Spatiotemporal traffic forecasting: review and proposed directions

This paper systematically reviews studies that forecast short-term traffic conditions using spatial dependence between links. We extract and synthesise 130 research papers, considering two perspectives: (1) methodological framework and (2) methods for capturing spatial information. Spatial information boosts the accuracy of prediction, particularly in congested traffic regimes and for longer horizons. Machine learning methods, which have attracted more attention in recent years, outperform the naïve statistical methods such as historical average and exponential smoothing. However, there is no guarantee of superiority when machine learning methods are compared with advanced statistical methods such as spatiotemporal autoregressive integrated moving average. As for the spatial dependency detection, a large gulf exists between the realistic spatial dependence of traffic links on a real network and the studied networks as follows: (1) studies capture spatial dependency of either adjacent or distant upstream and downstream links with the study link, (2) the spatially relevant links are selected either by prejudgment or by correlation-coefficient analysis, and (3) studies develop forecasting methods in a corridor test sample, where all links are connected sequentially together, assume a similarity between the behaviour of both parallel and adjacent links, and overlook the competitive nature of traffic links.     

In search of surrogate safety indicators for vulnerable road users: a review of surrogate safety indicators

Surrogate indicators are meant to be alternatives or complements of safety analyses based on accident records. These indicators are used to study critical traffic events that occur more frequently, making such incidents easier to analyse. This article provides an overview of existing surrogate indicators and specifically focuses on their merit for the analyses of vulnerable road users and the extent to which they have been validated by previous research. Each indicator is evaluated based on its ability to consider the collision risk, which can be further divided into the initial conditions of an event, the magnitude of any evasive action and the injury risk in any traffic event. The results show that various indicators and their combinations can reflect different aspects of any traffic event. However, no existing indicator seems to capture all aspects. Various studies have also focused on the validity of different indicators. However, due to the use of diverse approaches to validation, the large difference in how many locations were investigated and variations in the duration of observation at each location, it is difficult to compare and discuss the validity of the different surrogate safety indicators. Since no current indicator can properly reflect all the important aspects underlined in this article, the authors suggest that the choice of a suitable indicator in future surrogate safety studies should be made with considerations of the context-dependent suitability of the respective indicator.     

Assessing the impact of reduced visibility on traffic crash risk using microscopic data and surrogate safety measures

Due to the difficulty of obtaining accurate real-time visibility and vehicle based traffic data at the same time, there are only few research studies that addressed the impact of reduced visibility on traffic crash risk. This research was conducted based on a new visibility detection system by mounting visibility sensor arrays combined with adaptive learning modules to provide more accurate visibility detections. The vehicle-based detector, Wavetronix SmartSensor HD, was installed at the same place to collect traffic data. Reduced visibility due to fog were selected and analyzed by comparing them with clear cases to identify the differences based on several surrogate measures of safety under different visibility classes. Moreover, vehicles were divided into different types and the vehicles in different lanes were compared in order to identify whether the impact of reduced visibility due to fog on traffic crash risk varies depending on vehicle types and lanes. Log-Inverse Gaussian regression modeling was then applied to explore the relationship between time to collision and visibility together with other traffic parameters. Based on the accurate visibility and traffic data collected by the new visibility and traffic detection system, it was concluded that reduced visibility would significantly increase the traffic crash risk especially rear-end crashes and the impact on crash risk was different for different vehicle types and for different lanes. The results would be helpful to understand the change in traffic crash risk and crash contributing factors under fog conditions. We suggest implementing the algorithms in real-time and augmenting it with ITS measures such as VSL and DMS to reduce crash risk.     

客滚码头前沿功能区布置及交通组织仿真优化

以安全事故频发的客滚码头前沿区域作为研究对象,提出通过将该区域进行精细化布置以及合理交通组织的方式,提高码头前沿作业的安全性与便捷性。为获得区域行人密度、在港时间、行走距离及卸船作业时间等评价指标,建立客滚码头前沿作业系统微观仿真模型。并以某客滚码头泊位为例,分析码头前沿功能区布置及交通组织方案对客滚码头前沿作业评价指标的影响,并给出推荐方案。实例证明,客滚码头前沿功能区精细化布置及合理交通组织有利于减少安全事故,并提升前沿作业便捷性。     

多智能体仿真在LNG码头选址及港口规划中的应用

液化天然气(LNG)船舶进出港进行的严格交通管制会影响港口的运营,因此在港口规划阶段须审慎决策LNG码头选址及布局问题。应用自主开发的《基于多智能体信息交互的港口运营系统仿真软件(简称:APSS)V1.0》,建立可模拟涵盖LNG船舶通航影响机制的港口运营系统仿真模型;以实际工程为例,系统探讨LNG船舶通航对近、远期港口运营影响问题的思路和方法;结合水域通航环境特点提出远期优化措施。研究结果可辅助港口规划制定。     

基于Vissim仿真软件的交通组织方案研究

高速公路在改扩建作业过程中,必然对路段行车道有所侵占,形成分合流区段,而交通流发生紊乱往往就发生在分合流区段。本文以沪宁高速扩建无锡段桥梁施工为例,运用Vissim软件进行交通流微观仿真分析,对交通组织过程中最小施工路段间隔、中分带开口长度以及合流路段长度进行了研究,降低了因施工造成的对交通运行的影响。     

山区双车道公路驾驶员疲劳特性研究

山区双车道公路由于地形条件限制线形复杂多变,山体及路侧植被遮挡视线的现象时有发生,增加了驾驶操作的难度和强度.导致驾驶操作频繁、精神紧张.长时间高强度、大负荷的驾驶操作极易产生驾驶疲劳,进而发生交通事故.因此,研究山区双车道公路驾驶员的驾驶疲劳特性,可为山区公路的交通安全设计和管理提供参考,预防交通事故,保证人车安全.     

交通环境变化下的交通与经济互动性分析

交通和经济是区域的两大职能因素,它们是相互影响并且相互制约的。交通环境的变化将对区域经济产生重要的影响,同时经济发展也将进一步地刺激交通发展。以宜昌市为例,在分析宜昌交通环境变化及交通和经济发展的数据基础上,从对交通和经济相互影响的两个方面的分析,探讨了交通与经济的互动性。