The phrase "availability of traffic" is correct and usable in written English.
It can be used in contexts related to transportation, internet connectivity, or data flow, where the presence or accessibility of traffic is being discussed.
Example: "The availability of traffic data is crucial for optimizing our delivery routes."
Alternatives: "traffic accessibility" or "traffic presence".
"Level of Service" (LOS) in transportation science is a concept to break the continuous range of traffic-state dependent quality and availability of traffic infrastructure into a manageable number of (mostly six) levels.
LUR models can effectively model traffic effects in our study, due to adequate representation of traffic sites in the ESCAPE study and good availability of traffic predictors compared to other sources (e.g., wood burning).
The availability of traffic counts provide a high spatial resolution of exposure as opposed to relying on data from a few monitoring stations, and therefore reduces misclassification of exposure.
In view of the well-recognized need for the availability of simple traffic noise prediction methods for non-straight roads, such as interchanges and flyovers, an approximate traffic noise prediction model, suitable for non-straight roads, was developed following the modeling process of the Federal Highway Administration's (FHWA) traffic-noise model.
However, both these schemes rely on prior availability of the traffic demands of each mesh node, which is not always feasible.
The explosion of computer capacity, the revival of the "science of networks", and the growing availability of maritime traffic data soon gave birth to numerous analyses of global maritime flows, which greatly varied in objectives and outcomes.
Modern transportation systems rely increasingly on the availability and accuracy of traffic detector data to monitor traffic operational conditions and assess system performance.
The new station will significantly enhance the availability of inter-regional traffic and promote the local industrial development.
Taking advantage of the availability of crash and traffic data disaggregated by time and space, it is possible to identify the factors that may contribute to crash risks in Hong Kong, including traffic flow, road design, and weather conditions.
Examples include passenger load in public transports, availability of parking spots, traffic jams, waste production, energy consumption and revenues of a shop in a specific street.
They are land-use patterns, transport systems, urban design, green space, availability of pavements, heavy traffic, street lights, unattended dogs, enjoyable scenery, high levels of crime, and easy access to recreation and retail shops [ 15, 16].
