Understanding and predicting trends in urban freight transport
dc.citation.epage | 133 | en_US |
dc.citation.spage | 124 | en_US |
dc.contributor.author | Mrazovic, P. | en_US |
dc.contributor.author | Eravci, Bahaeddin | en_US |
dc.contributor.author | Larriba-Pey, J. L. | en_US |
dc.contributor.author | Ferhatosmanoğlu, Hakan | en_US |
dc.contributor.author | Matskin, M. | en_US |
dc.coverage.spatial | Daejeon, South Korea | |
dc.date.accessioned | 2018-04-12T11:45:40Z | |
dc.date.available | 2018-04-12T11:45:40Z | |
dc.date.issued | 2017-05-06 | en_US |
dc.department | Department of Computer Engineering | en_US |
dc.description | Date of Conference: 29 May-1 June 2017 | |
dc.description | Conference name: 18th IEEE International Conference on Mobile Data Management (MDM), 2017 | |
dc.description.abstract | 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. © 2017 IEEE. | en_US |
dc.description.provenance | Made available in DSpace on 2018-04-12T11:45:40Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 179475 bytes, checksum: ea0bedeb05ac9ccfb983c327e155f0c2 (MD5) Previous issue date: 2017 | en |
dc.identifier.doi | 10.1109/MDM.2017.26 | en_US |
dc.identifier.uri | http://hdl.handle.net/11693/37614 | en_US |
dc.language.iso | English | en_US |
dc.publisher | IEEE | en_US |
dc.relation.isversionof | http://dx.doi.org/10.1109/MDM.2017.26 | en_US |
dc.source.title | Proceedings - 18th IEEE International Conference on Mobile Data Management, MDM 2017 | en_US |
dc.subject | Parking availability prediction | en_US |
dc.subject | Smart cities | en_US |
dc.subject | Smart mobility | en_US |
dc.subject | Urban freight transport | en_US |
dc.subject | Availability | en_US |
dc.subject | Forecasting | en_US |
dc.subject | Information management | en_US |
dc.subject | Smart city | en_US |
dc.subject | Transportation | en_US |
dc.subject | Urban planning | en_US |
dc.subject | Urban transportation | en_US |
dc.subject | Availability predictions | en_US |
dc.subject | Data-driven optimization | en_US |
dc.subject | Freight deliveries | en_US |
dc.subject | Freight distribution | en_US |
dc.subject | Freight transport | en_US |
dc.subject | Freight transportation networks | en_US |
dc.subject | Traffic infrastructure | en_US |
dc.subject | Urban freight transport | en_US |
dc.subject | Freight transportation | en_US |
dc.title | Understanding and predicting trends in urban freight transport | en_US |
dc.type | Conference Paper | en_US |
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