replacement. The hydrodynamic model was verified using daily water levels and water temperatures from 2009 and 2010. The water exchange processes in the Hongfeng Reservoir are very complex with temporal, vertical, and spatial variations. The temporal water age was primarily controlled by the variable inflow and outflow, and the maximum and minimum ages for the site near the dam were 406.10 d (15th June) and 90.74 d (3rd August), respectively, in 2010. Distinct vertical differences in water age showed that surface flow, interflow, and underflow appeared alternately, depending on the season and water depth. The worst water exchange situation was found in the central areas of the North Lake with the highest water ages in the bottom on both 15th June and 3rd August, in 2010. Comparison of the spatial water ages revealed that the more favorable hydraulic conditions on 3rd August mainly improved the water exchange in the dam areas and most areas of the South Lake, but had little effect on the bottom layers of the other deepest areas in the South and North Lakes. The presented framework can be applied in other data-scarce waterbodies worldwide to provide better understanding of water exchange processes.

Use of cellular phone while driving is one of the top contributing factors that induce traffic crashes, resulting in significant loss of life and property. A dilemma zone is a circumstance near sig...

Congestion charging is being considered as a potential measure to address the issue of substantially increased traffic congestion and vehicle emissions in Beijing. This study assessed the impact of congestion charging on traffic and emissions in Beijing using macroscopic traffic simulation and vehicle emissions calculation. Multiple testing scenarios were developed with assumptions in different charging zone sizes, public transit service levels and charging methods. Our analysis results showed that congestion charging in Beijing may increase public transit use by approximately 13%, potentially reduce CO and HC emissions by 60–70%, and reduce NOx emissions by 35–45% within the charging zone. However, congestion charging may also result in increased travel activities and emissions outside of the charging zone and a slight increase in emissions for the entire urban area. The size of charging zone, charging method, and charging rate are key factors that directly influence the impact of congestion charging; improved public transit service needs to be considered as a complementary approach with congestion charging. This study is used by Beijing Transportation Environment and Energy Center (BTEC) as reference to support the development of Beijing’s congestion charging policy and regulation.

Not all drivers are motivated by the same values (i.e., they may have proself [Editor’s note: The authors use the term “proself” to describe individuals who focus on their own well-being.] or prosocial [Editor’s note: The authors use the term “prosocial” to describe individuals who focus more on the well-being of others than on their own well-being.] values), and different drivers might prefer to learn in different ways (i.e., through learning or performance). To improve drivers’ acceptance of ecodriving and to enhance the effectiveness of ecodriving training, an ecodriving behavior optimization method that considers the characteristics of driver types and the effectiveness of approaches to ecodriving training was developed in this study. On the basis of the relationship between drivers’ values and goal orientations and their driving behaviors, data on in-trip driving behavior were collected, and then the drivers were classified into four types: those with proself and learning orientations, those with proself and performance orientations, those with prosocial and learning orientations, and those with prosocial and performance orientations. The effectiveness of three common ecodriving training methods (i.e., education, coaching, and feedback through smartphone applications) was then tested and compared to determine those that met the fuel consumption characteristics of the driver types. In addition, feedback corresponding to driver types was designed (e.g., how much money they saved from ecodriving and their ecodriving rankings compared with the rankings of their peers). Finally, the appropriate ecodriving training method and suitable feedback were identified for each driver type. A validation test showed that this ecodriving behavior optimization method was more effective in reducing fuel consumption than the generic ecodriving training method (by which the reductions in fuel consumption were 9.60% and 4.62%, respectively). The study results provide guidance for ecodriving applications, contributing to the improvement of vehicle fuel use efficiency.

Travelers' route choice behavior, a dynamical learning process based on their own experience, traffic information, and influence of others, is a type of cooperation optimization and a constant day-to-day evolutionary process. Travelers adjust their route choices to choose the best route, minimizing travel time and distance, or maximizing expressway use. Because route choice behavior is based on human beings, the most intelligent animals in the world, this swarm behavior is expected to incorporate more intelligence. Unlike existing research in route choice behavior, the influence of other travelers is considered for updating route choices on account of the reality, which makes the route choice behavior from individual to swarm. A new swarm intelligence algorithm inspired by travelers' route choice behavior for solving mathematical optimization problems is introduced in this paper. A comparison of the results of experiments with those of the classical global Particle Swarm Optimization (PSO) algorithm demonstrates the efficacy of the Route Choice Behavior Algorithm (RCBA). The novel algorithm provides a new approach to solving complex problems and new avenues for the study of route choice behavior.

Introduction: Numerous studies have demonstrated the close relationship between alcohol availability and alcohol-related crashes. However, there is still a lack of spatial empirical analysis regarding this relationship, particularly in large cities of developing countries. Differences in alcohol outlets and drinking patterns in these cities may lead to quite different patterns of crash outcomes. Method: 3356 alcohol-related crashes were collected from the blood-alcohol test report of a forensic institution in Tianjin, China. Density of alcohol outlets such as retail locations, entertainment venues, restaurants, hotels, and companies were extracted based on 2114 Traffic Analysis Zones (TAZ) together with the residential and demographic characteristics. After applying the exploratory spatial data analysis, this research developed and compared the traditional Ordinary Least Square model (OLS), Spatial Lag Model (SLM), Spatial Error Model (SEM) and Spatial Durbin Model (SDM) to explore spatial effects of all the variables. Results: The results of incremental spatial autocorrelation show that the most significant distance threshold of alcohol-related roadway traffic crashes is 3 km. The SDM is found to be the optimal spatial model to characterize the relationship between alcohol outlets and crashes. The number of alcohol-involved traffic crashes is positively related to population density and retail density, but negatively related to the company density, hotel density, and residential density within the same TAZ. Meanwhile, dense population and hotels have reverse spillover effects in adjacent zones. Conclusions: The significant spatial direct effect and spillover effect of alcohol outlet densities on drunk driving crashes should not be neglected. These findings could help improve transportation planning, traffic law enforcement and traffic management for large cities in developing countries.