Real‐time estimation of travel speed using urban traffic information system and filtering algorithm

Abstract

Travel speed is an important parameter for measuring road traffic. urban traffic information system (UTIS) was developed as a mobile detector for measuring link travel speeds in South Korea. However, UTIS incur errors, such as those caused by irregular vehicle trajectories and communication delays. This study describes an algorithm developed for estimating reliable and accurate average roadway link travel speeds using UTIS data. The algorithm estimates link travel times using a robust data-filtering procedure to identify valid observations within a sampling interval using a varying data validity window. The size of the data validity window varies as a function of the standard deviation of observations in previous intervals. A field test showed that the variance of the percent errors of link travel times was reduced when measured using the new model. Therefore it can be concluded that the proposed model significantly improves travel speed measuring accuracy.

The Influence of Transport Link Density on Conductivity If Junctions and/or Links Are Blocked

“…The values of network blocking values are specified in brackets, calculated from the analysis of real transport systems using numerical simulation. The blocking value is calculated using the following equation: one minus the percolation threshold calculated in Equation (3) or Equation (4). The values found in the analysis of the network graph are specified in brackets.…”

“…In [3], the authors developed an algorithm to calculate the exact average speed of flow movement using mobile detector data for measuring movement speed. The algorithm developed indicates average speed on a given road section, ignoring repetitive messages, and a travel time filter is used to compensate such time selection exceeding the road speed limit.…”

Mining the spatio‐temporal pattern using matrix factorisation: a case study of traffic flow

“…On the other hand, traffic‐related database management has emerged as a popular research field due to the rapidly increasing collection of all kinds of location‐based data. With the improvement and maturity of traffic flow estimation techniques [1–3], the real‐time traffic condition data is widely accessible and accurately available from traffic service providers such as mapping servers. Particularly, these massive traffic datasets record the traffic state information for almost all road segments all the time and hence give us opportunities to work on traffic data mining and data analysis for solving the problems existing in the ITS.…”

Compression algorithm of road traffic data in time series based on temporal correlation

“…The essence of road traffic state data compression is to represent the signal information with less data. Through effective compression and reconstruction, traffic data transmission and storage can be achieved [6–8]. Communication time, transmission bandwidth, and storage space may be directly related to the effect of data compression.…”