Change search
Refine search result
1 - 9 of 9
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1. Kolbay, B.
    et al.
    Mrazovic, Petar
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Larriba-Pey, J. L.
    Analyzing last mile delivery operations in barcelona’s urban freight transport network2018In: 2nd EAI International Conference on ICT Infrastructures and Services for Smart Cities, IISSC 2017 and 2nd International Conference on Cloud, Networking for IoT systems, CN4IoT 2017, Springer Verlag , 2018, p. 13-22Conference paper (Refereed)
    Abstract [en]

    Barcelona has recently started a new strategy to control and understand Last Mile Delivery, AreaDUM. The strategy is to provide freight delivery vehicle drivers with a mobile app that has to be used every time their vehicle is parked in one of the designated AreaDUM surface parking spaces in the streets of the city. This provides a significant amount of data about the activity of the freight delivery vehicles, their patterns, the occupancy of the spaces, etc. In this paper, we provide a preliminary set of analytics preceded by the procedures employed for the cleansing of the dataset. During the analysis we show that some data blur the results and using a simple strategy to detect when a vehicle parks repeatedly in close-by parking slots, we are able to obtain different, yet more reliable results. In our paper, we show that this behavior is common among users with 80\% prevalence. We conclude that we need to analyse and understand the user behaviors further with the purpose of providing predictive algorithms to find parking lots and smart routing algorithms to minimize traffic.

  • 2.
    Mrazovic, Petar
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Crowdsensing-driven Route Optimisation Algorithms for Smart Urban Mobility2018Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Urban mobility is often considered as one of the main facilitators for greener and more sustainable urban development. However, nowadays it requires a significant shift towards cleaner and more efficient urban transport which would support for increased social and economic concentration of resources in cities. A high priority for cities around the world is to support residents’ mobility within the urban environments while at the same time reducing congestions, accidents, and pollution. However, developing a more efficient and greener (or in one word, smarter) urban mobility is one of the most difficult topics to face in large metropolitan areas. In this thesis, we approach this problem from the perspective of rapidly evolving ICT landscape which allow us to build mobility solutions without the need for large investments or sophisticated sensor technologies.

    In particular, we propose to leverage Mobile Crowdsensing (MCS) paradigm in which citizens use their mobile communication and/or sensing devices to collect, locally process and analyse, as well as voluntary distribute geo-referenced information. The mobility data crowdsensed from volunteer residents (e.g., events, traffic intensity, noise and air pollution, etc.) can provide valuable information about the current mobility conditions in the city, which can, with the adequate data processing algorithms, be used to route and manage people flows in urban environments.

    Therefore, in this thesis we combine two very promising Smart Mobility enablers – MCS and journey/route planning, and thus bring together to some extent distinct research challenges. We separate our research objectives into two parts, i.e., research stages: (1) architectural challenges in designing MCS systems and (2) algorithmic challenges in MCS-driven route planning applications. We aim to demonstrate a logical research progression over time, starting from fundamentals of human-in-the-loop sensing systems such as MCS, to route optimisation algorithms tailored for specific MCS applications. While we mainly focus on algorithms and heuristics to solve NP-hard routing problems, we use real-world application examples to showcase the advantages of the proposed algorithms and infrastructures.

  • 3.
    Mrazovic, Petar
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    De La Rubia, Ivan
    Urmeneta, Jordi
    Balufo, Carlos
    Tapias, Ricard
    Matskin, Mihhail
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Larriba-Pey, Josep L.
    CIGO! Mobility Management Platform for Growing Efficient and Balanced Smart City Ecosystem2016In: IEEE SECOND INTERNATIONAL SMART CITIES CONFERENCE (ISC2 2016), IEEE, 2016, p. 106-109Conference paper (Refereed)
    Abstract [en]

    The massive amount of tourists, citizens and traffic in big cities usually collapse busy areas causing transport inefficiency, unbalanced economic growth, crime, and nuisance among citizens and visitors. Therefore, the Smart City strategies such as Smart Mobility and Smart Governance naturally arise as means to improve mobility in urban areas. In this paper we propose a novel mobility management platform and business model that can attract numerous actors and still be orchestrated by the city government. The proposed platform integrates mobility data from various sources such as Open Data, mobile applications, sensors and government data, allowing for its visualisation and analysis while making it actionable through associated third party mobile applications. We propose to inject the city mobility policies to the third party mobile applications which provide services related to the city resources. In this way we form a value chain which connects different actors (city governments, mobile application providers, POI owners, companies that require logistics in cities, and final users) who both take a part in improving the mobility in urban areas, and benefit from the way mobility policies being executed. In this paper we discuss the business model and logical architecture of the proposed platform which has been already deployed in the city of Barcelona.

  • 4.
    Mrazovic, Petar
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Eravci, B.
    Larriba-Pey, J. L.
    Ferhatosmanoglu, H.
    Matskin, Mihhail
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Understanding and predicting trends in urban freight transport2017In: Proceedings - 18th IEEE International Conference on Mobile Data Management, MDM 2017, Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 124-133Conference paper (Refereed)
    Abstract [en]

    Among different components of urban mobility, urban freight transport is usually considered as the least sustainable. Limited traffic infrastructures and increasing demands in dense urban regions lead to frequent delivery runs with smaller freight vehicles. This increases the traffic in urban areas and has negative impacts upon the quality of life in urban populations. Data driven optimizations are essential to better utilize existing urban transport infrastructures and to reduce the negative effects of freight deliveries for the cities. However, there is limited work and data driven research on urban delivery areas and freight transportation networks. In this paper, we collect and analyse data on urban freight deliveries and parking areas towards an optimized urban freight transportation system. Using a new check-in based mobile parking system for freight vehicles, we aim to understand and optimize freight distribution processes. We explore the relationship between areas' availability patterns and underlying traffic behaviour in order to understand the trends in urban freight transport. By applying the detected patterns we predict the availabilities of loading/unloading areas, and thus open up new possibilities for delivery route planning and better managing of freight transport infrastructures.

  • 5.
    Mrazovic, Petar
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Eser, Elif
    Bilkent Univ, Dept Comp Engn, Ankara, Turkey..
    Ferhatosmanoglu, Hakan
    Univ Warwick, Dept Comp Sci, Coventry, W Midlands, England..
    Larriba-Pey, Josep L.
    Univ Politecn Cataluna, Dept Comp Architecture, Barcelona, Spain..
    Matskin, Mihhail
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Multi-vehicle Route Planning for Efficient Urban Freight Transport2018In: 2018 9TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS (IS) / [ed] JardimGoncalves, R Mendonca, JP Jotsov, V Marques, M Martins, J Bierwolf, R, IEEE , 2018, p. 744-753Conference paper (Refereed)
    Abstract [en]

    The urban parking spaces for loading/unloading are typically over-occupied, which shifts delivery operations to traffic lanes and pavements, increases traffic, generates noise, and causes pollution. We present a data analytics based routing optimization that improves the circulation of vehicles and utilization of parking spaces. We formalize this new problem and develop a novel multi vehicle route planner that avoids congestions at loading/unloading areas and minimizes the total duration. We present the developed tool with an illustration and analysis for the urban freight in the city of Barcelona, which monitors tens of thousands of deliveries every day. Our system includes an effective evaluation of candidate routes by considering the waiting times and further delays of other deliverers as a first class citizen in the optimization. A two-layer local search is proposed with a greedy randomized adaptive method for variable neighborhood search. Our approach is applied and validated over data collected across Barcelona's urban freight transport network, which contains 3,704,034 parking activities. Our solution is shown to significantly improve the use of available parking spaces and the circulation of vehicles, as evidenced by the results. The analysis also provides useful insights on how to manage delivery routes and parking spaces for sustainable urban freight transport and city logistics.

  • 6.
    Mrazovic, Petar
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Larriba-Pey, J. L.
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS. Dept. of Computer Architecture, UPC Polytechnic University of Catalonia, Barcelona, Spain.
    Matskin, Mihhail
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    A Deep Learning Approach for Estimating Inventory Rebalancing Demand in Bicycle Sharing Systems2018In: Proceedings - International Computer Software and Applications Conference, IEEE Computer Society , 2018, p. 110-115Conference paper (Refereed)
    Abstract [en]

    Meeting user demand is one of the most challenging problems arising in public bicycle sharing systems. Various factors, such as daily commuting patterns or topographical conditions, can lead to an unbalanced state where the numbers of rented and returned bicycles differ significantly among the stations. This can cause spatial imbalance of the bicycle inventory which becomes critical when stations run completely empty or full, and thus prevent users from renting or returning bicycles. To prevent such service disruptions, we propose to forecast user demand in terms of expected number of bicycle rentals and returns and accordingly to estimate number of bicycles that need to be manually redistributed among the stations by maintenance vehicles. As opposed to traditional solutions to this problem, which rely on short-term demand forecasts, we aim to maximise the time within which the stations remain balanced by forecasting user demand multiple steps ahead of time. We propose a multi-input multi-output deep learning model based on Long Short-Term Memory networks to forecast user demand over long future horizons. Conducted experimental study over real-world dataset confirms the efficiency and accuracy of our approach.

  • 7.
    Mrazovic, Petar
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Larriba-Pey, J. L.
    Matskin, Mihhail
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Improving Mobility in Smart Cities with Intelligent Tourist Trip Planning2017In: 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC), IEEE Computer Society, 2017, Vol. 1, p. 897-907Conference paper (Refereed)
    Abstract [en]

    Selecting the most interesting tourist attractions and planning optimal sightseeing tours can be a difficult task for individuals visiting unfamiliar tourist destinations. On the other hand, the massive amounts of tourists in big cities can collapse certain areas causing transport inefficiency, unbalanced economic growth and nuisance among tourists and citizens. Therefore, the tourist trip planning problem should take into account the possibility for the city government to manage the urban environment and achieve a balanced and sustainable growth. In this paper we introduce the tourist trip planning problem which covers both individual (tourist) and global (city) needs. The planning problem is modelled as an extension of the mixed orienteering problem and can be controlled by deployment of mobility policies which put restrictions on points of interest and routes between them. We propose an algorithmic approach and a software tool to solve this hard combinatorial optimisation problem using variable neighbourhood search. The performance of the proposed algorithm and the tool is assessed over a real-life dataset related to the city of Barcelona. Computational results confirm the efficiency of the algorithm and ability to help both individuals in planning their trips and city governments in achieving sustainable mobility objectives.

  • 8.
    Mrazovic, Petar
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Matskin, Mihhail
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    MobiCS: Mobile Platform for Combining Crowdsourcing and Participatory Sensing2015Conference paper (Refereed)
    Abstract [en]

    Current participatory sensing approaches usuallydo not consider device carriers as intelligent participants insensing processes. However, modern mobile communicationdevices allow users express their opinions and judgementswhich can complement to captured sensor data. In this paperwe bring together different modes of mobile crowdsourcinginto a general sensing platform which treats device carriersas intelligent problem solvers. We propose a conceptual archi-tecture for versatile context-aware mobile crowdsourcing, andaddress issues related to data representation, quality control,trust and reputation management, and task allocation. Toprove the potential advantages of the proposed conceptualarchitecture we developedMobiCS, a prototype platform whichallows crowdsourcers formulate and distribute both sensingand human intelligence tasks to Android-powered mobilecommunication devices.

  • 9.
    Mrazovic, Petar
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Matskin, Mihhail
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Dokoohaki, Nima
    Trajectory-Based Task Allocation for Reliable Mobile Crowd Sensing Systems2015In: Proceedings - 15th IEEE International Conference on Data Mining Workshop, Institute of Electrical and Electronics Engineers (IEEE), 2015, Vol. 1, p. 398-406, article id 7395697Conference paper (Refereed)
    Abstract [en]

    Mobile crowd sensing (MCS) is as a promising people-centric sensing paradigm which allows ordinary citizens to contribute sensing data using mobile communication devices. In this paper we study correlation between users’ mobility and their role as contributors in MCS applications. We propose a new trajectory-based approach for task allocation in MCS environments and model participants’ spatio-temporal competences by analyzing their mobile traces. By allocating MCS tasks only to participant who are familiar with the target location we significantly increase the reliability of contributed data and reduce total communication cost. We introduce novel metric to estimate participants’ competence to conduct MCS tasks and propose fair ranking approach allowing newcomers to compete with experienced senior contributors. Additionally, we group similar expert contributors and thus open up new possibilities for physical collaboration between them. We evaluate our work using GeoLife trajectory dataset and the experimental results show the advantages of our approach.

1 - 9 of 9
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf