Trend analysis of activity generation attributes over time

Activity generation models are relatively poorly developed in activity-based travel demand modelling frameworks. This research investigates whether observed distributions of activity attributes (activity frequency, start time and duration) used as inputs in the activity generation component of an activity-based travel demand model have changed over time. This research empirically examines changes in the distributions of activity generation attributes over time in the Greater Montreal area (GMA), Quebec, Canada. It also focuses on how these attributes vary with peoples’ socio-demographic characteristics. This research relies on the 1998, 2003 and 2008 origin–destination (O–D) household travel surveys of the GMA. The comparative analysis at three time points in a 10-year period clearly reveals that distributions of activity attributes are significantly changing over time. Work and school activities show similar trends; frequency “1” has increased and frequency “2+” has decreased over time. The occurrence of shopping activity on weekdays is decreasing over time. Start time and duration distributions for each activity have also changed significantly over time. The research allows preparing activity attributes for the application of an activity-based model, TASHA, such that they reflect temporal changes in travel behaviour of the GMA.     

城市水岸再开发:中国临水城市发展的下一个前沿

战结束后不久,水岸再开发这一概念就成为了西方国家关注的焦点,因为很多城市都开始考虑该如何对待那些已经变得如此刺眼的水岸区域.     

From broken windows to a renovated research agenda: A review of the literature on vandalism and graffiti in the rail industry

The execution of vandalism and graffiti on rail property has a significant impact on rail authorities, the patronisation of rail services, expenditure, and the timely operation of services. There are also important social costs which stem from passengers feeling unsafe, not to mention the environmental costs of removing graffiti and repairing vandalism. In this review paper, we focus on the social, non-technical determinants of and deterrents to vandalism and graffiti in the rail industry. First, we consider the definitions of graffiti and vandalism that are often conflated. After providing some clarification on terminology, and proposing a media-centred approach to vandalism and graffiti, we consider various theorisations of the psychosocial determinants of vandalism and graffiti behaviour. We then turn to an empirical discussion of different technical and social, non-technical prevention programmes that have been trialled. With a focus on identifying what works and under what circumstances, we refer to international case studies of successful vandalism reduction initiatives from Europe, the United Kingdom, the United States of America and Australia. Based on a review of literature and practice, we outline a future research agenda to address vandalism and graffiti. We recommend lines of further research covering: theory, empirical data collection and practical initiatives. Specifically, we note the need for a trans-theoretical model of vandalism and graffiti, further ethnographic research and improved evaluation and benchmarking strategies. This is the first review dedicated to the topic of vandalism and graffiti in the rail industry and the first review of non-technical, social deterrents to vandalism and graffiti broadly.     

International comparison of market risks across shipping-related industries

This paper compares the behaviour of shipping and shipping-related company stock returns to reveal whether systematic risk differs from the average in the market and across sub-sectors of the maritime industry. Following an extensive collection of information through a postal questionnaire survey, 108 publicly listed shipping and shipping-related companies, across stock exchanges of the world, are classified by sector according to their core business activity. The Capital Asset Pricing Model (CAPM) is employed for the period 1996-1999 to model stock returns and measure sector βs (systematic risk). Stock returns over the period are mostly negative. The systematic risks of the Drilling and Offshore sectors are significantly higher than those of all other sectors, but are not different from each other. There is no significant difference between the systematic risks of the Bulk, Tanker, Container and Ferry sectors. The systematic risk of the Cruise sector lies somewhere between these two groups. There is no difference in the systematic risk of companies that diversified within shipping or shipping-related industries when compared to companies that diversified in other areas. Over all companies in the sample, βis lower than the market average, and so are the βs of the Ferry, Tanker, Bulk, Container and Yard sectors. Only the βof the Drilling sector is statistically higher than one, while the Cruise, Diversified and Offshore sectors are statistically one.     

Leveraging socio-ecological resilience theory to build climate resilience in transport infrastructure

ABSTRACT

Anthropogenic climate change poses risks to transport infrastructure that include disrupted operations, reduced lifespan and increased reconstruction and maintenance costs. Efforts to decrease the vulnerability of transport networks have been largely limited to understanding projected risks through governance and administrative efforts. Where physical adaptation measures have been implemented, these have typically aligned with a traditional “engineering resilience” approach of increasing the strength and rigidity of assets to withstand the impacts of climate change and maintain a stable operating state. Such systems have limited agility and are susceptible to failure from “surprise events”. Addressing these limitations, this paper considers an alternate approach to resilience, inspired by natural ecosystems that sense conditions in real-time, embrace multi-functionality and evolve in response to changing environmental conditions. Such systems embrace and thrive on unpredictability and instability. This paper synthesises key literature in climate adaptation and socio-ecological resilience theory to propose a shift in paradigm for transport infrastructure design, construction and operation, towards engineered systems that can transform, evolve and internally manage vulnerability. The authors discuss the opportunity for biomimicry (innovation inspired by nature) as an enabling discipline for supporting resilient and regenerative infrastructure, introducing three potential tools and frameworks. The authors conclude the importance of leveraging socio-ecological resilience theory, building on the achievements in engineering resilience over the past century. These findings have immediate practical applications in redefining resilience approaches for new transport infrastructure projects and transport infrastructure renewal.     

ITS Solutions and Accident Risks: Prospective and Limitations

Abstract

This article investigates the prospective and limitations in the application of potential intelligent transport system (ITS) functions to reduce accident risks, using a cause‐treatment relationship. The main causes of road accidents are described and appropriate ITS solutions (including advanced driver assistance systems and advanced traveller information systems) are presented as countermeasures. Anticipated impacts are discussed and indicate that several ITS have the potential of improving road safety and addressing specific accident causes. However, attention is required on particular aspects of their implementation as they may trigger adverse effects by imposing behavioural adaptation risks, and overestimation and over‐reliance on system capabilities. Further, user acceptability and strategic implementation issues are paramount to the successful introduction of these systems.     

交通拥堵与温室气体排放

缓解拥堵项目可减少温室气体排放,但基于单个变量(如仅基于出行距离)的CO2排放预测并不精确.为了准确预测缓堵项目减少的CO2排放量,利用车辆运行检测技术、车辆活动数据库及根据车辆类型设计的排放模型,通过分析速度与排放的关系绘制速度-排放曲线,并结合交通运行检测数据,评价缓堵策略、速度管理策略、交通平滑策略等交通运行管理...     

Energy and emissions impacts of a freeway-based dynamic eco-driving system

Surface transportation consumes a vast quantity of fuel and accounts for about a third of the US CO₂ emissions. In addition to the use of more fuel-efficient vehicles and carbon-neutral alternative fuels, fuel consumption and CO₂ emissions can be lowered through a variety of strategies that reduce congestion, smooth traffic flow, and reduce excessive vehicle speeds. Eco-driving is one such strategy. It typically consists of changing a person’s driving behavior by providing general static advice to the driver (e.g. do not accelerate too quickly, reduce speeds, etc.). In this study, we investigate the concept of dynamic eco-driving, where advice is given in real-time to drivers changing traffic conditions in the vehicle’s vicinity. This dynamic strategy takes advantage of real-time traffic sensing and telematics, allowing for a traffic management system to monitor traffic speed, density, and flow, and then communicates advice in real-time back to the vehicles. By providing dynamic advice to drivers, approximately 10–20% in fuel savings and lower CO₂ emissions are possible without a significant increase in travel time. Based on simulations, it was found that in general, higher percentage reductions in fuel consumption and CO₂ emission occur during severe compared to less congested scenarios. Real-world experiments have also been carried out, showing similar reductions but to a slightly smaller degree.     

Cluster-based correlation of severe driving events with time and location

In this article, a systematic strategy is proposed to identify severe driving events occurrence correlation with time and location. The proposed approach, which is constructed based on batch clustering and real-time clustering techniques, incorporates historical and real-time data to predict the time and location of severe driving events. Batch clustering is implemented with the combination of subtractive clustering and fuzzy c-means clustering to generate clusters representing the initial correlation patterns. Real-time clustering is then developed to create and update real-time correlation patterns on the foundation of the batch clustering using the evolving Gustafson–Kessel like (eGKL) algorithm. In both clustering processes, the correlation of the events within time domain is identified first, and then two different levels of accurate correlations are conducted for the location domain. Real-time data of operating vehicles each equipped with a data acquisition and wireless communication platform are used to validate the proposed strategy. Batch clustering results reveal the severe braking events distribution and concentration at daytime and nighttime. Real-time clustering provides and updates the variation of the correlations/intercorrelation of different regions. Drivers can be notified of the potential severe driving locations through maps showing the driving routes. Through the variation of the correlations, drivers can recognize the events occurrence at different times and locations. The generated time series can be potentially used to develop spatial-time models for regions to model and forecast the events occurrence.