scholarly journals Comparing Google Maps and Uber Movement Travel Time Data

10.32866/5115 ◽  
2018 ◽  
Author(s):  
Hao Wu
Keyword(s):  
Travel Time ◽  
Data Sources ◽  
Research Subjects ◽  
Time Data ◽  
Google Maps ◽  
Travel Time Data ◽  
Data Source ◽  
Different Sources ◽  
Selection Of ◽  
Sydney Australia

The emergence and availability of crowd sourced data provide transport researchers with comprehensive coverage in their research subjects. However, difficulties in data validation and consistency between different sources pose a threat to the credibility of research based on such data. In this paper, travel time data for Sydney, Australia from Google Maps and from Uber Movement are compared for their consistency. Although the results show the two data sources are similar in measuring travel time, travel times from Uber Movement are systematically lower than from Google. This study recommends due caution in the selection of data source, and in comparing research results using different data sources.

Use of Multisensor Data in Reliable Short-Term Travel Time Forecasting for Urban Roads

2015 ◽  
Vol 2526 (1) ◽  
pp. 61-69 ◽  
Author(s):  
Qinghui Nie ◽  
Jingxin Xia ◽  
Zhendong Qian ◽  
Chengchuan An ◽  
Qinghua Cui
Keyword(s):  
Travel Time ◽  
Model Development ◽  
Data Sources ◽  
Alternative Methods ◽  
Time Data ◽  
Short Term ◽  
Real World Data ◽  
Travel Time Data ◽  
Data Source ◽  
Travel Time Forecasting

As multiple traffic data sources have become available recently, a new opportunity has been provided for improving the accuracy of short-term travel time forecasting by fusing different but valid data sources. However, previous studies seldom quantified and integrated the reliability of data sources into model development to achieve the potential promised by data fusion. This paper proposes a combined method for short-term travel time forecasting for urban road links that uses travel time extracted from fixed vehicle detectors and probe vehicle data. The method uses the generalized autoregressive conditional heteroscedasticity model to forecast the mean and variance of each type of travel time data source, and the Dempster–Shafer model is used to calculate the fusion weights iteratively. Real-world data collected on urban roads in Kunshan, China, were used to validate and evaluate the proposed method. Empirical results show that the proposed method can effectively capture the variance of each type of travel time data source for iteratively calculating the fusion weights and hence can produce accurate travel time forecasts. Moreover, through a comparison with the alternative methods, the proposed method is shown to be able to consistently generate improved performance under varying traffic conditions.

scholarly journals Comparative Evaluation of Technologies and Data Sources to Capture Travel Time at Section-Level on Urban Streets

2015 ◽  
Author(s):  
Srinivas S. Pulugurtha ◽  
Rahul C. Pinnamaneni ◽  
Venkata R. Duddu ◽  
R.M. Zahid Reza
Keyword(s):  
Travel Time ◽  
Time Data ◽  
Network Characteristics ◽  
Urban Streets ◽  
Urban Street ◽  
Percentage Difference ◽  
Travel Time Data ◽  
Data Source ◽  
Street Segments ◽  
Section Level

This paper focuses on capturing section-level (a signalized intersection to the next) travel times on urban street segments using Bluetooth detectors as well as from INRIX data source and comparing it with manual and Global Positioning System (GPS) floating test car methods (test car with a trained technician and GPS unit to capture travel time between selected points) for each travel time run. Results obtained indicate that section-level travel time data captured using Bluetooth detectors on urban street segments are less accurate and not dependable when compared with GPS unit and INRIX. The role of various on-network characteristics on the percentage difference in travel time from GPS unit, INRIX, and Bluetooth detectors was also examined.

scholarly journals Big data actionable intelligence architecture

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Tian J. Ma ◽  
Rudy J. Garcia ◽  
Forest Danford ◽  
Laura Patrizi ◽  
Jennifer Galasso ◽  
...  
Keyword(s):  
Big Data ◽  
Real Time ◽  
Digital Media ◽  
Decision Makers ◽  
Time Data ◽  
Human In The Loop ◽  
Course Of Action ◽  
Insufficient Time ◽  
Data Source ◽  
Different Sources

AbstractThe amount of data produced by sensors, social and digital media, and Internet of Things (IoTs) are rapidly increasing each day. Decision makers often need to sift through a sea of Big Data to utilize information from a variety of sources in order to determine a course of action. This can be a very difficult and time-consuming task. For each data source encountered, the information can be redundant, conflicting, and/or incomplete. For near-real-time application, there is insufficient time for a human to interpret all the information from different sources. In this project, we have developed a near-real-time, data-agnostic, software architecture that is capable of using several disparate sources to autonomously generate Actionable Intelligence with a human in the loop. We demonstrated our solution through a traffic prediction exemplar problem.

Depth solution from borehole and travel time data using three-variable hypersurface splines

2001 ◽  
Vol 46 (3) ◽  
pp. 201-211 ◽  
Author(s):  
P.F. Xu ◽  
Z.W. Yu ◽  
H.Q. Tan ◽  
J.X. Ji
Keyword(s):  
Travel Time ◽  
Time Data ◽  
Travel Time Data

Crustal structure in the Transvaal*

1956 ◽  
Vol 46 (4) ◽  
pp. 293-316
Author(s):  
P. G. Gane ◽  
A. R. Atkins ◽  
J. P. F. Sellschop ◽  
P. Seligman
Keyword(s):  
Travel Time ◽  
Crustal Structure ◽  
Time Data ◽  
Lower Velocity ◽  
Travel Time Data ◽  
The Mean

abstract Travel-time data are given at 25 km. intervals between 50 and 500 km. for traverses west, south, east, and north of Johannesburg. These derive from numerous seismograms of Witwatersrand earth tremors taken by means of a triggering technique. The only phases considered to be consistent are those mentioned below, and few signs of a change of velocity with depth were discovered. There were no great differences in the results for the various directions, and the mean results were: P 1 = + 0.24 + Δ / 6.18 sec . S 1 = + 0.37 + Δ / 3.66 sec . P n = + 7.61 + Δ / 8.27 sec . S n = + 11.4 + Δ / 4.73 sec . which give crustal depths of 35.1 and 33.3 km. from P and S data respectively. These depths include about 1.3 km. of superficial material of lower velocity.

Study of teleseismic P I — travel-time data

1970 ◽  
Vol 4 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Jack F. Evernden ◽  
Don M. Clark
Keyword(s):  
Travel Time ◽  
Time Data ◽  
Travel Time Data
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