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。     

利用时空密度聚类的高速公路交通事故黑点路段鉴别

研究高速公路交通事故黑点路段的时空分布规律和关联因素,一直是交通领域的关注重点。本文针对事故统计的交通事故黑点路段鉴别方法存在地理学中的可塑面积单元(MAUP)问题,提出一种基于时空密度聚类的高速公路交通事故黑点路段鉴别方法。该方法改进了传统的DBSCAN空间聚类算法,引入一种顾及时间周期性和事故严重程度的事故时空邻近计算方法,通过密度连接规则自适应鉴别各种时空尺度的交通事故黑点路段。以2012—2016年湖南省的高速公路交通事故为例进行试验,结果表明,本文方法可有效克服不同划分单元的可塑面积单元问题,自适应鉴别不同长度的黑点路段,同时可进一步挖掘黑点路段上交通事故时空聚集模式。     

道路交通安全地理信息服务系统开发探讨

针对道路交通事故信息管理的具体要求和特点,提出了一种基于GIS(地理信息系统)技术的道路交通安全信息系统(GIS-RTSIS)的设计方案。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。     

城市道路交通事故时空分布模式分析

为分析城市道路交通事故的时空分布模式,本文基于网络空间线性最邻近指数和Knox时空检验方法,对武汉市青山区道路交通事故的热点路段分布以及时空交互模式进行检验。实验结果表明,城市道路交通事故在空间和时空层面均呈现显著聚集分布。事故热点主要分布在主干道和道路交叉口等人流量大和路网密集的区域,显著性时空交互现象发生于近时空区域内。本文结果可为交通事故防控、交通设施优化提供理论参考。     

MapX与PowerBuilder集成技术在交通事故信息系统中的应用

针对复杂的道路交通事故信息,利用MapInfo公司的组件式控件MapX5.0,在PowerBuilder8.0开发环境下,使用SQLServer2005作为系统后台属性数据库,研究路段采用高分辨率的遥感数据为地图背景,设计开发了城市道路交通事故信息系统。该系统具有城市道路交通信息与道路交通事故信息的查询、分析和统计功能,使道路交通事故信息查询更迅速、直观。实际开发表明,PowerBuilder与地图控件MapX结合开发GIS应用系统,不仅开发过程简单、开发速度快,而且能充分发挥PowerBuilder快速高效管理、访问MapX空间数据库的优势。     

基于空间回归模型的道路交通事故影响因子分析

为分析影响交通事故的主要道路影响因子,选取道路结构、道路附属设施、交通基础设施等方面的16个解释变量,考虑交通事故分布的过度分散性、零膨胀性及空间相关性,构建空间滞后-零膨胀负二项回归模型,并利用赤池信息准则（Akaike information criterion,AIC）和贝叶斯信息准则（Bayesian information criterion,BIC）、模型残差的空间依赖性检验等指标将其与常用的普通最小二乘（ordinary least square,OLS）等回归模型进行对比。结果显示,所提模型的拟合效果最优;车道数、道路宽度、平均车速等因子对交通事故的发生具有显著影响。     

道路交通事故现场快速测绘系统

介绍道路交通事故现场快速测绘系统的设计原理、系统构成、系统功能和主要技术。     

交通安全分析评价GIS的设计与实现

基于面向对象思想完成了交通安全分析评价GIS系统的概要框架设计、详细设计以及数据库设计,提出选用C#语言结合SuperMap Objects组件在Microsoft.NET平台上实现系统开发的技术方案。交通安全专业分析模型以可执行程序或动态链接库的形式与GIS进行集成。系统主要功能包括:交通事故数据库建立、事故数据录入及编辑、动态分段、黑点鉴别、评价指标统计、事故严重程度分级、事故时间/成因分布分析、多元线性回归等。     

上期幸运读者名单

2003年暑假,丹巴境内发生泥石流,两对来此旅游的青年教师夫妇不幸遇难。为此我曾撰写过一篇短文强调为防万一,到川西高原旅游之前,须就地势、天气和道路状况作一基础性的了解——看来此文还不是“无的放矢”。就在2004年国庆节期间,此地接连发生两起重大交通事故:一广州游客自驾车坠入大金川,死3人;一重庆大巴在去九寨沟的途中于平武境内坠河,死21人。此外,不久前丹巴境内还发生过岩崩。所以,川西高原有诱人的一面,也有不尽理想的一面,陌生人进入须谨慎从事。因看到《地图》2005年第2期的《丹巴,一朵暗香梅》一文,特此提醒。王祖荫/四川成都…     

智能出行重塑人车路关系

不断加速的城市化进程加剧了交通供需矛盾,道路拥堵、交通事故、资源过度消耗,成为摆在交通管理部门和用户出行面前的三个难题。数据显示,在所有获取交通信息方式中,通过手机地图APP获取交通信息的高达72.3%。因此,以移动导航应用为抓手的"智能+出行"模式,就成为连接出行者与管理者的关键。     

Traffic safety prediction model for identifying spatial degrees of exposure to the risk of road accidents based on fuzzy logic approach

This article, we propose a traffic accident prediction system based on fuzzy logic which allows to identify ‘the degree of exposure to road accidents’ risk’ and to analyse the level of complexity of the factors involved. We focus our study on the possible influence of a series of local criteria observed and selected for each kilometre per segment of the road network studied. The study was conducted on a road network within the rural area of the Wilaya of Mascara in the north-western region of Algeria. A Geographic Information System was integrated into the analysis process to enable a spatial visualization of the degrees of exposure to road accidents’ risk, providing a cartographically measurable solution to establish and attenuate accident risk. Results show that the developed system can be effectively applied as an useful Road Safety tool capable of identifying risk factors related to the characteristics of the road.     

基于介数中心性的交通拥堵指数计算

本文以北京城市交通为例,选取北京城市路网数据,计算城市路网介数中心性,以反映城市各道路路段的通达情况;并利用北京出租车GPS定点数据,计算实际的交通轨迹.传统的路网介数中心性主要依据各路段最短路径的比重评价理论上的道路通达性,而本文以网络介数中心性为基础,提出动态介数的方法,从城市各路段交通量比重的角度评价各道路实际拥...     

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.     

利用词向量模型分析城市道路交通空间相关性

刻画城市道路之间的交通相关性是提高交通插值及预测水平的基础。现有研究及应用通常假设一定空间或拓扑距离内的道路相互之间具有相关性,这种方式忽视了道路之间交通影响的时空异质性。例如,上游道路交通流通常不会均匀扩散到所有下游道路,而是集中在特定方向上。道路之间产生交通影响和交互作用的根本原因是大量机动车辆穿梭其中。为从数据驱动的角度度量道路之间的交通相关性,从而顾及其时空异质性,本文利用词向量模型Word2Vec从大量机动车出行路径中挖掘道路之间的交通交互影响关系。首先把"路段-路径"类比为"词-文档";其次利用Word2Vec模型从大量路径(文档)中为每条路段(词)训练出一个实数向量(词向量);然后以向量之间的余弦相似度度量对应路段之间的交通相关性;最后利用交通状态数据对结果进行验证。以北京市200万条出租车出行路径为数据进行试验,结果表明:(1)平均水平上,向量相似度越高的邻近路段,其交通状态变化趋势也越相似,证明了本文方法可以正确度量道路之间的交通相关性,并刻画出其空间异质性;(2)工作日早、晚高峰及节假日路段之间的交通相关性大于工作日平峰和周六日,其合理性体现了本文方法可以正确捕捉道路交通相关性的时间异质性。本文方法及分析可为交通规划、诱导等提供方法论和理论基础。     

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.     

From taxonomies to ontologies: formalizing generalization knowledge for on-demand mapping

Automation of the cartographic design process is central to the delivery of bespoke maps via the web. In this paper, ontological modeling is used to explicitly represent and articulate the knowledge used in this decision-making process. A use case focuses on the visualization of road traffic accident data as a way of illustrating how ontologies provide a framework by which salient and contextual information can be integrated in a meaningful manner. Such systems are in anticipation of web-based services in which the user knows what they need, but do not have the cartographic ability to get what they want.     

基于道路标线布设方案的交通路网数据库构建研究

道路标线是交通规则的载体,它们布设在物理道路网络上为交通参与者分配路权,并规范其行为。通常,称这种加载了交通规则的物理道路网络为交通路网。本文首先提出了物理道路网络的表示模型;然后结合线性参考技术,以物理道路网络为基础,构建了基于的GIS道路标线布设方案数据模型;最后,本文选取示范路网,运用道路标线布设方案数据库,进行路网交通规则的推导,构建了基于车道级路网数据模型的交通路网数据库,为交通路网数据库的构建提供了一种参考。     

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.