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  • 101.
    Zhao, Xiaoyun
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Carling, Kenneth
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Håkansson, Johan
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    An evaluation of the reliability of GPS-based transportation data2017In: Proceedings of IAC in Vienna 2017, 2017, p. 323-334, article id IAC201711035Conference paper (Refereed)
    Abstract [en]

    GPS-based data are becoming a cornerstone for real-time transportation applications. Tracking data of vehicles from GPS receivers are however susceptible to measurement errors. The assessment of the reliability of data from GPS receiver is a neglected issue, especially in a real road network setting and in the phase after data transfer but before information identification. An evaluation method is outlined and carried out by conducting a randomized experiment. We assess the reliability of GPS-based transportation data on geographical position, speed, and elevation from three varied receivers GlobalSat BT-338X, Magellan SporTrak Pro and smart phone for three transportation modes: bicycle, car, and bus. The positional error ranging from 0158 meters, and 74% to 100% with an error within 5 meters depending on the transportation mode and route, there is also a non-negligible risk for aberrant positioning. Speed is slightly underestimated or overestimated with errors around 5km/h except for SporTrak Pro which had an error of -10 km/h. Elevation measurements are unreliable with errors bigger than 100 meters.

  • 102.
    Zhao, Xiaoyun
    et al.
    Dalarna University, School of Technology and Business Studies, Statistics.
    Carling, Kenneth
    Dalarna University, School of Technology and Business Studies, Statistics.
    Håkansson, Johan
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    An Evaluation of the Reliability of GPS-Based Transportation Data2014Report (Other academic)
    Abstract [en]

    GPS-based data are becoming a cornerstone for real-time transportation applications. Tracking data of vehicles from GPS receivers are however susceptible to measurement errors. The assessment of the reliability of data from GPS receiver is a neglected issue, especially in a real road network setting and in the phase after data transfer but before information identification. An evaluation method is outlined and carried out by conducting a randomized experiment. We assess the reliability of GPS-based transportation data on geographical position, speed, and elevation from three varied receivers GlobalSat BT-338X, Magellan SporTrak Pro and smart phone for three transportation modes: bicycle, car, and bus. The positional error ranging from 0158 meters, and 74% to 100% with an error within 5 meters depending on the transportation mode and route, there is also a non-negligible risk for aberrant positioning. Speed is slightly underestimated or overestimated with errors around 5km/h except for SporTrak Pro which had an error

  • 103.
    Zhao, Xiaoyun
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Carling, Kenneth
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Håkansson, Johan
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    On assessing governmental sustainable residential planning and its alignment with residents’ and estate investors’ objectives2017Report (Other academic)
    Abstract [en]

    There are three key actors in forming the sustainable spatial distribution of residency in an area, (local) government, the estate investor and the resident, each with its own objective. Most urban planning studies have mainly focused on the ex-post evaluation of residential development by considering the objective of each actor separately. This paper outlines a conceptual model where the three key actors and their unique objectives are integrated with the aim of providing an ex-ante evaluation of residential development for government to make policies operational on a micro level. The methodology is implemented on a Swedish city, where sustainable residential development is in high need due to the influx of immigrants. The case study demonstrates that the model can integrate the macro and micro actors well. The model can provide noteworthy insights for the government on where the objectives of sustainability, livability and profit can be met. A sensitivity check of the parameter settings shows that the implementation of the model is robust for replication in other cities.

  • 104.
    Zhao, Xiaoyun
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Carling, Kenneth
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Håkansson, Johan
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Residential planning, driver mobility and CO2 emission: a microscopic look at Borlänge in Sweden2017In: European Planning Studies, ISSN 0965-4313, E-ISSN 1469-5944, Vol. 25, no 9, p. 1597-1614Article in journal (Refereed)
    Abstract [en]

    In a city there are hotspots that attract citizens, and most of the transportation arises when citizens move between their residence and primary destinations (i.e. hotspots). However, an ex ante evaluation of energy-efficient mobility and urban residential planning has seldom been conducted. Therefore, this paper proposes an ex ante evaluation method to quantify the impacts, in terms of CO2 emissions induced by intra-urban car mobility, of residential plans for various urban areas. The method is illustrated in a case study of a Swedish midsize city, which is presently preoccupied with urban planning of new residential areas in response to substantial population growth due to immigration. In general, CO2 emissions increase from the continued urban core area (CUCA), to the sub-polycentric area (SPA), to the edge urbanization area (EUA), where CO2 emission of EUA is twice that of the CUCA. The average travel distances also increase in the same pattern, though the relative increase is more than four times. Apartment buildings could be more effective in meeting residential needs and mitigating CO2 emissions than dispersed single-family houses. 

  • 105.
    Zou, Luyi
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    An Investigation of the LDA based Topic Model Approachfor Data Mining Twitter Social Network2015Independent thesis Advanced level (degree of Master (Two Years)), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    In recent years, Twitter has become a highly popular form of social media.Twitter provides a platform for users to post short messages for followers to read inan on-or off-line fashion. Twitter is used in a variety of ways, from posting aboutpersonal daily life, to keeping up to date with current events.This thesis aims to find a reliable pipeline to analyse and visualize hottest topics(or trends) that people are talking about on Twitter during a period of time. Topicmodel is used to cluster Twitter messages and identify topic words, then topic wordscombined with the tweets’ influences are graphically represented by visualizationsoftware to reflect the trend under the topic. However, two limitations of Twittermessages prevent normal topic model tools from being applied their full potentials:Twitter messages are short and and colloquial. Twitter message provides little usefulinformation for the topic model to work properly. Thus, we proposed an poolingschema to enhance the performance of a topic model on Twitter data. Meanwhile, toidentify a reliable pipeline to do the task, we compared different methodologiesduring the process. We compared performance with and without pooling schema inthe data sampling step, performance with and without TF*IDF in the data processingstep; and finally compare performance of Latent Dirichlet allocation (LDA) withCorrelated Topic Models (CTM) to identify a topic. The results show thatLDA-TF*IDF with pooling schema is the most accurate model to identify Twittertrend.

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