Fuzzy Data Mining Approach for Quantifying Signalized Intersection Level of Services Based on User Perceptions

Fang, Fang Clara; Pecheux, Kelley Klaver

doi:10.1061/(asce)0733-947x(2009)135:6(349)

Cited by 19 publications

(23 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most of the existing studies use Kmeans clustering in transportation problems, such as defining LOS for urban arterial roads, LOS for freeways, and vehicle LOS at intersections [34]. Few researchers have utilized fuzzy c-means clustering technique for defining vehicle LOS classification at intersections and urban roads [35][36][37][38]. None of the studies, at least to the author's knowledge, have utilized fuzzy c-means clustering techniques for defining threshold values for PLOS at signalized intersections in India.…”

Section: Review Of Earlier Studiesmentioning

confidence: 99%

Pedestrian perception-based level-of-service model at signalized intersection crosswalks

Marisamynathan

Vedagiri

2019

J. Mod. Transport.

View full text Add to dashboard Cite

Pedestrian level of service (PLOS) is an important measure of performance in the analysis of existing pedestrian crosswalk conditions. Many researchers have developed PLOS models based on pedestrian delay, turning vehicle effect, etc., using the conventional regression method. However, these factors may not effectively reflect the pedestrians' perception of safety while crossing the crosswalk. The conventional regression method has failed to estimate accurate PLOS because of the primary assumption of an arbitrary probability distribution and vagueness in the input data. Moreover, PLOS categories in existing studies are based on rigid threshold values and the boundaries that are not well defined. Therefore, it is an important attempt to develop a PLOS model with respect to pedestrian safety, convenience, and efficiency at signalized intersections. For this purpose, a video-graphic and user perception surveys were conducted at selected nine signalized intersections in Mumbai, India. The data such as pedestrian, traffic, and geometric characteristics were extracted, and significant variables were identified using Pearson correlation analysis. A consistent and statistically calibrated PLOS model was developed using fuzzy linear regression analysis. PLOS was categorized into six levels (A-F) based on the predicted user perception score, and threshold values for each level were estimated using the fuzzy c-means clustering technique. The developed PLOS model and threshold values were validated with the fieldobserved data. Statistical performance tests were conducted and the results provided more accurate and reliable solutions. In conclusion, this study provides a feasible alternative to measure pedestrian perception-based level of service at signalized intersections. The developed PLOS model and threshold values would be useful for planning and designing pedestrian facilities and also in evaluating and improving the existing conditions of pedestrian facilities at signalized intersections.Keywords Pedestrian Á Signalized intersection Á Level of service Á Fuzzy regression Á Fuzzy c-means Pedestrian perception-based level of service model at signalized intersection crosswalks 267 123

show abstract

Section: Review Of Earlier Studiesmentioning

confidence: 99%

Pedestrian perception-based level-of-service model at signalized intersection crosswalks

Marisamynathan

Vedagiri

2019

J. Mod. Transport.

View full text Add to dashboard Cite

show abstract

“…The researchers designed a genetically designed regression model having very high precision. Fang and Pecheux (2009) studied about the LOS of a signalized intersection taking user perception into account. Unsupervised data clustering technique such as fuzzy c-means clustering was used to get distinct cluster of user perceived delay and service rating.…”

Section: Background Of the Studymentioning

confidence: 99%

Pedestrian Level of Service Criteria for Urban Off-Street Facilities in Mid-Sized Cities

Sahani

Bhuyan

2014

Transport

View full text Add to dashboard Cite

Levels Of Service (LOS) evaluation criteria for off-street pedestrian facilities are not well defined in urban Indian context; hence an in-depth research is carried out in this regard. Defining Pedestrian Level of Service (PLOS) criteria is basically a classification problem; therefore a comparative study is made using three methods of clustering i.e. Affinity Propagation (AP), Self-Organizing Map (SOM) in Artificial Neural Network (ANN) and Genetic AlgorithmFuzzy (GA-Fuzzy) clustering. Pedestrian data are used on validation measure of clustering method to obtain optimal number of cluster used in defining PLOS categories. To decide the most suitable algorithm applicable in defining PLOS criteria for urban off-street facilities in Indian context, Wilk’s Lambda is used on results of the three clustering methods. It is observed from the analysis that GA-Fuzzy is the most suitable clustering analysis among the three methods. With the help of GA-Fuzzy clustering analysis the ranges of the four measuring parameters (average pedestrian space, flow rate, speed of pedestrian and volume to capacity ratio) are defined by using the data collected from two mid-sized cities located in the state of Odisha, India. It is also observed that at >16.53 m2/ped average space, ≤0.061 ped/sec/m flow rate, >1.21 speed and ≤0.34 v/c ratio pedestrians can move in their desired path at LOS ‘A’ without changing movements and it is the best condition for off-street facilities. But in the pedestrian facility having ≤4.48 m2/ped average space, >0.146 ped/sec/m flow rate, ≤0.62 average speed and >1.00 v/c ratio, pedestrian movement is severely restricted and frequent collision among users occurs. The ranges of the parameters used for LOS categories found in this study for Indian cities are different from that mentioned in HCM (Highway Capacity Manual 2010) because of differences in population density, traffic flow condition, geometric structure and some other factors.

show abstract

“…In the case of the latter, the expressions such as 'big' or 'small' are of greater importance than the precise values expressed in standardized units. Some researchers propose the use of fuzzy clustering to create boundaries between the LOS factors [7], [8].…”

Section: Survey Localisation and Proceduresmentioning

confidence: 99%

Evaluation of Distance Between Pedestrian Crossings by Students in One of the Polish Cities

Kruszyna

2013

Archives of Civil Engineering

View full text Add to dashboard Cite

In this paper, the distances between pedestrian crossings in twenty one places in the city of Wrocław, together with their evaluation by the researched groups of students, were analyzed. The database created from the collected questionnaires contains a set of two-dimensional variables: the distance between crossings and the rating of the students. The database set was analyzed using a fuzzy data mining approach to create particular clusters. Various numbers of clusters were analyzed, and the division of data into three clusters made it possible to relate the analysis to the LOS methodology. Each variable was enriched with a third dimension representing a membership value. The obtained evaluated distances are similar to values recommended in literature, although the distances highly evaluated by the students do not often occur in reality. This might suggest that there is the need to create new crossings, especially in the city centre, where pedestrian trafic is or should be important.

show abstract

Fuzzy Data Mining Approach for Quantifying Signalized Intersection Level of Services Based on User Perceptions

Cited by 19 publications

References 4 publications

Pedestrian perception-based level-of-service model at signalized intersection crosswalks

Pedestrian perception-based level-of-service model at signalized intersection crosswalks

Pedestrian Level of Service Criteria for Urban Off-Street Facilities in Mid-Sized Cities

Evaluation of Distance Between Pedestrian Crossings by Students in One of the Polish Cities

Contact Info

Product

Resources

About