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| Titel |
Application of GPS data for benefits of air quality assessment and fleet management |
| VerfasserIn |
Song Hao, Yun fat Lam, Chi Cheong Ying, Ka Lok Chan |
| Konferenz |
EGU General Assembly 2017
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 19 (2017) |
| Datensatznummer |
250149825
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| Publikation (Nr.) |
 EGU/EGU2017-14217.pdf |
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| Zusammenfassung |
| In the modern digitizedsociety, traffic data can be easily collected for use in roadway
development, urban planning and vehicle emission. These data are then further parameterized
to support traffic simulation and roadside emission calculations. With the commercialization
of AGPS/GPS technology, GPS data are widely utilized to study habit and travelling
behaviors.
GPS on franchised buses can provide not only positioning information for fleet management
but also raw data to analyze traffic situations. In HK, franchised buses account for 6% of RSP
and 20% of NOx emissions among the whole vehicle fleet. Being the most heavily
means of public transport, the setting up of bus travelling trajectories and service
frequency always raise concern from citizens. On this basis, there is an increasing
interest and as well as to design and realize an effective cost benefit fleet management
strategy.
In this study, data collection analysis is carried out on all bus routes (i.e. 112) in Shatin
district, one of the 18 districts in Hong Kong. The GPS/AGPS data through Esri
ArcGIS investigate the potential benefit of GPS data in different emission scenarios
(such as engine type over whole bus fleet). Building on the emission factors from
EMFC-HK model, we accounted for factors like travelling distance, idling time,
occupancy rate, service frequency, tire and break emissions. Through the simple
emission developed model we demonstrate how GPS are data are utilized to assess
bus fleet emissions. Further amelioration on the results involve tuning the model
with field measurement so as to assess district level emission change after fleet
optimization. |
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