GIS-Based Multivariate Spatial Clustering for Traffic Pattern Recognition using Continuous Counting Data

2020 ◽  
Vol 2674 (10) ◽  
pp. 583-598 ◽  
Author(s):  
Md Mehedi Hasan ◽  
Jun-Seok Oh
Keyword(s):  
Pattern Recognition ◽  
Travel Behavior ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Seasonal Adjustment ◽  
Traffic Data ◽  
Traffic Patterns ◽  
Traffic Pattern ◽  
Adjustment Factors ◽  
Clustering Approach

Traffic count stations play a key role in measuring roadway characteristics and traffic performance by collecting and monitoring travel behavior and vehicle data. Continuous counting stations (CCSs), which count traffic volumes continuously throughout the year, are used to develop seasonal adjustment factors to convert short-term traffic counts (average daily traffic) to annual average daily traffic (AADT). As data collection is conducted at limited locations, many state Departments of Transportation (DOTs) group the CCSs based on different traffic patterns and estimate the AADT at specific locations by considering seasonal adjustment factors. Computer-based clustering approaches are widely used in grouping continuous traffic data for their accuracy in traffic pattern recognition. However, most of the clustering techniques do not consider the spatial constraints and therefore overlooked the locational proximity and inference from nearby traffic data. In this study, a GIS-based multivariate spatial clustering approach was developed to recognize statewide traffic patterns based on temporal and spatial variables. A total of 12 optimal clusters were automatically computed and labeled based on the proposed clustering algorithm. The proposed clustering approach was compared and validated based on machine learning classifiers. The results showed that it outperformed the traditional Michigan DOT clustering approach and was consistent in nature across different years. The model was applied to estimate the AADT, and good accuracy was detected relative to other approaches. The proposed clustering method offers a new approach to group traffic patterns by simultaneously incorporating proximity and similarity of traffic data.

A statistical practical methodology of statewide traffic pattern grouping and precision analysis

2009 ◽  
Vol 36 (3) ◽  
pp. 427-438 ◽  
Author(s):  
Shy Bassan
Keyword(s):  
Traffic Volume ◽  
Seasonal Adjustment ◽  
Traffic Data ◽  
Annual Average ◽  
Traffic Pattern ◽  
Traffic Characteristics ◽  
Annual Average Daily Traffic ◽  
Adjustment Factors ◽  
And Performance ◽  
Day By Day

Traffic data in general and traffic volume in particular are collected to determine the use and performance of the roadway system. Due to budget limitations, traffic volume cannot be counted day by day for every roadway within the state. Therefore, the volume on roadways without automatic traffic recorders (ATRs) can be determined by taking portable short-duration counts and using adjustment factors to produce annual average daily traffic (AADT) at a specific location. This study presents a statistical practical methodology that develops traffic pattern groups (TPGs) by combining roadways with similar traffic characteristics such as volume, seasonal variation, and land use in Delaware, USA. Monthly seasonal adjustment factors and their coefficient of variance (FCV) are analyzed for each group. To meet the desired confidence level and precision intervals, the TPGs’ ATR inventory is examined such that the required sample size is determined by the critical month.

Observed Traffic Pattern Changes Due to Variable Tolls

2000 ◽  
Vol 1732 (1) ◽  
pp. 55-59 ◽  
Author(s):  
Mark Burris ◽  
Michael C. Pietrzyk ◽  
Chris R. Swenson
Keyword(s):  
Travel Behavior ◽  
Test Group ◽  
Time Of Day ◽  
Control Group ◽  
Traffic Data ◽  
Traffic Patterns ◽  
Traffic Pattern ◽  
Variable Pricing ◽  
Traffic Volumes ◽  
Group Variable

In August 1998 the Midpoint and Cape Coral Bridges in Lee County, Florida, began charging variable tolls based on the time of day. Traffic data collected at these two bridges are examined, including the number of vehicles, by transaction payment type and by time of day. These data were analyzed to determine if variable pricing toll discounts have changed traffic patterns at the toll bridges. Data collected from January through July 1998 (before the implementation of variable pricing) were compared to data collected from August through December 1998 (during variable pricing). The data were examined in both aggregate and half-hour time increments throughout the day. This allowed for initial analysis of ways that traffic volumes have shifted by time of day due to discounted tolls. Data were also collected on the payment methods of all bridge users. This information is critical, since only those users paying their toll electronically (approximately 23 percent of transactions) are eligible for the variable pricing toll discounts. Therefore, two groups were examined separately—eligible users (the test group) and ineligible users (the control group). Variable pricing was found to have caused significant changes in the travel behavior of eligible users. On the Cape Coral and Midpoint Bridges, the number of eligible users increased significantly during the discount periods and decreased significantly during the peak periods. In contrast, changes in the traffic patterns of ineligible users were found to be statistically insignificant.

scholarly journals Massively scalable density based clustering (DBSCAN) on the HPCC systems big data platform

2021 ◽  
Vol 10 (1) ◽  
pp. 207
Author(s):  
Yatish H. R. ◽  
Shubham Milind Phal ◽  
Tanmay Sanjay Hukkeri ◽  
Lili Xu ◽  
Shobha G ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Computation Time ◽  
Large Data ◽  
Single Node ◽  
Data Set ◽  
Traffic Pattern ◽  
Density Based Clustering ◽  
Data Points ◽  
Hpcc Systems

<span id="docs-internal-guid-919b015d-7fff-56da-f81d-8f032097bce2"><span>Dealing with large samples of unlabeled data is a key challenge in today’s world, especially in applications such as traffic pattern analysis and disaster management. DBSCAN, or density based spatial clustering of applications with noise, is a well-known density-based clustering algorithm. Its key strengths lie in its capability to detect outliers and handle arbitrarily shaped clusters. However, the algorithm, being fundamentally sequential in nature, proves expensive and time consuming when operated on extensively large data chunks. This paper thus presents a novel implementation of a parallel and distributed DBSCAN algorithm on the HPCC Systems platform. The algorithm seeks to fully parallelize the algorithm implementation by making use of HPCC Systems optimal distributed architecture and performing a tree-based union to merge local clusters. The proposed approach* was tested both on synthetic as well as standard datasets (MFCCs Data Set) and found to be completely accurate. Additionally, when compared against a single node setup, a significant decrease in computation time was observed with no impact to accuracy. The parallelized algorithm performed eight times better for higher number of data points and takes exponentially lesser time as the number of data points increases.</span></span>

scholarly journals Navigation Decision Support: Discover of Vessel Traffic Anomaly According to the Historic Marine Data

2020 ◽  
Vol 15 (3) ◽  
Author(s):  
Andrius Daranda ◽  
Gintautas Dzemyda
Keyword(s):  
Anomaly Detection ◽  
Traffic Safety ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
K Nearest Neighbors ◽  
Traffic Pattern ◽  
Dbscan Clustering ◽  
Marine Traffic ◽  
The Right ◽  
Traffic Anomaly

During the last years, marine traffic dramatically increases. Marine traffic safety highly depends on the mariner’s decisions and particular situations. The watch officer must continuously observe the marine traffic for anomalies because the anomaly detection is crucial to predict dangerous situations and to make a decision in time for safe marine navigation. In this paper, we present marine traffic anomaly detection by the combination of the DBSCAN clustering algorithm (Density- Based Spatial Clustering of Applications with Noise) with k-nearest neighbors analysis among the clusters and particular vessels. The clustering algorithm is applied to the historic marine traffic data – a set of vessel turn points. In our experiments, the total number of turn points was about 3 million, and about 160 megabytes of computer store was used. A formal numerical criterion to com-pare anomaly with normal traffic flow case has been proposed. It gives us a possibility to detect the vessels outside the typical traffic pattern. The proposed meth-od ensures the right decisions in different oceanic scale or hydro meteorology conditions in the detection of anomaly situation of the vessel.

Regional Traffic Control System Study Based on Pattern Identification

2011 ◽  
Vol 121-126 ◽  
pp. 4552-4559
Author(s):  
Bai Zhan Xia ◽  
Huai Wen He
Keyword(s):  
Control System ◽  
Traffic Control ◽  
Clustering Algorithm ◽  
Pattern Identification ◽  
System Structure ◽  
Traffic Patterns ◽  
Traffic Pattern ◽  
Signal Coordination ◽  
Traffic Control System ◽  
And Control

The thesis introduces traffic patterns definition and identification. Combined with actual project it has established the regional traffic signal coordination and control system based on particle swarm K-means clustering algorithm pattern identification. It puts forward system structure and working principles with discussions focused on several key problems existing in traffic pattern identification process.

Fuzzy ART Neural Network Model for Automated Detection of Freeway Incidents

1998 ◽  
Vol 1634 (1) ◽  
pp. 56-63 ◽  
Author(s):  
Sherif S. Ishak ◽  
Haitham M. Al-Deek
Keyword(s):  
Neural Networks ◽  
False Alarm ◽  
False Alarm Rate ◽  
Back Propagation ◽  
Incident Detection ◽  
Traffic Patterns ◽  
Traffic Pattern ◽  
Detection Algorithms ◽  
Fuzzy Art ◽  
Freeway Incidents

Pattern recognition techniques such as artificial neural networks continue to offer potential solutions to many of the existing problems associated with freeway incident-detection algorithms. This study focuses on the application of Fuzzy ART neural networks to incident detection on freeways. Unlike back-propagation models, Fuzzy ART is capable of fast, stable learning of recognition categories. It is an incremental approach that has the potential for on-line implementation. Fuzzy ART is trained with traffic patterns that are represented by 30-s loop-detector data of occupancy, speed, or a combination of both. Traffic patterns observed at the incident time and location are mapped to a group of categories. Each incident category maps incidents with similar traffic pattern characteristics, which are affected by the type and severity of the incident and the prevailing traffic conditions. Detection rate and false alarm rate are used to measure the performance of the Fuzzy ART algorithm. To reduce the false alarm rate that results from occasional misclassification of traffic patterns, a persistence time period of 3 min was arbitrarily selected. The algorithm performance improves when the temporal size of traffic patterns increases from one to two 30-s periods for all traffic parameters. An interesting finding is that the speed patterns produced better results than did the occupancy patterns. However, when combined, occupancy–speed patterns produced the best results. When compared with California algorithms 7 and 8, the Fuzzy ART model produced better performance.

scholarly journals Changes in Passengers’ Travel Behavior Due to COVID-19

2021 ◽  
Vol 13 (14) ◽  
pp. 7974
Author(s):  
Dong-Gyun Ku ◽  
Jung-Sik Um ◽  
Young-Ji Byon ◽  
Joo-Young Kim ◽  
Seung-Jae Lee
Keyword(s):  
Travel Behavior ◽  
Occupancy Rate ◽  
Traffic Patterns ◽  
Similar Time ◽  
Usage Rate ◽  
Contagious Nature ◽  
Passenger Volume ◽  
Service Rates ◽  
Time Periods ◽  
Usage Rates

The COVID-19 outbreak in 2020 has changed the way people travel due to its highly contagious nature. In this study, changes in the travel behavior of passengers due to COVID-19 in the first half of 2020 were examined. To determine whether COVID-19 has affected the use of transportation by passengers, paired t-tests were conducted between the passenger volume of private vehicles in Seoul prior to and after the pandemic. Additionally, the passenger occupancy rate of different modes of transportation during the similar time periods were compared and analyzed to identify the changes in monthly usage rate for each mode. In the case of private vehicles and public bicycles, the usage rates have recovered or increased when compared to those of before the pandemic. Conversely, bus and rail passenger service rates have decreased from the previous year before the pandemic. Furthermore, it is found that existing bus and rail users have switched to the private auto mode due to COVID-19. Based on the results, traffic patterns of travelers after the outbreak and implications responding to the pandemic are discussed.

scholarly journals An Enhanced Spectral Clustering Algorithm with S-Distance

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 596
Author(s):  
Krishna Kumar Sharma ◽  
Ayan Seal ◽  
Enrique Herrera-Viedma ◽  
Ondrej Krejcar
Keyword(s):  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Clustering Algorithms ◽  
Rank Test ◽  
Customer Churn ◽  
Signed Rank ◽  
Signed Rank Test ◽  
Spectral Clustering Algorithm ◽  
Industrial Databases

Calculating and monitoring customer churn metrics is important for companies to retain customers and earn more profit in business. In this study, a churn prediction framework is developed by modified spectral clustering (SC). However, the similarity measure plays an imperative role in clustering for predicting churn with better accuracy by analyzing industrial data. The linear Euclidean distance in the traditional SC is replaced by the non-linear S-distance (Sd). The Sd is deduced from the concept of S-divergence (SD). Several characteristics of Sd are discussed in this work. Assays are conducted to endorse the proposed clustering algorithm on four synthetics, eight UCI, two industrial databases and one telecommunications database related to customer churn. Three existing clustering algorithms—k-means, density-based spatial clustering of applications with noise and conventional SC—are also implemented on the above-mentioned 15 databases. The empirical outcomes show that the proposed clustering algorithm beats three existing clustering algorithms in terms of its Jaccard index, f-score, recall, precision and accuracy. Finally, we also test the significance of the clustering results by the Wilcoxon’s signed-rank test, Wilcoxon’s rank-sum test, and sign tests. The relative study shows that the outcomes of the proposed algorithm are interesting, especially in the case of clusters of arbitrary shape.

The prediction of aquifer groundwater level based on spatial clustering approach using machine learning

2021 ◽  
Vol 193 (4) ◽  
Author(s):  
Hamid Kardan Moghaddam ◽  
Sami Ghordoyee Milan ◽  
Zahra Kayhomayoon ◽  
Zahra Rahimzadeh kivi ◽  
Naser Arya Azar
Keyword(s):  
Machine Learning ◽  
Groundwater Level ◽  
Spatial Clustering ◽  
Clustering Approach

A Hard C-Means Clustering Algorithm Incorporating Membership KL Divergence and Local Data Information for Noisy Image Segmentation

2017 ◽  
Vol 32 (04) ◽  
pp. 1850012 ◽  
Author(s):  
R. R. Gharieb ◽  
G. Gendy ◽  
H. Selim
Keyword(s):  
Image Segmentation ◽  
Membership Function ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cluster Center ◽  
Local Data ◽  
Cluster Membership ◽  
Kl Divergence ◽  
Clustering Approach ◽  
Center Distance

In this paper, the standard hard C-means (HCM) clustering approach to image segmentation is modified by incorporating weighted membership Kullback–Leibler (KL) divergence and local data information into the HCM objective function. The membership KL divergence, used for fuzzification, measures the proximity between each cluster membership function of a pixel and the locally-smoothed value of the membership in the pixel vicinity. The fuzzification weight is a function of the pixel to cluster-centers distances. The used pixel to a cluster-center distance is composed of the original pixel data distance plus a fraction of the distance generated from the locally-smoothed pixel data. It is shown that the obtained membership function of a pixel is proportional to the locally-smoothed membership function of this pixel multiplied by an exponentially distributed function of the minus pixel distance relative to the minimum distance provided by the nearest cluster-center to the pixel. Therefore, since incorporating the locally-smoothed membership and data information in addition to the relative distance, which is more tolerant to additive noise than the absolute distance, the proposed algorithm has a threefold noise-handling process. The presented algorithm, named local data and membership KL divergence based fuzzy C-means (LDMKLFCM), is tested by synthetic and real-world noisy images and its results are compared with those of several FCM-based clustering algorithms.

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