1. Pedestrian Crossing Speed Model Using Multiple Regression Analysis

2. Is the system of moving of people and goods from one point to the other in any given time. I. Introduction Transportation pedestrians

3. Transportation cycle Introduction SUB MODE (TRICYCLE / JEEPNEYS) MAIN MODE (LRT / MRT / BUSES) SUB MODE (TRICYCLE / JEEPNEYS) DESTINATION ORIGIN MAIN MODE (LRT / MRT / BUSES) WALKING

4. Main Problem The typical crosswalk signal is of fixed time. The time allotted is not actuated to the number of pedestrians. No apparent provisions for the elderly and disabled. Introduction

5. Objectives To enumerate and identify the different variables that may affect pedestrian speed. To formulate a model to predict the pedestrian speed in a crosswalk for any given cycle. To establish an algorithm for an alternative computation of pedestrian speed.

6. Conceptual Framework Design of Pedestrian Crossing Time Pedestrian Speed Profile Existing Signal Timing Volume Of Pedestrians Crosswalk Dimensions Vehicular Volume and Movement

7. Significance of the Study The result may help the Traffic Engineers for the proper allocation of time for crossing pedestrians. The output of this study is a call to Urban Planners of the importance of pedestrian flow to the transportation system.

8. IV. Methodology ANALYSIS DATA COLLECTION INTRODUCTION REVIEW OF RELATED LITERATURE CROSSWALK INVENTORY SURVEY PEDESTRIAN VOLUME COUNT AND PEDESTRIAN TRAVEL TIME SURVEY CROSSWALK ANALYSIS PEDESTRIAN CROSSING SPEED MODEL CONCLUSION CONCLUSION AND RECOMMENDATION RECOMMENDATION

9. Scope and Limitations Criteria for selecting crosswalk –Crosswalks must be in the proximity of school or any educational establishments. –Must have a functioning signal at the time of survey. –All must be in one corridor. –Must have existing median or refuge island. IV. Methodology

10. Study Area Four (4) crosswalks have been chosen to be the study area for this research. These crosswalks are: –CCP –Stop&Shop –Lacson –Gilmore

11. V. Presentation, Analysis and Interpretation of data Existing Situation of the Crosswalk

12. TABLE 4-6 - Corresponding properties of crosswalk CrosswalkLength (m) Width (m) Green time (s) Cycle time (s) CCP22.62.425106 Stop & Shop22.62.412179 Lacson262.426195 Gilmore122.41381

13. Speed Profile Cumulative frequency curve of crossing speeds taken at all crosswalks on i and j direction

14. Speed Profile DirectionmeanStandard deviation 15th percentile 85th percentile i1.2260.2411.0001.413 j1.2030.2690.9831.444 Table 4.5 - 15th and 85th percentiles on each direction of all crosswalks

15. Non Compliance Ratio Non-Compliance Ratio for each crosswalk and all sites combined.

16. Design Speed Profile Column graph of length –green time ratio per crosswalk

17. Relationships between variables

18. Speed – Density Relationship Speed on i direction vs. total density (K) Speed on j direction vs. total density (K)

19. Speed – Density Relationship Speed vs. density relationship along i direction (ki) Speed vs. density relationship along j direction (kj)

20. Speed – Length Relationship

21. Model Estimation

22. Model 1 Ws = f (length (Lc), density along i direction (ki), density along j direction (kj))

23. Observations246.000 Sum of weights246.000 DF242.000 R²0.215 Adjusted R²0.205 Goodness of fit statistics (Ws) Model 1 SourceDFSum of squaresMean squaresFPr > F Model33.0761.02522.114< 0.0001 Error24211.2210.046 Corrected Total24514.297 Analysis of variance (Variable Ws)

24. Model 1 Equation of the model Ws = 0.725 + 0.027*Lc - 0.308*ki -0.052*kj

25. Model 2 Ws = f (length (Lc), flow along i direction (qi), flow along j direction (qj))

26. Observations246.000 Sum of weights246.000 DF242.000 R²0.212 Adjusted R²0.202 Goodness of fit statistics (Ws) Model 2 SourceDFSum of squaresMean squaresFPr > F Model33.0761.00921.653< 0.0001 Error24211.2720.046 Corrected Total24514.297 Analysis of variance (Variable Ws)

27. Model 2 Equation of the model Ws = 0.722 + 0.026*Lc - 0.182*qi - 0.009*qj

28. Model 3 Ws = f (length (Lc), volume along i direction (ki), volume along j direction (kj), green time (G))

29. Observations246.000 Sum of weights246.000 DF242.000 R²0.218 Adjusted R²0.205 Goodness of fit statistics (Ws) Model 3 SourceDFSum of squaresMean squaresFPr > F Model43.1140.77916.735< 0.0001 Error24011.1650.047 Corrected Total24414.279 Analysis of variance (Variable Ws)

30. Model 3 Equation of the model Ws = 0.691 + 0.025*Lc - 0.005*Wvi - 0.001*Wvj + 0.003*G

31. Model Selection After determining the possible variables and its relation to speed, we select MODEL 1 as our final model. Ws = 0.725 + 0.027*Lc - 0.308*ki -0.052*kj This model has the highest value of F. Even if MODEL 3 obtained the highest value of R 2, the researchers would like to emphasize that in multiple regression, the high value of F rather than R 2.

32. Conclusion and Recommendations

33. Findings There are a significant number of pedestrians who do not clearly understand the meaning conveyed by the pedestrian signalization. About 60% or 6 out of 10 pedestrians do not follow the pedestrian signals. The pedestrian speed profile in all four crosswalk is slightly lower than the recommended speed profile. The signal timing allocation in these crosswalks is not based the length of the crosswalk. Even if the crosswalk length is increased, the signal timing does not necessarily be increased. (Existing Case)

34. Conclusions To enumerate and identify the different variables that may affect pedestrian speed. Several factors may affect the speed of the pedestrians and the researchers came to conclusion that it is not merely by density. The dimension of the crosswalk affect the speed in terms that when the length of the crosswalk is lengthen, the tendency is to increase their speed as not to be caught up by the movement of traffic. In the course of our research, we conclude that the presented variables are not enough to explain the variation on speed.

35. Conclusions To formulate a model to predict the pedestrian speed in a crosswalk for any given cycle. The researchers had formulated a model to predict the pedestrian signal but due to its low coefficient of determination, the researchers conclude that the model may not predict the correct pedestrian speed located outside the study area.

36. Conclusions To establish an algorithm for an alternative computation of pedestrian speed. Due to the low coefficient of determination of the model, the researchers failed to present an alternative computation of pedestrian speed.

37. Recommendations Additional model for obstructions and wall avoidance of the microscopic pedestrian simulation model is suggested to perform a better capacity analysis. Integration of pedestrian flow in planning and design. Inclusion of pedestrians to the education system. Use of fully actuated signals for pedestrians and of countdown pedestrian timers to minimize vehicular conflicts and non compliancy.

38. Improvement of the automatic video data collection toward the occlusion problem is highly recommended to enhance the performance of the system for higher pedestrian traffic density. Further studies on pedestrian flow which can include: –Larger study area that can include all metro manila. –Analysis of both signalized and unsignalized crosswalks. –Analysis for both one-way and two-way pedestrian lane.

39. Inclusion of model variables such as: delay pedestrian generators (schools, offices, railway station, malls) pavement markings dissipation time vehicular movement and speed pedestrian interaction queuing time Pedestrian analysis using: Benefit cost cellular model Cellular automata model Magnetic force model Social Force model Queuing network model

40. THANK YOU!