Greenhouse gas from ridership on the Jubilee Line Extension

This is the final version of the article.It first appeared form Taylor & Francis via https://doi.org/10.1680/jensu.15.00015This paper examines changes in travel behaviour associated with ridership on the Jubilee Line Extension in east London and the resulting impacts on greenhouse gas (GHG) emissions. The paper looks at initial changes in mode choice after the line opened in 1999 and on-going mode share trends through to 2011. The initial mode shift is assessed through an analysis of published travel survey data and the annual TfL Rolling Origin Destination Survey. Longitudinal changes in mode share are assessed using the London Travel Demand Survey and the relationship between metro accessibility and mode choice. From 2000 to 2011 the calculated GHG savings are 338 ktCO2e; approximately equivalent to the annual average GHG emissions of 43,000 UK residents.The authors would like to thank The Commonwealth Scholarship Commission in the UK for the scholarship funding that facilitated this work

Ground source heat storage and thermo-physical response of soft clay

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 137-141).Ground source heat storage can condition buildings with reduced consumption of fossil fuels, an important issue in modem building design. However, seasonal heat storage can cause soil temperature fluctuations and possibly deformation of soft clays. This thesis evaluates the thermo-mechanical response of soft clays to seasonal heat storage and associated temperature fluctuations. A literature review reveals that, in normally consolidated to lightly overconsolidated clays, increases in soil temperature can lead to significant plastic strains and a reduction in soil strength. This behavior can be modeled through constitutive formulations that include thermal strain within the elasto-plastic framework of the well-known Modified Cam-Clay Model. The current research uses the MCC Picard (1994) model to study the ground response to a buried heat exchange pipe. The spacing of the pipe was found to govern the effectiveness of ground heat storage. With only one pipe in semi-infinite soil, heat transfer to the ground dissipates quickly and thermal-mechanical interaction is negligible; however, seasonal heat storage is not possible. Closely spaced heat pipes would permit effective seasonal heat storage, but could undergo significant thermally induced consolidation deformations.by Shoshanna Dawn Saxe.S.M

The Relationship between Airport Infrastructure and Flight Arrivals in Remote Northern Canadian Communities

Much of Canada’s northern population resides in communities that are inaccessible by road for a substantial portion of the year. Residents of these “fly-in” communities rely on aircraft to provide a wide range of social, economic, and transportation services. However, for numerous reasons, including the often extreme environmental conditions in the circumpolar regions of Canada, a substantial number of flights to these communities are cancelled or diverted. Using a dataset from two airlines that serve the western portion of this region with information about schedules, delays, and cancellations of more than 18 500 flights, we examined the links between airport infrastructure, flight arrival reliability, and a variety of socioeconomic variables in 23 northern communities. Results show that runway length has a significant impact on the reliability of flight arrival, but also that the reliability of flights may not affect the cost of food in the communities included in our analysis. These findings provide evidence that lengthening runways could improve air service in the Canadian North. Une grande partie de la population du Nord canadien réside dans des localités inaccessibles par voie terrestre pendant une grande partie de l’année. Les habitants de ces localités desservies par voie aérienne dépendent des avions pour une vaste gamme de services sociaux, économiques et de transport. Toutefois, pour maintes raisons, dont les conditions environnementales souvent extrêmes qui sévissent dans les régions circumpolaires du Canada, un grand nombre de vols à destination de ces localités est annulé ou dévié. En nous appuyant sur des données en provenance de deux sociétés aériennes qui desservent ces régions, données portant sur les horaires de vol, les retards et les annulations concernant plus de 18 500 vols, nous avons examiné les liens entre les infrastructures aéroportuaires, la fiabilité de l’arrivée des vols et un éventail de variables socioéconomiques propres à 23 localités nordiques. Les résultats ont permis de constater que la longueur des pistes exerce une grande incidence sur la fiabilité de l’arrivée des vols, mais aussi, que la fiabilité des vols n’a pas nécessairement d’influence sur le coût des aliments dans les localités visées par notre analyse. Grâce à ces constatations, nous pouvons soutenir que l’allongement des pistes pourrait améliorer les dessertes aériennes dans le Nord canadien

A Machine Learning Approach to Solving Large Bilevel and Stochastic Programs: Application to Cycling Network Design

We present a novel machine learning-based approach to solving bilevel programs that involve a large number of independent followers, which as a special case include two-stage stochastic programming. We propose an optimization model that explicitly considers a sampled subset of followers and exploits a machine learning model to estimate the objective values of unsampled followers. Unlike existing approaches, we embed machine learning model training into the optimization problem, which allows us to employ general follower features that can not be represented using leader decisions. We prove bounds on the optimality gap of the generated leader decision as measured by the original objective function that considers the full follower set. We then develop follower sampling algorithms to tighten the bounds and a representation learning approach to learn follower features, which can be used as inputs to the embedded machine learning model. Using synthetic instances of a cycling network design problem, we compare the computational performance of our approach versus baseline methods. Our approach provides more accurate predictions for follower objective values, and more importantly, generates leader decisions of higher quality. Finally, we perform a real-world case study on cycling infrastructure planning, where we apply our approach to solve a network design problem with over one million followers. Our approach presents favorable performance compared to the current cycling network expansion practices

AutoLTS: Automating Cycling Stress Assessment via Contrastive Learning and Spatial Post-processing

Cycling stress assessment, which quantifies cyclists' perceived stress imposed by the built environment and motor traffics, increasingly informs cycling infrastructure planning and cycling route recommendation. However, currently calculating cycling stress is slow and data-intensive, which hinders its broader application. In this paper, We propose a deep learning framework to support accurate, fast, and large-scale cycling stress assessments for urban road networks based on street-view images. Our framework features i) a contrastive learning approach that leverages the ordinal relationship among cycling stress labels, and ii) a post-processing technique that enforces spatial smoothness into our predictions. On a dataset of 39,153 road segments collected in Toronto, Canada, our results demonstrate the effectiveness of our deep learning framework and the value of using image data for cycling stress assessment in the absence of high-quality road geometry and motor traffic data

Rethinking environmental LCA life stages for transport infrastructure to facilitate holistic assessment

Life cycle assessment (LCA) aims to provide a near full accounting of impacts from the complete life of a product, to avoid burden shifting between different parts of the life cycle. However, this is exceptionally difficult with transport infrastructure because important parts of their impact lie outside the widely-applied industrial-product-oriented LCA life stages: production, manufacturing, use, and end of life. To account for those missing impacts, we propose a new framework for assessing the life cycle impacts of transport infrastructure. This framework takes account of the differences between transport infrastructure and the industrial product system to which LCA is most attuned. First, rather than a linear process from material extraction to disposal, this LCA framework accommodates the multiple iterations of transport infrastructure through circular life stages. These reflect the long-life times, durability, persistence and feedback loops of transport infrastructure. Second, this framework recognizes the impact at the start of the life cycle created by demolition of previous infrastructure or land clearing. Third, the tightly linked external impacts that transport infrastructure induces, including influences on travel behaviour, local land use, land use, land use change and forestry, and network effects. Fourth, this framework recharacterizes “end of life” as “partial end of life”, in reflection of the widespread reconstruction, major refurbishment of and persistence of indirect impacts from transport infrastructure

Embodied emissions in rail infrastructure: a critical literature review

This paper investigates the state of knowledge in quantifying the embodied greenhouse gas (GHG) emissions in rail infrastructure and develops a sketch model for estimating the GHG impact of rail infrastructure based on the literature. A literature review identified 22 publications, containing 57 case studies, at least touching on the embodied GHG for different types of rail infrastructure. The cases studies include high speed rail, intercity rail, light rail, commuter rail, heavy rail, freight, and metro rail. The paper examines the GHG impact per kilometre of rail infrastructure reported across the case studies and compares the boundaries, functional units, methods, and data used. Most studies employed process-based LCA for an attributional analysis. The embodied emissions associated with the case studies range from 0.5 to 12 700 tCO _2 km ^−1 ; much of the variation is dependent on the proportion of the rail line at-grade, elevated, or in a tunnel. However, large ranges in GHG per kilometre remain after controlling for elevated and tunneled distance. Comparing the embodied emissions across the rail types was challenging, due to the large variations in system boundaries, study goals, and inventory methods adopted in the publications. This review highlights the need for standardization across the reporting of embodied GHG for rail infrastructure to better facilitate hot spot detection, engineering design and GHG policy decision making. The statistical model finds that overall ∼941(±168) tCO _2 e are embodied per kilometre of rail at-grade, and tunneling has 27 (±5) times more embodied GHG per kilometre than at-grade construction. The statistical model is based on the findings of published literature and does not explicitly consider function, geometry, specifications, emphasis on whole lifecycle, legislative constraints, socio-economic factors, or the physical and environmental conditions of the construction site

THE GREENHOUSE GAS IMPACT OF THE SHEPPARD SUBWAY LINE RIDERSHIP, TORONTO, CANADA

This is a metadata record relating to an article that cannot be shared due to publisher copyright. Changes in travel behavior near the Sheppard Subway Line in Toronto, Ontario, Canada, and the associated greenhouse gas impacts were examined. A study looked at initial changes in mode share after the line opened in 2002 and examined ongoing mode share trends through 2012. The initial mode shift was assessed through an analysis of bus boardings, subway platform counts, and traffic counts made between 2000 and 2012. Longitudinal changes in mode share were assessed with the use of transit survey data. For the first 6 years of operation, the Sheppard Subway Line produced more greenhouse gas (GHG) per passenger kilometer traveled (PKT) than the bus that it had replaced. A net GHG reduction of 66.4 kilotons of CO2 equivalent was calculated but was wholly dependent on avoided car PKTs that may have been offset in their entirety by induced travel on Sheppard Avenue. </jats:p

Accessing the Subway in Toronto, Canada: Access Mode and Catchment Areas

A buffer of ½ mi (805 m) is commonly used to define the service area of a subway (metro) station. This buffer is based on an approximation of the distance people are willing to walk to access the subway. This research compared the ½-mi pedestrian catchment area with the service areas reported in the Transportation Tomorrow Survey for access to the Toronto Transit Commission Subway System in Canada. This paper assesses the breakdown of access by mode to the subway and the pedestrian, bus–streetcar, and automobile catchment areas of stations in Toronto. This analysis finds two major drawbacks with the use of the ½-mi pedestrian catchment area. The service areas of buses and streetcars that connect to the subway are critical; they account for more than a third of all riders. Spatially, the size and the shape of the service area predicted by the ½-mi approach do not accurately represent what is observed in Toronto. Pedestrian catchment areas are commonly less than ½ mi in radius, and the bus–streetcar and automobile catchment areas are often many times larger

A Street-Specific Analysis of Level of Traffic Stress Trends in Strava Bicycle Ridership and its Implications for Low-Stress Bicycling Routes in Toronto

This study uses Strava bicycling data to investigate network level patterns of bicycle ridership in Toronto, Canada based on Level of Traffic Stress (LTS). We found that most bicycling occurred on a small fraction of the network, with just 10% of all roads and paths accounting for 75% of all bicycle kilometres travelled in 2022. Low-stress routes (LTS 1 and LTS 2) were more popular than high-stress routes for the top 80% most popular streets. The majority of bicycle kilometres travelled (84%) in LTS 2 occurred on routes with no bicycle infrastructure, highlighting the importance of quiet residential streets in forming a low-stress bike network. Despite high-stress conditions, some LTS 3 and LTS 4 streets were heavily used, suggesting infrastructure gaps in Toronto's bicycle network