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Tytuł artykułu

Evaluation of distance between pedestrian crossings by students in one of the Polish cities

Autorzy
Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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 traffic is or should be important.
Rocznik
Strony
547--559
Opis fizyczny
Bibliogr. 17 poz., il., tab.
Twórcy
autor
  • Wroclaw University of Technology, Department of Civil Engineering, Wrocław
Bibliografia
  • 1. de LAVALETTE, B.C., TIJUS, CH., POITRENAUD, T., LEPROUX, CH., BERGERON, J., THOUEZ, J.-P., 2009. Pedestrian crossing decision-making: A situational and behavioral approach. Safety Science 47 (10), 1248-1253.
  • 2. PAPADIMITRIOU, E., 2012. Theory and models of pedestrian crossing behaviour along urban trips. Transportation Research Part F 15 (1) 75–94.
  • 3. SISIOPIKU, V.P., AKIN, D., 2003. Pedestrian behaviors at and perceptions towards various pedestrian facilities: an examination based on observation and survey data. Transportation Research Part F 6 (4), 249-274.
  • 4. HOLLAND, C., HILL, R., 2007. The effect of age, gender and driver status on pedestrians’ intentions to cross the road in risky situations. Accident Analysis and Prevention 39 (2), 224-237.
  • 5. CAVALLO, V., LOBJOIS, R., 2007. Age-related differences in street-crossing decision: the effects of vehicle speed and time constraints on gap detection in an estimation task. Accident Analysis and Prevention 39 (6), 934-943.
  • 6. Transportation Research Board, 2000. Highway capacity manual. Special Rep. No. 209, National Research Council, Washington, D.C.
  • 7. FANG, C.F., PECHEUX, K.K., 2009. Fuzzy data mining approach for quantifying signalized intersection level of services based on user perceptions. Journal of Transportation Engineering 135 (6), 349-358.
  • 8. KIKUCHI, S., CHAKROBORTY, P., 2006. Frameworks to represent the uncertainty when determining the level of service. Transportation Research Record. 1968, Transportation Research Board, Washington, D.C., 53-62.
  • 9. ADELI H., KARIM, A., 2000. Fuzzy-wavelet RBFNN model for freeway incident detection. Journal of Transportation Engineering 126 (6), 464-471.
  • 10. HAWAS, Y.E., 2004. Development and calibration of route choice utility models: neuro-fuzzy approach. Journal of Transportation Engineering 130 (2), 171-182.
  • 11. YIN, H., WONG, S.C., XU, J., WONG, C.K., 2002. Urban traffic flow prediction using a fuzzy-neural approach. Transportation Research Part C 10 (2), 85-98.
  • 12. FANG, C., ELEFTERIADOU, L., PECHEUX, K.K., PIETRUCHA, M.T., 2003. Using fuzzy clustering of user perception to define levels of service at signalized intersections. Journal of Transportation Engineering 129 (6), 657-663.
  • 13. ZHANG, J.S., LEUNG, Y.W., 2004. Improved Possibilistic C-Means Clustering Algorithms. IEEE Transactions On Fuzzy Systems, 12 (2), 209-217.
  • 14. LESKI, J., 2003. Towards a robust fuzzy clustering. Fuzzy Sets and Systems 137 (2), 215-233.
  • 15. ALI, Y.M., ZHANG, L., 2001. A methodology for fuzzy modeling of engineering systems. Fuzzy Sets and Systems 118 (2), 181-197.
  • 16. BEZDEK, J.C., 1981. Pattern recognition with fuzzy objective function algorithm. Plenum Press, New York.
  • 17. STRONG, CH., YE, Z., 2010. Spillover effects of yield-to-pedestrian channelizing devices. Safety Science 48 (3), 342-347.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-141bef64-6537-49d8-bb1d-f1672973c418
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