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.

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.

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.

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.

[Deep neck space infections: a retrospective cohort study of surgical risk factors]

Objective: To explore risk factors affecting treatment for deep neck space infections (DNSIs) so as to provide guidance for appropriate early managements. Methods: A retrospective cohort study was conducted on inpatients with DNSIs admitted to the Department of Otolaryngology, Head and Neck Surgery, Affiliated Hospital of Qingdao University from March 2013 to February 2021. Patients were divided into surgical and non-surgical groups based on whether they had surgery or not. Information collected included demographic data, disease-related signs and symptoms, treatment history, systemic comorbidities, imaging data and laboratory indicators. Hypothesis testing, univariate Logistic regression and multivariate Logistic regression were used for data processing. Resuts A total of 61 patients were included, including 37 males and 24 females, aged 6-96 years. There were 35 cases (57.4%) in the surgical group and 26 cases (42.6%) in the non-surgical group. Multivariate analysis showed that risk factors for surgery as followings: neck dyskinesia (OR=0.03, 95%CI: 0.00-0.24), dysphagia (OR=0.10, 95%CI: 0.02-0.72), serum white blood cell count≥16.74×10⁹/L (OR=1.18, 95%CI: 1.01-1.39) and interspace gas (OR=0.03, 95%CI: 0.00-0.30). Conclusion: Clinicians should be alert to these risk factors for surgery in the course of treatment and timely surgical treatment for patients who meet the conditions.

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.

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.

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.

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.

[Current status and progress in the standardized surgical management of specimens after radical gastric cancer surgery]

Standardized surgical management of postoperative specimens of gastric cancer is an important part of the standardized diagnosis and treatment of gastric cancer. It can reflect the accurate number and detailed distribution of lymph nodes in the specimen and lay the foundation for accurate and standardized pathological reports after surgery. Meanwhile, it can evaluate the scope of intraoperative lymph node dissection, the safety of cutting edge, and the standardization of surgery (principle of en-bloc dissection), which is an important means of surgical quality control. It also provides accurate research samples for further research and is an important way for young surgeons to train their clinical skills. The surgical management of postoperative specimens for gastric cancer needs to be standardized, including specimen processing personnel, processing flow, resection margin examination, lymph node sorting, measurement after specimen dissection, storage of biological specimens, documentation of recorded data, etc. The promotion of standardized surgical management of specimens after radical gastrectomy can promote the homogenization of gastric cancer surgical diagnosis and treatment in medical institutions and further promote the high-quality development of gastric cancer surgery in China.

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.

Enhanced strong metal–support interactions between Pt and WO3–x nanowires for the selective hydrogenation of p-chloronitrobenzene

Pt/WO3–x nanocatalysts demonstrate significantly enhanced catalytic performance due to the strong interaction between Pt and WO3–x nanowires.

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-CoO_x hybrid cores and mesoporous SiO₂ shells were designed and prepared to overcome these problems. The Ir-CoO_x hybrid cores were converted from IrCo alloy nanoparticles (NPs) inside SiO₂ through in situ calcination and reduction pretreatments. The SiO₂ mesoporous shells in Ir-CoO_x@SiO₂ nanoreactors prevented the agglomeration/sintering of IrCo NPs, while allowing the free reactants and products (big molecules). The synergy between Ir and CoO_x species improved H₂ adsorption, thus affecting the reaction rate as well as the selectivity to aromatic amines. As a result, the obtained Ir-CoO_x@SiO₂ nanocatalyst showed tremendous improvement in catalytic activity, selectivity, and stability.

One-pot synthesis of Au–Fe2O3@SiO2 core–shell nanoreactors for CO oxidation

The combination of the Au–Fe₂O₃ phase and core–shell structure helps in achieving high activity and good thermal 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.

Synergetic effect of Na-doping and carbon coating on the electrochemical performances of Li3-x Na x V2(PO4)3/C as cathode for lithium-ion batteries

Carbon coated Li_3-x Na _x V₂(PO₄)₃/C (x = 0.04, 0.06, 0.10, 0.12, 0.18) cathode materials for lithium-ion batteries were synthesized via a simple carbothermal reduction reaction route using methyl orange as the reducing agent, which also acted as the Na and carbon sources. The influence of various Na-doping levels on the structure and electrochemical performance of the Li_3-x Na _x V₂(PO₄)₃/C composites was investigated. The valence state of vanadium, the form of residual carbon and the overall morphology of the Li_2.90Na_0.10V₂(PO₄)₃/C, which showed the highest initial specific discharge capacity of 128 mA h g^-1 at the current density of 0.1C (1C = 132 mA g^-1) among this series of composites, were further examined by X-ray photoelectron spectroscopy, Raman spectroscopy, scanning electron microscopy and high-resolution transmission electron microscopy, respectively. The results indicated that a well crystallized structure of Na-doped Li_2.90Na_0.10V₂(PO₄)₃ coated by a carbon matrix is obtained. In the further electrochemical measurements, the Li_2.90Na_0.10V₂(PO₄)₃/C cathode material shows superior discharge capacities of 124, 118, 113, 106 and 98 mA h g^-1 at 0.3, 0.5, 1, 2 and 5C, respectively. High capacity retention of 97% was obtained after 1100 cycles in long-term cyclic performance tests at 5C. The reason for such a promising electrochemical performance of the as-prepared Li_2.90Na_0.10V₂(PO₄)₃/C has also been explored, which revealed that the synergetic effect of the Na-doping and carbon coating provide enlarged Li⁺ diffusion channels and the increased electronic conductivity.

Optimizing evacuation efficiency under emergency with consideration of social fairness based on a cell transmission model

Traffic assignment and management objectives are considered as two significant parts in developing the emergency evacuation plan, which can directly influence the evacuation performance and efficiency. From the perspective of disaster response operators, the evacuation objective frequently is to minimize the total evacuation time to reduce losses, which may lead to an unreasonable and unfair phenomenon where people in highest risk areas may be forced to sacrifice their priorities of evacuation to improve the system evacuation efficiency. In this paper, considering both efficiency and social fairness in emergency evacuation, a weight function consisting of risk evaluation index as variable and the emphasis degree of managers on social fairness principle as coefficient was initially proposed and embedded in system optimal (SO) objective function. Combining the weight function and other constraints based on an extended cell transmission model (CTM), the linear program (LP) model was established to realize the simulation of dynamic traffic assignment in emergency evacuation. Employing this model, the impact of the management strategy of balancing both efficiency and social fairness on evacuation results was studied in the “Tianjin Explosions” case. In the end, the conclusion of “balancing social fairness is valuable during evacuation” was obtained.

How does intersection field of view influence driving safety in an emergent situation?

Restricted intersection field of view (IFOV) can influence drivers' hazard detection abilities and driving safety in an emergent traffic event. However, no field studies or crash-data analyses have been conducted to prove the adequateness of the current intersection sight-distance design standards, which are adopted to ensure that the approaching-intersection drivers have a sufficient field of view to detect traffic hazards and travel safely at intersections. In this study, we conducted a driving simulator experiment to compare drivers' behavioral and eye-movement measures between different IFOV conditions that met the current intersection sight distance design standards. We examined the influencing mechanism of IFOV on the drivers' collision avoidance process being composed of three consequential stages, respectively in terms of search stage, decision stage and action stage. Our experiment results showed that restricted IFOV impacts the three-stage driving performance interlockingly. Enlarging IFOV can significantly improve drivers' performance in detecting a conflicting vehicle more timely, having a longer perception-reaction time in monitoring the hazard, spending more time on observing intersection surroundings, and taking brake actions earlier and more smoothly so that drivers were more likely to successfully avoid colliding with the conflicting vehicle. In addition, we found that compared with female drivers, male drivers were less likely to take brake actions to avoid a potential collision and had a lower deceleration rate in the braking stage of collision avoidance while there was no significant gender difference in crash involvement rates. The findings indicated that male drivers were more skillful in vehicle control than female drivers. Nevertheless, male drivers had less traffic-crash expectation, which degraded their overall crash avoidance effect. Considering the traffic safety that more than five million intersection-related crashes occur in American each year, these experimental findings have implications for public safety and health.

Using perceptual cues for brake response to a lead vehicle: Comparing threshold and accumulator models of visual looming

Previous studies have shown the effect of a lead vehicle's speed, deceleration rate and headway distance on drivers' brake response times. However, how drivers perceive this information and use it to determine when to apply braking is still not quite clear. To better understand the underlying mechanisms, a driving simulator experiment was performed where each participant experienced nine deceleration scenarios. Previously reported effects of the lead vehicle's speed, deceleration rate and headway distance on brake response time were firstly verified in this paper, using a multilevel model. Then, as an alternative to measures of speed, deceleration rate and distance, two visual looming-based metrics (angular expansion rate θ˙ of the lead vehicle on the driver's retina, and inverse tau τ^-1, the ratio between θ˙ and the optical size θ), considered to be more in line with typical human psycho-perceptual responses, were adopted to quantify situation urgency. These metrics were used in two previously proposed mechanistic models predicting brake onset: either when looming surpasses a threshold, or when the accumulated evidence (looming and other cues) reaches a threshold. Results showed that the looming threshold model did not capture the distribution of brake response time. However, regardless of looming metric, the accumulator models fitted the distribution of brake response times better than the pure threshold models. Accumulator models, including brake lights, provided a better model fit than looming-only versions. For all versions of the mechanistic models, models using τ^-1 as the measure of looming fitted better than those using θ˙, indicating that the visual cues drivers used during rear-end collision avoidance may be more close to τ^-1.

Comparison of proposed countermeasures for dilemma zone at signalized intersections based on cellular automata simulations

The Type II dilemma zone describes the road segment to a signalized intersection where drivers have difficulties to decide either stop or go at the onset of yellow signal. Such phenomenon can result in an increased crash risk at signalized intersections. Different types of warning systems have been proposed to help drivers make decisions. Although the warning systems help to improve drivers' behavior, they also have several disadvantages such as increasing rear-end crashes or red-light running (RLR) violations. In this study, a new warning system called pavement marking with auxiliary countermeasure (PMAIC) is proposed to reduce the dilemma zone and enhance the traffic safety at signalized intersections. The proposed warning system integrates the pavement marking and flashing yellow system which can provide drivers with better suggestions about stop/go decisions based on their arriving time and speed. In order to evaluate the performance of the proposed warning system, this paper presents a cellular automata (CA) simulation study. The CA simulations are conducted for four different scenarios in total, including the typical intersection without warning system, the intersection with flashing green countermeasure, the intersection with pavement marking, and the intersection with the PMAIC warning system. Before the specific CA simulation analysis, a logistic regression model is calibrated based on field video data to predict drivers' general stop/go decisions. Also, the rules of vehicle movements in the CA models under the influence by different warning systems are proposed. The proxy indicators of rear-end crash and potential RLR violations were estimated and used to evaluate safety levels for the different scenarios. The simulation results showed that the PMAIC countermeasure consistently offered best performance to reduce rear-end crash and RLR violation. Meanwhile, the results indicate that the flashing-green countermeasure could not effectively reduce either rear-end crash risk or RLR violations. Also, it is found that the pavement-marking countermeasure has positive effects on reducing the rear-end risk while it may increase the probability of RLR violation. Lastly, the implementation of the proposed warning system is discussed with the consideration of connected-vehicle technology. It is expected that the dilemma zone issues can be efficiently addressed if the proposed countermeasure can be employed within connected vehicle technology.

Red-Light-Running Crashes' Classification, Comparison, and Risk Analysis Based on General Estimates System (GES) Crash Database

Red-light running (RLR) has been identified as one of the prominent contributing factors involved in signalized intersection crashes. In order to reduce RLR crashes, primarily, a better understanding of RLR behavior and crashes is needed. In this study, three RLR crash types were extracted from the general estimates system (GES), including go-straight (GS) RLR vehicle colliding with go-straight non-RLR vehicle, go-straight RLR vehicle colliding with left-turn (LT) non-RLR vehicle, and left-turn RLR vehicle colliding with go-straight non-RLR vehicle. Then, crash features within each crash type scenario were compared, and risk analyses of GS RLR and LT RLR were also conducted. The results indicated that for the GS RLR driver, the speed limit displayed the highest effects on the percentages of GS RLR collision scenarios. For the LT RLR driver, the number of lanes displayed the highest effects on the percentages of LT RLR collision scenarios. Additionally, the drivers who were older than 50 years, distracted, and had a limited view were more likely to be involved in LT RLR accidents. Furthermore, the speeding drivers were more likely to be involved in GS RLR accidents. These findings could give a comprehensive understanding of RLR crash features and propensities for each RLR crash type.

Individuals' Acceptance to Free-Floating Electric Carsharing Mode: A Web-Based Survey in China

Carsharing is growing rapidly in popularity worldwide. When the vehicles involved are Battery Electric Vehicles (BEV), carsharing has been proven to remarkably contribute to easing energy and environment crises. In this study, individuals’ acceptance to carsharing in China was measured from three aspects: carsharing mode choice behavior, highest acceptable price to use carsharing, and willingness to forgo car purchases. The data were collected by a web-based survey. The hierarchical tree-based regression (HTBR) method was applied to explore the effects of potential influencing factors on individuals’ acceptance, and some interesting findings were obtained: participants who know about carsharing were more likely to use carsharing, pay higher prices and forgo car purchases; the most competitive trip purpose and trip distance for choosing carsharing were, respectively, business activities and 11–20 km; most participants (47.1%) were willing to pay 1–2 Yuan per minute to use carsharing, and males or participants with higher income-level could accept higher price; and when car purchase restrain policy (CPRP) was carried out in a city or the urban public transport service level (UPTSL) was high, participants were more willing to forgo car purchases. Based on the above findings, corresponding policies were proposed to provide guidance for successful establishment of carsharing in China.

Traffic-related trace elements in soils along six highway segments on the Tibetan Plateau: Influence factors and spatial variation

The accumulation of traffic-related trace elements in soil as the result of anthropogenic activities raises serious concerns about environmental pollution and public health. Traffic is the main source of trace elements in roadside soil on the Tibetan Plateau, an area otherwise devoid of industrial emissions. Indeed, the rapid development of tourism and transportation in this region means it is becoming increasingly important to identify the accumulation levels, influence distance, spatial distribution, and other relevant factors influencing trace elements. In this study, 229 soil samples along six segments of the major transportation routes on the Tibetan Plateau (highways G214, S308, and G109), were collected for analysis of eight trace elements (Cr, Co, Ni, As, Cu, Zn, Cd, and Pb). The results of statistical analyses showed that of the eight trace elements in soils, Cu, Zn, Cd, and Pb were primarily derived from traffic. The relationship between the trace element accumulation levels and the distance from the roadside followed an exponential decline, with the exception of Segment 3, the only unpaved gravel road studied. In addition, the distance of influence from the roadside varied by trace element and segment, ranging from 16m to 144m. Background values for each segment were different because of soil heterogeneity, while a number of other potential influencing factors (including traffic volume, road surface material, roadside distance, land cover, terrain, and altitude) all had significant effects on trace-element concentrations. Overall, however, concentrations along most of the road segments investigated were at, or below, levels defined as low on the Nemero Synthesis index.

A rear-end collision risk assessment model based on drivers' collision avoidance process under influences of cell phone use and gender-A driving simulator based study

Driver's collision avoidance performance has a direct link to the collision risk and crash severity. Previous studies demonstrated that the distracted driving, such as using a cell phone while driving, disrupted the driver's performance on road. This study aimed to investigate the manner and extent to which cell phone use and driver's gender affected driving performance and collision risk in a rear-end collision avoidance process. Forty-two licensed drivers completed the driving simulation experiment in three phone use conditions: no phone use, hands-free, and hand-held, in which the drivers drove in a car-following situation with potential rear-end collision risks caused by the leading vehicle's sudden deceleration. Based on the experiment data, a rear-end collision risk assessment model was developed to assess the influence of cell phone use and driver's gender. The cell phone use and driver's gender were found to be significant factors that affected the braking performances in the rear-end collision avoidance process, including the brake reaction time, the deceleration adjusting time and the maximum deceleration rate. The minimum headway distance between the leading vehicle and the simulator during the rear-end collision avoidance process was the final output variable, which could be used to measure the rear-end collision risk and judge whether a collision occurred. The results showed that although cell phone use drivers took some compensatory behaviors in the collision avoidance process to reduce the mental workload, the collision risk in cell phone use conditions was still higher than that without the phone use. More importantly, the results proved that the hands-free condition did not eliminate the safety problem associated with distracted driving because it impaired the driving performance in the same way as much as the use of hand-held phones. In addition, the gender effect indicated that although female drivers had longer reaction time than male drivers in critical situation, they were more quickly in braking with larger maximum deceleration rate, and they tended to keep a larger safety margin with the leading vehicle compared to male drivers. The findings shed some light on the further development of advanced collision avoidance technologies and the targeted intervention strategies about cell phone use while driving.

[Abnormal expression of insulin-like growth factor-I receptor and inhibitory effect of its transcription intervention on nude mice xenograft tumor]

Objective: To investigate the expression of insulin-like growth factor-I receptor (IGF-IR) in liver cancer and the inhibitory effect of its transcription intervention on nude mice xenograft tumor. Methods: A total of 40 patients with primary liver cancer were enrolled, and 40 samples of cancer lesions, peri-cancerous tissues (with a distance of 2 cm to the margin of cancer lesion), or distal liver tissues (with a distance of 5 cm to the margin of cancer lesion), with a weight of 200 mg, were collected after surgery. Some of these samples were used for pathological examination, and the rest were stored at -85°C. A total of 18 BALB/c nude mice aged 4-6 weeks with a body weight of 18-20 g (9 male and 9 female mice) were randomly divided into control group, negative control group, and co-intervention group, with 6 mice in each group, and fed under specific pathogen-free conditions. The cell line was cultured in the dimethyl sulfoxide complete medium containing 10% fetal bovine serum in a CO₂incubator at 37°C. When the cell confluence reached 90% after cell inoculation, shRNA was divided into co-intervention group, negative control group, and untreated control group and were transfected to hepatoma cells using PolyJetTM transfection reagent. Stable cell clones obtained by G418 screening and used for the in vivo study. Immunohistochemistry, Western blotting, and quantitative real-time PCR were used to analyze the expression of IGF-IR in the human hepatoma tissue and cell line. The IGF-IR shRNA eukaryotic expression plasmids were established and screened for the most effective sequence; they were transfected to PLC/PRF/5 hepatoma cells, and the CCK-8 assay was used to analyze the changes in cell proliferation. The stable cell line screened out by G418 was inoculated to establish the subcutaneous xenograft tumor in nude mice. The tumor growth curve was plotted and histological examination was performed. Graphpad Prism 5.0 and SPSS 18.0 were used for plotting and data analysis; the variance test and Q test were used for comparison of means between multiple samples, the t-test was used for comparison of means between any two samples, the chi-square test or Fisher's exact test was used for comparison of rates between samples, and a rank correlation analysis was performed for expression intensity. Results: The liver cancer group had a significantly higher positive rate of IGF-IR than the peri-cancerous group and distal tissue group (82.5% vs 42.5%/10%,χ²= 13.653 and 42.29, bothP< 0.01), as well as significantly higher expression intensity than these two groups (Z= 4.771 and 6.579, bothP< 0.01). IGF-IR was not significantly expressed in the L02 cell line and was strongly expressed in the PLC/PRF/5 hepatoma cells, and the expression intensity of IGF-IR in the PLC/PRF/5 hepatoma cells was 4 and 5 times that in Bel-7404 cells and HepG2 cells, respectively. After the PLC/PRF/5 hepatoma cells were transfected with shRNA4 with the best co-intervention effect, the mean inhibition rate of tumor cell growth reached 63.9% at 72 hours, and the mean inhibition rate of IGF-IR transcription reached 59.6%. Tumor cells were arrested in G1 phase, and there was a significant increase in apoptosis rate. As for the subcutaneous hepatoma xenograft in nude mice, the intervention group had significantly slower tumor growth than the blank control group and negative control group (143±24 mm3 vs 372±46 mm3/350±50 mm3,t= 10.776 and 9.142, bothP< 0.01); the intervention group had significantly downregulated IGF-IR expression, which was significantly lower than that in the blank control group and negative control group (t= 11.184 and 9.450, bothP< 0.01). Conclusion: Intervention of IGF-IR transcription can effectively inhibit the growth of xenograft tumor in nude mice, suggesting that IGF-IR gene might become a new potential target for the treatment of liver cancer.

Drivers' eye movements as a function of collision avoidance warning conditions in red light running scenarios

The intersection collision avoidance warning systems (ICAWSs) have substantial potentials in improving driving performance and reducing the number and severity of intersection collisions, through helping drivers timely detect hazardous conflicting vehicles in precrash scenarios. However, the influences of ICAWS on drivers' visual performance have barely been discussed. This study focuses on exploring the patterns in drivers' eye movements as a function of ICAWS's warning conditions in red light running scenarios based on a driving simulation experiment. Two types of speech warning conditions including warning timings (varied form 2.5s to 5.5s) and directional information (with or without) are examined, and the no-warning condition is the baseline. The results revealed that more subjects would be likely to benefit from the ICWAS under the earlier warning timings. The warning condition of 4.5s ahead of a collision had the best effectiveness in terms of visual performances. Under such a warning timing, drivers had shorter fixation duration and higher frequency of searching for the red light running (RLR) vehicles. Compared to the warning condition without directional information, the directional warning information could capture drivers' attention more efficiently, help driver direct fixations toward the RLR vehicles more quickly and lead to more scanning activities. Compared to female drivers, male drivers had more scanning activities when approaching intersections, detected the RLR vehicles more quickly and were more likely to avoid the RLR collisions. Besides, the experiment results indicated that the female drivers were more inclined to trust the warning information and got more benefits from the RLR-ICAWS in terms of the crash risk reduction rate than male drivers. Finally, the conclusions lead the way toward warning condition design recommendations for improving the effectiveness of the RLR-ICAWSs.

A Method for Formulizing Disaster Evacuation Demand Curves Based on SI Model

The prediction of evacuation demand curves is a crucial step in the disaster evacuation plan making, which directly affects the performance of the disaster evacuation. In this paper, we discuss the factors influencing individual evacuation decision making (whether and when to leave) and summarize them into four kinds: individual characteristics, social influence, geographic location, and warning degree. In the view of social contagion of decision making, a method based on Susceptible-Infective (SI) model is proposed to formulize the disaster evacuation demand curves to address both social influence and other factors’ effects. The disaster event of the “Tianjin Explosions” is used as a case study to illustrate the modeling results influenced by the four factors and perform the sensitivity analyses of the key parameters of the model. Some interesting phenomena are found and discussed, which is meaningful for authorities to make specific evacuation plans. For example, due to the lower social influence in isolated communities, extra actions might be taken to accelerate evacuation process in those communities.

Changes in Drivers' Visual Performance during the Collision Avoidance Process as a Function of Different Field of Views at Intersections

The intersection field of view (IFOV) indicates an extent that the visual information can be observed by drivers. It has been found that further enhancing IFOV can significantly improve emergent collision avoidance performance at intersections, such as faster brake reaction time, smaller deceleration rate, and lower traffic crash involvement risk. However, it is not known how IFOV affects drivers’ eye movements, visual attention and the relationship between visual searching and traffic safety. In this study, a driving simulation experiment was conducted to uncover the changes in drivers’ visual performance during the collision avoidance process as a function of different field of views at an intersection by using an eye tracking system. The experimental results showed that drivers’ ability in identifying the potential hazard in terms of visual searching was significantly affected by different IFOV conditions. As the IFOVs increased, drivers had longer gaze duration (GD) and more number of gazes (NG) in the intersection surrounding areas and paid more visual attention to capture critical visual information on the emerging conflict vehicle, thus leading to a better collision avoidance performance and a lower crash risk. It was also found that female drivers had a better visual performance and a lower crash rate than male drivers. From the perspective of drivers’ visual performance, the results strengthened the evidence that further increasing intersection sight distance standards should be encouraged for enhancing traffic safety.

Investigation of work zone crash casualty patterns using association rules

Investigation of the casualty crash characteristics and contributory factors is one of the high-priority issues in traffic safety analysis. In this paper, we propose a method based on association rules to analyze the characteristics and contributory factors of work zone crash casualties. A case study is conducted using the Michigan M-94/I-94/I-94BL/I-94BR work zone crash data from 2004 to 2008. The obtained association rules are divided into two parts including rules with high-lift, and rules with high-support for the further analysis. The results show that almost all the high-lift rules contain either environmental or occupant characteristics. The majority of association rules are centered on specific characteristics, such as drinking driving, the highway with more than 4 lanes, speed-limit over 40mph and not use of traffic control devices. It should be pointed out that some stronger associated rules were found in the high-support part. With the network visualization, the association rule method can provide more understandable results for investigating the patterns of work zone crash casualties.

Discrepancy analysis of driving performance of taxi drivers and non-professional drivers for red-light running violation and crash avoidance at intersections

Due to comfort, convenience, and flexibility, taxis have become increasingly more prevalent in China, especially in large cities. However, many violations and road crashes that occurred frequently were related to taxi drivers. This study aimed to investigate differences in driving performance between taxi drivers and non-professional drivers from the perspectives of red-light running violation and potential crash involvement based on a driving simulation experiment. Two typical scenarios were established in a driving simulator, which includes the red-light running violation scenario and the crash avoidance scenario. There were 49 participants, including 23 taxi drivers (14 males and 9 females) and 26 non-professional drivers (13 males and 13 females) recruited for this experiment. The driving simulation experiment results indicated that non-professional drivers paid more attention to red-light running violations in comparison to taxi drivers who had a higher probability of red-light running violation. Furthermore, it was found that taxi drivers were more inclined to turn the steering wheel in an attempt to avoid a potential collision and non-professional drivers had more abrupt deceleration behaviors when facing a potential crash. Moreover, the experiment results showed that taxi drivers had a smaller crash rate compared to non-professional drivers and had a better performance in terms of crash avoidance at the intersection.

A hybrid finite mixture model for exploring heterogeneous ordering patterns of driver injury severity

Debates on the ordering patterns of crash injury severity are ongoing in the literature. Models without proper econometrical structures for accommodating the complex ordering patterns of injury severity could result in biased estimations and misinterpretations of factors. This study proposes a hybrid finite mixture (HFM) model aiming to capture heterogeneous ordering patterns of driver injury severity while enhancing modeling flexibility. It attempts to probabilistically partition samples into two groups in which one group represents an unordered/nominal data-generating process while the other represents an ordered data-generating process. Conceptually, the newly developed model offers flexible coefficient settings for mining additional information from crash data, and more importantly it allows the coexistence of multiple ordering patterns for the dependent variable. A thorough modeling performance comparison is conducted between the HFM model, and the multinomial logit (MNL), ordered logit (OL), finite mixture multinomial logit (FMMNL) and finite mixture ordered logit (FMOL) models. According to the empirical results, the HFM model presents a strong ability to extract information from the data, and more importantly to uncover heterogeneous ordering relationships between factors and driver injury severity. In addition, the estimated weight parameter associated with the MNL component in the HFM model is greater than the one associated with the OL component, which indicates a larger likelihood of the unordered pattern than the ordered pattern for driver injury severity.

Modeling the probability of freeway lane-changing collision occurrence considering intervehicle interaction

OBJECTIVE

To better capture the relationships between lane-changing collisions and explanatory variables, a microscopic model is developed for freeway lane-changing collisions based on the interactions between lane-changing vehicles.

METHODS

The model applies an intervehicle interaction structure to account for the occurrence mechanism of lane-changing collisions. The occurrence mechanism can be described as the failure of a vehicle driver of an adjacent lane in avoiding the lane-changing vehicle, which disturbs the smooth movement of the adjacent lane vehicle and requires the driver's brake action to avoid an angle collision. This model is examined using data collected from freeways in Washington State during 2010 to 2011 and validated using lane-changing collision data for the SR 520 freeway.

RESULTS

The findings of this study show that generalized truck percentage has a significant decreasing effect on lane-changing collision risk, whereas average spacing and several roadway characteristics have significant increasing effects. The frequency of slight collisions during peak hours is higher than that during off-peak hours. Young female drivers are more likely to be involved in collisions during lane-changing than young male drivers, but the result for senior drivers is opposite, with older male drivers having a higher probability of lane-changing collisions than female drivers in the same age group.

CONCLUSION

The process of lane-changing collisions is a complicated maneuver. Truck percentage, average spacing, and good roadway characteristics, such as straight and level segment, in the target lane have a significant effect on the occurrence of lane-changing collisions. Age and gender are also 2 important factors contributing to the relationship between lane-changing collisions and explanatory variables.

Effects of fog, driver experience and gender on driving behavior on S-curved road segments

Driving on curved roads has been recognized as a significant safety issue for many years. However, driver behavior and the interactions among variables that affect driver performance on curves is complicated and not well understood. Previous studies have investigated various factors that influence driver performance on right- or left-turn curves, but have paid little attention to the effects of foggy weather, driver experience and gender on driver performance on complex curves. A driving simulator experiment was conducted in this study to evaluate the relationships between driving behavior on a continuous S-curve and foggy weather, driver experience and gender. The process of negotiating a curve was divided into three stages consisting of a straight segment, the transition from the straight segment to the S-curve and the S-curve. The experimental results indicated that drivers tended to drive more cautiously in heavy fog, but the driving risk was still increased, especially in the transition stage from the straight segment to the S-curve. The non-professional (NP) drivers were less sensitive to the impending change in the road geometry, and less skilled in both longitudinal and lateral vehicle control than the professional drivers. The NP female drivers in particular were found to be the most vulnerable group in S-curve driving.

In-depth analysis of drivers' merging behavior and rear-end crash risks in work zone merging areas

This study investigates the drivers' merging behavior and the rear-end crash risk in work zone merging areas during the entire merging implementation period from the time of starting a merging maneuver to that of completing the maneuver. With the merging traffic data from a work zone site in Singapore, a mixed probit model is developed to describe the merging behavior, and two surrogate safety measures including the time to collision (TTC) and deceleration rate to avoid the crash (DRAC) are adopted to compute the rear-end crash risk between the merging vehicle and its neighboring vehicles. Results show that the merging vehicle has a bigger probability of completing a merging maneuver quickly under one of the following situations: (i) the merging vehicle moves relatively fast; (ii) the merging lead vehicle is a heavy vehicle; and (iii) there is a sizable gap in the adjacent through lane. Results indicate that the rear-end crash risk does not monotonically increase as the merging vehicle speed increases. The merging vehicle's rear-end crash risk is also affected by the vehicle type. There is a biggest increment of rear-end crash risk if the merging lead vehicle belongs to a heavy vehicle. Although the reduced remaining distance to work zone could urge the merging vehicle to complete a merging maneuver quickly, it might lead to an increased rear-end crash risk. Interestingly, it is found that the rear-end crash risk could be generally increased over the elapsed time after the merging maneuver being triggered.

Synchronously synthesized Si@C composites through solvothermal oxidation of Mg2Si as lithium ion battery anode

Si@C composites have been synchronously synthesized by solvothermal oxidation of Mg₂Si at 650 °C.

Development of a subway operation incident delay model using accelerated failure time approaches

This study aims to develop a subway operational incident delay model using the parametric accelerated time failure (AFT) approach. Six parametric AFT models including the log-logistic, lognormal and Weibull models, with fixed and random parameters are built based on the Hong Kong subway operation incident data from 2005 to 2012, respectively. In addition, the Weibull model with gamma heterogeneity is also considered to compare the model performance. The goodness-of-fit test results show that the log-logistic AFT model with random parameters is most suitable for estimating the subway incident delay. First, the results show that a longer subway operation incident delay is highly correlated with the following factors: power cable failure, signal cable failure, turnout communication disruption and crashes involving a casualty. Vehicle failure makes the least impact on the increment of subway operation incident delay. According to these results, several possible measures, such as the use of short-distance and wireless communication technology (e.g., Wifi and Zigbee) are suggested to shorten the delay caused by subway operation incidents. Finally, the temporal transferability test results show that the developed log-logistic AFT model with random parameters is stable over time.

Analysis of work zone rear-end crash risk for different vehicle-following patterns

This study evaluates rear-end crash risk associated with work zone operations for four different vehicle-following patterns: car-car, car-truck, truck-car and truck-truck. The deceleration rate to avoid the crash (DRAC) is adopted to measure work zone rear-end crash risk. Results show that the car-truck following pattern has the largest rear-end crash risk, followed by truck-truck, truck-car and car-car patterns. This implies that it is more likely for a car which is following a truck to be involved in a rear-end crash accident. The statistical test results further confirm that rear-end crash risk is statistically different between any two of the four patterns. We therefore develop a rear-end crash risk model for each vehicle-following pattern in order to examine the relationship between rear-end crash risk and its influencing factors, including lane position, the heavy vehicle percentage, lane traffic flow and work intensity which can be characterized by the number of lane reductions, the number of workers and the amount of equipment at the work zone site. The model results show that, for each pattern, there will be a greater rear-end crash risk in the following situations: (i) heavy work intensity; (ii) the lane adjacent to work zone; (iii) a higher proportion of heavy vehicles and (iv) greater traffic flow. However, the effects of these factors on rear-end crash risk are found to vary according to the vehicle-following patterns. Compared with the car-car pattern, lane position has less effect on rear-end crash risk in the car-truck pattern. The effect of work intensity on rear-end crash risk is also reduced in the truck-car pattern.

Effect of audio in-vehicle red light-running warning message on driving behavior based on a driving simulator experiment

OBJECTIVE

Drivers' incorrect decisions of crossing signalized intersections at the onset of the yellow change may lead to red light running (RLR), and RLR crashes result in substantial numbers of severe injuries and property damage. In recent years, some Intelligent Transport System (ITS) concepts have focused on reducing RLR by alerting drivers that they are about to violate the signal. The objective of this study is to conduct an experimental investigation on the effectiveness of the red light violation warning system using a voice message.

METHODS

In this study, the prototype concept of the RLR audio warning system was modeled and tested in a high-fidelity driving simulator. According to the concept, when a vehicle is approaching an intersection at the onset of yellow and the time to the intersection is longer than the yellow interval, the in-vehicle warning system can activate the following audio message "The red light is impending. Please decelerate!" The intent of the warning design is to encourage drivers who cannot clear an intersection during the yellow change interval to stop at the intersection.

RESULTS

The experimental results showed that the warning message could decrease red light running violations by 84.3 percent. Based on the logistic regression analyses, drivers without a warning were about 86 times more likely to make go decisions at the onset of yellow and about 15 times more likely to run red lights than those with a warning. Additionally, it was found that the audio warning message could significantly reduce RLR severity because the RLR drivers' red-entry times without a warning were longer than those with a warning.

CONCLUSIONS

This driving simulator study showed a promising effect of the audio in-vehicle warning message on reducing RLR violations and crashes. It is worthwhile to further develop the proposed technology in field applications.