Active Travel Co-Benefits of Travel Demand Management Policies that Reduce Greenhouse Gas Emissions, MTI Report 12-12

There is increasing evidence that improved health outcomes may be significant co-benefits of land use plans and transport policies that increase active transport (or walking and biking for purposeful travel) and reduce greenhouse gas emissions (GHGs) from vehicle miles traveled (VMT). A greater understanding of these benefits may broaden the constituency for regional planning that supports local and national GHG reduction goals. In this study, California’s activity-based travel demand model (ABM) is applied to (1) demonstrate how this new generation of travel models can be used to produce the active travel data (age and sex distributions) required by comparative risk assessment models to estimate health outcomes for alternative land use and transport plans and to (2) identify the magnitude of change in active travel that may be possible from land use, transit, and vehicle pricing policies for California and its five major regions for a future 2035 time horizon. The results of this study suggest that distance-based vehicle pricing may increase walking by about 10% and biking by about 17%, and concurrently GHG from VMT may be reduced by about 16%. Transit expansion and supportive development patterns may increase active travel by about 2% to 3% for both walk and bike modes while also reducing VMT by about 4% on average. The combination of all three policies may increase time spent walking by about 13% and biking by about 19%, and reduce VMT by about 19%

Energy Conservation in Existing Housing Sites; a Comparative Case Analysis\ud in the Netherlands

The housing sector in the Netherlands is responsible for a significant fraction of primary energy use and CO2 emissions. Great energy conservation opportunities are to be found in the existing housing stock, especially in large renovation projects on existing sites. Energy conservation savings of up to 90% are technically feasible. Despite this, there is little empirical evidence available about processes that influence the achievement of energy conservation goals in such locations. Moreover, no systematic, bottom-up research on the matter is available. This paper attempts to answer questions about the factors – size, direction and significance – that explain variation in the degree of energy conservation. Four main propositions were tested, comprising the following variables: actor characteristics, policy instruments, interorganizational collaboration and context. The study used a comparative research design. Data were collected from eleven existing housing sites where renovation projects had been executed, involving 70 personal interviews, a survey, and the collection of project documents. A mixed methods approach was applied for data analysis. The results show that interorganizational, collaborative efforts, policy instruments and the presence of wealthy housing associations have a positive influence on energy conservation outcomes. The mean energy conservation was slightly less than 40%, and outcomes varied between 26.5% and 69.8%. Strikingly, planning does not have a beneficial influence and the actual outcome is lower than predicted. The results are useful for national and local government policy makers, as they clearly argue that ambitious policy goals should be tempered

On the Effect of Semantically Enriched Context Models on Software Modularization

Many of the existing approaches for program comprehension rely on the linguistic information found in source code, such as identifier names and comments. Semantic clustering is one such technique for modularization of the system that relies on the informal semantics of the program, encoded in the vocabulary used in the source code. Treating the source code as a collection of tokens loses the semantic information embedded within the identifiers. We try to overcome this problem by introducing context models for source code identifiers to obtain a semantic kernel, which can be used for both deriving the topics that run through the system as well as their clustering. In the first model, we abstract an identifier to its type representation and build on this notion of context to construct contextual vector representation of the source code. The second notion of context is defined based on the flow of data between identifiers to represent a module as a dependency graph where the nodes correspond to identifiers and the edges represent the data dependencies between pairs of identifiers. We have applied our approach to 10 medium-sized open source Java projects, and show that by introducing contexts for identifiers, the quality of the modularization of the software systems is improved. Both of the context models give results that are superior to the plain vector representation of documents. In some cases, the authoritativeness of decompositions is improved by 67%. Furthermore, a more detailed evaluation of our approach on JEdit, an open source editor, demonstrates that inferred topics through performing topic analysis on the contextual representations are more meaningful compared to the plain representation of the documents. The proposed approach in introducing a context model for source code identifiers paves the way for building tools that support developers in program comprehension tasks such as application and domain concept location, software modularization and topic analysis