Sentence examples for traffic predictor from inspiring English sources

In this context, the multiresolution learning neural-network-based traffic predictor is rigorously examined.

Recently, scholars have put efforts on the pertinent work ranging from developing traffic predictors to understanding human mobility and activity patterns.

Possible explanations are that the same traffic data and similar traffic predictors (traffic flow at the nearest road and a distance variable) were used in both models.

Our land use regression models had the best fit for these elements because traffic predictors were available and traffic sites were overrepresented in the measurement campaign.

In terms of source characterization, missing low-traffic hours as well as morning and evening rush hours limited the range of traffic conditions and may have contributed to the lack of significance for traffic predictors.

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).

Despite the inclusion of similar (traffic) predictors in the OP and other models, the relative importance of predictors may differ in the OP model versus models for other pollutants.

The generated models can be used as traffic rate predictors during the network operation phase (online traffic modeling).

This study examined contextual variables of location, vegetation, weather, safety, and traffic as predictors of affect, feeling states, and activity in order to gain preliminary understanding of relationships relevant to adolescent health.

The final LUR models mainly comprised traffic-related predictor variables, such as vehicle counts on major roads, and had determination coefficients (R) of 0.71.

To achieve this goal, we use the artificial neural networks technique as input traffic flow data predictor.