Future Traffic Prediction from Short Period Traffic Data
نویسندگان
چکیده
منابع مشابه
Traffic volume estimation from short period traffic counts
This paper considers the problem of estimating the yearly traffic volume at a count site, when traffic counts are available for only a limited part of the year, perhaps only a few hours or days. A new method for estimating annual average daily traffic (AADT) based on regression is presented. In addition to being more precise than the traditional factor approach, the new method supplies the prec...
متن کاملShort-Term Traffic Flow Prediction Using Neuro-Genetic Algorithms
This paper presents a new short-term traffic flow prediction system based on an advanced Time Delay Neural Network (TDNN) model, the structure of which is synthesized using a Genetic Algorithm (GA). The model predicts flow and occupancy values at a given freeway section based on contributions from their recent temporal profile (over a few minutes) as well the spatial profile (including inputs f...
متن کاملPrediction of Short-interval Traffic Dynamics in Multidimensional Spaces
A radial basis function neural network (RBFNN) model is employed to predict the short-interval (within 15-minute) traffic series, including flow, speed and occupancy, which are measured in different time intervals, time lags, dimensions of state spaces, and times of day. Aside from describing entirely the methodology of RBFNN, the paper also uses two deterministic functions to test prediction p...
متن کاملAutomated Traffic Signal Prediction from Surveillance Videos
The traffic signals available in the present are based on the static feed of time without considering the actual available traffic. This leads to a situation where vehicles wait unnecessarily in one of the lanes while the traffic flow is not up to the considerable amount in the other lane. This paper provides a system to monitor the traffic flow automatically in traffic signals where video came...
متن کاملShort - Term Traffic Prediction Using a Binary Neural Network
This paper presents a binary neural network algorithm for short-term traffic flow prediction. The algorithm can process both univariate and multivariate data from a single traffic sensor using time series prediction (temporal lags) and can combine information from multiple traffic sensors with time series prediction ( spatial-temporal lags). The algorithm provides Intelligent Decision Support (...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IOP Conference Series: Materials Science and Engineering
سال: 2020
ISSN: 1757-899X
DOI: 10.1088/1757-899x/1006/1/012029