The flow rate was determined based on the traffic flow theory using traffic density data that was collected by counting the number of incoming messages of the requested vehicles.
Highway Capacity Manual (HCM) [1] uses traffic density (Density), in terms of passenger cars per kilometer per lane as the primary level of service (LOS) measure for multi-lane highways.
The decision-making node uses traffic-density information based on friends, friends-of-friends, and non-friends information on each road segment and implement fuzzy inference system to determine best route towards destination.
Other inputs included area land use, traffic density, and meteorological data (Gryparis et al. 2007; Zanobetti et al. 2014).
We used four methods to assess traffic proximity, and two methods were used for traffic density.
We used the traffic density as the coordinated information, as well as representing the state of an intersection.
Another study, conducted in Massachusetts, used cumulative traffic density as an exposure metric, which is a more rough exposure metric than DWTD as the products of the traffic intensities and the lengths of the road segments are not weighted.
Furthermore, several studies using traffic indicators, such as traffic-density or distance to a major road, have reported associations with coronary heart disease (Hoffmann et al. 2006), atherosclerosis (Hoffmann et al. 2007; Künzli et al. 2010), diabetes (Krämer et al. 2010), and myocardial infarction (Tonne et al. 2006).
The simulation model was calibrated and validated using traffic characteristics such as volume, density, speeds, occupancy time, etc., and then used to predict the exposure.
