Multidimensional urban sprawl in Europe: A self-organizing map approach

In this paper, we present a hybrid approach based on the Analytical Hierarchy Process (AHP) and Dempster-Shafer theory for evaluating the impact of environment-friendly transport measures like mode sharing, multi-modal transport solutions, intelligent transport solutions, etc. on city sustainability. The proposed approach is a mix of curiosity driven and client-driven research in the sense that the problem is guided by the client for practical applicability and the solution is motivated by technical or scientific contribution to research. The solution approach comprises multiple steps. In the first step, we identify the criteria for sustainability evaluation. AHP is used to structure and rate the criteria. In the second step, we test the transportation measure for sustainability and collect data from multiple information sources like human experts, questionnaire, sensors, models, etc on the selected criteria for evaluation purposes. The information from multiple data sources is combined using Dempster-Shafer theory. In the third step, we estimate the state of sustainability of the city using a Transport Sustainability Index (TSI). The Transport Sustainability Index is computed at two stages: pre- and post-test stages of the transportation measure. In the fourth step, we assess the impacts of the transportation measure on the city sustainability by observing the difference between the values at the pre- and the post-test stages. If an increase in the value of TSI is observed, then the impact of the transportation measure on city sustainability is judged as positive and it is recommended for adoption. We illustrate our approach by application on the transportation measure ''Carsharing''.

Using AHP and Dempster-Shafer theory for evaluating sustainable transport solutions

It is challenging to map large spatial flow data due to the problem of occlusion and cluttered display, where hundreds of thousands of flows overlap and intersect each other. Existing flow mapping approaches often aggregate flows using predetermined high-level geographic units (e.g. states) or bundling partial flow lines that are close in space, both of which cause a significant loss or distortion of information and may miss major patterns. In this research, we developed a flow clustering method that extracts clusters of similar flows to avoid the cluttering problem, reveal abstracted flow patterns, and meanwhile preserves data resolu- tion as much as possible. Specifically, our method extends the traditional hierarchical clustering method to aggregate and map large flow data. The new method considers both origins and destinations in determining the similarity of two flows, which ensures that a flow cluster represents flows from similar origins to similar destinations and thus minimizes information loss during aggregation. With the spatial index and search algorithm, the new method is scalable to large flow data sets. As a hierarchical method, it generalizes flows to different hierarchical levels and has the potential to support multi-resolution flow mapping. Different dis- tance definitions can be incorporated to adapt to uneven spatial distribution of flows and detect flow clus- ters of different densities. To assess the quality and fidelity of flow clusters and flow maps, we carry out a case study to analyze a data set of 243,850 taxi trips within an urban area.

Mapping Large Spatial Flow Data with Hierarchical Clustering

Changing food consumption patterns and associated greenhouse gas (GHG) emissions have been a matter of scientific debate for decades. The agricultural sector is one of the major GHG emitters and thus holds a large potential for climate change mitigation through optimal management and dietary changes. We assess this potential, project emissions, and investigate dietary patterns and their changes globally on a per country basis between 1961 and 2007. Sixteen representative and spatially differentiated patterns with a per capita calorie intake ranging from 1,870 to >3,400 kcal/day were derived. Detailed analyses show that low calorie diets are decreasing worldwide, while in parallel diet composition is changing as well: a discernable shift towards more balanced diets in developing countries can be observed and steps towards more meat rich diets as a typical characteristics in developed countries. Low calorie diets which are mainly observable in developing countries show a similar emission burden than moderate and high calorie diets. This can be explained by a less efficient calorie production per unit of GHG emissions in developing countries. Very high calorie diets are common in the developed world and exhibit high total per capita emissions of 3.7-6.1 kg CO(2eq.)/day due to high carbon intensity and high intake of animal products. In case of an unbridled demographic growth and changing dietary patterns the projected emissions from agriculture will approach 20 Gt CO(2eq.)/yr by 2050.

/pdf/embodied-greenhouse-gas-emissions-in-diets-1krk53v4i8.pdf

Embodied greenhouse gas emissions in diets.

Social area analysis, data mining, and GIS

Given that many spatial interaction (SI) systems are often constituted in large databases with high thematic dimensionality, data complexity reduction tasks are essential. The opportunity exists for researchers to examine the formation of different types of SIs as well as their interdependencies by exploring the patterns embedded in the data. To circumvent the limitations of existing methods of flow data compression and visual exploration, we propose an integrated computational and visual approach, known as VISIDAMIN, for handling both SI data projection and SI data quantization at once. The computational method of self-organizing maps serves as the data mining engine in this process. Using a large domestic air travel dataset as a case study, we examine how the characteristics of the air transport system interact with the SI system to create relationships and structures within the US domestic airline market.

Visual Data Mining in Spatial Interaction Analysis with Self-Organizing Maps

Evaluating benefits and costs of intelligent transportation systems elements from a planning perspective

Visual Exploration of Spatial Interaction Data with Self‐Organizing Maps

This paper presents the delay model that sits at the core of the ITSOAM (Intelligent Transportation Systems Options Analysis Model) evaluation framework of many ITS elements. For the sake of the exposition, the model is described in the context of the evaluation of a variable message sign (VMS) deployment along a freeway corridor. The scenario that is assumed here is that of an incident (accident or vehicle disablement) that occurs on the freeway and reduces capacity. The same model can serve to handle other types of disrupting events such as road construction or a scheduled special event (e.g., a fair or football game)

Delay modeling for corridor-level benefits evaluation of vms deployments

The scarcity of good benefits evaluation tools for intelligent transportation systems (ITS) deployments at the programming stage is a known hindrance for ready deployment of new ITS infrastructures. In many states, capital planning and programming procedures require that projects with an ITS content compete for scarce resources with projects whose approach lies with the traditional capacity-increase philosophy. This paper presents a Windows-based modeling tool dubbed the ITS Options Analysis Model (ITSOAM) to evaluate the benefits of a broad selection of ITS deployment elements. Developed for the New York State Department of Transportation (NYSDOT), the model serves as a sketch planning tool and has limited data requirements, which makes it a tool suitable for use at the programming stage of the department's capital investment plan. The paper will briefly present the tool’s requirements and capabilities and the modeling design approach of the entire system. The central part of the paper will focus on the evaluation of benefits of deploying selected elements of an Incident Management System (IMS), namely variable message signs, detection and surveillance devices, and highway emergency local patrols. Results of several scenarios are introduced, with an emphasis on the sensitivity of model results to key parameters such as the traffic diversion rate, reduction in detection time, response time, and clean-up time.

Modeling Benefits of Incident Management Systems with the ITS Options Analysis Model (ITSOAM): A Tool for Economic Analysis

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