Speed characteristics of urban streets based on driver behaviour studies and simulation
Sammanfattning: The objective of the study was to gain in-depth knowledge of speed relationships for urban streets. The speed characteristics were examined using a number of methods for data collection. Throughout the research, a special focus was placed on capturing the influence on driver speed of interactions with pedestrians, cyclists and other road users, called sidefriction events in this study.First, driver behaviour and travel time data was collected from field and driving simulator studies for a range of street types and traffic conditions. The collected data was used to calibrate a microscopic traffic simulation model. Production runs with this model were performed for various traffic conditions. Second, aggregated speed data was collected at the link level, i.e. the macro level, for three street types. In combination with street site variables, speed and flow data was analysed using multiple regression techniques with space mean speed as dependent variable. This analysis was also performed for average travel speed data produced by microscopic traffic simulation.Two central results were attained and utilized for the model development:- In-depth knowledge of which factors influence speed choice on urban street links with minor intersections, on a micro and macro level.- A comprehensive research methodology for study of speed characteristics on urban streets in which the knowledge gained at the micro and macro level was applied.Results from the micro study showed that Average number of crossing pedestrians and Traffic flow had significant impact on average travel speed (R2=0.91). Results from the macro study performed for three street types showed that Street function and Number of lanes also had a high degree of explanation (R2 close to 0.70). The variables Separated bicycle lane, Roadside parking permitted and Number of minor intersections per 1 km were significant for some of the street types modelled in the macro study. The variables Ratio of through vehicles and Gender of the driver were also investigated and were found not to influence space-mean speed. The macro study demonstrated that speed choice and driver behaviour were consistent for each street type investigated regardless of city type and population size. The speed-flow relationships of the micro model for an urban street type showed good agreement with the macro model for traffic flows in the upper range. In conclusion, the research effort showed that the included side-friction variables added explanatory value to the estimation of speed, and thus can enhance the knowledge of traffic impacts of different urban street designs.
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