Sample Size Calculation for Studying Transportation Modes from GPS Data

Bolbol, A; Cheng, Tao; Tsapakis, Ioannis; Chow, Ahf

doi:10.1016/j.sbspro.2012.06.1271

Cited by 14 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Calculating the most effective sample size for the inference mainly depends on the variability of variables that are generally used. Another example, Bolbol et al (2012) utilized traffic count data to study the variability across different seasonal divisions. The hourly and daily seasonal divisions were proved to have the highest variability.…”

Section: Data Tap: Advance / Higher Miningmentioning

confidence: 99%

Calibration of Big Traffic Data for a Transport Smart City

Wang¹

2015

Cictp 2015

View full text Add to dashboard Cite

Within a smart city, Intelligent Transportation Systems (ITS), especially all kinds of sensors, provide valuable resource for traffic knowledge discovery. This paper presents the critical path of ITS data visualization by literature review, focusing on short-term traffic forecast models that are divided into two categories, namely the sequential spatiotemporal traffic forecasting and unstable characteristics such as big events or abnormal weather, and points out that cloud computing and communication technology, like 4G/5G and WiFi or LiFi, brings great opportunities and challenges for managing big traffic data. Finally the paper illustrates Beijing's traffic data and looks into the possibility of delicateregulation due to the diversity of the spatiotemporal features.

show abstract

Section: Data Tap: Advance / Higher Miningmentioning

confidence: 99%

Calibration of Big Traffic Data for a Transport Smart City

Wang¹

2015

Cictp 2015

View full text Add to dashboard Cite

show abstract

“…e estimation is usually made by attempting a statistical analysis of the travel time distribution of traveling vehicles, but the estimation performance is reduced when the number of the probes is low. Hence, there were a few studies that examined the minimum conditions of MPR [14,15]. ere were also some studies that provided techniques for properly estimating travel time even with low MPR conditions [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

A Study on Travel Time Estimation of Diverging Traffic Stream on Highways Based on Timestamp Data

Kim

Yeo

2021

Journal of Advanced Transportation

View full text Add to dashboard Cite

Travel time is valuable information for both drivers and traffic managers. While properly estimating the travel time of a single road section, an issue arises when multiple traffic streams exist. In highways, this usually occurs at the upstream of diverge bottleneck. The aim of this paper is to provide a new framework for travel time estimation of a diverging traffic stream using timestamp data only. While providing the framework, the main focus of this paper is on performing a few analyses on the stage of travel time data classification in the proposed framework. Three sequential steps with a few statistical approaches are provided in this stage: detection of data divergence, classification of divergent data, and outlier filtering. First, a divergence detection index (DDI) of data has been developed, and the analysis results show that this new index is useful in finding the threshold of determining data divergence. Second, three different methods are tested in terms of properly classifying the divergent data. It is found that our modified method based on the approach used by Korea Expressway Corporation shows superior performance. Third, a polynomial regression-based method is used for outlier filtering, and this shows reasonable performance even at a relatively low market penetration rate (MPR) of probe vehicles. Then, the overall performance of the travel time estimation framework is tested, and this test demonstrates that the proposed framework can show improved performance in distinctively estimating the travel times of two different traffic streams in the same road section.

show abstract

“…In fact, devices from which transportation data can be obtained concern a portion of a population whose representativeness is not always easily available to researchers/technicians (e.g., private cars equipped with on-board units to reduce insurance costs [48]. Secondly, the statistical significance of the estimates obtained [49][50][51][52], as well as goodness in reproducing vehicle link flows, should be pointed out. Thus, the opportunity offered by AVM data is explored.…”

Section: Introductionmentioning

confidence: 99%

Private Car O-D Flow Estimation Based on Automated Vehicle Monitoring Data: Theoretical Issues and Empirical Evidence

et al. 2021

View full text Add to dashboard Cite

Data on the daily activity of private cars form the basis of many studies in the field of transportation engineering. In the past, in order to obtain such data, a large number of collection techniques based on travel diaries and driver interviews were used. Telematics applied to vehicles and to a broad range of economic activities has opened up new opportunities for transportation engineers, allowing a significant increase in the volume and detail level of data collected. One of the options for obtaining information on the daily activity of private cars now consists of processing data from automated vehicle monitoring (AVM). Therefore, in this context, and in order to explore the opportunity offered by telematics, this paper presents a methodology for obtaining origin–destination flows through basic info extracted from AVM/floating car data (FCD). Then, the benefits of such a procedure are evaluated through its implementation in a real test case, i.e., the Veneto region in northern Italy where full-day AVM/FCD data were available with about 30,000 vehicles surveyed and more than 388,000 trips identified. Then, the goodness of the proposed methodology for O-D flow estimation is validated through assignment to the road network and comparison with traffic count data. Taking into account aspects of vehicle-sampling observations, this paper also points out issues related to sample representativeness, both in terms of daily activities and spatial coverage. A preliminary descriptive analysis of the O-D flows was carried out, and the analysis of the revealed trip patterns is presented.

show abstract

Sample Size Calculation for Studying Transportation Modes from GPS Data

Cited by 14 publications

References 8 publications

Calibration of Big Traffic Data for a Transport Smart City

Calibration of Big Traffic Data for a Transport Smart City

A Study on Travel Time Estimation of Diverging Traffic Stream on Highways Based on Timestamp Data

Private Car O-D Flow Estimation Based on Automated Vehicle Monitoring Data: Theoretical Issues and Empirical Evidence

Contact Info

Product

Resources

About