Minimum Sampling Size of Floating Cars for Urban Link Travel Time Distribution Estimation

2019 ◽  
Vol 2673 (3) ◽  
pp. 24-43 ◽  
Author(s):  
Meiping Yun ◽  
Wenwen Qin
Keyword(s):  
Sample Size ◽  
Travel Time ◽  
Real Time ◽  
Time Distribution ◽  
Ground Truth ◽  
Sample Size Determination ◽  
Minimum Sample Size ◽  
Travel Time Distribution ◽  
Minimum Sample ◽  
Link Travel Time

Despite the wide application of floating car data (FCD) in urban link travel time estimation, limited efforts have been made to determine the minimum sample size of floating cars appropriate to the requirements for travel time distribution (TTD) estimation. This study develops a framework for seeking the required minimum number of travel time observations generated from FCD for urban link TTD estimation. The basic idea is to test how, with a decreasing the number of observations, the similarities between the distribution of estimated travel time from observations and those from the ground-truth vary. These are measured by employing the Hellinger Distance (HD) and Kolmogorov-Smirnov (KS) tests. Finally, the minimum sample size is determined by the HD value, ensuring that corresponding distribution passes the KS test. The proposed method is validated with the sources of FCD and Radio Frequency Identification Data (RFID) collected from an urban arterial in Nanjing, China. The results indicate that: (1) the average travel times derived from FCD give good estimation accuracy for real-time application; (2) the minimum required sample size range changes with the extent of time-varying fluctuations in traffic flows; (3) the minimum sample size determination is sensitive to whether observations are aggregated near each peak in the multistate distribution; (4) sparse and incomplete observations from FCD in most time periods cannot be used to achieve the minimum sample size. Moreover, this would produce a significant deviation from the ground-truth distributions. Finally, FCD is strongly recommended for better TTD estimation incorporating both historical trends and real-time observations.

scholarly journals Methods and Rule-of-Thumbs in The Determination of Minimum Sample Size When Appling Structural Equation Modelling: A Review

2020 ◽  
Vol 15 ◽  
pp. 102-107
Author(s):  
Hunuwala Malawarage Suranjan Priyanath ◽  
Ranatunga RVSPK ◽  
Megama RGN
Keyword(s):  
Structural Equation Modeling ◽  
Sample Size ◽  
Structural Equation ◽  
Sample Size Determination ◽  
Equation Modeling ◽  
Minimum Sample Size ◽  
Research Project ◽  
Rules Of Thumb ◽  
Minimum Sample

Basic methods and techniques involved in the determination of minimum sample size at the use of Structural Equation Modeling (SEM) in a research project, is one of the crucial problems faced by researchers since there were some controversy among scholars regarding methods and rule-of-thumbs involved in the determination of minimum sample size when applying Structural Equation Modeling (SEM). Therefore, this paper attempts to make a review of the methods and rule-of-thumbs involved in the determination of sample size at the use of SEM in order to identify more suitable methods. The paper collected research articles related to the sample size determination for SEM and review the methods and rules-of-thumb employed by different scholars. The study found that a large number of methods and rules-of-thumb have been employed by different scholars. The paper evaluated the surface mechanism and rules-of-thumb of more than twelve previous methods that contained their own advantages and limitations. Finally, the study identified two methods that are more suitable in methodologically and technically which have identified by non-robust scholars who deeply addressed all the aspects of the techniques in the determination of minimum sample size for SEM analysis and thus, the prepare recommends these two methods to rectify the issue of the determination of minimum sample size when using SEM in a research project.

scholarly journals Estimation of Bimodal Urban Link Travel Time Distribution and Its Applications in Traffic Analysis

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Yuxiong Ji ◽  
Shengchuan Jiang ◽  
Yuchuan Du ◽  
H. Michael Zhang
Keyword(s):  
Travel Time ◽  
Time Distribution ◽  
Urban Traffic ◽  
Traffic Operations ◽  
Time Model ◽  
Urban Streets ◽  
Travel Time Distribution ◽  
Proposed Model ◽  
Estimate Travel Time ◽  
Link Travel Time

Vehicles travelling on urban streets are heavily influenced by traffic signal controls, pedestrian crossings, and conflicting traffic from cross streets, which would result in bimodal travel time distributions, with one mode corresponding to travels without delays and the other travels with delays. A hierarchical Bayesian bimodal travel time model is proposed to capture the interrupted nature of urban traffic flows. The travel time distributions obtained from the proposed model are then considered to analyze traffic operations and estimate travel time distribution in real time. The advantage of the proposed bimodal model is demonstrated using empirical data, and the results are encouraging.

scholarly journals Estimation of Urban Link Travel Time Distribution Using Markov Chains and Bayesian Approaches

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Wenwen Qin ◽  
Meiping Yun
Keyword(s):  
Markov Chain ◽  
Travel Time ◽  
Time Distribution ◽  
State Transition ◽  
Transition Function ◽  
Travel Times ◽  
Travel Time Distribution ◽  
Multimodal Distributions ◽  
State Transition Function ◽  
Link Travel Time

Despite the wide application of Floating Car Data (FCD) in urban link travel time and congestion estimation, the sparsity of observations from a low penetration rate of GPS-equipped floating cars make it difficult to estimate travel time distribution (TTD), especially when the travel times may have multimodal distributions that are associated with the underlying traffic states. In this case, the study develops a Bayesian approach based on particle filter framework for link TTD estimation using real-time and historical travel time observations from FCD. First, link travel times are classified by different traffic states according to the levels of vehicle delays. Then, a state-transition function is represented as a Transition Probability Matrix of the Markov chain between upstream and current links with historical observations. Using the state-transition function, an importance distribution is constructed as the summation of historical link TTDs conditional on states weighted by the current link state probabilities. Further, a sampling strategy is developed to address the sparsity problem of observations by selecting the particles with larger weights in terms of the importance distribution and a Gaussian likelihood function. Finally, the current link TTD can be reconstructed by a generic Markov Chain Monte Carlo algorithm incorporating high weighted particles. The proposed approach is evaluated using real-world FCD. The results indicate that the proposed approach provides good accurate estimations, which are very close to the empirical distributions. In addition, the approach with different percentage of floating cars is tested. The results are encouraging, even when multimodal distributions and very few or no observations exist.

scholarly journals BioCCP.jl: Collecting Coupons in combinatorial biotechnology

2021 ◽  
Author(s):  
Kirsten Van Huffel ◽  
Michiel Stock ◽  
Bernard De Baets
Keyword(s):  
Sample Size ◽  
Design Space ◽  
Source Code ◽  
Sample Size Determination ◽  
Mathematical Framework ◽  
Minimum Sample Size ◽  
Size Determination ◽  
Screening Experiments ◽  
Coupon Collector ◽  
Minimum Sample

In combinatorial biotechnology, it is crucial for screening experiments to sufficiently cover the design space. In the BioCCP.jl package, we provide functions for minimum sample size determination based on the mathematical framework coined the Coupon Collector Problem. BioCCP.jl, including source code, documentation and a Pluto notebook is available at https://github.com/kirstvh/BioCCP.

scholarly journals A Step-by-Step Process on Sample Size Determination for Medical Research

2021 ◽  
Vol 28 (2) ◽  
pp. 15-27
Author(s):  
Mohamad Adam Bujang
Keyword(s):  
Sample Size ◽  
Type I Error ◽  
Early Stage ◽  
Sample Size Determination ◽  
Type I ◽  
Minimum Sample Size ◽  
Size Determination ◽  
Prerequisite Knowledge ◽  
Minimum Sample

Determination of a minimum sample size required for a study is a major consideration which all researchers are confronted with at the early stage of developing a research protocol. This is because the researcher will need to have a sound prerequisite knowledge of inferential statistics in order to enable him/her to acquire a thorough understanding of the overall concept of a minimum sample size requirement and its estimation. Besides type I error and power of the study, some estimates for effect sizes will also need to be determined in the process to calculate or estimate the sample size. The appropriateness in calculating or estimating the sample size will enable the researchers to better plan their study especially pertaining to recruitment of subjects. To facilitate a researcher in estimating the appropriate sample size for their study, this article provides some recommendations for researchers on how to determine the appropriate sample size for their studies. In addition, several issues related to sample size determination were also discussed.

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