Nothing Special   »   [go: up one dir, main page]
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 

Saved Queries

Sign in to use this feature.

Search Filter Reset All

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
Add Subjects Reset
Select Subjects

Journals

remove_circle_outline
remove_circle_outline
Add Journals Reset
Select Journals

Article Types

remove_circle_outline
Add Article Types Reset
Select Article Types

Countries / Regions

remove_circle_outline
Add Countries / Regions Reset
Select Countries / Regions
Update Search
share announcement

Search Results (2)

Search Parameters:
Keywords = cellular floating vehicle data

Order results
Result details
Results per page
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
2160 KiB  
Article
Vehicle Positioning and Speed Estimation Based on Cellular Network Signals for Urban Roads
by Wei-Kuang Lai and Ting-Huan Kuo
ISPRS Int. J. Geo-Inf. 2016, 5(10), 181; https://doi.org/10.3390/ijgi5100181 - 2 Oct 2016
Cited by 11 | Viewed by 4484
Abstract
In recent years, cellular floating vehicle data (CFVD) has been a popular traffic information estimation technique to analyze cellular network data and to provide real-time traffic information with higher coverage and lower cost. Therefore, this study proposes vehicle positioning and speed estimation methods [...] Read more.
In recent years, cellular floating vehicle data (CFVD) has been a popular traffic information estimation technique to analyze cellular network data and to provide real-time traffic information with higher coverage and lower cost. Therefore, this study proposes vehicle positioning and speed estimation methods to capture CFVD and to track mobile stations (MS) for intelligent transportation systems (ITS). Three features of CFVD, which include the IDs, sequence, and cell dwell time of connected cells from the signals of MS communication, are extracted and analyzed. The feature of sequence can be used to judge urban road direction, and the feature of cell dwell time can be applied to discriminate proximal urban roads. The experiment results show the accuracy of the proposed vehicle positioning method, which is 100% better than other popular machine learning methods (e.g., naive Bayes classification, decision tree, support vector machine, and back-propagation neural network). Furthermore, the accuracy of the proposed method with all features (i.e., the IDs, sequence, and cell dwell time of connected cells) is 83.81% for speed estimation. Therefore, the proposed methods based on CFVD are suitable for detecting the status of urban road traffic. Full article
(This article belongs to the Special Issue Applications of Internet of Things)
►▼ Show Figures

Figure 1

Figure 1
<p>The case study of CFVD for highway and urban roads.</p> Full article ">Figure 2
<p>The case study of an urban road network and cell coverage.</p> Full article ">Figure 3
<p>The steps of vehicle positioning method.</p> Full article ">Figure 4
<p>The steps of speed estimation method.</p> Full article ">Figure 5
<p>The urban road segments in the experimental environment.</p> Full article ">
2149 KiB  
Article
Vehicle Speed Estimation and Forecasting Methods Based on Cellular Floating Vehicle Data
by Wei-Kuang Lai, Ting-Huan Kuo and Chi-Hua Chen
Appl. Sci. 2016, 6(2), 47; https://doi.org/10.3390/app6020047 - 5 Feb 2016
Cited by 21 | Viewed by 6819
Abstract
Traffic information estimation and forecasting methods based on cellular floating vehicle data (CFVD) are proposed to analyze the signals (e.g., handovers (HOs), call arrivals (CAs), normal location updates (NLUs) and periodic location updates (PLUs)) from cellular networks. For traffic information estimation, analytic models [...] Read more.
Traffic information estimation and forecasting methods based on cellular floating vehicle data (CFVD) are proposed to analyze the signals (e.g., handovers (HOs), call arrivals (CAs), normal location updates (NLUs) and periodic location updates (PLUs)) from cellular networks. For traffic information estimation, analytic models are proposed to estimate the traffic flow in accordance with the amounts of HOs and NLUs and to estimate the traffic density in accordance with the amounts of CAs and PLUs. Then, the vehicle speeds can be estimated in accordance with the estimated traffic flows and estimated traffic densities. For vehicle speed forecasting, a back-propagation neural network algorithm is considered to predict the future vehicle speed in accordance with the current traffic information (i.e., the estimated vehicle speeds from CFVD). In the experimental environment, this study adopted the practical traffic information (i.e., traffic flow and vehicle speed) from Taiwan Area National Freeway Bureau as the input characteristics of the traffic simulation program and referred to the mobile station (MS) communication behaviors from Chunghwa Telecom to simulate the traffic information and communication records. The experimental results illustrated that the average accuracy of the vehicle speed forecasting method is 95.72%. Therefore, the proposed methods based on CFVD are suitable for an intelligent transportation system. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) The scenario diagram for vehicle movement and the handover on the road; (<b>b</b>) the timing diagram for the handover on the road segment covered by Cell<span class="html-italic">i</span>. BSC, base station controller; RNC, radio network controller.</p> Full article ">Figure 2
<p>The scenario diagram of location update events. LA, location area.</p> Full article ">Figure 3
<p>(<b>a</b>) The scenario diagram for vehicle movement and call arrivals on the road; (<b>b</b>) the timing diagram for call arrivals on the road segment covered by Cell<span class="html-italic">i</span>.</p> Full article ">Figure 4
<p>Intelligent transportation system based on the proposed methods.</p> Full article ">Figure 5
<p>Vehicle speed estimation based on the back-propagation neural network algorithm.</p> Full article ">Figure 6
<p>Trace-driven simulation for the traffic information estimations.</p> Full article ">Figure 7
<p>The distribution of cells, location areas and vehicle detectors (VDs) in the simulation experiments.</p> Full article ">
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying article 1-50 on page 1 of 1.
Back to TopTop