The aim of research is the study of driving behavior in relation to the geometric characteristics of the urban road infrastructure. This study allows the identification of factors which influence driving speed. The purpose is realizing mathematical models which link the driving behavior with the geometric characteristics of the infrastructure. Up to now, the research has focused mostly on suburban roads but the models about the suburban roads are not applicable to Italian urban roads because you must consider many factors. For example, in urban roads there are several factors that affect speed such as road characteristics, car parks on the edge of the road, weather conditions, pedestrians, bicycles, vehicles and their category. The parameter used to describe driving behavior is space mean speed. This is very important because it considers the speed of vehicles traveling a given segment of roadway during a specified period of time and it is calculated using the average travel time and length for the roadway segment. Furthermore, this speed is used to understand the behavior of driving in two scenarios: normal traffic and under free flow conditions when vehicles are isolated. Particular attention was given to define the isolated vehicle. In the literature the definition of isolated vehicle does not exist for urban roads. For this reason, the time intervals were defined. The application of statistical techniques has shown that a vehicle is isolated when the vehicle ahead is at a temporal distance greater than or equal to six seconds and the vehicle following it is at a temporal distance greater than or equal to four seconds. Therefore the mathematical model construction includes the following phases. First of all, accidents analysis was carried out in order to identify the roads of interest. This analysis performed for Cagliari’s city has revealed that the streets most affected by this phenomenon are urban roads with two lanes in each direction. In particular for these urban roads, the portion of the studied tangent. Then, surveys campaign was conducted in daylight and with two instruments: radar EasyData, used to detect vehicular traffic variables, and digital cameras, useful to analyze the driver behaviors and to identify any external or internal traffic factors able to affect driver behavior. Afterwards data were processed and analyzed. Finally predictive models were constructed and validated in two traffic conditions. In both cases we used a multiple linear regression. In the normal traffic condition, two models were developed and validated to predict speed, statistically significant variables include traffic characteristics (flow, number of vehicles entering and leaving traffic stream) and geometric design attributes (width of lines, type of median, length of tangent, presence of sidewalk, type of obstacle left-lateral). Under free flow conditions, three models were developed but only two were validated. The statistically significant variables include presence of sidewalk, length of tangent, presence and type of median, width of obstacle right-lateral.

Incidentalità in ambito urbano: studio del comportamento di guida in relazione alle caratteristiche geometriche dell’infrastruttura stradale

ZEDDA, MARIANGELA
2016-02-09

Abstract

The aim of research is the study of driving behavior in relation to the geometric characteristics of the urban road infrastructure. This study allows the identification of factors which influence driving speed. The purpose is realizing mathematical models which link the driving behavior with the geometric characteristics of the infrastructure. Up to now, the research has focused mostly on suburban roads but the models about the suburban roads are not applicable to Italian urban roads because you must consider many factors. For example, in urban roads there are several factors that affect speed such as road characteristics, car parks on the edge of the road, weather conditions, pedestrians, bicycles, vehicles and their category. The parameter used to describe driving behavior is space mean speed. This is very important because it considers the speed of vehicles traveling a given segment of roadway during a specified period of time and it is calculated using the average travel time and length for the roadway segment. Furthermore, this speed is used to understand the behavior of driving in two scenarios: normal traffic and under free flow conditions when vehicles are isolated. Particular attention was given to define the isolated vehicle. In the literature the definition of isolated vehicle does not exist for urban roads. For this reason, the time intervals were defined. The application of statistical techniques has shown that a vehicle is isolated when the vehicle ahead is at a temporal distance greater than or equal to six seconds and the vehicle following it is at a temporal distance greater than or equal to four seconds. Therefore the mathematical model construction includes the following phases. First of all, accidents analysis was carried out in order to identify the roads of interest. This analysis performed for Cagliari’s city has revealed that the streets most affected by this phenomenon are urban roads with two lanes in each direction. In particular for these urban roads, the portion of the studied tangent. Then, surveys campaign was conducted in daylight and with two instruments: radar EasyData, used to detect vehicular traffic variables, and digital cameras, useful to analyze the driver behaviors and to identify any external or internal traffic factors able to affect driver behavior. Afterwards data were processed and analyzed. Finally predictive models were constructed and validated in two traffic conditions. In both cases we used a multiple linear regression. In the normal traffic condition, two models were developed and validated to predict speed, statistically significant variables include traffic characteristics (flow, number of vehicles entering and leaving traffic stream) and geometric design attributes (width of lines, type of median, length of tangent, presence of sidewalk, type of obstacle left-lateral). Under free flow conditions, three models were developed but only two were validated. The statistically significant variables include presence of sidewalk, length of tangent, presence and type of median, width of obstacle right-lateral.
9-feb-2016
flusso libero
free flow
multiple linear regression
regressione multipla lineare
rettilineo
space mean speed
strade urbane
urban roads
validation
validazione
velocità media spaziale
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/266861
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