METHODS OF TEST VEHICLE TRAVEL TIME DATA COLLECTION TECHNIQUES ON A ROUTE AND REQUIREMENTS TO THE SAMPLE SIZES

The article describes the methods of travel time data collection techniques using a test vehicle, their comparative characteristics are given. The requirements for the volume of the sample values of the travel time, which are based on the values of the following parameters: Student's criterion (or normal distribution), coefficient of variation and relative error are considered

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Bilevel Programming Model for Minimum-Cost Travel Time Data Collection with Time Windows

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/2197-04 ◽

2010 ◽

Vol 2197 (1) ◽

pp. 29-35 ◽

Cited By ~ 1

Author(s):

Haifeng Yu ◽

Steven I-Jy Chien ◽

Ching-Jung Ting

Keyword(s):

Data Collection ◽

Travel Time ◽

Bilevel Programming ◽

Programming Model ◽

Time Windows ◽

Minimum Cost ◽

Time Data ◽

Travel Time Data ◽

Bilevel Programming Model

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Advanced Techniques for Travel Time Data Collection

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198196155100107 ◽

1996 ◽

Vol 1551 (1) ◽

pp. 51-58 ◽

Cited By ~ 8

Author(s):

Shawn M. Turner

Keyword(s):

Data Collection ◽

Travel Time ◽

Real Time ◽

Cellular Phone ◽

License Plate ◽

Time Data ◽

Video Imaging ◽

Advantages And Disadvantages ◽

Travel Time Data ◽

Time Information

Travel time information is becoming more important for applications ranging from congestion measurement to real-time travel information. Several advanced techniques for travel time data collection are discussed, including electronic distance-measuring instruments (DMIs), computerized and video license plate matching, cellular phone tracking, automatic vehicle identification (AVI), automatic vehicle location (AVL), and video imaging. The various advanced techniques are described, the necessary equipment and procedures are outlined, the applications of each technique are discussed, and the advantages and disadvantages are summarized. Electronic DMIs are low in cost but typically limited to congestion monitoring applications. Computerized and video license plate matching are more expensive and would be most applicable for congestion measurement and monitoring. Cellular phone tracking, AVI, and AVL systems may require a significant investment in communications infrastructure, but they can provide real-time information. Video imaging is still in testing stages, with some uncertainty about costs and accuracy.

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Development and Benefits of Computer-Aided Travel Time Data Collection

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198196155100101 ◽

1996 ◽

Vol 1551 (1) ◽

pp. 1-7 ◽

Cited By ~ 4

Author(s):

Robert J. Benz ◽

Michael A. Ogden

Keyword(s):

Data Collection ◽

Travel Time ◽

Automated Analysis ◽

Future Research ◽

Measuring Instruments ◽

Time Data ◽

Computer Data ◽

Distance Information ◽

Travel Time Data ◽

Computer Aided

Link travel times are one of the most widely used and valuable measurements of congestion. Travel time measures are compatible with multimodal analyses and are understood by nontechnical audiences, yet are rigorous enough for technical analyses by transportation engineers and planners. The “average” car and floating car techniques are the most widely used travel time–based measures. Although there are cost, safety, and data limitation problems associated with collecting travel time data manually, the use of computer-aided travel time technology solves most of these problems. Detailed speed, time, and distance information can be safely collected in up to 0.1-sec intervals for a reasonable cost. The consistent format of the computer data lends itself to an automated analysis process. The development and benefits of using computer-aided travel time data collection techniques using distance-measuring instruments (DMI) and laptop computers are discussed. Automated analysis techniques and developmental software can produce results such as speed profiles, average speeds, level of service, and vehicle accelerations. Current and future research on air quality methods, fuel consumption information, and planning model potential is also presented.

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Antenna Characterization for Bluetooth-Based Travel Time Data Collection

Journal of Intelligent Transportation Systems ◽

10.1080/15472450.2012.696452 ◽

2012 ◽

Vol 17 (2) ◽

pp. 142-151 ◽

Cited By ~ 23

Author(s):

J. David Porter ◽

David S. Kim ◽

Mario E. Magaña ◽

Panupat Poocharoen ◽

Carlos Antar Gutierrez Arriaga

Keyword(s):

Data Collection ◽

Travel Time ◽

Time Data ◽

Travel Time Data

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Research on Link Travel Time Reliability Model Based on Massive Expressway Toll Data

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.505-506.719 ◽

2014 ◽

Vol 505-506 ◽

pp. 719-726 ◽

Cited By ~ 2

Author(s):

Tao Wen ◽

Chang Cheng Li ◽

Chun Jiang Che ◽

Lian De Zhong ◽

Xin Xin

Keyword(s):

Normal Distribution ◽

Travel Time ◽

Estimation Method ◽

Model Parameters ◽

Reliability Model ◽

Travel Time Reliability ◽

Time Data ◽

Moment Estimation ◽

Travel Time Data ◽

Link Travel Time

Massive expressway toll data contained lots of valuable information. However, the skills of mining and analyzing toll data were limited currently. This study explored the modeling method of road network travel time reliability based on massive toll data. Firstly, this study obtained travel time data sample of each link at different months, and analyzed travel time statistical properties preliminarily. Secondly, this study used normal distribution, gamma distribution and Weibull distribution to fit travel time data sample, and different statistical indicators were involved to measure the fitting effect. Fitting results showed that normal distribution for link travel time was more rational and acceptable than the others. Thus, this study established link travel time reliability model, and proposed moment estimation method of calibrating the model parameters. In practical application, the reliability model can be used to judge traffic operating posture for expressway management department, and also can be used to forecast travel time information, to provide valuable reference on decision-making for drivers travel plan or route choice.

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