Weather conditions may impact traffic flow in different ways. Both the human decisions regarding the trip (route, mode, time) and the trip itself can significantly vary. Driver’s behavior may be affected by weather resulting in, among others, deterioration of the network ’s travel times and speeds. Therefore to study and analyse travel times under different weather conditions, represents an important instrument to support Intelligent Transport Systems (ITS). With the correct knowledge and information, travellers would be able to plan their trips in a cost-efficient way, while traffic managers could take advantages of these predictions to deploy control strategies (e.g. weather-responsive signal timing plans). In this project Automatic Number Plate Recognition (ANPR) data from summer 2012 to summer 2013 from three different arterial routes in Stockholm city is used in order to analyze travel times, at a link level, under different weather conditions. To determine to what extent weather variables such as rain, snowfall, temperature and visibility impact the speeds in the network, weather data is integrated with traffic data (ANPR) and analyzed through linear regression models. Results show that there is in fact a negative effect on speed but also on speed’s variability. This knowledge can be useful for trip planning and for traffic management under different weather conditions.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kth-149461 |
| Date | January 2014 |
| Creators | Rondon, Abraham |
| Publisher | KTH, Trafik och logistik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | TSC-MT ; 14-001 |
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