Abstract:
With unprecedented advancement in mobility technology, most of the vehicles in the
world are still operating on fuels from natural resources. On burning these vehicles
contribute to air pollution significantly affecting life of every individual with maximum
concern for the older as well as to newest generations. Thus, it becomes extremely
important to measure and model air pollution and take preventive actions as efficiently
and quickly as possible. For modelling, traffic characteristics like volume, and density
near fixed monitoring sites plays an important role. These flow characteristics can also
be coupled with nearby land use to give a better spatio-temporally varied model for
pollutant prediction. Real-time congestion data can provide a fast and accurate measure
of various pollutants that a person can expect on a particular route. It can significantly
help non-motorized transit users and active users to plan their route based on the greenest
route available.
In the present study, real-time congestion information is fused in a land-use regression
model. The former is obtained from HERE maps Traffic Flow API (Application programming
interface). To integrate land use information in the model, each raster pixel for the data
inside the buffer region can be converted to a point, and a value is assigned to it, which is
based on its distance from the monitoring station, land use, and traffic flow. Using these
point data, regression analysis can be done to obtain a predictive model which can be used
along any route to give a better-estimated value of pollutant concentration experienced
by the user. These results can be integrated with map services to give a greener and safer
route for active and non-motorized users leading to sustainable development.
