China In-Depth Accident Study Research Articles

Analysis of following vehicle driver injury severity in rear-end collision on straight road based on LightGBM and SHAP

Rear-end accidents, as one of the common accident types, may cause serious injuries to the driver and passengers. There are mutual coupling effects among causal features of rear-end accidents, and traditional analytical methods may lead to modeling distortion due to assumption constraints. In this study, the Light Gradient Boosting Machine (LightGBM), a machine learning algorithm, is used to model the injury severity of the following vehicle driver in a rear-end crash accident on a straight road, based on the data from the China In-depth Accident Study data. The Shapley Additive Explanations (SHAP) method was used to interpret the results of the LightGBM model and to analyze the relationship between factors and driver injury severity. The results show that location familiarity, willingness to take risks, and driving time have a significant impact on the injury severity for following vehicle drivers involved in rear-end crashes. Cloudy conditions increase a driver’s risk of being involved in a fatal or injured rear-end collision. In rain, snow, hail, and foggy conditions, have a higher propensity to cause driver fatalities in the crash event. The rear-end crash of passenger cars resulted in a higher death probability of the following vehicle driver compared to sedan and truck, and female drivers are more likely to be involved in uninjured accidents compared to males. These results are informative for preventing rear-end accidents and reducing the extent of accidental injuries.

Assessing the Impact of Different Population Density Scenarios on Two-Wheeler Accident Characteristics at Intersections

Examining 1192 intersection car and two-wheeled vehicle collision accidents from the China In-Depth Accident Study (CIDAS) database, this study employs population density heat maps for precise assessment of surrounding population densities at accident sites. The K-Medoid clustering algorithm and silhouette coefficient were used to classify accidents into two distinct groups based on population density. Subsequent application of the random parameter logit model revealed key contributing factors to these accidents in varying population densities. The results show notable differences in factors such as collision direction of two-wheeled vehicles, types of accident conflict, road conditions, and traffic flow, depending on the population density. Based on these conclusions, the research can inform differentiated risk prediction for two-wheeled vehicle accidents at intersections and provide insights for intersection design in various population density scenarios.

Pedestrian crash causation analysis and active safety system calibration

Over 20 % of global crash fatalities involve pedestrians, but pedestrian crash causation and pedestrian protection systems have not been thoroughly developed or reliably tested. To understand the causation characteristics of pedestrian crashes, 398 pedestrian crashes were extracted from the China in-depth accident study (CIDAS), and most of these crashes were aggregated into five scenarios. The two scenarios with the highest proportion of crashes were analyzed by the driving reliability and error analysis method (DREAM) to identify high-risk causation patterns. From these patterns, three main contributing factors were identified: 1) extremely environmental light disturbance; 2) distracted driving caused by drivers’ own thoughts; 3) drivers violating pedestrian yield law. Based on these patterns and factors, a pedestrian protection system was designed. It consists of a forward vision sensor and radar to sense the environment and the three-stage autonomous emergency braking (AEB) algorithm to automatically avoid pedestrian collisions. Crash scenarios from CIDAS data were recreated in MATLAB Simulink to test the pedestrian protection system proposed in this study. This system was found to reduce pedestrian crashes by more than 90 %. The optimal parameters for three AEB stages were obtained, with decelerations of 0.2 g, 0.3 g, and 0.6 g. This study designed an active safety system based on causation analysis of the vehicle–pedestrian crashes and calibrated the AEB algorithm of it, thus providing reference and insight for further development of pedestrian protection systems.

Predicting and factor analysis of rider injury severity in two-wheeled motorcycle and vehicle crash accidents based on an interpretable machine learning framework

Objective As one of the vulnerable road users in accidents, how to improve the two-wheeled motorcyclist’s driving safety and reduce accident injury is a public health issue. Accurate identification of the factors influencing the severity of accidents is an important prerequisite for mitigating injury from crashes. Methods Based on a vehicle and a two-wheeled motorcycle crash accident data from the China in-depth accident study database (CIDAS), this study uses the performance evaluation indicators of accuracy, precision, recall, F1-score, AUC, and the ROC curve. The classification and prediction performances of the six machine learning methods on the dataset are compared, and the LightGBM algorithm with the best performance is selected to model the accident injury severity of the motorcyclists. The SHAP method is used to extend the interpretability of the LightGBM model results. Based on the SHAP method, the importance, main effect, and the interaction effect of factors under each accident injury severity are quantitatively analyzed. Results The model prediction accuracy is 92.6%, the F1-Score is 92.8%, and the AUC value is 0.986. The importance of factors varies with the accident injury severity of motorcyclists. The kilometers traveled per year by the driver, the throwing distance of the motorcyclist, and the road speed limit are the three most important factors. The motorcyclist is more likely to suffer fatal injuries when the throwing distance is >1,000 cm. Conclusions The prediction model of driver injury severity based on LightGBM algorithm has a good prediction performance. It can be used to analyze the influence factors of injury severity in two-wheeled motorcyclist accident by combining the model with SHAP method. These results could help the traffic management department to take measures to reduce accident injury of motorcyclists.

A case study of unavoidable accidents of autonomous vehicles

Objective: Autonomous driving technology eliminates human errors, and thus it is a promising solution for reducing road traffic fatalities and injuries. While future autonomous driving technology may be able to reduce the number of collision accidents, it will not be able to avoid all collision accidents. This study is aimed to demonstrate why some accidents will still be unavoidable even with advanced perceiving and controlling capabilities. Methods: Because fully autonomous vehicles are currently in the laboratory stage, we used the prospective method to study the unavoidable accident of autonomous vehicles. Suitable traffic accident cases were screened from the China In-Depth Accident Study (CIDAS). Videos of the accidents were analyzed and the accidents were reconstructed using PC-Crash software. We assumed that target vehicle possesses near-perfect autonomous driving capabilities. Unavoidable accidents were determined based on vehicle dynamics and traffic constraints. The time from perceiving hazard to collision was calculated for each accident. Results: Among the 112 accidents screened, 15 cases of unavoidable accidents were identified. Three typical cases are presented in detail in this study. Based on the reasons why the target vehicles cannot avoid the collisions, we classified the unavoidable accidents into time-limit type and space-limit type. Time-limit means that vehicle cannot stop or steer out of danger in time, and space-limit means that the traffic environment does not have sufficient space for vehicle to avoid collision. Conclusions: Collision accidents will still occur even with perfect autonomous driving technology. We used the prospective method to investigate scenarios and characteristics of unavoidable accidents of autonomous vehicles. The time-limit type and the space-limit type were identified as two categories of unavoidable accidents. For the time-limit unavoidable accidents, the time from perceiving hazard to collision is typically not longer than 1.5s. The characteristics of unavoidable accidents and the estimated pre-crash warning time can provide some reference for establishing future occupant protection strategies. This study also showed the limitations of active safety and the necessity of passive safety.

Automatic Emergency Braking System for Trucks for Two-Wheeler Longitudinal Collision Avoidance

Among traffic crashes, truck crashes are usually more severe and cause greater harm to other involved traffic participants, particularly vulnerable road users such as the drivers of two-wheelers. Automatic emergency braking (AEB), also known as autonomous emergency braking, has been demonstrated to be a safe, efficient, and high benefit–cost ratio advanced driving assistance system that can reduce driver errors and collisions, but at present, AEB research is mainly carried out for passenger cars with little specific attention to trucks. In this study, a three-stage AEB algorithm was designed to control longitudinal collision scenarios between trucks and two-wheelers. A total of 121 crashes were collected from the China In-Depth Accident Study Database (CIDAS). The crashes were reconstructed and verified in the MATLAB/Simulink simulation platform. The experimental results show that our proposed method avoided 35% of the collisions. Analysis of the simulation results showed that the scenarios in which the proposed system failed to avoid collisions mainly had the characteristics of rapid increase in the urgency of the conflict, insufficient truck deceleration time, and insufficient braking distance. Random forest was used to analyze the factors affecting the AEB’s collision avoidance, and it was found that collision angle, reflecting the urgency of two-wheeler lane changing, was the most significant factor. The results have at least two practical implications. It is of significance to (1) educate two-wheeler drivers to carefully observe the road conditions before making decisions and (2) develop multi-sensor AEB systems for trucks.

Evaluation of Automatic Emergency Braking Systems in Two-Wheeler Crash Scenarios

The two-wheeler (TW) is a popular means of transportation in China, but TWs often suffer from serious traffic crashes because of their highly flexible trajectory and low detectability. Therefore, they present a challenge for the sensing and decision-making systems in autonomous vehicles (AVs). Collision avoidance systems, such as automatic emergency braking (AEB), have provided an effective way for AVs to avoid collisions with different objects, including TWs. The effectiveness of the AEB system is highly dependent on its parameter configurations, however, which vary among TW crash scenarios. This study, therefore, evaluates the AEB parameters, including time to collision (TTC), deceleration, and detection area (including detection range, field of vision, and trigger width) in two AEB systems: one-stage AEB and three-stage AEB. A total of 243 crashes extracted from the China In-Depth Accident Study database were simulated in Matlab’s Simulink. Results show: (i) one-stage AEB crash avoidance rates range from 15.2% to 81.5%, while three-stage AEB has crash avoidance rates as high as 87.2%; (ii) deceleration, TTC, and detection area all have significant main effects on crash rate, but detection area has less influence in longitudinal than in crossing scenarios; (iii) higher crash avoidance rates resulted in lower traffic efficiency for both AEB systems, but resulted in greater speed reduction only for one-stage AEB; and (iv) collisions are less likely to be avoided in scenarios with high initial speed of TW and AV. This study demonstrates the performance of AEB algorithms in multiple actual crash scenarios and provides a reliable basis for the development of AEB systems.

Investigation on AEB Key Parameters for Improving Car to Two-Wheeler Collision Safety Using In-Depth Traffic Accident Data

Automatic emergency braking (AEB) system play an essential role in avoiding traffic collisions and minimizing impact strengths, which contributes to improving road traffic safety. However, the present research of the AEB system is mainly concentrated on avoiding the car to car and car to pedestrian collisions. China has typical mixed traffic characteristics, especially the frequent car to two-wheeler collision accidents. Research on the AEB system to prevent car to two-wheeler collisions in complex multi-traffic scenarios is of great significance to further improve the comprehensive performance of the AEB system. In this article, based on the cases of the car to two-wheeler collisions from China In-Depth Accident Study (CIDAS) database, the effects of three key parameters of the AEB system, braking deceleration, braking advance time, and detection range, on the accident rate and severity are studied by accident reconstruction and virtual experiment. Results show that the theoretical optimal detection range of the AEB system is 180°–35 m for the car to two-wheeler collision prevention. Moreover, the current feasible optimal detection range is 120°–35 m due to the limitation of environmental awareness technology. When the braking advance time is 1 s, the detection angle is 120°, and the detection distance is 35 m, the installation of the AEB system can avoid 22.3% of the car to two-wheeler collision accidents. Meanwhile, in the inevitable accidents, the average collision speed of vehicles decreases from 33.93 to 21.34 km/h, which effectively reduces the collision strengths. Findings of this study could help car manufacturers and makers of AEB hardware select the appropriate detection radar. They can also provide ideas and data support for formulating relevant laws and regulations of the AEB system, future development of AEB system, or other driver assistance systems.

Left-turn-across-path-from-opposite-direction accidents in China: CIDAS accident study

Abstract Left-turn-across-path-from-opposite-direction (LTAP/OD) conflicts are one of the most common crash types at intersections. The research aims to reveal the general and dynamic information about the conflict for the most relevant street layouts for each conflict configuration of the LTAP/OD accidents involving passenger cars, motorcycles and Ebikes. The analysis was based on 276 LTAP/OD accidents collected by the China in-depth accident study (CIDAS 2011–2019). The LTAP/OD accidents include 44 car-to-car conflicts, 157 car-to-motorcycle conflicts and 75 car-to-Ebike conflicts. Most accidents belonging to the three types were observed at the W0 street layout without a green belt separating the oncoming lane and no offset lane between the turning car and the oncoming traffic, the main distance between both vehicles in the beginning of the critical situation being about four metres, occurring in the clear day with no rain and at junctions lighted either because of daylight or based on street lighting. In terms of the turning car initial speed, the range is 15–30 km/h for most car-to-car and car-to-motorcycle accidents, but 30–40 km/h for most car-to-Ebike accidents. As for the collision speed, this range is between 10 km/h and 20 km/h for car-to-car and car-to-Ebike accidents and between 10 km/h and 25 km/h for car-to-motorcycle crashes. Based on the distributions of objective motorcycles’ and Ebikes' positions in collisions with passenger cars, the maximum longitudinal distance is 60 m for both types of accidents and the maximum lateral distance ranges from −20 m to 20 m and from −15 m to 15 m, respectively.

Risk prediction and factor analysis of rider’s injury severity in passenger car and E-bike accidents based on interpretable machine learning

Casualties and property losses caused by the passenger car and electric bicycle crash accidents increased year by year. Assessment of the relevant risk factors of injury severity in passenger car and electric bicycle crashes could help to mitigate crash severity. This study uses an emerging machine learning method to predict the relationship between the risk factors and bicyclist accident injury severity in passenger car-electric bicycle collision accidents. The model’s performance is compared and evaluated based on accuracy, precision, recall, F1-Score, area under curve (AUC), and receiver operating characteristic curve (ROC). An interpretable machine learning framework Shapley additive explanations (SHAP) is used to further analyze the relationship between risk factors and bicyclist injury severity. It is found that we can adopt the light gradient boosting machine (LightGBM) algorithm after hyper-parameter optimization to get the highest accuracy (94.85%), precision (95.2%), recall (94.9%), F1-Score (95%), and AUC (0.993) based on the accident data of electric bicycles and passenger cars in the China in-depth accident study dataset from 2014 to 2018. The model can be used to assess new accident cases based on the model learning rate. There are some new findings in the aspects of bicyclists’ physical factors and electric vehicle characteristics. The throwing distance of the bicyclist has a positive impact on the injury severity. The bicyclist is more likely to suffer more serious injuries in a crash accident when the bicyclist is male, or shorter. Electric bicycles have a smaller handlebar width. In general, the lower handlebar height is, or lower saddle height is, the more serious the bicyclist’s injury is. Safety training for drivers can help reduce the injury severity in crash accidents and improve traffic safety.

Investigating the severity of non-urban road traffic accidents in typical regions of Sichuan and Guizhou, China

Objective The traffic characteristics of Sichuan and Guizhou differ from those of other regions due to its unique geographical features. In addition, accident studies in China mainly focus on urban roads in the eastern and central regions. However, studies on western regions, especially non-urban roads, are scarce. Thus, this study aims to explore the factors that influence the severity of accidents on non-urban roads in typical regions of Sichuan and Guizhou. Methods A total of 541 cases from 2014 to 2020 were selected from the database of the China In-Depth Accident Study, where 18 variables, which may exert an impact on accident severity, were extracted after screening. First, heterogeneity of data was eliminated through latent class analysis (LCA). The ordered probit (OP) model was then conducted for each class to obtain significant variables that exert an impact on accident severity. The study quantified the degree of influence of the significant variables using marginal effect analysis. Results The LCA results demonstrate that data were categorized into the following classes, namely, (a) two-vehicle accidents involving trucks, (b) pedestrian and multiple-vehicle accidents, (c) two-wheeler accidents, and (d) single-vehicle accidents. The OP results show that most variables could exert impact on accident severity, and some of them exerted varying levels of influence on the severity of different classes, whereas others only influence a specific class. Conclusion According to this study, we obtained the accident characteristics of these regions and put forward some targeted suggestions to further improve the level of road traffic safety. The findings can provide support for the construction of transportation in line with the regional characteristics in China.

Development of Test Scenarios for Vehicle and Two-Wheeler at Intersection Based on China In-Depth Accident Study

Development of Test Scenarios for Vehicle and Two-Wheeler at Intersection Based on China In-Depth Accident Study

Study of typical electric two‐wheelers pre-crash scenarios using K-medoids clustering methodology based on video recordings in China

Crash safety of electric two-wheelers (ETWs) has been one of the most important safety issues in China due to their high proportion of involvement in traffic accidents. Automated Emergency Braking (AEB) systems have proven to be effective in reducing the number of fatalities and injuries in traffic accidents. Providing test scenarios is one of the fundamental tasks required for establishing a set of AEB test programs for ETWs. Compared to traditional in-depth accident data, accident data accompanied with video recordings provide more accurate accident information prior to a crash as both the traffic environment and the crash process can be observed from the video. In this study, a set of typical AEB test scenarios for ETWs was developed using accident data with video information. Video recordings of 630 car-to-ETW crashes in China from 2010 to 2021 were selected from the VRU Traffic Accident database with Video (VRU-TRAVi). A K-medoids11See Section 2.3 Cluster Analysis. cluster analysis was carried out based on variables including the collision time, visual obstruction, motion of the car and ETW before the collision, relative motion direction between the car and ETW, and the ETW type. The velocity information of cars and ETWs was also accounted for in each clustering scenario. Seven typical pre-crash scenarios were obtained, including five electric-scooter (E-scooter) scenarios (representing two scenarios where the ETWs are approaching the car from the left side, two scenarios where the ETWs are approaching the car in the same direction and another scenario where the ETWs are approaching the car in the opposite direction) and two electric-bike (E-bike) scenarios where the E-bikes are approaching the car in the perpendicular direction. Both E-bike scenarios are consistent with the E-scooter scenario except for the ETW type and velocity range; therefore, by combining the E-bike and E-scooter scenarios, five ETW scenarios were finally recommended as AEB test scenarios. By comparing with typical scenarios extracted based on the China In-Depth Accident Study (CIDAS) data and the China New Car Assessment Program (C-NCAP) test scenarios, the results show that future AEB test scenarios for ETWs should focus on scenarios with visual obstructions and scenarios where either the car or the ETW is turning, with a velocity range of 15–30 km/h for ETWs.

Research on risky driving behavior evaluation model based on CIDAS real data

This paper constructs an evaluation system that reflects dangerous driving behavior. The evaluation system has a three-layer structure model of “Evaluation Index-Performance Mode-Driving behavior score.” Verification of the feasibility of the model based on the relationship between the driver and the cause of the accident based on behavioral characteristics. First, the driving return survey data and accident form information of the real traffic accident cases of China In-Depth Accident Study (CIDAS) database are counted, and the character variables are converted into digital variables. Then, a three-tier structure of the dangerous driving behavior evaluation system is built, and the correlation between the driver and the cause of the accident is conducted to verify the feasibility of the model. The research shows that the individual characteristics of drivers with dangerous driving behavior are closely related to the cause of accidents, and the evaluation system constructed in this paper can quantify and describe this relationship effectively.

Head kinematics study of E-bicycle rider in car to E-bicycle side collisions

In recent years, car to bicycle collisions take place more and more frequently, which have attracted the attention of some organizations and engineers. They are trying to make some rules for cars to realize better bicyclist protection, including defining new head impact location area and head impact velocity in pedestrian protection tests. However, car to E-bicycle collisions occur more than car to bicycle collisions in China. Therefore, vehicle to E-bicycle collisions should be researched to define appropriate head impact location area and head impact velocity in pedestrian protection tests for cars in Chinese market. In this article, the head kinematics of E-bicycle riders in car to E-bicycle side collisions are studied. First, through analyzing data from China In-Depth Accident Study Database (which conducts field investigation, analysis, and research on traffic accidents in China), some representative conditions and key parameters in car to E-bicycle collisions are extracted. Second, a condition of car to E-bicycle side collision from above analysis is simulated. Third, an experiment according to the above condition is performed and the finite element models in the above simulation are validated. Then, a series of conditions are simulated by using the validated models, and some factors affecting head impact locations and head impact velocities in car to E-bicycle side collisions are studied. Finally, some factors that affect the head kinematics of E-bicycle riders in car to E-bicycle collisions are identified and some results of head movement in these collisions are concluded, such as head impact locations and head impact velocities.

A Study on Correlation of Traffic Accident Tendency with Driver Characters Using In-Depth Traffic Accident Data

Traffic accidents are often related to the driver’s driving behavior, which is mainly decided by his or her characters. In order to explore the correlation of traffic accident risk with driver characters, the age, driving experience, and driving style were statistically analyzed based on the China In-Depth Accident Study (CIDAS) database. Taking the number of casualties in the accident as evaluation indicators, the grey cluster analysis was used to classify the drivers into four accident risk ranks: low, medium to low, medium to high, and high. The results show that drivers aged 18–30 years are more likely to induce accidents; drivers with 6–10 years of driving experience have the highest risk to accidents, followed by drivers with 4-5 years of driving experience; and the driving style is also highly correlated with accident risk tendency.

Casualty risk of e-bike rider struck by passenger vehicle using China in-depth accident data

Objective: Traffic deaths involving e-bike (electric bike) riders are increasing in China. This study aims to quantitatively investigate the association between e-bike rider casualty and impact speed in electric bike-passenger vehicle collisions based on China in-depth accident study data.Methods: According to the collision location and driving direction of the e-bike and the vehicle, electric bike-passenger vehicle collisions are divided into five collision types: frontal collision, e-bike side collision, vehicle side collision, scrape collision and rear-end collision. Since e-bike side collision (the side of e-bike impacted with the front of vehicle) is the leading type and has the highest likelihood of severe or fatal injury in all collision types, e-bike side collisions are further selected to build the casualty risk functions of e-bike rider in relation to the rider age and the impact speed (vehicle impact speed and e-bike impact speed).Results: The analysis results show that, as for e-bike side collisions and e-bike impact speed is 20 km/h, the fatality risk of riders is approximately 2.9% at vehicle impact speed of 30 km/h, 23% at 50 km/h, 50% at 60 km/h, and 90% at 80 km/h. Rider age is also significantly associated with a higher risk of severe and fatality injury. The e-bike impact speed is not significantly associated with the severe and fatality risk in e-bike side collisions.Conclusions: The findings of this study provide meaningful insights to formulate effective policies especially for speed limit management to improve the safety of e-bikes.

The injury epidemiology of adult riders in vehicle-two-wheeler crashes in China, Ningbo, 2011–2015

Introduction: We used road crashes between vehicles and two-wheelers from Yinzhou District Ningbo in 2011–2015 from the China In-depth Accident Study (CIDAS) as sample cases. The risk factors of different injury severity grades were analyzed. Method: The classification tree model was used to screen the possible interaction items, and the corresponding regression model was constructed according to the results of the tree model to explore the influencing factors of cyclist injury. Results: The road types, weather types, gender, age of the riders, and vehicle speed were significantly correlated with the dependent variables. The interaction effect of gender*road type, weather*age, weather*speed and speed*age were obtained through a tree model. Conclusions: The risk of male casualties at the crossroads was 3.31 times higher than that of female casualties at the straight roads. On sunny days, the risk of crash casualties in Ningbo was low, and the fatality risk when the speed reached 38 km/h was 10%. Under the interaction effect of weather and age, the fatality risk in cloudy/foggy and rainy days was almost coincident, and the serious risk in cloudy/foggy conditions was the highest. Practical applications: Through factor analysis, it is confirmed that there is interaction effect among the factors, and it can provide reference for relevant departments to formulate more targeted and effective governance strategies.

A clustering approach to developing car-to-two-wheeler test scenarios for the assessment of Automated Emergency Braking in China using in-depth Chinese crash data

Two-wheeled vehicles (motorized and non-motorized, referred to as TWs) are an important part of the transport system in China. They also represent an important challenge for road safety, with many TW user fatalities and injuries every year. Recently, active safety systems for cars, such as Automated Emergency Braking (AEB), promise to reduce road traffic fatalities and injuries. For these systems to work effectively, it is necessary to understand and define the complex traffic scenarios to be addressed. The aim of this study is to contribute to the development of test procedures for AEB specifically, drawing on the China In-Depth Accident Study (CIDAS) data from July 2011 to February 2016 to describe typical scenarios for crashes between cars and TWs by means of cluster analysis. In total, 672 car-to-TW crashes were extracted. The data was clustered according to five main crash characteristics: time of crash, view obstruction, pre-crash driving behavior of the car driver and the TW driver, and relative moving direction. The analysis resulted in six car-to-TW crash scenarios typical of China. In three scenarios the car and the TW travel perpendicularly to each other before the crash, in two they travel in the same direction, and in one they travel in opposite directions. Further, each scenario can be described with three characteristics (the road speed limit, the TW’s first contact point on the car, and the car’s first contact point on the TW) that can be included in an AEB test suite.Some scenarios were similar to those in the Euro New Car Assessment Program (Euro NCAP). For example, in one, a TW moving straight ahead was hit by a car moving perpendicularly, and in the other the car hit a TW traveling in the same direction. Both occurred in daytime, without a visual obstruction. However, in contrast to the Euro NCAP, typical scenarios in China included night-time scenarios, scenarios where the car or the TW was turning, and those in which the TW was hidden from the car by an obstruction. The results contribute to a proposed novel AEB test suite with realistic scenarios specific to China.

Vehicle-Pedestrian Accidents Characteristic Analysis and Study

This paper summarizes the character of vehicle-pedestrian accidents based on the statistics and analysis on the data of vehicle-pedestrian accidents selected from China In-Depth Accident Study (CIDAS) database. Pedestrian Accidents Characteristic is analyzed and studied. It has great significance for improving vehicle safety.