基于GIS与主成份分析的道路交通事故黑点研究

为准确找出道路交通事故发生的原因,提出了一种基于主成份分析的黑点识别方法,并将GIS相关技术应用到道路交通事故黑点研究中。通过实实例,验证所述的黑点研究方法能够为交通管理部门制定减少交通事故发生,提高道路安全水平的措施提供决策支持。     

基于Android的地下管线巡检系统的设计与实现

近年来,城市地下管线事故频繁发生,造成了巨大的人员和财产损失。为了保障城市地下管线的安全运行,加强对城市地下管网的全方位掌控,本文基于Android平台,结合GIS技术、三维建模技术、GPS技术和3G无线通信技术,采用Java语言设计和开发了一款地下管线巡检终端。此外,通过在远程服务器上部署一套巡检管理系统,成功实现了与移动端的信息交互。分析表明,该巡检系统具有直观显示城市地下管线的空间层次和位置,精确、迅速定位事故地点,以及控制地下管线事故风险等特点,适合大规模推广利用。     

Collaborative Web‐GIS Design: A Case Study for Road Risk Analysis and Monitoring

This article presents a methodology for designing a WebGIS framework intended for automatically analyzing spatial data and updating statistics of interest with new information inserted daily by multiple users via a Web portal. A practical example is used on vehicle accident data for assessing risk in specific road segments. Two main blocks integrated together will be described: the collaborative block and the data‐analysis block. The former gives end‐users computer‐aided tools to view, insert, modify and manage data related to accidents and traffic monitoring sensors, whereas the latter is developed to automatically analyze the accident data coming from user's collaboration. Because different agencies can survey accident sites, a collaborative environment is necessary – and a Web‐based solution is ideal – for permitting multi‐user access and data insertion. A centralized approach to process the data in real time is described in all its components. Server‐side Structured Query Language functions optimize performance by using dedicated libraries for spatial processing and re‐structuring the attributes associated with elements which are consequently re‐classified for correct color‐scaling. The end‐product is a system that provides a seamless integration of front‐end tools for user collaboration and back‐end tools to update accident risk statistics in real time and provide them to stakeholders.     

二三维一体化城市综合管网系统设计与实现

2014年6月,国务院办公厅印发了《关于加强城市地下管线建设管理的指导意见》,提出"2015年底之前,完成城市地下管线普查,建立综合管理信息系统"。本文以此为契机,提出了基于普查完成的管线数据成果,建立城市二三维一体化综合管网系统。该系统能够满足政府管理部门的需求,确保地下管线的建设适应社会发展的需要,降低管网事故率,避免重大事故发生,大幅度提升应急防灾能力。     

基于GIS技术的道路交通安全信息系统研究

以GIS技术为基础,针对道路交通事故信息管理的具体要求和特点,分析交通事故的发生原因、概率及各种隐患表现的定性或定量具体内容,利用GIS—RTSIS对交通事故进行分析,标示事故黑点信息和空间分布,并研究引发交通事故的各种因素,在有效掌握道路交通安全系统问题的同时,找到预防交通安全事故发生的各种途径,为道路安全评价和控制提供决策依据。文章以哈尔滨市GIS—RTSIS模拟示范工程为例,详细探讨了有关基于GIS的道路交通安全信息系统的框架设计、系统研制及开发思路。     

基于百度地图的国家安全生产事故时空分析技术

国家安全生产事故信息化管理和分析是关系国家资产和群众安全的重要工作。针对国家安全生产事故信息化管理在时空分析上的薄弱环节,文中以国家安监局提供的2000—2015年国家安全生产事故数据为支撑,研究基于百度在线地址解析服务的事故空间位置在线解析技术,采用百度地图热力图可视化技术对国家安全生产事故进行时空分析,为政府部门管理和预防安全生产事故提供决策支持。     

Improving Spatial Accuracy of Roadway Networks and Geocoded Addresses

Exposure to traffic‐related pollutants is associated with both morbidity and mortality. Because vehicle‐exhaust are highly localized, within a few hundred meters of heavily traveled roadways, highly accurate spatial data are critical in studies concerned with exposure to vehicle emissions. We compared the positional accuracy of a widely used U.S. Geological Survey (USGS) roadway network containing traffic activity data versus a global positioning system (GPS)‐validated road network without traffic information; developed a geographical information system (GIS)‐based methodology for producing improved roadway data associated with traffic activities; evaluated errors from geocoding processes; and used the CALINE4 dispersion model to demonstrate potential exposure misclassifications due to inaccurate roadway data or incorrectly geocoded addresses. The GIS‐based algorithm we developed was effective in transferring vehicle activity information from the less accurate USGS roadway network to a GPS‐accurate road network, with a match rate exceeding 95%. Large discrepancies, up to hundreds of meters, were found between the two roadway networks, with the GPS‐validated network having higher spatial accuracy. In addition, identifying and correcting errors associated with geocoding resulted in improved address matching. We demonstrated that discrepancies in roadway geometry and geocoding errors, can lead to serious exposure misclassifications, up to an order of magnitude in assigned pollutant concentrations.     

层次分析法支持下的道路洪涝灾害风险评价——以武夷山地区为例

本文首先采用基于多准则决策的层次分析评价法,根据自然灾害风险理论,将洪涝风险影响因子分为危险性和脆弱性两类,子准则层包括平均降雨量、汇流累积量、坡度、海拔和土地覆盖度、道路级别、地表产流能力7个因子,构建了道路洪涝灾害风险评价模型。然后以福建省武夷山地区为研究区,利用地形数据、气象数据及遥感影像提取土地覆盖类型数据,通过道路洪涝灾害风险评价模型,绘制了道路风险分区图。结果表明,中、高风险积水道路占比较高,主要集中在东部、西部和中南部地区。本文对道路洪涝灾害风险所进行的评价,可服务于洪涝灾害风险预警和应急救援规划。     

Multi-objective route planning for dangerous goods using compromise programming

The transportation of dangerous goods (DG) can significantly affect the human and natural environment if accidents occur during the transportation process. Hong Kong is a large city with high population density and narrow streets. Due to the land constraints, vehicles carrying DG inevitably have to pass through densely populated areas or their vicinities. Therefore, safe DG transportation is of paramount importance. There is thus an urgent need to review and improve the way trucks carrying DG are being routed on the road networks. Routing of such vehicles should consider not only the operating cost, but also the safety of travelers in the network, the population potentially exposed, and the possible damage inflicted to the surrounding properties and facilities in the event of a DG incident. This research develops a novel methodology for the determination of optimal routes for DG transportation under conflicting objectives by means of the compromise programming approach. With the support of geographical information system (GIS), a case study is carried out for the transportation of DG in the road network of Hong Kong. The experimental results confirm the effectiveness of the proposed approach.     

A novel framework for detecting non-recurrent road traffic anomalies by combining a temporal graph convolutional network and hierarchical time memory detector

A non-recurrent road traffic anomaly refers to a sudden change in the capacity of a road segment, which deviates from the general traffic patterns, and is usually caused by abnormal traffic events such as traffic accidents and unexpected road maintenance. Timely and accurate detection of non-recurrent road traffic anomalies facilitates immediate handling to reduce the wastage of resources and the risk of secondary accidents. Compared with other types of traffic anomaly detection methods, prediction algorithms are suitable for detecting non-recurrent anomalies for their potential ability to distinguish non-recurrent anomalies from recurrent congestion (e.g., rush hours). A typical prediction algorithm detects an anomaly when the difference between the predicted traffic parameter (i.e., speed) and the actual one is greater than a threshold. However, the subjective setting of thresholds in many prediction algorithms greatly affects the detection performance. This study proposes a novel framework for non-recurrent road traffic anomaly detection (NRRTAD). The temporal graph convolutional network (T-GCN) model acts as the predictor to learn the general traffic patterns of road segments by capturing both the topological effects and temporal patterns of traffic flows, and to predict the “normal” traffic speeds. The hierarchical time memory detector (HTM-detector) algorithm acts as the detector to evaluate the differences between the predicted speeds and the actual speeds to detect non-recurrent anomalies without setting a threshold. In the experiments with traffic datasets of Beijing, NRRTAD outperformed other methods, not only achieving the highest detection rates but also exhibiting higher resilience to noise. The main advantages of NRRTAD are as follows: (1) adopting the T-GCN with a weighted graph to integrate differentiated connection strengths of multiple types of topological relations between road segments as well as temporal traffic patterns improves the prediction performance; and (2) utilizing a flexible mechanism in the HTM-detector to adapt to changing stream data not only avoids subjective setting of a threshold, but also improves the accuracy and robustness of anomaly detection.     

基于灰度和方向一致性的遥感影像道路网提取

从遥感影像中提取道路网是一个经典课题,根据遥感影像中的道路具有灰度和方向一致性的特征,提出一种从遥感影像中提取道路网的新方法。首先根据遥感影像的灰度信息和方向方差信息建立灰度和方向一致性准则分割模型,由此可从遥感影像中提取基本的道路网轮廓,然后再针对道路区域存在非道路点:空洞和裂缝等情况,采用膨胀、腐蚀等操作去除杂乱物块,最后通过数学形态学操作提取出道路网。实验结果表明,该方法能够适用道路、建筑物、植被等多种复杂地物的城市遥感影像中提取道路网,且能获得较好地提取效果。     

一种支持城市路网交通关系自动化的智能模型

针对车载导航、地图网站等应用中路网要素之间交通关系维护的难题,提出一种支持路网要素交通关系自动化的智能过程模型,将路网要素交通关系自动化过程理解为由路网要素间的空间和语义关系、规则集和控制系统组成的产生式系统.该产生式系统可在路网要素空间和语义信息基础上,通过定义可扩展的交通关系规则集,自动化地生成符合应用需求的路网要素间的交通关系.此外,在该产生式系统中引入触发器概念监控和响应几何网络及其交通关系规则集的变更,实时更新逻辑网络中的连通关系信息,实现几何网络、规则集和逻辑网络三者的一致性.同时,提出路网要素交通关系处理的控制策略和关键流程,并对该智能网络模型的有效性进行了验证.

Abstract：

Maintaining the traffic connectivity relations between road features has always been a time consuming task for in-vehicle navigation, map website, and other traveling service related applications. Such a task has been commonly conducted artificially and inevitably inefficient, yet makes data quality control difficult. Considering the intrinsic rules of traffic connectivity formed by the geometrical structures, spatial and semantic relationships between road features in city road networks, an intelligent processing model is set forward in this paper for traffic connectivity automation. It is argued that traffic connectivity automation between road features is fundamentally a production system composed of the spatial and semantic relations between road features, connectivity rules and control system. With the implementation of an extendable connectivity rule set, the traffic connectivity relations between road network features are built automatically based on the spatial and semantic information of road network. The trigger concept is adopted to monitor and respond the changes in geometrical network and connectivity rules, and then dynamically updates the traffic connectivity between road network features in logical network so as to guarantee the consistence between geometrical network, connectivity rules and logical network. A series of control strategies and a conducting engine are developed to maintain the traffic connectivity relations. A case study conducted on a real road network verifies the effects of the proposed intelligent model.     

顾及上下级空间关系相似性的道路网联动匹配方法

现有道路网匹配方法中,大多利用道路自身结点和弧段特征进行匹配,而较少注意道路邻域要素在道路网匹配中的重要定位参考作用,从而影响匹配效率和正确率的进一步提高。针对上述问题,本文提出了一种顾及上下级空间关系相似性的道路网联动匹配方法,即模仿人在读图时通过特征地物和空间关联寻找目标地物的思维过程,将匹配看作是一种特征目标寻找、信息关联传递的推理过程。首先,运用Stroke技术将复杂道路网进行等级划分。其次,通过道路骨架关联关系树构建道路网联动匹配模型。最后,选取高等级骨干道路作为起始特征对象,计算道路间的上下级空间关系相似性,逐级迭代使匹配信息在道路网联动匹配模型中传递,从而得到匹配结果。试验表明,本文算法缩小了待匹配数据的搜索范围,能够有效提高匹配正确率和效率,尤其在数据位移较大、存在非系统性几何位置偏差的情况下优势明显。     

对偶图节点重要度的道路网自动选取方法

采用对偶拓扑方法构建基于广义路网拓扑的复杂交通网络,通过引入m阶邻居节点概念,考虑复杂交通网络中节点度、介中心及节点间距离等因素,顾及节点自身及1到m阶邻居节点的重要度贡献,定义了节点重要度评价模型,进而提出一种基于m阶邻居节点重要度贡献的道路网自动选取方法,并基于网络最小规模原则保持选取道路的连通性。为检验方法的有效性,引入度分布评价所选取道路对路网整体结构及拓扑特性的保持程度,并采用实际城市路网进行试验,结果表明本文方法选取的路网能较好地保持了原始路网的整体结构、拓扑特性及道路连通性,且算法稳定、可靠。     

AIS船舶污染风险定量评估模型

针对目前海上船舶污染风险评估模型的不足,本文提出一种基于船舶自动识别系统(AIS)的海上船舶污染风险定量评估数学模型。该模型的特点是对特定海域船舶污染历史事故进行统计分析,通过AIS中的信息对船舶事故频率进行建模及数值计算;并且模型考虑到事故海域资源类型,船舶种类、大小、风险程度以及污染事故等级等因素,使污染风险评估更科学化和定量化。     

宝鸡市道路几何网络模型的建立与应用

主要是结合MapGIS6.5软件对宝鸡市的道路网进行网络分析,进而在此基础上对该系统进行二次开发,建立道路网数据库,为宝鸡市的城市建设、交通规划以及道路管理提供空间数据;同时也为人们的旅游出行提供方便;更为智能交通和“数字宝鸡”的建设提供地理空间基础数据。通过对宝鸡市几何道路模型的研究,探索出一条适合中小城市空间信息应用和地理数据资源集成共享的模式与解决方案。     

Determining the road traffic accident hotspots using GIS-based temporal-spatial statistical analytic techniques in Hanoi,Vietnam

ABSTRACT

This study applied GIS-based statistical analytic techniques to investigate the influence of accident Severity Index (SI) on temporal-spatial patterns of accident hotspots related to the specific time intervals of day and seasons. Road Traffic Accident (RTA) data in 3 years (2015 ? 2017) in Hanoi, Vietnam were used to analyze and test this approach. Firstly, the RTA data were divided into four seasons in accordance with Hanoi’s weather conditions and the time intervals such as the daytime, nighttime, or peak hours. Then, the Kernel Density Estimation (KDE) method was applied to analyze hotspots according to the time intervals and seasons. Finally, the results were presented by using the comap technique. This study considered both analyses with and without SI. The accident SI measures the seriousness of an accident. The approach method is to give higher weights to the more serious accidents, but not with the extremely high values calculated on a direct rate to the accident expenditures. The results showed that both analyses determined the relatively similar hotspots, but the rankings of some hotspots were quite different due to the integration of SI. It is better to take into account SI in determining RTA hotspots because the gained results are more precise and the rankings of hotspots are more accurate. From there, the traffic authorities can easily understand the causes behind each accident and provide reasonable solutions to solve the most dangerous hotspots in case of limited budget and resources appropriately. This is also the first study about this issue in Vietnam, so the contribution of the article will help the traffic authorities easily solve this problem not only in Hanoi but also in other cities.     

道路网层次骨架控制的道路选取方法

提出了基于路网层次骨架控制的道路自动选取方法。首先,该方法以stroke为单位,利用中介中心性值对stroke进行层次结构划分;其次,采用结构特征识别完善道路层次骨架;第三,基于层次骨架间的连通关系建立逐层传递的stroke重要性计算模型;最后,通过该计算模型自上而下的逐层计算,得到stroke重要性,并据此完成道路网选取。采用3种典型道路网数据,对本方法与其他主要常规stroke选取方法进行对比,试验结果表明本方法不但对语义的依赖性极低,同时消除了中介中心性在评价道路重要性时对边缘道路的不利影响,在保持道路网整体结构和层次性上有较为明显的优势,可适用于各种形态的道路网选取。     

改进蚁群算法支持下的交通流量分配

灾后城市交通运输能力下降,原有的流量分配方案不再适用。为保障正常的经济社会活动,本文提出了一种基于改进蚁群算法的交通流量分配方法。首先评估路网通行能力影响因素并建立道路质量评价体系,利用路段质量改进蚁群算法中的启发式因子;然后为扩大蚁群搜索范围加入随机节点并改进信息素的更新机制;最后应用改进算法对城市交通总量进行分批分配并得到流量分配图。结果表明,改进算法综合考虑了出行距离和道路质量,较改进前更符合交通流量分配要求,具有较好的路径寻优性,可为灾后救援工作和灾后路网交通分配决策提供建议和支持。     

北京交通网络拓扑结构及可达性格局历史变化研究

基于过去800年（1160～2000年）北京城市交通网络时间序列数据,研究了北京交通网络的发展变化。主要从交通网络的拓扑结构、可达性等角度分别对网络直径、平均最短路径长度、平均聚集指数、相对可达性等指标进行了定性定量分析。研究结果表明：第一,北京交通网络扩张呈“两段式”特点：前数百年只加密路网,近53年网络直径扩张了2．62倍;第二,从网络拓扑结构的角度分析,北京过去800年交通网络度分布结构呈现良好的一致性,交通网络中顶点与路段的比例保持不变,网络内部联系日趋紧密;第三,北京交通网络可达性格局趋于有序：从金元时期的不规则格局发展为明清时期的椭圆形格局,建国后呈圆盘形格局扩张,但可达性中心由天安门向西北方向偏移至阜成门处。通过搜集研究北京过去800年的交通网络数据,本文记录了北京交通网络演化的全过程,并揭示了其演化过程中蕴含的宏观规律。