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

Author(s):  
Srinivas S. Pulugurtha ◽  
Rahul C. Pinnamaneni ◽  
Venkata R. Duddu ◽  
R.M. Zahid Reza

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.

10.32866/5115 ◽  
2018 ◽  
Author(s):  
Hao Wu

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.


Author(s):  
Qinghui Nie ◽  
Jingxin Xia ◽  
Zhendong Qian ◽  
Chengchuan An ◽  
Qinghua Cui

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.


2021 ◽  
Author(s):  
Sarvani Duvvuri ◽  
Srinivas S. Pulugurtha

Trucks serve significant amount of freight tonnage and are more susceptible to complex interactions with other vehicles in a traffic stream. While traffic congestion continues to be a significant ‘highway’ problem, delays in truck travel result in loss of revenue to the trucking companies. There is a significant research on the traffic congestion mitigation, but a very few studies focused on data exclusive to trucks. This research is aimed at a regional-level analysis of truck travel time data to identify roads for improving mobility and reducing congestion for truck traffic. The objectives of the research are to compute and evaluate the truck travel time performance measures (by time of the day and day of the week) and use selected truck travel time performance measures to examine their correlation with on-network and off-network characteristics. Truck travel time data for the year 2019 were obtained and processed at the link level for Mecklenburg County, Wake County, and Buncombe County, NC. Various truck travel time performance measures were computed by time of the day and day of the week. Pearson correlation coefficient analysis was performed to select the average travel time (ATT), planning time index (PTI), travel time index (TTI), and buffer time index (BTI) for further analysis. On-network characteristics such as the speed limit, reference speed, annual average daily traffic (AADT), and the number of through lanes were extracted for each link. Similarly, off-network characteristics such as land use and demographic data in the near vicinity of each selected link were captured using 0.25 miles and 0.50 miles as buffer widths. The relationships between the selected truck travel time performance measures and on-network and off-network characteristics were then analyzed using Pearson correlation coefficient analysis. The results indicate that urban areas, high-volume roads, and principal arterial roads are positively correlated with the truck travel time performance measures. Further, the presence of agricultural, light commercial, heavy commercial, light industrial, single-family residential, multi-family residential, office, transportation, and medical land uses increase the truck travel time performance measures (decrease the operational performance). The methodological approach and findings can be used in identifying potential areas to serve as truck priority zones and for planning decentralized delivery locations.


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

1956 ◽  
Vol 46 (4) ◽  
pp. 293-316
Author(s):  
P. G. Gane ◽  
A. R. Atkins ◽  
J. P. F. Sellschop ◽  
P. Seligman

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.


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

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