UMSI Master's Thesis: BITDANFO : a peer-to-peer traffic information system
dc.contributor.author | Ndubuisi-Obi Jr., Innocent | |
dc.contributor.advisor | Toyama, Kentaro | |
dc.date.accessioned | 2019-06-25T18:41:27Z | |
dc.date.available | 2019-06-25T18:41:27Z | |
dc.date.issued | 2019-05-01 | |
dc.identifier.uri | https://hdl.handle.net/2027.42/149635 | |
dc.description.abstract | Twelve million residents of Lagos, Nigeria use on public transit daily. However limited mobility options strain the existing transit infrastructure making daily commutes slow, unreliable and expensive. Transit planners lack adequate data to manage and control traffic flows. Residents lack transit information to support them in optimizing their transit decision. This paper presents BITDANFO: a native Android application that supports the real-time sensing and aggregation of traffic patterns and transit information. BITDANFO was built to assess the viability of using GPS-enabled mobile phones to collect structured, crowd-sourced and machine-generated traffic data. The goal is the development an information collection mechanism capable of supporting the collection of real-time information on traffic conditions from the crowd. The main contribution of this project is a design for a scalable, peer-to-peer, cloud-based traffic information collection system. We also explore features in the design of BITDANFO that were generated from a thorough review of current approaches to crowdsourced traffic information collection. | |
dc.subject | UMSI Master's Thesis | |
dc.subject | urban sensing | |
dc.subject | gps | |
dc.subject | traffic | |
dc.subject | Nigeria | |
dc.subject | peer to peer | |
dc.title | UMSI Master's Thesis: BITDANFO : a peer-to-peer traffic information system | |
dc.type | Thesis | |
dc.description.thesisdegreename | Master of Science (MS) | en_US |
dc.description.thesisdegreegrantor | University of Michigan | |
dc.contributor.committeemember | Gilbert, Eric | |
dc.subject.hlbsecondlevel | Information Science | |
dc.subject.hlbtoplevel | Social Sciences | |
dc.contributor.affiliationum | Information, School of | |
dc.identifier.uniqname | innoobi | |
dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/149635/1/Ndubuisi_Innocent_20190507_Final-MTOP-Thesis.pdf | |
dc.owningcollname | Dissertations and Theses (Ph.D. and Master's) |
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