Evaluation of driving behavior based on massive vehicle trajectory data

Based on the driver surveillance video data and controller area network(CAN)data,the methods of studying commercial vehicles’driving behavior is relatively advanced.However,these methods have difficulty in covering private vehicles.Naturalistic driving studies have disadvantages of small sample size and high cost,one new driving behavior evaluation method using massive vehicle trajectory data is put forward.An automatic encoding machine is used to reduce the noise of raw data,and then driving dynamics and self-organizing mapping(SOM)classification are used to give thresholds or the judgement method of overspeed,rapid speed change,rapid turning and rapid lane changing.The proportion of different driving behaviors and typical dangerous driving behaviors is calculated,then the temporal and spatial distribution of drivers’driving behavior and the driving behavior characteristics on typical roads are analyzed.Driving behaviors on accident-prone road sections and normal road sections are compared.Results show that in Shenzhen,frequent lane changing and overspeed are the most common unsafe driving behaviors;16.1%drivers have relatively aggressive driving behavior;the proportion of dangerous driving behavior is higher outside the original economic special zone;dangerous driving behavior is highly correlated with traffic accident frequency.

The development of real time data driving multi-axis linkage and synergic movement control system of 3D variable cross-section roll forming machine

The three dimensional variable cross-section roll forming is a kind of new metal forming technol- ogy which combines large forming force, multi-axis linkage movement and space synergic movement, and the sequential synergic movement of the ganged roller group is used to complete the metal sheet forming according to the shape of the complicated and variable forming part data. The control system should meet the demands of quick response to the test requirements of the product part. A new kind of real time data driving multi-axis linkage and synergic movement control strategy of 3D roll forming is put forward in the paper. In the new control strategy, the forming data are automatically generated according to the shape of the parts, and the multi-axis linkage movement together with cooperative motion among the six stands of the 3D roll forming machine is driven by the real-time information, and the control nodes are also driven by the forming data. The new control strategy is applied to a 48 axis 3D roll forming machine developed by our research center, and the control servo period is less than 10ms. A forming experiment of variable cross section part is carried out, and the forming preci- sion is better than ＋ 0.5mm by the control strategy. The result of the experiment proves that the control strategy has significant potentiality for the development of 3D roll forming production line with large scale, multi-axis ganged and svner^ic movement

Analysis of Stopping Behavior at Rural T-Intersections Using Naturalistic Driving Study Data

Rural intersections account for around 30% of crashes in rural areas and 6% of all fatal crashes, representing a significant but poorly understood safety problem. Crashes at rural intersections are also problematic since high speeds on intersection approaches are present which can exacerbate the impact of a crash. Additionally, rural areas are often underserved with EMS services which can further contribute to negative crash outcomes. This paper describes an analysis of driver stopping behavior at rural T-intersections using the SHRP 2 Naturalistic Driving Study data. Type of stop was used as a safety surrogate measure using full/rolling stops compared to non-stops. Time series traces were obtained for 157 drivers at 87 unique intersections resulting in 1277 samples at the stop controlled approach for T-intersections. Roadway (i.e. number of lanes, presence of skew, speed limit, presence of stop bar or other traffic control devices), driver (age, gender, speeding), and environmental characteristics (time of day, presence of rain) were reduced and included as independent variables. Results of a logistic regression model indicated drivers were less likely to stop during the nighttime. However presence of intersection lighting increased the likelihood of full/rolling stops. Presence of intersection skew was shown to negatively impact stopping behavior. Additionally drivers who were traveling over the posted speed limit upstream of the intersection approach were less likely to stop at the approach stop sign.

A New FERA Bus Drive and Memory Module for High Speed Data Acquisition System

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Design of Driving Behavior Pattern Measurements Using Smartphone Global Positioning System Data

The emergence of new technologies such as GPS,cellphone,Bluetooth device,etc.offers opportunities for collecting high-fidelity temporal-spatial travel data in a cost-effective manner.With the vehicle trajectory data achieved from a smartphone app Metropia,this study targets on exploring the trajectory data and designing the measurements of the driving pattern.Metropia is a recently available mobile traffic app that uses prediction and coordinating technology combined with user rewards to incentivize drivers to cooperate,balance traffic load on the network,and reduce traffic congestion.Speed and celeration(acceleration and deceleration)are obtained from the Metropia platform directly and parameterized as individual and system measurements related to traffic,spatial and temporal conditions.A case study is provided in this paper to demonstrate the feasibility of this approach utilizing the trajectory data from the actual app usage.The driving behaviors at both individual and system levels are quantified from the microscopic speed and celeration records.The results from this study reveal distinct driving behavior pattern and shed lights for further opportunities to identify behavior characteristics beyond safety and environmental considerations.

Studying Driving Risk Factors using Multi-Source Mobile Computing Data

Traffic congestion can largely be attributed to the issues related with driving behavior,which may cause vehicle crash,stop-and-go traffic due to frequent lane changing behaviors,etc.,and makes the driving behavior research also of significance in the realm of traffic management and demand management.The emergence and subsequent rapid advances with new information and communication technologies(ICT)now offers the capability of collecting high-fidelity and high-resolution trajectory data in a cost-effective manner.In this research,we use a smartphone app to collect data for the purpose of studying driving risk factors.What’s unique about the data in this research is its backend server also estimates traffic speed and volume for each link that the vehicle traverses.In order words,the data collected with build-in GPS modules in the smartphone include not only the vehicle spatial-temporal dimension location,which could be used to correlate the network geography attributes and/or real-time traffic condition,but also the detailed information about the vehicle dynamics including speed,acceleration,and deceleration,whereby a driver’s control and maneuver of a vehicle can be analyzed in detail.Such type of dataset combining both user trajectory and link speed/volume information is rarely seen in prior research,permitting a unique opportunity to link critical traffic congestion factors leading to driving behavior and crash potential.In this paper,the overall research framework used in this research is presented,which mainly includes data collection,data processing,calibration and analysis methodology.A preliminary case study-including data summary statistics and correlation analysis-is also presented.The results of our study will further existing knowledge about driving exposure factors that are closely linked to crash risk,and provide the foundation for advanced forms of Usage Based Insurance.

大小兴安岭地区森林生态城市带林业经济增长动力分析——基于Panel Data模型

大小兴安岭地区森林生态城市带具有大兴安岭地区、伊春市和黑河市3个核心发展区辐射周边区域,该城市带的建立有利于整合大兴安岭地区森林资源,发展区域林业经济优势产业。文章运用Panel Data模型对大小兴安岭地区林业经济增长动力进行分析,通过根据固定影响变系数模型计算,测试大小兴安岭地区林业经济增长的主动力,分别测算林业三次产业内部增长动力,根据大小兴安岭地区林业经济发展的实际情况,给出若干提升区域林业经济增长动力,促进林业经济发展的建议。

多源数据融合的山东半岛城市群建成区时空演变

随着城市化进程的推进,快速精准地提取城市群建成区,并深入剖析其时空特征,对未来城市的可持续发展至关重要。夜间灯光数据能够很好的对城市区域进行识别,现已广泛的用于城市建成区的提取,但是单一的灯光数据无法完全准确的对建成区进行提取,会导致建成区的提取误差。为了准确揭示山东半岛城市群时空演化特征和主导因素,本文运用了EVI(增强植被指数)、道路密度网和夜间灯光数据等多源数据融合的REANI指数对建成区进行提取,分析山东半岛城市群建成区的时空演变。通过REANI指数提取城市群建成区,结果表明,山东半岛城市群近从20年扩张速度和扩张强度稳步提升,形成以西部为主,东部为辅的单一扩张模式,并且城市群扩张受到经济因素最显著,社会、自然因素次之。

Driving Forces and Their Effects on Water Conservation Services in Forest Ecosystems in China

Identifying the driving forces that cause changes in forest ecosystem services related to water conservation is essential for the design of interventions that could enhance positive impacts as well as minimizing negative impacts. In this study, we propose an assessment concept framework model for indirect-direct-ecosystem service （IN-DI-ESS） driving forces within this context and method for index construction that considers the selection of a robust and parsimonious variable set. Factor analysis was integrated into two-stage data envelopment analysis （TS-DEA） to determine the driving forces and their effects on water conservation services in forest ecosystems at the provincial scale in China. The results showed the following. 1） Ten indicators with factor scores more than 0.8 were selected as the minimum data set. Four indicators comprising population density, per capita gross domestic product, irrigation efficiency, and per capita food consumption were the indirect driving factors, and six indicators comprising precipitation, farmland into forestry or pasture, forest cover, habitat area, water footprint, and wood extraction were the direct driving forces. 2） Spearman＇s rank correlation test was performed to compare the overall effectiveness in two periods： stage 1 and stage 2. The calculated coefficients were 0.245, 0.136, and 0.579, respectively, whereas the tabulated value was 0.562. This indicates that the driving forces obviously differed in terms of their contribution to the overall effectiveness and they caused changes in water conservation services in different stages. In terms of the variations in different driving force effects in the years 2000 and 2010, the overall, stage 1, and stage 2 variances were 0.020, 0.065, and 0.079 in 2000, respectively, and 0.018, 0.063, and 0.071 in 2010. This also indicates that heterogeneous driving force effects were obvious in the process during the same period. Identifying the driving forces that affect service changes and evaluating their efficiency have significant policy implications for the management of forest ecosystem services. Advanced effectiveness measures for weak regions could be improved in an appropriate manner. In this study, we showed that factor analysis coupled with TS-DEA based on the IN-D1-ESS framework can increase the parsimony of driving force indicators, as well as interpreting the interactions among indirect and direct driving forces with forest ecosystem water conservation services, and reducing the uncertainty related to the internal consistency during data selection.

基于ISODATA的实时动态电动汽车运行工况与耗电特性研究

针对电动汽车行驶工况进行研究是确定电动汽车能耗、电动汽车新型技术和评估的核心方法。采集电动汽车在实际道路行驶的数据,使用主成分分析算法(Principal Component Analysis, PCA)、迭代自组织数据分析算法(Iterative Self Organizing Data Analysis Techniques Algorithm, ISODATA)以及运动学片段分析法对实测的数据进行降维和聚类,利用Silhouette函数验证聚类结果的合理性。根据聚类中心的大小,筛选提取的运动学片段,构建电动汽车实际行驶的代表性工况,通过测试数据进行了差异性检验。在建立代表性工况的基础上,提出电动汽车能耗特性和电量实时估算方法。

A Study of the Driving Force Model Revealing Changes in Land Utilization Level Based on 3S Technologies ——The Example of Yuanmou, Yunnan, China

This paper introduced the theory and approaches of building driving forcemodels revealing the changes in land utilization level by integrating RS, GPS, and GIS technologiesbased on the example of Yuanmou County of Yunnan Province. We first created the land utilizationtype database, natural driving forces for land utilization database, and human driving forces forland utilization database. Then we obtained the dependent and the independent variables of changesin land utilization level by exploring various data. Lastly we screened major factors affectingchanges in land utilization level by using the powerful spatial correlation analysis and maincomponent analysis module of GIS and obtained a multivariable linear regression model of thechangesin land utilization level by using GIS spatial regression analysis module.

Persistent Data Layout in File Systems

Data layout in a file system is the organization of data stored in external storages. The data layout has a huge impact on performance of storage systems. We survey three main kinds of data layout in traditional file systems： in-place update file system, log-structured file system, and copy-on-write file sys- tem. Each file system has its own strengths and weaknesses under different circumstances. We also include a recent us- age of persistent layout in a file system that combines both flash memory and byte- addressable non- volatile memory. With this survey, we conclude that persistent data layout in file systems may evolve dramatically in the era of emerging non-volatile memory.

基于Filter-Hook Drive设计个人防火墙

结合Filter-Hook Driver的工作原理和特点,在Windows2000/XP下使用Filter-Hook Driver创建过滤函数,并利用设定好的过滤规则对访问的数据包进行过滤,实现防火墙的功能,给出程序实例。

Low Cost Driving Trainer Assistance System

The multiple tasks involved in real-time driving are challenging tasks for any new learner of driving. The proposed Low Cost Driving Trainer Assistance System (DTAS) helps the amateur drivers to learn the basic skills involved while driving a vehicle, in particular a 4 wheeler like a car. The proposed system not only helps the novice drivers to gain confidence but also saves money spent on fuels while learning. The proposed DTAS system uses a steering wheel, an accelerator pedal, a brake pedal, gear mechanism and virtual (simulated) road environment. We also monitor and record the vital system parameters during the training period and analyze the same. The proposed DTAS involves operations like taking a turn, braking, accelerating, using dashboard functions and changing gears.

Assessing Factors for Occurrence of Road Accidents in Tanzania Using Panel Data Analysis: Road Safety Perspective

This study assessed critical factors for road traffic accidents and associated mitigation to reduce the accidents by the year 2030. The study was guided by research questions, what are the major causatives of road accidents and how to mitigate the problem. The study used secondary data collected from the repository database of traffic police at the division of Tanzania Road Safety Squad. Data were collected at the events of accident occurrences and reported annually by regions. Panel data analysis was used to allow for controlling variables which cannot be observed over time and across areas such as regions. Pooled Poisson model, fixed effect and random effect Poisson model was applied to assess factors for road traffic accidents. Fixed effect model was the best model with a reasonably good fit. Results indicated that all predictors are significant under fixed effect Poisson model with a p-value less than 0.05 but Passengers and Railway crossing road was found insignificant and dropped in the final model. Laws and regulatory frameworks should be formulated and enforced promptly for Tanzania may reach the target of 2nd decade of action for roads safety 2021-2030.

How to Link Accident Data and Road Traffic Measurements to Enable ADAS/AD Simulation?

The progress of safety technologies,based on the continuous advances in vehicle crash worthiness,restraint systems and active safety functions made traffic safer than ever before.Latest developments heading from assisted Advanced Driver Assistance System(ADAS)to Automated Driving(AD),lead to more and more complex real-world situations to be handled,going from standard driving tasks up to critical situations,which may cause a collision.Therefore,throughout the development process of such systems,it becomes common to use simulation technologies in order to assess these systems in advance.To gain results out of the simulation,input data are required;typically,from various sources,so the requirements can be covered.Thus,the challenge of scoping with different input sources arises.To come along with that problem,two main kinds of input data will be needed in general:(1)the descriptive parameter covering all border conditions,so called parameter room;(2)the system specifications for estimation.The quality of the results correlates strongly with the quality of inputs given.In case of ADAS systems and AD functions,the second kind of input data is very well known.Major challenges relate to the first kind of input data.Thus,the paper will describe a way to create input data that cover all descriptive parameters needed from normal driving up to the collision by the combination of accident analysis and real-world road traffic observations.The method aims at being applicable to different data sources and to different countries.

Evaluation of Factors Affecting Driver’s Behaviors Using Association Rule

In this paper,association rule mining algorithm is utilized to analyze the correlations of various factors of causing traffic accidents,from which the relationship model of dangerous driving behaviors is established.In this model,the factors and their correlations include:ability of risk control,ability of driving self-confidence,individual characteristics,and incorrect driving operations.By selecting the drivers in the city of Chengdu to be the objects of investigation,a group of valid sample data is obtained.Based on these data,the Support and Confidence for association rules are analyzed.In the analysis,the two stage computing of Apriori algorithm programming is simulated,and from which some important rules are obtained.With these rules,departments of traffic administration can focus on these key factors in their processing of traffic transactions.By the training of drivers’skills and their physical and mental behaviors,the incorrect driving operations can be greatly reduced and the traffic safety can be effectively guaranteed.

引力理论框架下基于综合竞争力的自动驾驶拟人换道决策模型

为有效刻画城市快速路中自动驾驶环境下的车辆换道决策机理,考虑主车与周边车辆的位置属性、驾驶风格属性及车辆运动属性对主车换道行为的影响,建立基于综合竞争力的拟人换道决策模型。首先,以邻近前车间距、邻近前车速度差及驾驶风格这3种因素作为自动驾驶车辆的拟人化换道意愿属性,量化表征主车的换道意愿;然后,基于人类决策中的悲观主义准则,分析换道过程中周边车辆与主车可能产生的竞争行为,利用车头间距比和驾驶风格差异提出潜在竞争强度概念;其次,考虑环境稳定性对驾驶舒适性的影响,提出“速度伪距”“加速度伪距”概念,衡量换道后的环境稳定性;最后,结合引力理论建立以车辆横向速度为求解目标的综合竞争力换道决策模型。在模型标定中,筛选Ubiquitous Traffic Eyes开源数据集,得到非强制换道片段数据,利用蚁群算法标定模型参数。采用随机排列交叉验证方法进行验证,以正确率为模型精度和泛化能力的评价指标,并将其与传统模型进行对比。结果表明,将训练与验证比设置为72%∶28%、65%∶35%、57%∶43%和50%∶50%时,平均正确率区间为87.67%~90.34%,说明该模型具有较强的鲁棒性和可行性,相比于传统模型,本文模型具有更高的预测精度,可为自动驾驶环境下车辆的车道选择提供依据。

以学习操作系统构建数字化转型的数智动能

伴随着数据要素价值的凸显,如何以教育数据的有效治理,充分发挥数智驱动教育变革的强大潜力,成为教育数字化转型的关键所在。在智能教育大脑的隐喻下,学习技术系统依托于充当“数智大脑”角色的核心构件,搭建“数据组织—数据建模—数据分析”的教育数据治理通路,深度挖掘并最大化释放教育数据价值。基于此,文章以数据为主线重塑新一代学习技术系统框架,并以学习操作系统作为核心构件的隐喻,从“为何”“是何”及“如何”三个方面深度阐释其来源、内涵与体系架构,聚焦于数智动能的系统实现。同时,基于团队研发的“数智大脑”平台,文章以案例故事的形式描绘其在学校教育中的多元化使用场景,展现出以学习操作系统构建的数智动能的强大应用潜力,以期为数智驱动教育数字化转型提供全新的视角和思路。