Advancing urban traffic accident forecasting through sparse spatio-temporal dynamic learning.

Traffic accidents have emerged as one of the most public health safety matters, raising concerns from both the public and urban administrators. The ability to accurately predict traffic accident not only supports the governmental decision-making in advance but also enhances public confidence in safety measures. However, the efficacy of traditional spatio-temporal prediction models are compromised by the skewed distributions and sparse labeling of accident data. To this end, we propose a Sparse Spatio-Temporal Dynamic Hypergraph Learning (SST-DHL) framework that captures higher-order dependencies in sparse traffic accidents by combining hypergraph learning and self-supervised learning. The SST-DHL model incorporates a multi-view spatiotemporal convolution block to capture local correlations and semantics of traffic accidents, a cross-regional dynamic hypergraph learning model to identify global spatiotemporal dependencies, and a two-supervised self-learning paradigm to capture both local and global spatiotemporal patterns. Through experimentation on New York City and London accident datasets, we demonstrate that our proposed SST-DHL exhibits significant improvements compared to optimal baseline models at different sparsity levels. Additionally, it offers enhanced interpretability of results by elucidating complex spatio-temporal dependencies among various traffic accident instances. Our study demonstrates the effectiveness of the SST-DHL framework in accurately predicting traffic accidents, thereby enhancing public safety and trust.

Modeling occupant injury severities for electric-vehicle-involved crashes using a vehicle-accident bi-layered correlative framework with matched-pair sampling.

This study seeks to investigate occupant injury severities for electric-vehicle-involved crashes and inspect if electric vehicles lead to more serious injuries than fuel-powered vehicles, which have commonly been neglected in past studies. A Bayesian random slope model is proposed aiming to capture interactions between occupant injury severity levels and electric vehicle variable. The random slope model is developed under a vehicle-accident bi-layered correlative framework, which can account for the interactive effects of vehicles in the same accident. Based on the crash report sampling system (CRSS) 2020 and 2021 database, the extracted observations are formed into inherently matched pairs under certain matching variables including restraint system use, air bag deployed, ejection and rollover. The introduced data structure is able to ensure the standard error of the modeling parameters are not affected by these matching variables. Meanwhile, a comprehensive modeling performance comparison is conducted between the Bayesian random slope model and the Bayesian random intercept model, the Bayesian basic model. According to the empirical results, the bi-layered Bayesian random slope model presents a strong ability in model fitting and analysis, even when the sample size is small and the error structure is complex. Most importantly, occupants in electric vehicles are more likely to suffer serious injuries, especially incapacitating and fatal injuries, in the event of an accident compared to fuel-powered vehicles, which disproving the long-held misconception that green and safety are related.

The balance between traffic control and economic development in tourist cities under the context of COVID-19: A case study of Xi'an, China.

Selecting an appropriate intensity of epidemic prevention and control measures is of vital significance to promoting the two-way dynamic coordination of epidemic prevention and control and economic development. In order to balance epidemic control and economic development and suggest scientific and reasonable traffic control measures, this paper proposes a SEIQR model considering population migration and the propagation characteristics of the exposed and the asymptomatic, based on the data of COVID-19 cases, Baidu Migration, and the tourist economy. Further, the factor traffic control intensity is included in the model. After determining the functional relationship between the control intensity and the number of tourists and the cumulative number of confirmed cases, the NSGA-II algorithm is employed to perform multi-objective optimization with consideration of the requirements for epidemic prevention and control and for economic development to get an appropriate traffic control intensity and suggest scientific traffic control measures. With Xi'an City as an example. The results show that the Pearson correlation coefficient between the predicted data of this improved model and the actual data is 0.996, the R-square in the regression analysis is 0.993, with a significance level of below 0.001, suggesting that the predicted data of the model are more accurate. With the continuous rise of traffic control intensity in different simulation scenarios, the cumulative number of cases decreases by a significant amplitude. While balancing the requirements for epidemic prevention and control and for tourist economy development, the model works out the control intensity to be 0.68, under which some traffic control measures are suggested. The model presented in this paper can be used to analyze the impacts of different traffic control intensities on epidemic transmission. The research results in this paper reveal the traffic control measures balancing the requirements for epidemic prevention and control and for economic development.

Recovery of driving skills after endoscopy under propofol sedation: a prospective pilot study to assess the driving skills after endoscopic sedation using driving simulation.

BACKGROUND: Patients are recommended not to drive for at least the first 24 h after endoscopy with propofol sedation. However, the evidence underlying these recommendations is scarce. We hypothesized that after endoscopic procedures performed under propofol sedation, the subject's driving ability was restored in less than 24 h. METHODS: We prospectively enrolled thirty patients between 20 and 70 years possessing a legitimate driver's license scheduled for endoscopy at our hospital. The sample chosen was a convenience sample. Gastroscopy or colonoscopy was performed with propofol sedation. Before and after endoscopy, the investigator drove the subjects to the laboratory to assess their driving skills using a driving simulation system, which employs 3 driving scenarios designed by professional transportation researchers. The blood propofol concentration was estimated before endoscopy, and 2 and 4 h after endoscopy. The primary outcome was the time required for subjects to recover their driving ability after propofol sedation. The secondary outcome was the blood propofol concentration before and after endoscopic procedures under propofol anesthesia. RESULTS: Thirty volunteers participated in the study and 18 of them completed all the interventions. In the low-risk S-curve scene, the mean acceleration, lane deviation, and number of deviations from the path at baseline (0.016 cm/s2, 42.50 cm, and 0.83, respectively) were significantly less than that at post-2 h (0.029 cm/s2, P = 0.001; 53.80 cm, P = 0.014; 2.06, P = 0.022). In the moderate-(overtaking) and high-risk (emergency collision avoidance) scenes, the tested parameters at baseline and post-2 h were statistically comparable. In the low-, moderate-, and high-risk scenes the tested parameters at baseline and post-4 h were statistically comparable. The total range of propofol was 120-280 mg.The mean blood concentration of propofol at post-2 h was 0.81 ± 0.40 µg/mL, and at post-4 h was below the limit of detection. CONCLUSION: After endoscopy performed under propofol sedation, subjects' driving abilities were completely restored at 4 h when tested on a simulator.

Seasonal precipitation regulates magnitude and direction of the effect of nitrogen addition on net ecosystem CO2 exchange in saline-alkaline grassland of northern China.

Increased nitrogen (N) deposition and altered precipitation regimes have profound effects on carbon (C) flux in semi-arid grasslands. However, the interactive effects between N enrichment and precipitation alterations (both increasing and decreasing) on ecosystem CO2 fluxes and ecosystem resource use efficiency (water use efficiency (WUE) and carbon use efficiency (CUE)) remain unclear, particularly in saline-alkaline grasslands. A four-year (2018-2021) field manipulation experiment was conducted to investigate N enrichment and precipitation alterations (decreased and increased by 50 % of ambient precipitation) and their interactions on ecosystem CO2 fluxes (gross- ecosystem productivity (GEP), ecosystem respiration (ER), and net ecosystem CO2 exchange (NEE)), as well as their underlying regulatory mechanisms under severe salinity stress in northern China. Our results showed that N addition and precipitation alteration alone did not significantly affect the GEP, ER and NEE. While the interaction of N addition and increased precipitation over the four years significantly improved the mean GEP and NEE by 24.9 % and 15.9 %, respectively. The interactive effects of N addition and increased precipitation treatment significantly stimulated the mean value of WUE by 39.1 % compared with control, but had no significant effects on CUE over the four years. Based on the four-year experiment, the magnitude and direction of the effects of N addition on the NEE were related to seasonal precipitation. Nitrogen addition increased the NEE under increased precipitation and decreased it during extreme drought. Soil salinization (pH and base cations) could directly or indirectly affect GEP and NEE via plants productivity, plant communities, as well as ecosystem resource use efficiency (WUE and CUE) based on structural equation model. Our results address lacking investigations of ecosystem C flux in saline-alkaline grasslands, and highlight that precipitation regulates the magnitude and direction of N addition on NEE in saline-alkaline grasslands.

The impact of traffic control measures on the spread of COVID-19 within urban agglomerations based on a modified epidemic model.

With the spatial structure of urban agglomerations, well-developed transportation networks and close economic ties can increase the risk of intercity transmission of infectious diseases. To reveal the epidemic transmission mechanism in urban agglomerations and to explore the effectiveness of traffic control measures, this study proposes an Urban-Agglomeration-based Epidemic and Mobility Model (UAEMM) based on the reality of urban transportation networks and population mobility factors. Since the model considers the urban population inflow, along with the active intracity population, it can be used to estimate the composition of urban cases. The model was applied to the Chang-Zhu-Tan urban agglomeration, and the results show that the model can better simulate the transmission process of the urban agglomeration for a certain scale of epidemic. The number of cases within the urban agglomeration is higher than the number of cases imported into the urban agglomeration from external cities. The composition of cases in the core cities of the urban agglomeration changes with the adjustment of prevention and control measures. In contrast, the number of cases imported into the secondary cities is consistently greater than the number of cases transmitted within the cities. A traffic control measures discount factor is introduced to simulate the development of the epidemic in the urban agglomeration under the traffic control measures of the first-level response to major public health emergency, traffic blockades in infected areas, and public transportation shutdowns. If none of those traffic control measures had been taken after the outbreak of COVID-19, the number of cases in the urban agglomeration would theoretically have increased to 3879, which is 11.61 times the actual number of cases that occurred. If only one traffic control measure had been used alone, each of the three measures would have reduced the number of cases in the urban agglomeration to 30.19 %-57.44 % of the theoretical values of infection cases, with the best blocking effect coming from the first-level response to major public health emergency. Traffic control measures have a significant effect in interrupting the spread of COVID-19 in urban agglomerations. The methodology and main findings presented in this paper are of general interest and can also be used in studies in other countries for similar purposes to help understand the spread of COVID-19 in urban agglomerations.

Predicting collision cases at unsignalized intersections using EEG metrics and driving simulator platform.

Unsignalized intersection collision has been one of the most dangerous accidents in the world. How to identify road hazards and predict the potential intersection collision ahead are challenging problems in traffic safety. This paper studies the feasibility of EEG metrics to forecast road hazards and presents an improved neural network model to predict intersection collision based on EEG metrics and driving behavior. It is demonstrated that EEG metrics show significant differences between collision and non-collision cases. It indicates that EEG metrics can serve as effective indicators to predict the collision probability. The drivers with higher relative power in fast frequency band (alpha and beta), lower relative power in slow frequency band (delta and theta) are more likely to have conflicts. The prediction using three machine learning models (Multi-layer perceptron (MLP), Logistic regression (LR) and Random forest (RF)) based on three input datasets (only EEG metrics, only driving behavior and combined EEG metrics with driving behavior) are compared. The results show that for single time point prediction, MLP model has the highest accuracy among three machine learning models. The model solely based on EEG metrics datasets has higher accuracy than driving behavior as well as combined datasets. However, for multi-time point prediction, the accuracy of MLP is only 73.9%, worse than LR and RF. We improved the MLP model by adding attention mechanism layer and using random forest model to select important features. As a consequence, the accuracy is greatly improved and reaches 88%. This study demonstrates the importance and feasibility of EEG signals to identify unsafe drivers ahead. The improved neural network model can be helpful to reduce intersection accidents and improve traffic safety.

Using a V2V- and V2I-based collision warning system to improve vehicle interaction at unsignalized intersections.

INTRODUCTION: Unsignalized intersections are critical components of the road network where traffic collisions occur frequently. METHOD: This study aims to design a Vehicle-to-Vehicle (V2V)- and Vehicle-to-Infrastructure (V2I)-based unsignalized intersection collision warning system (UICWS) to improve driver performance and enhance driver safety at unsignalized intersections. A multi-user driving simulator experiment was conducted with 48 participants divided into 24 pairs. The dynamic interaction of each participant pair as they approached the intersection from straight-crossing directions was examined under different warning conditions (i.e., with vs without UICWS) and intersection field of view (IFOV) conditions (i.e., standard vs improved IFOV). RESULTS AND CONCLUSIONS: The experimental results showed that the UICWS could effectively help drivers make appropriate operation decisions and reduce the number of right-angle collisions and near-collisions at unsignalized intersections. In the condition without UICWS, improved IFOV could prompt drivers to make crossing decisions in advance and adjust speed smoothly. Moreover, drivers' crossing maneuvers changed with the relative distance between the subject and conflict vehicles and the intersection. The risks of collisions and near-collisions increased significantly when the two vehicles were at a similar distance to the intersection before they initiated any actions. PRACTICAL APPLICATIONS: The findings show that the proposed UICWS can effectively reduce collisions or near-collisions at unsignalized intersections in a connected vehicle environment. On this basis, some active intervention strategies, such as specific speed guidance depending on the dynamics of the conflict vehicle, can be developed to ensure vehicles passing through unsignalised intersections safely.

An exploratory study of drivers' EEG response during emergent collision avoidance.

INTRODUCTION: EEG (electroencephalogram) has been applied as a valuable measure to estimate drivers' mental status and cognitive workload during driving tasks. However, most previous studies have focused on the EEG features at particular driver status, such as fatigue or distraction, with less attention paid to EEG response in emergent and safety-critical situations. This study aims to investigate the underlying patterns of different EEG components during an emergent collision avoidance process. METHOD: A driving simulator experiment was conducted with 38 participants (19 females and 19 males). The scenario included a roadside pedestrian who suddenly crossed the road when the driver approached. The participants' EEG data were collected during the pedestrian-collision avoidance process. The log-transformed power and power ratio of four typical EEG components (i.e., delta, theta, alpha and beta) were extracted from four collision avoidance stages: Stage 1-normal driving stage, Stage 2-hazard perception stage, Stage 3-evasive action stage, and Stage 4-post-hazard stage. RESULTS: The activities of all four EEG bands changed consistently during the collision avoidance process, with the power increased significantly from Stage 1 to Stage 4. Drivers who collided with the pedestrian and drivers who avoided the collision successfully did not show a significant difference in EEG activity across the stages. Male drivers had a higher delta power ratio and lower alpha power ratio than females in both hazard perception and evasive action stages. CONCLUSIONS: Enhanced activities of different EEG bands could be concurrent at emergent and safety-critical situations. Female drivers were more mentally aroused than male drivers during the collision avoidance process. PRACTICAL APPLICATIONS: The study generates more understanding of drivers' neurophysiological response in an emergent and safety-critical collision avoidance event. Driver state monitoring and warning systems that aim to assist drivers in impending collisions may utilize the patterns of EEG activity identified in the collision avoidance process.

Influence of in-Vehicle Audio Warning on Drivers' Eye-Movement and Behavior at Flashing Light-Controlled Grade Crossings.

OBJECTIVE: This study aims to evaluate the effect of in-vehicle audio warning at flashing-light-controlled grade crossings based on driving simulation and eye-tracking systems. BACKGROUND: Collisions at flashing-light-controlled grade crossings have severe consequences. In-vehicle audio warning has the potential to regulate driver behavior. However, whether this improvement occurs through priming drivers' visual search patterns is not yet clear. METHOD: Drivers' visual activity and behaviors were recorded. The effect of a warning was tested with a series of flashing light trigger times (FLTTs) ranging from 2s to 6s with a 1s increment. Different driving conditions (i.e., clear and fog) and driver experience were considered in the experiment design. RESULTS: Warnings could guide the allocation of both overt and covert attention, as well as raise drivers' situation awareness, manifesting as the enhanced perception of signs and better understanding of the flashing red light. Significant improvement in the stop-compliance rate was found in warning scenarios, particularly with a late FLTT. The decreased saccade duration and increased fixation duration on the signal implied a dilemma-zone effect when the FLTT was lower than 4s. Furthermore, reduced fixation duration on signs and signals was found in foggy conditions. Non-professional drivers had a wider search range than their counterparts. CONCLUSION: In-vehicle audio warning is an effective countermeasure for improving crossing safety by optimizing visual search strategy. APPLICATION: In-vehicle audio warnings warrant promotion at grade crossings based on the driver assistance system.

Developing a two-stage auditory warning system for safe driving and eco-driving at signalized intersections: A driving simulation study.

In-vehicle intersection warning systems represent a promising approach for informing drivers of potential danger to reduce crashes and improve intersection safety. However, there is limited research on drivers' eco-driving performances, such as fuel consumption and emission, when drivers adapt their behaviors to the systems. In this study, an innovative two-stage in-vehicle intersection warning system was proposed to reduce red-light running (RLR) violations. Forty-five drivers participated in a simulated driving experiment and their driving performances at the intersections were evaluated to examine the effectiveness of the warning system. The measures included stop/go decision, RLR rate, average speed and deceleration, brake transition time, brake level, fuel consumption, and emission of CO and NOx. The results indicated that the warning system had a positive effect on drivers' safe driving and eco-driving performances, such as reducing the RLR rate, advancing and smoothing the deceleration and reducing fuel consumption and emission. Moreover, the impact of warning on drivers' performances varied with the time to the onset of yellow light. The research has practical implications for the automobile industry to use vehicle-to-infrastructure technology in the design of in-vehicle warning systems to improve driver behaviors at intersections.

In-vehicle warnings for work zone and related rear-end collisions: A driving simulator experiment.

Work zone area on roads is a critical component of road networks which concerns the safety of workers and passing by drivers. However, the passive speed reduction and lane changes caused by lane closure have led to frequent rear-end collisions in work zone areas. To help drivers better anticipate work zone situation and reduce collision risks, this paper proposes two types of in-vehicle warnings for work zone areas: Leading Vehicle Brake Warning (LVBW), and Lane-Closed Warning & Leading Vehicle Brake Warning (LCW & LVBW). The LVBW delivers a danger warning message to drivers upon the brake of the leading vehicle, while the LCW & LVBW provides an additional work-zone position message to remind drivers to decelerate in advance. A driving simulator experiment was conducted with 44 participants (24 males and 20 females) to test drivers' performance in work zone area under different conditions, comprising two warning types (LVBW vs. LCW & LVBW), four warning times (3 s, 5 s, 7 s and 9 s) and two visibility conditions (clear and foggy weather). The results showed significant safety benefits of the lane-closed warning message under the LCW & LVBW condition. In contrast, the warning of leading vehicle's brake in both LVBW and LCW & LVBW conditions had limited efficacy, which indicates that earlier warning about lane-closure is important to assist drivers in anticipating the complex situations in work zones. Drivers' speed control and collision avoidance performances were impaired in fog, but the impairment was compensated by the warning messages. Compared with male drivers, female drivers tend to be more cautious when approaching the work zone areas. Overall, this study plays a pioneering role in developing effective safety countermeasures for work zone areas and providing strong support for implementing in-vehicle warning technologies.

Eye movement as a function to explore the effects of improved signs design and audio warning on drivers' behavior at STOP-sign-controlled grade crossings.

Train-vehicle collisions at STOP-sign-controlled grade crossings attract many concerns in China and across the world. Researchers have demonstrated that the cost-effective approaches to improve grade crossing safety are the redesign of signs and pavement markings as well as the application of in-vehicle audio warning. However, the impacts of improved signs design and audio warning on drivers' visual performance have barely been discussed. This study explored the effects of improved signs design and audio warning on drivers' eye movement patterns and driving behavior at STOP-sign-controlled grade crossings, by conducting a driving simulator experiment. Three types of grade crossing scenarios: 1) the conventional signs design (Baseline), 2) improved signs design (PS), and 3) improved signs design and three-stage audio warning (PSW), were modeled in a driving simulation system and tested under a series of train TTC (no train, 4 s, 7 s, 10 s, 13 s) conditions. Foggy conditions and drivers' characteristics, i.e., gender and vocation were considered in the experiment design. Seven variables describing both drivers' fixation patterns and driving performance were collected and analyzed in this study, e.g., total fixation duration, distance to stop line at the first fixation, fixation transition probability, stop compliance, speed, maximum deceleration rate and minimum time-to-collision. Results revealed that the improved design of signs and the audio warning could prime drivers' expectation of the grade crossing in advance since drivers could drive at a lower speed, perceive signs timely, and conduct an earlier visual search for the train with these countermeasures. Besides, in PS and PSW scenarios, drivers attached more importance to the STOP sign, and they were more cautious in estimating the time-to-arrival of the train by repeatedly fixating on these two areas. The improvement in fixation performance of drivers in PS and PSW contributed to a more comfortable deceleration. Compared with no warning scenarios, higher compliance rates were observed with audio warning, especially with a short train TTC (4 s and 7 s). However, no significant difference was found between PS and Baseline, indicating the limited safety benefits of improved signs design. Minimum time-to-collision for those drivers who ignored the warning did not increase significantly in both PS and PSW. Additionally, heavy fog limited drivers' perception of signs and led to a later and shorter fixation. For gender effect, males had a lower fixation duration on the STOP sign and lower compliance rate than females. Moreover, female drivers could perceive the approaching train earlier than males, especially in PS and PSW. These findings suggested that the improved signs design and in-vehicle audio warning improved drivers' visual and behavioral performance and had the potential to enhance safety at STOP-sign-controlled grade crossings.

An improved automated braking system for rear-end collisions: A study based on a driving simulator experiment.

INTRODUCTION: To assist drivers in avoiding rear-end collisions, many early warning systems have been developed up to date. Autonomous braking technology is also used as the last defense to ensure driver's safety. METHOD: By taking the accuracy and timeliness of automatic system control into account, this paper proposes a rear-end Real-Time Autonomous Emergency Braking (RTAEB) system. The system inserts brake intervention based on drivers' real-time conflict identification and collision avoidance performance. A driving simulator-based experiment under different traffic conditions and deceleration scenarios were conducted to test the different thresholds to trigger intervention and the intervention outcomes. The system effectiveness is verified by four evaluation indexes, including collision avoidance rate, accuracy rate, sensitivity rate, and precision rate. RESULTS: The results showed that the system could help avoid all collision events successfully and enlarge the final headway distance, and a TTC threshold of 1.5 s and a maximum deceleration threshold of -7.5 m/s2 could achieve the best collision avoidance effect. The paper demonstrates the situations that are more inclined to trigger the RTAEB (i.e., a sudden brake of the leading vehicle and a small car-following distance). Moreover, the study shows that driver characteristics (i.e., gender and profession) have no significant association with system trigger. Practical Applications: The study suggests that development of collision avoidance systems design should pay attention to both the real-time traffic situation and drivers' collision avoidance capability under the present situation.

Wielding and evaluating the removal composition of common artefacts in EEG signals for driving behaviour analysis.

Noninvasive EEG signals provide neural activity information at high resolution, of which human mental status can be properly detected. However, artefacts always exist in brain oscillatory EEG signals and thus impede the accuracy and reliability of relevant analysis, especially in real-world tasks. Moreover, the use of a mature artefact identification method cannot assure impeccable artefact separation; this leads to a trade-off between removing contaminated information and losing valuable information. This study addresses this problem by investigating a simulator-based driving behaviour analysis using a car-following scenario to correlate the EEG-based mental features with behavioural responses. The study develops an architecture for an artefact composition pool and proposes three integrated prediction models to evaluate the removal compositions of the EEG artefacts. Three errors (mean absolute, root mean square, mean absolute percentage) and R-squared index are considered for measuring the performance of the models. The results show that the best-performing composition outperformed the no-removal and all-removal cases by 11.75% and 4.28% improvements, respectively. Specifically, we investigate different common artefacts including eye blinks, horizontal eye movements, vertical eye movements, generic discontinuities and muscle artefacts. The gained knowledge on artefact removal, EEG spectral features and stimuli-response patterns can be further applied to properly manipulate real-world EEG signals and develop an effective brain-computer interface.

Effect of warning message on driver's stop/go decision and red-light-running behaviors under fog condition.

The red-light-running (RLR) warning system has substantial potentials in helping drivers make proper stop/go decisions and reducing the RLR violations. Adverse foggy weather degrades drivers' performances and may also affect the effectiveness of the RLR warning system. However, limited research has been conducted regarding the impact of the RLR warning on driving performances under foggy weather. Thus, this study aims to explore drivers' decision-making process and RLR behaviors at intersection dilemma zones and evaluate the effectiveness of the RLR auditory-warning (RLR-AW) system in both fog and clear weather conditions. A concept of the RLR-AW system was proposed and designed in a driving simulator experiment. The simulated driving with the RLR-AW system was conducted in both clear and foggy weather conditions. The results show that drivers took compensation actions in fog while approaching the intersection, such as driving at lower speeds and using harder maximum brakes. The RLR-AW was able to reduce RLR rates in both clear and fog conditions, and drivers tended to respond more quickly and take smoother brake reactions with the RLR-AW provided. Moreover, the RLR-AW showed more remarkable influences on drivers' behaviors in fog with higher decrement in brake reaction time and maximum deceleration rate. Overall, findings of the study shed light on the design of in-vehicle RLR-AW system and highlight the necessity of drivers applying the system in adverse weather conditions.

Modeling the service-route-based crash frequency by a spatiotemporal-random-effect zero-inflated negative binomial model: An empirical analysis for bus-involved crashes.

Previous studies related to bus crash frequencies modeling are limited and the statistical models are usually developed at the road segment or zonal level. This study focuses on modeling crash frequencies specifically at the bus-service-route level, which is useful and important to policymakers and bus operation companies toward the improvement of the safety level of bus networks, especially for developing countries where buses are still a major mode of urban travels. Using the observed data adopted from one of the bus operating companies in Beijing, China, we proposed a spatiotemporal-random-effect zero-inflated negative binomial (spatiotemporal ZINB) model to investigate bus crash occurrence and identity key influential factors at the bus-service-route level. The model was motivated to accommodate the special statistical characteristics of the excessive zeros and, more importantly, the potential spatiotemporal correlations of the data. Three degenerated versions of this model were also developed for comparison purposes. Results indicate that the proposed spatiotemporal ZINB model is statistically superior to the others according to a comprehensive judgment based on the EAIC, EBIC, and RMSE criteria. The estimated coefficients reveal the impacts of related factors on the likelihood of bus-involved crashes from bus operation factors including total passengers, number of drivers, and proportion of male drivers as well as planning factors including route length and stop density. On the other hand, the standard deviations of the introduced structured and unstructured spatiotemporal random-effects are statistically significant indicating that the observations are correlated within each route, between neighbor routes and across years. Corresponding policy and practical implications are provided for bus operating companies and planning departments toward the improvement of bus safety.

Fabrication of Ir-CoOx@mesoporous SiO2 Nanoreactors for Selective Hydrogenation of Substituted Nitroaromatics.

Nanosized Ir catalysts suffer from serious side reactions and poor stability during hydrogenation of substituted nitroaromatics to produce aromatic amines. In this work, core-shell nanostructures with sub-4 nm Ir-CoOx hybrid cores and mesoporous SiO2 shells were designed and prepared to overcome these problems. The Ir-CoOx hybrid cores were converted from IrCo alloy nanoparticles (NPs) inside SiO2 through in situ calcination and reduction pretreatments. The SiO2 mesoporous shells in Ir-CoOx@SiO2 nanoreactors prevented the agglomeration/sintering of IrCo NPs, while allowing the free reactants and products (big molecules). The synergy between Ir and CoOx species improved H2 adsorption, thus affecting the reaction rate as well as the selectivity to aromatic amines. As a result, the obtained Ir-CoOx@SiO2 nanocatalyst showed tremendous improvement in catalytic activity, selectivity, and stability.

How do drivers avoid collisions? A driving simulator-based study.

INTRODUCTION: Drivers' collision avoidance performance in an impending collision situation plays a decisive role for safety outcomes. This study explored drivers' collision avoidance performances in three typical collision scenarios that were right-angle collision, head-on collision, and collision with pedestrian. METHOD: A high-fidelity driving simulator was used to design the scenarios and conduct the experiment. 45 participants took part in the simulator experiment. Drivers' longitudinal/lateral collision avoidance performances and collision result were recorded. RESULTS: Experimental results showed that brake only was the most common response among the three collision scenarios, followed by brake combining swerve in head-on and pedestrian collision scenarios. In right-angle collision scenario with TTC (time to collision) largest among three scenarios, no driver swerved, and meanwhile drivers who showed slow brake reaction tended to compensate the collision risk by taking a larger maximum deceleration rate within a shorter time. Swerve-toward-conflict was a prevalent phenomenon in head-on and pedestrian collision scenarios and significantly associated with collision risk. Drivers that swerved toward the conflict object had a shorter swerve reaction time than drivers that swerved away from conflict. CONCLUSIONS: Long brake reaction time and wrong swerve direction were the main factors leading to a high collision likelihood. The swerve-toward-conflict maneuver caused a delay in brake action and degraded subsequent braking performances. The prevalent phenomenon indicated that drivers tended to use an intuitive (heuristic) way to make decisions in critical traffic situations. Practical applications: The study generated a better understanding of collision development and shed lights on the design of future advanced collision avoidance systems for semi-automated vehicles. Manufactures should also engage more efforts in developing active steering assistance systems to assist drivers in collision avoidance.

Evaluating and Diagnosing Road Intersection Operation Performance Using Floating Car Data.

: Urban road intersections play an important role in deciding the total travel time and the overall travel efficiency. In this paper, an innovative traffic grid model has been proposed, which evaluates and diagnoses the traffic status and the time delay at intersections across whole urban road networks. This method is grounded on a massive amount of floating car data sampled at a rate of 3 s, and it is composed of three major parts. (1) A grid model is built to transform intersections into discrete cells, and the floating car data are matched to the grids through a simple assignment process. (2) Based on the grid model, a set of key traffic parameters (e.g., the total time delay of all the directions of the intersection and the average speed of each direction) is derived. (3) Using these parameters, intersections are evaluated and the ones with the longest traffic delays are identified. The obtained intersections are further examined in terms of the traffic flow ratio and the green time ratio as well as the difference between these two variables. Using the central area of Beijing as the case study, the potential and feasibility of the proposed method are demonstrated and the unreasonable signal timing phases are detected. The developed method can be easily transferred to other cities, making it a useful and practical tool for traffic managers to evaluate and diagnose urban signal intersections as well as to design optimal measures for reducing traffic delay and increase operation efficiency at the intersections.