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.     

Intermodal transport in freight distribution: a literature review

There has been significant growth in research on intermodal transport in freight distribution since the 1990s. Differentiating itself from previously published literature reviews, this paper evaluates the current state of this research using Systematic Literature Review methodology. The complementary aims are: (a) to identify the research lines developed and to propose a criterion for classifying the literature, and (b) to discuss the empirical evidence that identifies existing interrelationships. The analysis has enabled three main lines of research to be identified. The first research line, basic principles of intermodal transport, groups together works related to the definition of intermodal transport and the results obtained using this transportation system. The second, improvements to the way that intermodal transport systems work, frames elements and variables that impact intermodal transport systems’ logistics efficiency, such as quality of service, information and communication systems, and freight planning and linkages among system operators to provide an adequate service. Finally, the third line, intermodal transport system modelling, identifies the main variables used to optimise these transport systems, the different focuses and approaches used in modelling, and the advantages and disadvantages of each focus. These research lines take in more specific sublines that incorporate articles that develop related research questions. Lastly, the discussion of the content of each of these research sublines enables us to identify gaps in the literature and comment on directions for future research.     

Long-term impact of the Shinkansen on rail and air demand: analysis with data from Northeast Japan

This paper analyses how the high-speed rail construction in Northeast Japan (Tohoku) has affected total demand and interregional travel patterns. We use annual interregional passenger data from 1989 to 2012 and apply regression analysis with the demand between Tokyo and the Tohoku prefectures as the dependent variable. We distinguish particularly between the ‘Full-’ and the ‘Mini-’ Shinkansen, where the latter are branch services running with reduced speed. We find that the ‘Full-Shinkansen’ quickly increases rail and total public transport trips and generates additional rail demand year on year. The ‘Mini-Shinkansen’ impacts are less pronounced. Furthermore, our analysis shows that the Shinkansen has shifted some demand from air to rail once it started operation and increased rail share gradually. We therefore suggest that predictions of demand impacts should carefully distinguish immediate from gradual impacts. We also discuss differences in regional demand in that not all prefectures have gained equally from Shinkansen construction.     

Genetics of traffic assignment models for strategic transport planning

This paper presents a review and classification of traffic assignment models for strategic transport planning purposes by using concepts analogous to genetics in biology. Traffic assignment models share the same theoretical framework (DNA), but differ in capability (genes). We argue that all traffic assignment models can be described by three genes. The first gene determines the spatial capability (unrestricted, capacity restrained, capacity constrained, and capacity and storage constrained) described by four spatial assumptions (shape of the fundamental diagram, capacity constraints, storage constraints, and turn flow restrictions). The second gene determines the temporal capability (static, semi-dynamic, and dynamic) described by three temporal assumptions (wave speeds, vehicle propagation speeds, and residual traffic transfer). The third gene determines the behavioural capability (all-or-nothing, one shot, and equilibrium) described by two behavioural assumptions (decision-making and travel time consideration). This classification provides a deeper understanding of the often implicit assumptions made in traffic assignment models described in the literature. It further allows for comparing different models in terms of functionality, and paves the way for developing novel traffic assignment models.     

A logit-based model for measuring the effects of transportation infrastructure on land value

Mutual interactions between transportation and land use have long been debated. Despite progress made in computational technology, the study of these interactions is not adequately developed. The most important aspect of such interactions is given by the changes in land values due to changes in transportation infrastructures. We consider the behavioural features of these interactions along with the constraints on the land and/or zoning restrictions and propose a reliable model for the first time to predict land value changes with respect to changes in transportation facilities and accessibility. The proposed model is a logit-based mathematical programming methodology where the relative price of land is predicted with respect to transportation accessibility, neighbourhood amenities, location premium, availability of land, and zoning regulations. A real-world case study is used to exhibit the applicability of the proposed methodology and demonstrate the efficacy of the algorithms and procedures.     

Drivers of transit service loyalty considering heterogeneity between user segments

Users’ loyalty to public transit service is fundamental to promote its popularity in the transportation market. A four-step analytical framework is advanced to investigate the importance of service attributes that heterogeneous transit user segments place on their public transit service loyalty, measured in terms of overall satisfaction and re-use intention. Critical service attributes perceived by transit users that are relevant for loyalty enhancement are explicitly determined, which vary between user segments. It is suggested that the design of strategies aimed to promote the use of public transit by increasing user loyalty towards transit service be targeted at specific attributes that contribute most to loyalty and specific user segments whose original loyalty level is significantly different to others.     

基于改进云模型的地铁站行人拥挤辨识方法

为降低城市地铁站内行人拥挤识别时多指标和多等级带来的不确定性和模糊性,采用改进云模型构建行人拥挤状态辨识模型。基于AHP-熵权法标定指标权重和行人拥挤状态等级,以标定的指标权重和各指标等级阈值作为输入,计算云数字特征值,采用正向正态云发生器,建立模板云和待识别云模型,计算两者之间的隶属度,根据最大隶属度原则辨识车站内各服务设施的行人拥挤状态,最后输出各服务设施的拥挤等级辨识结果。以宁波市鼓楼站为实证对象,对辨识结果进行实证分析。结果表明:采用四级行人拥挤状态划分方法合理可行。     

洋流作用下悬浮隧道动力学行为试验研究

本研究根据相似准则和试验条件设计试验模型。在试验模拟的1节悬浮隧道管段上分别布置2组和3组锚索支撑,测试在不同流速条件下,悬浮隧道管体结构的环向应变、轴向应变及张力腿锚索的轴力。根据试验结果,初步分析悬浮隧道应力的空间分布特征和张力腿的轴力情况;给出在相同环境条件下,张力腿锚索数量对结构应力的影响、洋流速度对悬浮隧道管段结构变形和张力腿轴力的影响关系等。     

平面二维k-ε紊流模型不同壁函数的对比及研究

在平面二维紊流模型中壁函数起到连接流固的桥梁作用,克服了边界附近网格过密的困难,也是紊流模型应用中的一个重要环节,因此壁函数的选择对模型的模拟结果起着至关重要的作用。将3种不同形式的壁函数引入平面二维k-ε模型中,对丁坝绕流进行了数值计算,并将计算结果与实测数据进行了对比,从流场、流速、紊动动能分布及紊动耗散率等方面对3种壁函数进行优选验证,结果表明对数率壁函数优于其他两种形式的壁函数,其数值计算结果更接近实测数据。     

鸭绿江河口潮流泥沙数值模拟

在分析鸭绿江河口实测水文泥沙资料的基础上,采用MIKE21 FM模块建立了基于多重嵌套网格模式的河口潮流场和泥沙场数学模型,模型范围为上游丹东市的江桥至河口江海分界线以外6~7 m等深线附近,并对2个潮位站、15个流速流向和含沙量测站进行了验证,对现状条件下的河口水域流场和泥沙场进行了数值模拟与分析。研究结果表明:落潮流速一般大于涨潮,沿程变化上浪头港以下2 km附近及斗流浦航槽内平均流速较大,至河口段水面逐渐放宽,致使落潮水流流速有减小趋势;含沙量沿程呈递增趋势,最大含沙量出现在各分汊水道的上口,即斗流浦水域。