ANALYSIS AND MODELING OF GAP ACCEPTANCE AT UNPROTECTED MID- BLOCK CROSSWALKS A CASE STUDY OF CHANDIGARH CITY

Transcription

1 TRANSPORTATION INFRASTRUCTURE PROJECTS : CONCEPTION TO EXECUTION 2019 (Theme : DESIGN SAFETY ISSUES) ANALYSIS AND MODELING OF GAP ACCEPTANCE AT UNPROTECTED MID- BLOCK CROSSWALKS A CASE STUDY OF CHANDIGARH CITY Presented by : Ankit Bansal (Research Scholar) Supervisor(s): Dr. Tripta Goyal & Dr. Umesh Sharma 1

2 CONTENTS Brief about Pedestrian Facilities Introduction Need of the Study Objectives Study Area Survey Methods Data Collection and Analysis Conclusions References 2

3 TYPES OF PEDESTRIAN FACILITIES Uninterrupted Pedestrian Facilities Sidewalks & Walkways Queueing Areas Shared Pedestrian Bicycle Facilities Interrupted Pedestrian Facilities Signalized Intersection Crosswalks Unsignalized Intersection Crosswalks Midblock Crosswalks

4 MIDBLOCK CROSSWALKS Mid-bock crosswalks are to be provided for people to cross the street safely between building entries or bus stop locations or active land uses on opposite sides of the street. It is completely different and complex hazardous crossing locations as compared to signalized and unsignalized intersection crosswalks. These crossings can be controlled and uncontrolled. Controlled or Protected crossings are either equipped with the traffic signals known as Pelican signals or with zebra crossings gaining priority of movement with respect to vehicular traffic. Uncontrolled or unprotected crossings are more critical locations for crossing as no designated markings have been provided, that encourage the pedestrians to adopt risky crossing behavior. 4

5 INTRODUCTION Urbanisation has resulted in the vast expansion of transportation infrastructure due to which there is incessant growth of traffic in Indian cities. Traffic congestion has become a major problem nowadays, which results in shifting of the people towards the nonmotorised modes. Walking being the traditional non-motorised mode, has always been the prime source of manoeuvrability for the short distance trips, but the facilities ensuring the adequate safety while walking are inefficient. Improper facilities hamper the effectiveness of non-motorised modes and result in accidents. Traffic accidents involving pedestrians have now become a major safety problem all over the world, particularly in developing countries (Mohan et al., 2015). The majority of the accidents involving pedestrians take place when pedestrians cross or enter intersections. This scenario is most commonly observed at unprotected mid-block crossing locations. 5

6 INTRODUCTION (cont..) Maximum number of pedestrian accidents take place at such crossing locations due to : uncontrolled higher speed of vehicles pedestrians tendency in taking risks due to higher waiting time (Garder, 2004; Diogenes and Lindau, 2010). Difficulty experienced by pedestrians in crossing at mid-block segments : increases with an increase in vehicle volume, speed of the vehicle, crossing width, and length of traffic signal cycles. decreases with the presence of a marked crosswalk, traffic signals, and restricted medians (Baltes and Chu, 2002; Zhao et al. 2014; Kadali and Vedagiri, 2015). 6

7 NEED OF THE STUDY Lack of adherence to traffic regulations at mid-block crossings particularly by drivers create a paradigm in which pedestrians engage in irrational crossing and forcefully acquire the right of way that leads to the unnecessary larger vehicular delays. Due negligence of the regulation of pedestrians are the major reason for the fatal accidents. So, decrease in the pedestrian accident rate is one of the major targets while planning the roads, which could be acquired could be acquired only by investigating the measures such as acceptability of gaps in the traffic stream, pedestrian behavioral characteristics while crossing the street at the mid-block crosswalks (Ponnaluri, 2012; Chutani and Parida, 2013). Gap acceptance by the pedestrian plays a major role in determining the safety of pedestrians. Lesser the gap size, more will be the accident risk to the pedestrian. Therefore, this parameter needs to be studied in detail. 7

8 What is Pedestrian GAP? The gap is defined as the time headway between two successive vehicles and it is termed as accepted gap when pedestrian from a minor traffic crosses or merges into the traffic, otherwise the gap is termed as rejected. The gap accepted by the pedestrians governs the safety margin of the pedestrians, and affects the quality of service provided by the mid-block crossings (Jain et al., 2014). The gap acceptance gets influenced by various factors such as traffic characteristics, pedestrian behavioral characteristics and socio-demographic characteristics. CRITICAL GAP : The minimum average length that is accepted by half of all pedestrians Maximum gap to rejected cross the is road smaller safely. than the critical gap. Maximum gap rejected is smaller than the critical gap 8

9 Pedestrian Safety Margin Safety margin of a pedestrian is defined as the time taken by the approaching vehicle to reach the point at the other end of road where the pedestrian ends crossing the road without any conflict (Sahani et al., 2018). Safety margin of the pedestrian depends on the variables such as critical gap, crossing time, demographic characteristics such as age and gender of the pedestrian. 9

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11 OBJECTIVES To analyse the gap acceptance by the pedestrians at unprotected mid-block crosswalks of Chandigarh city. To model the gap size so that pedestrians could safely cross the urban roads of the well-planned Chandigarh city 11

12 STUDY AREA City : Chandigarh One of the planned city more than 8 lakhs vehicles which are shared by 12 lakhs population averaging more than one vehicle for less than 2 persons. An integrated system of seven categories of roads was designed to ensure efficient traffic circulation. The city's vertical roads run Northeast/Southwest (The 'Paths').The horizontal roads run northwest/southeast ( The Margs ). Chandigarh has seven types of roads classified from V1 to V7. Later on, a pathway for cyclists called V8 were added to this circulation system.

13 STUDY AREA (cont ) 13 midblock crosswalks situated along the prominent arterial road of Chandigarh i.e. Dakshin Marg (also known as V-2 road) were selected for the present study. C1-C8 crosswalks were marked, while C9-C13 were without marking crosswalks.

14 SURVEY METHODS VIDEOGRAPHIC TEHNIQUE Data was extracted from the two hours video survey during 9.00 am am. OBSERVATIONAL SURVEY Geometric Data was collected. 14

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16 Pedestrian Socio-Demographic Characteristics Variable Type of Variable Attributes Count Percentage Gender Age Group Size Nominal* Ordinal* Ordinal* Male-1, Female , 18 to 30-2, 30 to 45-3, 45 to 60-4, > , 2-2, 3-3 Baggage Continuous Mobile Continuous Note: Nominal and Ordinal are Discrete Variables

17 TYPES OF CROSSING BEHAVIOR ADOPTED BY PEDESTRIANS One-Stage Perpendicular (OP) One-Stage Oblique (OO) One-Stage Mixed (OM) Two-Stage Perpendicular (TP) Two-Stage Oblique (TO) Two-Stage Mixed (TM) 17

18 ONE-STAGE CROSSING TWO-STAGE CROSSING 18

19 Pedestrian Socio-Demographic Characteristics Variable Type of Variable Attributes Count Percentage Crossing Behavior Pattern Continuous Note: Nominal and Ordinal are Discrete Variables OP OO OM TP TO TM

20 Geometric Characteristics Variable Type of Variable Attributes Count Percentage Width of Crosswalk (m) Continuous -- Min 0, Max 3.2 Length of Crosswalk (m) Continuous -- Min 20.72, Max Median Open Width (m) Continuous -- Min 2.1, Max 3.4 Visibility of Cross/Zebra Markings Presence of Guard Rails Ordinal Nominal Not Visible -1 Slightly Visible-2 Moderately Visible-3 Highly Visible-4 No-1 Yes

21 Land-Use Accessibility Variable Type of Variable Attributes Count Percentage Nature of Land-use Nominal Educational-1 Commercial-2 Residential-3 Shopping-4 Recreational-5 Mixed

22 Flow Characteristics Variable Type of Variable Attributes Count Average Conflicting Traffic Flow (PCU/h) Continuous -- Min 1952, Max 3201 Average Conflicting Traffic Flow (PCU/15min) Continuous -- Min 488, Max Average Vehicular Speed (km/h) Continuous -- Min 43.6, Max 55.5 Average Vehicular Speed (m/s) Continuous -- Min 12.1, Max Average Pedestrian Flow (ped/h) Continuous -- Min 19, Max 109 Average Pedestrian Flow (ped/15min) Continuous -- Min 4.75, Max Average Pedestrian Speed (m/s) Continuous -- Min 1.29, Max

23 Flow Characteristics Variable Type of Variable Attributes Count Number of Crossing Attempts Continuous -- Min 19, Max 36 Average Pedestrian waiting time at the curb side (s) Continuous -- Min 5.25, Max 9.75 Average Pedestrian waiting time at the median (s) Continuous -- Min 4.3, Max 8.2 Average Crossing Time (s) Continuous -- Min 6.2, Max 9.1 Pedestrian Speed Change Behaviour Driver Yield Behavior Number of pedestrians engaged in rolling gap Discrete No percent Yes percent Not Applicable percent Discrete No percent Yes percent Continuous -- Min 9, Max 39 23

24 Flow Characteristics Variable Type of Variable Attributes Count Gap Acceptance Discrete Rejected percent Accepted percent Accepted lag or gap Discrete Lag percent Gap percent Gap Type Discrete Near percent Far percent Average Accepted Vehicular Gap Size (s) Continuous -- Min 7.9, Max 11.9 Average Pedestrian Safety Margin (s) Continuous -- Min 1.2, Max 3.0 Number of pedestrians engaged in rolling gap Continuous -- Min 9, Max 39 24

25 ANOVA ANALYSIS Gender Variables Age (X) (Years) Group Size Crossing Pattern Males Females 18 18<X 30 30<X 45 45<X 60 > OP OO OM TP TO TM Pedestrian Crossing Time (s) p=0.048, Significant p=0.6, Non-Significant p=0.035, Significant p=0.003, Significant Pedestrian Waiting Time (s) p=0.02, Significant p=0.89, Non-Significant p=0.044, Significant p=0.56, Non-Significant Accepted Vehicular Gap Size (s) p=0.049, Significant p=0.18, Non-Significant p=0.99, Non-Significant p=0.017, Significant Pedestrian Safety Margin (s) p=0.03, Significant p=0.007, Significant p=0.012, Significant p=0.035, Significant Note: if p<0.05, then significant

26 VARIATION IN CROSSING TIME 26

27 VARIATION IN WAITING TIME 27

28 VARIATION IN ACCEPTED VEHICULAR GAP SIZE 28

29 VARIATION IN PEDESTRIAN SAFETY MARGIN 29

30 Co-Relation Analysis : Relationship between Factors and Average Accepted Vehicular Gap Size Interval by Interval Association : Pearson Co-relation Analysis Factors Co-relation with Average Pedestrian LOS Width of Crosswalk (m).758 ** Median Open Width (m) Length of crosswalk (m).251 Average Conflicting Traffic Flow (PCU/h).117 Avg. Pedestrian Flow (Ped/h).056 Avg. Pedestrian Speed (m/s).310 Average Vehicular Speed (km/h) ** Average Waiting Time (s) * Average Crossing Time (s).388* Average Pedestrian Safety Margin (s).710 ** Number of pedestrians engaged in rolling gap.214 Number of Crossing Attempts -.538** Note: **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

31 Co-Relation Analysis : Relationship between Factors and Average Accepted Vehicular Gap Size Ordinal by Interval Association : Goodman and Kruskal s Gamma Association/Spearman s Rho Factors Association with Average Pedestrian LOS Visibility of Cross/Zebra Markings.372 Note: **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

32 Co-Relation Analysis : Relationship between Factors and Pedestrian LOS Co-Relation Analysis : Relationship between Factors and Average Accepted Vehicular Gap Size Nominal by Interval : Eta-Squared Analysis Factors Eta-Value (Co-relation Value) Association with Average Pedestrian LOS Gender.35*.123* Age -.42**.176** Platoon Size Crossing Pattern.44**.194** Gap Type -.52**.270** Driver Yielding Behaviour.63**.397** Gap Acceptance.49**.240** Accepted Lag or Gap.51**.260** Presence of Guard Rails Land use on sides Pedestrian Speed Change Behaviour Note: **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

33 Multicollinear Diagnostics Tests Variance inflation factor (VIF) is the ratio of variance in a model with multiple terms, divided by the variance of a model with one term alone (Allison, 1999). It quantifies the severity of multicollinearity in an ordinary least squares regression analysis. It provides an index that measures how much the variance (the square of the estimate's standard deviation) of an estimated regression coefficient is increased because of collinearity. It s value found was less than 10, therefore, indicating less collinearity among the variables. 33

34 MULTIPLE LINEAR REGRESSION MODEL (MLR) Dependent Variable Logarithm ofaccepted Vehicular Gap Size (U) Independent Variables Gender (X1), Age (X2), Width of Crosswalk (X3), Vehicular Speed (X4), Crossing Pattern (X5), Number of Crossing Attempts (X6), Driver Yielding Behavior (X7), Gap Acceptance (X8), Accepted Lag or Gap (X9), Gap Type (X10), Waiting time (X11), Crossing Time (X12), Pedestrian Safety Margin (X13)

35 MULTIPLE LINEAR REGRESSION MODEL (MLR) Variables Unstandardized Coefficients Std. B Error t p value (Sig.) (Constant) Vehicular Speed (X4) Gap Acceptance (X8) Gap Type (X10) Crossing Time (X12) Pedestrian Safety Margin (X13)

36 MULTIPLE LINEAR REGRESSION MODEL (MLR) Log(gap) = (Traffic Speed) (Gap Acceptance) (Gap Type) (Crossing Time) (Pedestrian Safety Margin) U = (X4) (X8) (X10) (X12) (X13) 36

37 MULTIPLE LINEAR REGRESSION MODEL (MLR) MODEL SUMMARY R R Square Adjusted R Square Std. Error of the Estimate

38 Model Discussion and Results The model calibration was considered with 80 percent data and remaining 20 percent data was used for validation of model. The calibrated R² value was found as The pedestrian road crossing behavior is quite unpredictable at uncontrolled mid-block location which makes it very difficult for deciding the minimum gap size. Different characteristics were considered for modeling the gap size, out of which only five could explain the significant relation with the gap size. It was observed that vehicular speed and gap acceptance are the most influencing variables. Vehicular Speed negatively impacts the gap size. The gap to be accepted or rejected affects the size of the gap, as pedestrians do not tend to wait for long to accept the gap, hence goes for smaller gap size. Most of the pedestrians accept far vehicular gaps for crossing the road at mid-block location. The crossing time and safety margin also positively impact the gap size which indirectly depends on the pedestrian speed. 38

39 Conclusions In this study, pedestrian dynamic behavioral characteristics along with other variables such as traffic characteristics, socio-demographic characteristics, geometric characteristics, frequency of attempt, crossing time, waiting time, rolling gap are studied that affects the gap selection and the gap acceptance. The pedestrian behavioral characteristics are very useful to control pedestrian erratic crossing behavior and for improving the pedestrian safety. It is concluded that pedestrian waiting time, crossing time, accepted vehicular gap size and safety margin show significant variation for different socio-demographic factors. Females have higher crossing and waiting time as compared to males as they value safety more and do not engage in risks while crossing. The accepted gap size and safety margin is more for female pedestrians with age group between years. The waiting and crossing time increases with increase in group size because while crossing in the group, the tendency of a pedestrian is to follow his or her fellow pedestrian that also leads to higher acceptance of gap size and pedestrian safety margin. If pedestrians choose to cross in one-stage, they pace at greater speeds than the normal walking speed due to which their crossing time gets reduced in comparison to the two-stage crossing pattern.

40 Conclusions It is believed that the presence of markings give a sense of safety to pedestrian while crossing and influence their behavioral characteristics. It is also observed that as pedestrian waiting time increases, the pedestrians may get restless which leads to increase in accepting the rolling gaps behavior which in turn affects the gap size. The MLR modeling suggests that the significant variables affecting the accepted gap size are vehicle speed, gap acceptance, type of gap, crossing time and safety margin. Speed of the vehicle plays an important role in deciding the gap size to be accepted. Far vehicular gaps are usually preferred by the pedestrians. Safety margin directly influences the gap size. It is believed that the developed model will be useful for policy makers to regulate the pedestrian erratic crossing behavior at unprotected mid-block locations. 40

41 References Allison, P. D. (1999). Multiple Regression: A Primer. Thousand Oaks, CA: Pine Forge Press Baltes, M.R., and X. Chu. (2002). Pedestrian Level of Service for Midblock Street Crossings. Transportation Research Record: Journal of the Transportation Research Board, 1818: Braun, R. R. and M.F. Roddin. (1978). NCHRP Report 189: Quantifying the Benefits of Separating Pedestrians and Vehicles. TRB, National Research Council, Washington, D.C Chutani, C., and P. Parida. (2013). LOS for Pedestrian at Uncontrolled Mid-Block Crossing. Paper presented at Urban Mobility India Conference, New Delhi, India. Diogenes, M. C. and L.A. Lindau. (2010). Evaluation of Pedestrian Safety at Midblock Crossings, Porto Alegre, Brazil. Transportation Research Record: Journal of the Transportation Research Board, 2193: Garder, P. E. (2004). The Impact of Speed and Other Variables on Pedestrian Safety in Maine. Accident Analysis and Prevention, 36(4): Highway capacity manual. TRB, National Research Council, Washington, D.C.,

42 References Highway capacity manual. TRB, National Research Council, Washington, D.C., Highway capacity manual. TRB, National Research Council, Washington, D.C., IRC (Indian Road Congress):103. Guidelines for Pedestrian Facilities, New Delhi, India, IRC (Indian Road Congress):103. Guidelines for Pedestrian Facilities, New Delhi, India, Jain, A., A. Gupta, and R. Rastogi. (2014). Pedestrian Crossing Behavior Analysis at Intersections. International Journal for Traffic and Transport Engineering, 4(1): Kadali, B. R., and P. Vedagiri. (2013). Marked versus unmarked pedestrian road crossing behaviour at uncontrolled midblock crosswalk in mixed traffic condition. 92 nd TRB Annual Meeting, Transportation Research Board, National Research Council, Washington D.C. Kadali, B. R. and P. Vedagiri. (2013). Modelling Pedestrian Road Crossing Behaviour Under Mixed Traffic Condition. European Transport, 55(3):

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