Many US metropolitan cities are notorious for their severe shortage of parking spots. To this
end, we present a proactive prediction-driven optimization framework to dynamically adjust …

Despite urbanization benefiting modern society and the people living in the urban city, the
limited public resources, especially parking resources, remain a problem. Parking pricing …

Searching for an available parking space is a stressful and time-consuming task, which
leads to increasing traffic and environmental pollution due to the emission of gases. To solve …

This paper proposes a machine learning method to predict curbside parking occupancy in
dynamic parking policies. We apply the convolutional long short term memory neural …

Smart parking technologies are rapidly being deployed in cities and public/private places
around the world for the sake of enabling users to know in real time the occupancy of …

Traffic congestion causes heavily energy consumption, carbon dioxide emission and air
pollution in cities, which is usually created by cars searching on-street parking spaces …

Parking occupancy prediction (POP) can be used for many real-time parking-related
services to significantly reduce the unnecessary cruising for parking and additional …

Crowded streets are a major problem in large cities. A large part of the problem stems from
drivers seeking on-street parking. Cities such as San Francisco, Los Angeles and Seattle …

With the growing number of cars in cities, smart parking is gradually becoming a strategic
issue in building a smart city. As the precondition in smart parking, accurate parking …

Problems with parking have resulted in traffic congestion, social phobia, and smog, as well
as an inefficient allocation of resources as a result of the city's growing population. The …