Departure Time Research Articles

Comparative analysis of quantum two-way time transfer and quantum round-trip time transfer in consistent spatiotemporal settings

In this study, we conducted experiments using a single energy-time entangled biphoton source to compare the performance of quantum two-way time transfer (Q-TWTT) and quantum round-trip time transfer (Q-RTTT) under consistent spatiotemporal conditions. By conducting experiments with different fiber links of 11.3 km, 22.4 km, and 55.6 km, while ensuring uniform photon counts received by the single-photon detectors, we measured standard deviations (SDs) and stabilities of the time offsets. The measured SDs for Q-TWTT and Q-RTTT were 0.46 ps and 0.65 ps over the 11.3 km fiber, 1.14 ps and 1.3 ps over the 22.4 km fiber, 3.98 ps and 4.39 ps over the 55.6 km fiber, respectively. These results show good agreement with theoretical predictions, and the smaller SDs for Q-TWTT can be directly attributed to protocol-specific factors related to system symmetry. The long-term time stabilities of Q-TWTT and Q-RTTT were evaluated in terms of time deviation (TDEV). At an average time of 12680 s, the measured TDEVs were 0.49 ps and 0.63 ps for the 11.3 km fiber, 0.59 ps and 0.7 ps for the 22.4 km fiber, 1.01 ps and 1.36 ps for the 55.6 km fiber, respectively. The results validate that Q-TWTT exhibits superior time transfer performance compared to Q-RTTT, highlighting the advantages of Q-TWTT in practical applications.

85 Genomic regions associated with electronic measures indicative of structural soundness in pigs

Abstract The objective was to determine the genetic factors influencing mobility traits recorded from 5 mo-old gilts. Mobility was measured using a pressure-sensing mat (GAIT4) and 7 d of video recorded activity (NUtrack). Gilts (n = 3,659) were evaluated for the study, but only 2,172 gilts had data for both systems. GAIT4 system creates a series of measurements for each foot related to pressure, duration and step length of each foot generating a lameness score for each foot. Traits studied summarized values for all 4 feet: average stride length, average stance time, standard deviation of stance time, lameness score and total scaled pressure. NUtrack measurements were rotations, velocity, and distance walked as well as the time spent eating, sitting, standing, lying sternal and lying lateral. All NUtrack traits were standardized to a mean of zero and standard deviation of 1.0 based on each pen-day subgroup. Mixed model analyses were conducted in R fitting animal as a random effect and fixed effects of breed of sire and contemporary group, with day included for NUtrack data. ssGBLUP analyses were conducted in WOMBAT using animal effects from R models as phenotypes. Three generations of pedigree were included and genotypic data from the NeoGen Porcine 50K beadchip on 3,186 animals representing 60% of gilts with NUtrack data, 67% of gilts with GAIT4 data and most parents (>99%). Single trait analyses were conducted for all traits, gEBVs estimated and then SNP effects estimated by back-solving equations. Significance values were determined by 5,000 permutations and a Bonferroni adjustment factor applied. Estimates of heritability ranged from 0.08 to 0.41 and traits from the NUtrack system tended to have greater heritability than GAIT4 traits. There were 343 significant SNP effects in 39 unique chromosomal regions. There were 133 significant SNP associations located on chromosome 14 between 45-47 Mb associated with rotations, distance and times standing and lying. Pathway analysis identified proteoglycan and apelin pathways to be over-represented based on associations with SNP located at 2:45, 2:51, 13:79 and 14:40 (SSC:Mb). Proteoglycans are a subunit of cartilage and apelins affect blood flow and angiogenesis which make them candidates for the development of osteochondrosis, a leading cause of lameness in pigs. These results will facilitate selection of more robust animals capable of withstanding commercial production environments and group sow housing. USDA is an equal opportunity employer.

Enhancing Flight Delay Predictions Using Network Centrality Measures

Accurately predicting flight delays remains a significant challenge in the aviation industry due to the complexity and interconnectivity of its operations. The traditional prediction methods often rely on meteorological conditions, such as temperature, humidity, and dew point, as well as flight-specific data like departure and arrival times. However, these predictors frequently fail to capture the nuanced dynamics that lead to delays. This paper introduces network centrality measures as novel predictors to enhance the binary classification of flight arrival delays. Additionally, it emphasizes the use of tree-based ensemble models, specifically random forest, gradient boosting, and CatBoost, which are recognized for their superior ability to model complex relationships compared to single classifiers. Empirical testing shows that incorporating centrality measures improves the models’ average performance, with random forest being the most effective, achieving an accuracy rate of 86.2%, surpassing the baseline by 1.7%.

A Bayesian detector-based approach for determining arrival and departure times of negative pressure waves in pipeline leakage localization

A Bayesian detector-based approach for determining arrival and departure times of negative pressure waves in pipeline leakage localization

Investigating the relative accuracy of GPS, GSM and CDR data for inferring spatiotemporal travel trajectories

AbstractThe potential of passively generated big data sources in transport modelling is well‐recognised. However, assessing their accuracy and suitability for policymaking remains challenging due to the lack of ground‐truth (GT) data for validation. This study evaluates the accuracy of inferring human mobility patterns from global positioning system (GPS), call detail records (CDR), and global system for mobile communication (GSM) data. Using outputs from an agent‐based simulation platform (MATSim) as ‘synthetic GT’ (SGT), synthetic GPS, CDR, and GSM data were generated, considering their positional disturbances and conventional spatiotemporal resolutions. Mobility information, including activity location, departure time, and trajectory distance, derived from the synthetic data, was compared with SGT to evaluate the accuracy of passive trajectory data at both disaggregate and aggregate levels. The results indicated a higher accuracy of GPS data in identifying stay locations at high resolution. But, GSM data at a lower resolution effectively accounted for over 80% of the variability in stay locations. Comparisons of departure time distribution and travel distance revealed higher measurement errors in GSM and CDR data than in GPS data. The proposed simulation‐based accuracy assessment framework will aid transport planners select the most suitable data for specific analyses and understand the potential margin of error involved.

Dual-comb-enhanced microwave clock synchronization over commercial fiber

The large-scale clock network is the key ingredient to obtain high precision in many scenarios, from fundamental research to cutting-edge applications. The advantage of the time synchronization among microwave clocks is their cost, size, and accessibility. Here, we demonstrate a femtosecond-level time synchronization of microwave clocks through a commercial link of 205.86 km via dual-comb-enhanced optical two-way time transfer, which achieves a 6.23-fs residual time deviation between synchronized timescales at 1 s and an instability below 6×10−18 at 10,000 s. Further, the high-precision time synchronization of microwave clocks significantly enhances the probe ability of subtle reciprocity changes of fiber to the sub-picosecond level. This work provides a path toward secure fiber time-frequency networks to support future microwave-clock-based precise timing and sensing systems.

Insights into the Global Characteristics of Shipping Exhaust Emissions at Berth

The need for an accurate inventory of ship emissions is vital for atmospheric scientists assessing the environmental impact of shipping and for policymakers aiming to regulate and incentivize emission reduction. This study used data from 189 international ports, related to ship arrivals and departures, to develop emissions inventories. Emission characteristics were examined in detail, classifying emissions by factors like ship type, month, and region. The analysis identified oil tankers and container ships as main emitters among ship categories. A detailed analysis of the monthly distribution of emissions from vessels at berth worldwide was conducted based on precise arrival and departure times. Singapore, Rotterdam, and Antwerp were the ports with the highest emissions from ships at berth. Overall, this study presents the spatial and temporal emission characteristics of ships at berth in 3912 ports around the world, which can support the development of emission reduction strategies in port management.

Effect of human–machine interface infotainment systems and automated vehicles on driver distraction

AbstractDriver distraction is intricately linked to human behavior and cognitive ergonomics, as it explores how human engagement with various stimuli influences attention and decision‐making processes while driving. The main purpose of this study is to comprehensively explore whether using Human–Machine Interface infotainment systems in automated vehicles can affect driver distraction. To this end, driver distraction was measured by driving performance features (speed, lane position, and reaction time), behavioral features (fixation time and pupil dilation), physiological features (changes in oxyhemoglobin), and subjective assessment (NASA‐TLX workload). Twenty‐one participants equipped with an eye tracker and functional near‐infrared spectroscopy drove a driving simulator in the current investigation. The results revealed that interacting with the infotainment systems significantly affects the drivers' average speed (F2,40 = 13.60, p < .0001), reaction time (F2,40 = 4.74, p = .0142), fixation time (F2,40 = 88.61, p < .0001), pupil dilation (F2,28 = 3.63, p = .0356), and workload (F2,40 = 14.40, p < .0001). Moreover, driving mode significantly affects drivers' speed deviation (F2,40 = 6.12, p = .0048), standard deviation of lane position (F2,40 = 10.57, p = .0002), fixation time (F2,40 = 36.71, p < .0001), and workload (F2,40 = 28.08, p < .0001). Drawing from the findings of this article and emphasizing human‐centric design principles, researchers and engineers can craft automotive technologies that are intuitive, effective, and safer. This is vital for mitigating driver distraction and guaranteeing the beneficial influence of automated vehicles on both road safety and the overall driving experience.

Promoting human-centered manufacturing through Lean Ergonomics – a structural equation model for ergonomics and management data

ABSTRACT The Stress-Strain Concept (SSC) describes central mechanisms in Lean Ergonomics (LE) and Human Factors Engineering (HFE). The SSC can create additional value through content and methodological enrichment based on current economic, technological, and demographic constraints. This article uses the SSC to explore mechanisms of interactions of productivity and ergonomics in empirical shop floor data. Ergonomic (Borg, NASA-Task Load Index (N-TLX), Ergonomic Assessment Worksheet (EAWS), Health, Age) and business data (normalized and absolute standard deviations of work process times) were collected for 24 manual work processes operating large-scale chemical reactors. Data is analyzed using several Structural Equation Models (SEM). Stress (EAWS) and – depending on Health – Physical Strain with a model quality of Adjusted Goodness-of-Fit ≥0.9 were identified as statistically significant regressors for time data. In addition, carry-over effects from ergonomics to productivity were identified in an autoregressive model. Fit indices are in the good to acceptable range.

Imperfect diffusion-controlled reactions for stochastic processes with memory

Many physical processes are controlled by the time that a random walker needs to reach a target. In many practical situations, such as reaction kinetics, this target is imperfect: multiple random encounters may be necessary to actually trigger a reaction. So far, most analytical approaches of imperfect reaction kinetics have been limited to Markovian (memoryless) stochastic processes. However, as soon as the random walker interacts with its environment, its motion becomes effectively non-Markovian. Here, we present a theory that provides the mean reaction time for a non-Markovian Gaussian random walker in a large confining volume in the presence of a spatially localized reaction rate or a gated target. Remarkably, in the weakly reactive regime, for strongly subdiffusive processes, our theory predicts that the deviation of the mean reaction time to the reaction controlled time displays a non-trivial scaling with the reactivity, which we identify analytically. This effect illustrates how the memory of past passages to the target influences the statistics of next-return times, to the difference of Markovian processes. The theory is developed in one and two dimensions and agrees with stochastic simulations. These results provide a refined understanding of how non-Markovian transport and local reactivity influence the kinetics of diffusion controlled reactions.

Analysis of Implementation Delay in Road Construction Project (A Case Study of Road Improvement Project in Jayapura Regency)

Planning and control over time and cost are important parts of overall construction project management. Work achievement of a project can be assessed through its quality, time, cost and safety aspects. So, in completing work project, time and cost incurred are aspects that must be continously measured for its deviation from the original plan. İn this study, time and cost deviations makes the management of road improvement project of Jayapura Regency getting worse. By employing several project performance indicators such as time and cost, it is possible to take preventative steps to make the road improvement project of Jayapura Regency can be proceed as planned. The research method applied in this research were qualitative and quantitative method where the qualitative method used a structured interview to respondents, while the quantity method involved a questionnaire distribution to total 100 respondents (10 project owners, 45 implementer contractors, and 45 consultants). Based on the result of this study, factors causing delays in the completion of road improvement project in Jayapura Regency are: (1) labor factor with value of 3.5400, (2) equipment factor with value of 3.4400, (3) finance factor with value of 3.4200, (4) material factor with factor of 3.3900, (5) scope of work and contract/work documents with value of 3.3900, (6) site characteristic factor with value of 3.3700, (7) changes factor with value of 3.3400, (8) managerial factor with value of 3.3300, (9) situation factor with value of 3.3200, (10) planning and scheduling factor with value of 3.2900, and (11) inspection, control and work evaluation systems with value of 3.2700. As for the ranking of variables causing delays for the completion of road improvement project in Jayapura Regency are: (a) workforce discipline with value of 3.5600, (b) inadequate number of workers or in accordane with the existing work activities with value of 3.5600 and (c) workforce nationalism with value of 3.5300

Right ventricular dyssynchrony predicts outcome in pulmonary arterial hypertension when assessed in multiple CMR views

BackgroundRight ventricular (RV) dyssynchrony or post systolic contraction (PSC) cause inefficient pumping and have not been investigated as prognostic markers in pulmonary arterial hypertension (PAH). ObjectivesTo investigate if RV dyssynchrony and PSC are prognostic markers of transplantation-free survival in PAH and if multiple RV views improve the prognostication. MethodsPatients with PAH undergoing cardiac magnetic resonance (CMR) between 2003-2021 were included. For strain analysis, endocardial end-diastolic RV contours were delineated in RV 3-chamber (RV3ch), 4-chamber (4ch) and midventricular short axis slice (SAX). RV dyssynchrony was defined as standard deviation of time to peak strain in the walls from one (4ch), two (4ch and SAX) or three views (4ch, SAX and RV3ch). PSC was defined as peak strain occurring after pulmonary valve closure. Outcome was defined as death or lung transplantation. ResultsOne hundred-one patients (58±19 years, 66% women) were included. Median follow-up was 37 [51] months. There were 60 events (55 deaths and 5 lung transplantations). Outcome was associated with RV dyssynchrony from three views and with RV strain in 4ch. An increase of RV dyssynchrony - from 3 views - by 1% was associated with a 10% increased risk of lung transplantation or death. There was no association between outcome and RV dyssynchrony in one or two views nor with PSC. ConclusionRight ventricular dyssynchrony from three views were associated with outcome in PAH, whereas assessing dyssynchrony from one or two views and PSC were not. This implies that assessment of multiple instead of single RV views potentially could be used for prognostication in PAH.

Mobile phone conversation during nighttime driving: Effects on driving behavior

Objectives This study aims to investigate the impact of mobile phone use (specifically, conversation), considering various use modes, on driving behavior at night. Mobile phone use is a source of driver distraction and has been associated with increased accident risk. Driving at night also entails higher accident risk and severity compared to daytime driving. Several studies have investigated the impact of mobile phone use on driving behavior; however, only a few have explored the differences between the different use modes. Most present studies involved daytime driving, although mobile phone use at night is equally if not more prevalent. Method A driving simulator experiment was designed in which 55 participants drove under nighttime simulator conditions, in different road environments (urban and rural) and under different types of distraction: no distraction, handheld, wired earphone, and speaker mode. The drives were performed during late afternoon and evening hours to resemble nighttime conditions both in the simulator and in the actual environment. Participants also completed a questionnaire for collection of different types of data. Results Results highlight the effect of mobile phone use on driving behavior, through specific indicators. Mobile phone use resulted in reduced 85th percentile driving speed and 85th percentile acceleration and increased reaction time and lateral deviation. However, safer stopping distance was observed in rural roads. Parameters relative to mobile phone use familiarity and exposure were found to mitigate mobile phone use effects. Conclusions Mobile phones affect driving behavior at night in a similar manner to that noted in several different studies considering daytime driving. The hands-free regulation should be revisited, because driver distraction also occurred under this particular use mode. Further research is required considering mobile phone use familiarity and exposure and effects of mobile phone use, because the latter is reduced with an increase in the former. Stopping distance, an understudied but more immediate surrogate measure of road safety, was increased with mobile phone use, mainly as a result of the risk compensation behavior that drivers adopt, indicating that more research is required in this field.

Facile synthesis of a new chiral polyimine macrocycle and its application for enantioseparation in high-performance liquid chromatography

Macrocyclic compounds such as crown ethers and cyclodextrins play an important role in the field of chromatography and show excellent separation performance. The design of simple and convenient methods for the efficient synthesis of novel chiral macrocycles for chromatographic separation is of great significance. In this work, a novel chiral polyimine macrocycle (PIMC) was designed and synthesized by the simply one-step reaction of 2,6-diformyl-4-tert-butylphenol with (S)-(-)-1,2-propanediamine. Then, it was bonded onto silica by the thiol-ene click reaction to construct a new chiral stationary phase (CSP) for high-performance liquid chromatography (HPLC). The chiral separation performance of the proposed CSP was examined by separating various racemates in the normal-phase (NP) and reversed-phase (RP) HPLC. In total, twelve and nine racemates, including ethers, esters, amines, alcohols, organic acids, ketones, and epoxides, were separated to varying degrees via NP-HPLC and RP-HPLC, respectively, Moreover, the CSP offered good chiral separation complementarity to Chiralcel OD-H and Chiralpak AD-H columns for resolution of these test racemates, and it can separate several racemic compounds that either cannot be separated or cannot be separated well be separated by the two commercially available columns. After the column was used for hundreds of injections, the relative standard deviations of the retention time and resolution were below 0.56 % and 0.45 %, respectively, showing the good reproducibility and stability of the CSP. This study provides a simple and convenient approach to synthesize a novel chiral macrocycle and CSP and also indicates the broad application prospects of such chiral PIMCs in HPLC chiral separation.

Routing and scheduling of trains and engines in a railway marshalling station yard

In a busy railway marshalling station, train and engine traffic management in the receiving/departure yard plays a crucial role in efficient and stable operations. Traditionally, a track assignment problem (TAP) is solved to assign tracks to trains for berthing at a receiving/departure yard. However, the TAP does not encompass shunting operations in the yard (e.g., engine replacement and train disassembly), which can result in additional scheduling challenges for dispatchers and route conflicts between operations. This paper investigates a train and engine routing and scheduling problem (TERSP) in a receiving/departure yard of railway marshalling stations, which involves simultaneously assigning routes and scheduling route-setting start times for both train and shunting operations to be conducted in the yard. By introducing the concepts of task, activity, and pattern, we transform the original problem into assigning pre-generated patterns incorporating both route and route-setting start time alternatives to activities. The transformed problem is formulated into a compact binary integer linear programming model with a linear number of constraints and the objective of minimizing the total time deviation of all involved tasks. An improved technique that relies on listing all maximal (bi)cliques in a constructed graph is designed to effectively model the time coherence and track section occupation constraints. A heuristic that gradually expands the patterns for the identified key activities by adding more start time alternatives is applied to remedy an infeasible model caused by potential route conflicts. In addition, a rolling horizon algorithm that decomposes the original problem into consecutive smaller stages using either a time-rolling or a train-rolling rule is developed to efficiently solve instances. Finally, numerical experiments based on the physical layouts and real timetables of a receiving yard and a departure yard of a large marshalling station in China are conducted to assess the performance and applicability of our proposed approaches. The results demonstrate that our approaches typically find (near-)optimal solutions within several minutes for the investigated instances by simultaneously addressing different classes of yard operations and resources.

Behavioral insights on individual and social adaptation to automated mobility using a serious game

IntroductionSerious games generate a virtual environment where players are immersed in simulated conflicts, guided by distinct rules. They replicate intricate systems, like transportation, encouraging social engagement via reinforced learning. Unlike preference-based studies, these games offer enhanced real-time feedback on players' actions. Thus, they reveal how user experience and social interaction influence decision-making over time. We use a serious game to study the willingness of travelers to adopt automated mobility, specifically shared modes of transport, an important step toward alleviating congestion, enhancing the quality of urban living, and improving people's health and well-being. MethodsFor each scenario, 100 participants were randomly divided into ten groups of ten interacting players. They chose independently out of three automated transportation modes - shared ride, shared car, and automated transit-over 50 simulated days how to commute to work. They aimed to maximize their overall score by arriving punctually, which was influenced by their mode and departure time and the choices of fellow players. Cross-nested logit kernel choice models were estimated based on the game data. ResultsIn the recurring congestion scenario players learned to adopt the shared ride at the expense of transit; in the nonrecurring congestion scenario, random incidents increased the use of transit and shared car (ride alone). ConclusionsCongested traffic motivated a shift to ridesharing at the expense of private rides and public transport; however, the latter was highly demanded when traffic became unsmooth and travel times more uncertain. The implications can be translated to health promoting polices to encourage sustainable travel behaviors while also improving transportation efficiency.

Route Planning for Intra-Hospital Patient Transportation Using Metaheuristics and Mixed Integer Linear Programming.

Healthcare processes are complex and involve uncertainties to influence the service quality and health of patients. Patient transportation takes place between the hospitals or between the departments within the hospital (i.e., Inter- or Intra-Hospital Transportation respectively). The focus of our paper is route planning for transporting patients within the hospital. The route planning task is complex due to multiple factors such as regulations, fairness considerations (i.e., balanced workload amongst transporters), and other dynamic factors (i.e., transport delays, wait times). Transporters perform the physical transportation of patients within the hospital. In principle, each job allocation respects the transition time between the subsequent jobs. The primary objective was to determine the feasible number of transporters, and then generate the route plan for all determined transporters by distributing all transport jobs (i.e., from retrospective data) within each shift. Secondary objectives are to minimize the sum of total travel time and sum of total idle time of all transporters and minimize the deviations in total travel time amongst transporters. Our method used multi-staged Local Search Metaheuristics to attain the primary objective. Metaheuristics incorporate Mixed Integer Linear Programming to allocate fairly the transport jobs by formulating optimization constraints with bounds for satisfying the secondary objectives. The obtained results using formulated optimization constraints represent better efficacy in multi-objective route planning of Intra-Hospital Transportation of patients.

Adaptive Clustering and Scheduling for UAV-Enabled Data Aggregation

Using unmanned aerial vehicles (UAVs) is an effective way to gather data from Internet of Things (IoT) devices. To reduce data gathering time and redundancy, thereby enabling the timely response of state-of-the-art systems, one can partition a network into clusters and perform aggregation within each cluster. Existing works solved the UAV trajectory planning problem, in which the energy consumption and/or flight time of the UAV is the minimization objective. The aggregation scheduling within each cluster was neglected, and they assumed that data must be ready when the UAV arrives at the cluster heads (CHs). This paper addresses the minimum time aggregation scheduling problem in duty-cycled networks with a single UAV. We propose an adaptive clustering method that takes into account the trajectory and speed of the UAV. The transmission schedule of IoT devices and the UAV departure times are jointly computed so that (1) the UAV flies continuously throughout the shortest path among the CHs to minimize the hovering time and energy consumption, and (2) data are aggregated at each CH right before the UAV arrival, to maximize the data freshness. Intensive simulation shows that the proposed scheme reduces up to 35% of the aggregation delay compared to other benchmarking methods.

A machine learning-based simulation metamodeling method for dynamic scheduling in smart manufacturing systems

Conventional Digital Twins (DTs) in smart manufacturing rely on complex and time-intensive simulation models, hindering real-time DT-based decision-making. However, the availability of big data in Manufacturing Execution Systems (MES) enables training different Machine Learning (ML) models for fast and accurate predictions and decision assessments. Accordingly, this paper proposes an ML-Based Simulation Metamodeling Method (MLBSM) to facilitate DT-based decision-making for dynamic production scheduling in complex Stochastic Flexible Job Shop (SFJS) environments. The proposed MLBSM integrates three modules: a novel data vectorizing method (SPBM), multi-output Adaptive Boosting Regressor (ABR) models, and a new statistical risk evaluation method. SPBM converts unstructured production log data into numerical vectors for ABR training by calculating numeric penalty scores for each job based on the position of operations in the schedule queue. Each trained ABR predicts mean job completion times for various dynamic scenarios based on shift schedules. The risk evaluation method estimates the standard deviation of job completion times and calculates the delay probability scores for each job, aiding DT in promptly evaluating production schedules. Working seamlessly together, MLBSM modules present a novel way of using production log data for ML training and ultimately bypassing several computationally intensive simulation replications. In this research, a simulation model generates the synthetic MES data, focusing on the machining process at a photolithography workstation in the semiconductor manufacturing. Experiments demonstrate the MLBSM’s accuracy and efficiency, predicting high-risk jobs with over 80% recall and being at least 70 times faster than conventional simulation runs. Sensitivity analyses also confirm the MLBSM’s consistency under different workstation conditions.

Quantitative Analysis of Chondroitin Sulfate and Dermatan Sulfate Using Capillary Electrophoresis With Gaussian Distribution‐Based Peak Fitting for Improved Peak Resolution

ABSTRACTIn this work, capillary electrophoresis with Gaussian distribution‐based peak fitting for improved peak resolution for simultaneous quantitative analysis of chondroitin sulfate (CS) and dermatan sulfate (DS) was developed for the first time. The effects of different pH, voltage, and peak shapes on the peak fitting were investigated. The results indicate that separation conditions that affect peak shape have a significant impact on peak fitting. Under optimized separation conditions, the peak fitting achieves high accuracy and precision, the relative standard deviation (n = 5) of peak area and migration time of CS and DS were 1.1%, 1.8% and 0.94%, 0.84%, respectively. Furthermore, the results obtained by peak fitting were close to real values. The developed method was used to analyze the purity of CS. The results had a high consistency with the labeled value; furthermore, the peak fitting could point out the number of possible impurities. The developed method has good potential for the analysis of other glycosaminoglycans with peak overlap.