observational study for determining the percentage of vehicles completely stopping at an all-way...

OBSERVATIONAL STUDY FOR DETERMINING THE PERCENTAGE OF VEHICLES COMPLETELY STOPPING AT A STOP-SIGN

INTERSECTION

Submitted to: Dr. Tom Dingus

Submitted by: Group 6 Raj Kishore Kamalanathsharma

Nopadon Kronprasert (Venkateshwar Reddy Dhomadugu)

National Capital Region, Virginia Tech

December, 2009

This study is submitted in partial fulfillment of the requirements for the CEE4684 Transportation Safety course at the Department of Civil and Environment Engineering, Virginia

Tech in Fall Semester 2009.

CEE4684: Transportation Safety Final Report - Group 6

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OBSERVATIONAL STUDY FOR DETERMINING THE PERCENTAGE OF VEHICLES COMPLETELY STOPPING AT A STOP-SIGN INTERSECTION

Raj Kishore, Nopadon Kronprasert, (Venkateshwar Reddy)

Group 6 Virginia Tech, National Capital Region

ABSTRACT

Approximately, a whopping 700,000 police-reported vehicular crashes occur at stop-signs each year and 77% of these involve stop-sign violation. Crash statistics shows that in the year 2000, there were more stop-sign crashes than crashes at signalized intersections. Failure to stop at a stop-sign has been a serious enforcement issue as well as a safety issue.

For an all-way stop-sign intersection, safety issue arises out of whether the vehicles

completely stops or just do a rolling-stop. Accordingly, the vehicle behavior at a stop sign can be categorized into a complete stop, a rolling stop and no-stop. One of the methods used in quantifying this behavior is the percentage of vehicles in the general traffic which stops, roll-stops or which does not stop. In some studies, the duration of a stop (either complete or rolling) is also used as a measure. These measures are important on various aspects such as safety surrogate studies, deciding on many stop-sign intersection features as well as in inputting vehicular behavioral parameters in micro-simulation packages.

This project deals with the observational study done in an all-way stop sign intersection of Northern Virginia for determining the percentage of vehicles completely stopping, roll-stopping as well as which doesn’t stop at a stop-sign. Observations for peak and off-peak hours are done and values such as vehicle class, traffic situation, driver characteristics are also recorded for an in-depth analysis. This observational study is important since it can find answers to questions like which class of vehicles/drivers are likely to completely stop at the stop sign.

Other aspects of the project include:

1. Observations were done for peak and off-peak hours. 2. Vehicle class, occupancy, turn data as well as arrival pattern of vehicles were recorded. 3. Age-class and sex of the driver were noted. 4. Presence of conflicting vehicles and pedestrians was also observed.


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TABLE OF CONTENTS ABSTRACT.................................................................................................................................... ii TABLE OF CONTENTS............................................................................................................... iii LIST OF TABLES......................................................................................................................... iv LIST OF FIGURES ....................................................................................................................... iv INTRODUCTION .......................................................................................................................... 5 PURPOSES OF THE STUDY........................................................................................................ 5 LITERATURE REVIEW ............................................................................................................... 6 OPERATIONAL FACTORS.......................................................................................................... 7 STUDY AREA ............................................................................................................................... 8 STUDY METHODOLOGY ......................................................................................................... 10 DATA ANALYSIS....................................................................................................................... 10 STATISTICAL ANALYSIS ........................................................................................................ 15 DISCUSSION AND CONCLUSION........................................................................................... 17 RECOMMENDATIONS.............................................................................................................. 18 REFERENCES ............................................................................................................................. 18 APPENDIX A............................................................................................................................... 20 APPENDIX B ............................................................................................................................... 21 APPENDIX C ............................................................................................................................... 21 APPENDIX D............................................................................................................................... 21


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LIST OF TABLES Table 1 Stop Time at Stop Signs With and Without Competing Traffic........................................ 6 Table 2 Classification of Operational Factors ................................................................................ 7 Table 3 Cross-Tabulation and Chi-Square Test of Stopping Behavior ........................................ 16 Table 4 Summary of Chi-Square Test for Each Independent Variable ........................................ 17 LIST OF FIGURES Figure 1 Study Area ....................................................................................................................... 8 Figure 2 Intersection Layout.......................................................................................................... 9


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INTRODUCTION An all-way stop-sign intersection is where the vehicles from all approaches are supposed to stop [10]. But the word “stop” varies from person to person as well as time to time. For some drivers, it means a complete stop where the speed of vehicle will become zero at least for an instance. For some others, it means a reduction in speed nearing to zero which will allow them to carefully look at other approaches for conflicting pedestrians or vehicles. For a small percentage of drivers, stop sign just serve as a warning and they just slow down their vehicle [1]. Hence there are various meanings for a stop-sign and an all-way stop-sign intersection raises quite a number of safety issues. Analyzing the behavior of vehicles at a stop-sign intersection is inevitable for traffic-safety studies as well as transportation efficiency studies. The behavior of vehicles at a stop-sign intersection can be studied using two measures, one being the percentages of vehicles stopping, roll-stopping and not stopping at a stop sign and the other being the duration of stop (either complete or roll-stop). Percentage of vehicles stopping, roll-stopping and not stopping at a stop sign depends on several factors and is mainly associated with studies related to transportation safety. Duration of stop also depends on the same factors but this measure is used for understanding traffic behavior at a particular location and time to modify micro-simulation parameters. This report deals with an observational study to estimate the proportion of vehicles which completely stop or roll-stop or do not stop at the stop-sign of an all-way stop-sign intersection. Observations will be done for an hour during peak and off-peak hours and the study area was West Falls Church in Northern Virginia. Since the study area includes a feeder to a metro station, the subjects were cosmopolitan in nature. PURPOSES OF THE STUDY Unlike a signalized intersection where each vehicle has its own right-of-way and protected time to turn, an all-way stop-sign intersection relies on the drivers to choose the right-of-way as well as time at which they should proceed among themselves. Many at times, this raises a safety concern and especially when the driver population is cosmopolitan in nature [3]. Some drivers decide to completely stop, whereas some others just slow down scanning other conflicting pedestrians and vehicles. There is also another group of drivers who doesn’t stop either because of urgency, or their aggressive behavior, or because they are confident of the situation or they just miss to see the stop-sign. This study quantifies the driver behavior of Northern Virginia at an all-way stop sign intersection. It is an observational study to determine the percentage of vehicles which completely stop, roll-stop or does not stop at a non-signalized intersection near West Falls Church Metro station. This study also takes into account independent variables such as driver demographics, vehicle occupancy etc as well as dependent variables such as conflicting or non-conflicting movements of pedestrians or other traffic.


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The results of this study can draw conclusions regarding the safety of the study intersection as well as a general traffic behavior of the people in this area. The same stop-sign intersection may and may not pose serious safety threats for varying demographics, vehicle mix as well as time of day. Apart from turn volumes, these factors should also be taken into account when choosing between a signalized and non-signalized intersection. LITERATURE REVIEW Stop signs require the vehicles to “stop” regardless of presence of conflicting vehicles or pedestrians. They are the most common type of intersection in the United States [2]. A plenty of researches have been done so far regarding the performance, efficiency and safety of an all-way stop-sign intersection. The Fatality Analysis and Reporting System Web-based Encyclopedia maintained by the National Highway Traffic Safety Administration shows that nearly 700,000 police-reported crashes occur annually at stop-sign intersections [9]. Among these, stop-sign violations account for around 70% of the crashes [4]. Surveys done by Pietrucha et. al. (1990) showed that around 71% of stop-sign violators did so because they were confident about the absence of any conflicts [7]. Studies by Retting et. al. (2003) defines the correlation between age and stop-sign running. They said that drivers younger than 18 and older than 64 are more likely to miss or run a red-light than others [4]. There have also been studies regarding the correlation between race or ethnicity with stop-sign violations [5]. Harper et. al. (2000) studies shows that a cosmopolitan driver group shows more traffic indecency than a non-Hispanic white motorist’s community [8]. There have also been studies regarding the confusion caused by the rapid arrival of vehicles at all-way stop-sign intersections. Studies by Preusser et. al. (1998) shows that drivers aged 65 and above are 2.3 times more at risk at an all-way stop sign intersection when compared to the 1.3 times more at risk in other situations. Drivers aged 85 yrs and older are 10.6 times more at risk at a stop-sign intersection [6]. Many simulation tools have come up with driver behaviors at a stop sign. The following table shows the stop time at stop signs with and without competing traffic [11]

Table 1 Stop Time at Stop Signs With and Without Competing Traffic Percentage (%) No competing traffic (sec) With competing traffic (sec)

45.0 0.00 0.50 25.0 0.25 0.75 15.0 0.50 1.00 10.0 0.75 1.25 5.0 1.00 1.50

Observational studies such as this one can help in modifying these parameters for more realistic micro-simulations.


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OPERATIONAL FACTORS The stop-sign intersection studies are done by pure observation of vehicles at an all-way stop-sign intersection. In addition to the driver behavior at the stop sign, features such as presence of conflicting pedestrians or traffic and competing traffic and vehicle type, movement and occupancy are also noted for an in-depth study. Table 2 shows the operational factors used in this study.

Table 2 Classification of Operational Factors Category Variables

Vehicle Approach (EB) Eastbound, Westbound (WB), Southbound (SB), Northbound (NB)

Gender Male (M), Female (F)

Age Teenage (TN), Adult (AD), Elderly (EL)

Vehicle Occupancy 1, 2, more than 2 (>2)

Vehicle Type Passenger Car (PC), (SUV), Bus (B)

Vehicle Movement Left-turn (LT), Through (TH), Right-turn (RT)

Arrival Pattern Leading vehicle (LP), Following vehicle (FP) of the platoon, Single Vehicle (SP)

Conflict Type Vehicle-to-Pedestrian, Vehicle-to Vehicle

Stopping Completely Stop (CS), Rolling Stop (RS), No Stop (NS) The survey sheet used for the observational study is shown in Appendix A. The operational definitions used in the observation are given below:

1. Gender: The gender of the driver was noted for a cross-tabulated study. The values in the survey sheet will correspond to M for males, F for females and U, if the observation cannot guarantee a gender.

2. Age: Since, the observation is from a distance, only an approximation of the age of driver could be made. Accordingly, the values TN for teenagers, AD for adults and EL for elderly are used in the survey. The value UN is used when even an approximate category cannot be made based of observation.

3. Occupancy: The vehicle occupancy is the third field and the value 1, 2 or 2+ corresponds to the number of people in the vehicle including the driver.

4. Class: The classes of vehicles are categorized into PC, SUV, BUS and OTH. All the hatchbacks, sedans, coupes, convertibles and similar cars are marked as PC. SUVs include utility vehicles, crossovers, minivans and wagons. BUS is the value given for high occupancy vehicles such as commercial buses, shuttle buses and shuttle vans. All other vehicles are having the value OTH.

5. Movement: This indicates the vehicular direction of movement relative to their approach and values can be L for left, T for through and R for right.

6. Pedestrians: This field indicates the presence of pedestrians crossing any approach. The value CROSS stands for presence of pedestrians crossing the direction of movement of the vehicle. The value PARALLEL stands for presence of pedestrians crossing


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approaches other than the ones taken or to be taken by the vehicle. The value NO indicates absence of any pedestrians.

7. Vehicle on other approaches: The value of this field tells whether or not, there are vehicles on other approaches corresponding to a YES or NO.

8. Stop: This denote the final observation of whether the vehicle stopped completely or not. A complete stop, in this study, is defined as the instance where the vehicle comes to a complete stop at-least momentarily. A rolling stop is when the vehicle slows down to a complete stop, but doesn’t completely stop even momentarily. All other situations are considered as not a stop. The respective field values are COMP, ROLL and NO.

Other global parameters attributable to the entire survey such as the time-range of observation, intersection considered and weather at the time of survey were also noted. STUDY AREA For the observational study, the intersection chosen is the all-way stop-sign intersection in front of the Virginia Tech Northern Virginia Center (VT-NVC) building.

Figure 1 Study Area


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The approaches are from Haycock Road, I-66 Ramp (and Route 7), West Falls Church Metro Station (and metro parking lot) and the Northern Virginia Center Visitor Parking Bay. This intersection is an important connection between the I-66 and the West Falls Church Metro station. Since, it is not a residential, not a commercial area, the driver group will be a cosmopolitan one with people from all races, places and traffic cultures. The presence of Metro station has molded the traffic pattern of this intersection. In addition to a standard peak period behavior from 6:30AM to 9:00AM, this intersection shows a second peak period behavior after 9:30AM till 10:30AM. This is because of the HOV restrictions on I-66 till 9:00AM as well as higher peak hour metro rates till 9:30AM. Such a peak hour behavior is also observed after the evening peak hours. Unlike residential study areas where the off-peak movement is scarce and unlike commercial areas where the after-hours movement is scarce, this study area has a justifiable volume of vehicles even during off-peak as well as after hours and not a considerably heavy volume during peak hours. This has helped in an easy collection of observation data. The approach which serves as an entrance/exit to the Northern Virginia Center Visitor Parking Bay is not considered for the data collection since negligible hourly movement is found from and to this approach.

Figure 2 Intersection Layout


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STUDY METHODOLOGY The observational study involves observational analysis of the vehicular behavior at the stop signs of the three approaches. The observation was done simultaneously for the three approaches by three experimenters. The data described in the operational definitions are collected accordingly for all the vehicles for 30-minute intervals during both AM peak hours and off-peak hours. The data is collected purely by observation and values are noted in a datasheet as shown in Appendix A. The time periods considered for the observation are as follows: Peak period: 7:30 – 8:00 a.m. Off-peak period: 1:30 – 2:00 p.m. DATA ANALYSIS The data thus collected through observations are categorized and cross-tabulated based on various parameters like age category and the gender of the driver, vehicle movement, type, and occupancy of the vehicle, and arrival pattern and conflict type in each case. Results are shown in the following sections: Vehicle Movement: The percentages of vehicles stopping and not stopping for peak and off-peak hours for various vehicle movements are given below: Percent of “Not Completely Stop” vs. Vehicle Movement

Percent of "Not Completely Stop" Vehicle Movement Off-Peak Peak

Eastbound - Left-turn 13.2% 11.3% Eastbound - Through 33.3% 35.7% Westbound - Through 15.2% 11.5% Southbound - Left-turn 4.8% 2.6% Southbound - Right-turn 36.8% 36.4% Total 17.1% 15.6%

The following pie-charts show the above results in a more easily understandable way. According to the observations done, The Southbound, left-turners are more likely to stop at the intersection completely before proceeding, whereas the Eastbound, through vehicles as well as Southbound, right-turners are the least likely to stop at the intersection completely.


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Vehicle Type: The percentages of vehicles stopping and not stopping for peak and off-peak hours for various vehicle types are given below: Percent of “Not Completely Stop” vs. Vehicle Type

Percent of "Not Completely Stop" Vehicle Type Off-Peak Peak

Passenger Car 19.8% 16.6% SUV 14.8% 15.0% Bus 0.0% 7.1% Total 17.1% 15.6%

This data shows that passenger cars are less likely to completely stop than SUVs or buses. As the size of the vehicle increases, stop-signs are becoming more enforcing. Buses show a very less probability of non-stopping. It may be because buses are driven by trained drivers.


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Gender: The percentages of vehicles stopping and not stopping for peak and off-peak hours for males and female drivers are given below: Percent of “Not Completely Stop” vs. Gender

Percent of "Not Completely Stop" Gender Off-Peak Peak

Male 18.4% 16.7% Female 10.5% 8.0% Total 17.1% 15.6%

The above data is depicted in a pie chart as follows. It shows that female drivers are more likely to completely stop their vehicles at a stop sign than male drivers.


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Age Group: The percentages of vehicles stopping and not stopping for peak and off-peak hours for drivers of various age groups are given below: Percent of “Not Completely Stop” vs. Age Group

Percent of "Not Completely Stop" Age Group Off-Peak Peak

Teenage 50.0% 20.0% Adult 17.8% 16.2% Elderly 0.0% 6.7% Total 17.1% 15.6%

As age increases, traffic culture gets better. The results from the observational study show this trend. Teenage drivers are less likely to stop than elderly drivers. The following pie-charts shows the probability of stopping in red and not completely stopping in blue.

Arrival Pattern: The percentages of vehicles stopping and not stopping for peak and off-peak hours for various types of arrival pattern are given below: Percent of “Not Completely Stop” vs. Arrival Sequence

Percent of "Not Completely Stop" Arrival Sequence Off-Peak Peak

Single-Vehicle Arrival 17.9% 21.7% Leading Vehicle 8.3% 10.0% Following Vehicle 19.0% 23.4% Total 17.1% 15.6%

The following pie-charts show the probability of a vehicle stopping completely at a stop-sign in red, and of not completely stopping in blue. It shows that the leading vehicle of a platoon are more likely to stop completely than the case of a single vehicle arrival or a following vehicle in a platoon.


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Vehicle Occupancy: The percentages of vehicles stopping and not stopping for peak and off-peak hours for various vehicle occupancies are given below: Percent of “Not Completely Stop” vs. Occupancy

Percent of "Not Completely Stop" Vehicle Occupancy Off-Peak Peak

1 19.2% 16.2% 2 17.6% 17.1% >2 0.0% 5.6% Total 17.1% 15.6%

These results are represented in the following pie-charts. It shows that a vehicle with no passengers is less likely to stop completely than a vehicle with more than one passenger. Vehicles with more than one passenger in our situation represent buses and shuttle services with trained drivers.

Conflict Type: The percentages of vehicles stopping and not stopping for peak and off-peak hours for various conflict types are given below:


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Percent of “Not Completely Stop” vs. Conflict Type Percent of "Not Completely Stop" Vehicle Occupancy

Off-Peak Peak With Pedestrians 0.0% 3.8% With Other Vehicles 5.2% 5.7% No Conflict 36.2% 35.3% Total 17.1% 15.6%

These results are represented in the following pie-charts. It shows that vehicles are least likely to stop completely in the absence of any conflicting vehicles or pedestrians. Vehicles are more likely to stop when there are pedestrians conflict.

STATISTICAL ANALYSIS This study analyzes the chi-square statistical test to evaluate the hypotheses whether each independent variable is statistically independent with the stopping behavior or not. Hypothesis

H0: “The independent variable is statistically independent with stopping behavior” H1: “The independent variable is not statistically independent with stopping behavior”

The Chi-square value can be calculated by

( )∑ −=

i i

ii

EEO 2

2χ

where Oi = the observed value, and Ei = the estimated value, which is the joint frequency distribution. The degree of freedom (df) of the cross-tabulation can be calculated by

)1)(1( −−= crdf


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where r = number of row, and c = number of column. The cross-tabulation of stopping behavior, “Not completely stop (No CS)” and “Completely stop (CS)” and the calculation of chi-square values of each independent variable are shown in Table 3.

Table 3 Cross-Tabulation and Chi-Square Test of Stopping Behavior

No CS CS No CS CS No CS CSEastbound - Left-turn 12 88 100 16.14 83.86 100 1.06 0.20Eastbound - Through 7 13 20 3.23 16.77 20 4.41 0.85Westbound - Through 11 74 85 13.72 71.28 85 0.54 0.10Southbound - Left-turn 2 57 59 9.52 49.48 59 5.94 1.14Southbound - Right-turn 19 33 52 8.39 43.61 52 13.41 2.58Total 51 265 316 51 265 316

Vehicle Movement

4

TotalTotalEstimates Chi-Square

dfObservation

30.24

No CS CS No CS CS No CS CSPassenger Car 40 186 226 36.47 189.53 226 0.34 0.07SUV 10 57 67 10.81 56.19 67 0.06 0.01Bus 1 22 23 3.71 19.29 23 1.98 0.38Total 51 265 316 51 265 316 2

TotalChi-Square

df

2.84

ObservationTotal

EstimatesVehicle Type

No CS CS No CS CS No CS CSMale 47 225 272 43.90 228.10 272 0.22 0.04Female 4 40 44 7.10 36.90 44 1.35 0.26Total 51 265 316 51 265 316 1.88 1

Chi-Squaredf

ObservationTotal

EstimatesTotalGender

No CS CS No CS CS No CS CSTeenage 2 5 7 1.13 5.87 7 0.67 0.13Adult 48 238 286 46.16 239.84 286 0.07 0.01Elderly 1 22 23 3.71 19.29 23 1.98 0.38Total 51 265 316 51 265 316 3.25 2

ObservationTotal

EstimatesTotal

Chi-SquaredfAge Group

No CS CS No CS CS No CS CSSingle-Vehicle Arrival 25 151 176 28.41 147.59 176 0.41 0.08Leading Vehicle 4 38 42 6.78 35.22 42 1.14 0.22Following Vehicle 22 76 98 15.82 82.18 98 2.42 0.47Total 51 265 316 51 265 316 4.73 2

Chi-Squaredf

ObservationTotal

EstimatesTotalArrival Pattern

No CS CS No CS CS No CS CS1 33 156 189 30.50 158.50 189 0.20 0.042 18 86 104 16.78 87.22 104 0.09 0.02>2 1 22 23 3.71 19.29 23 1.98 0.38Total 52 264 316 51 265 316 2.71 2

ObservationTotal

EstimatesTotal

Chi-SquaredfVehicle Occupancy

No CS CS No CS CS No CS CSWith Pedestrians 1 37 38 6.13 31.87 38 4.30 0.83With Other Vehicles 9 154 163 26.31 136.69 163 11.39 2.19No Conflict 41 74 115 18.56 96.44 115 27.13 5.22Total 51 265 316 51 265 316 251.05

Chi-Squaredf

ObservationTotal

EstimatesTotalConflict Type


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Table 4 summarizes the chi-square values and the chi-square tests.

Table 4 Summary of Chi-Square Test for Each Independent Variable

Variables 2calcχ df 2

10.0χ Test

Vehicle Movement 30.24 4 7.78 2calcχ > 2

10.0χ Reject H0

Vehicle Type 2.84 2 4.60 2calcχ < 2

10.0χ Accept H0

Gender 1.88 1 2.71 2calcχ < 2

10.0χ Accept H0

Age Group 3.25 2 4.60 2calcχ < 2

10.0χ Accept H0

Arrival Pattern 4.73 2 4.60 2calcχ > 2

10.0χ Reject H0

Vehicle Occupancy 2.71 2 4.60 2calcχ < 2

10.0χ Accept H0

Conflict Type 51.05 2 4.60 2calcχ > 2

10.0χ Reject H0 The statistical tests show that the chi-square value of “conflicting type”, “vehicle movement”, and “arrival pattern”( 2

calcχ ) are greater than the chi-square value at 10% confident level ( 210.0χ ).

Hence, we can conclude that the presence of conflicting, vehicle movement, and vehicle arrival pattern are statistically dependent on stopping behavior of vehicle approaching at the intersection. DISCUSSION AND CONCLUSION The following arguments can be drawn from the results.

• Based on the results, the most contributing factors that cause most drivers completely stop are the presence of conflict, followed by movement of vehicles, vehicle arrival sequences, and driver’s age group.

• Vehicles during off-peak period have higher possibility not to completely stop than those during peak period because of less conflicting movement (either pedestrians or vehicles from other directions).

• Among different types of movement at the intersection, the through vehicles on eastbound approach (25.0%) and the right-turn vehicles on southbound approach (25.0%) have the highest possibility not to completely stop.

• Among different types of vehicle arrivals, the following vehicles are more likely not to completely stop at the intersection (22.4%) than the leading vehicles in the platoon (9.5%) and the single vehicle arrival (19.9%). This is because when they approach the intersection, they have more time to clearly see the entire intersection whether the vehicles in other directions exist or not.

• Among three age groups of drivers, teenage drivers are the most possible age groups that would not completely stop at the all-stop intersection (28.6%), compared to adult drivers (16.8%) and the elderly drivers (4.3%).


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• Among different vehicle classes, buses are the highest vehicle class that completely stops at the intersection (96.0%), compared to passenger cars (82.3%) and SUVs (85.1%). This may be because bus drivers are considered as well-trained drivers.

• Male drivers are more likely not to stop at the intersection (17.3%) than female drivers (9.1%)

RECOMMENDATIONS From the observations done on the behavior of vehicles at the above-said intersection, following recommendations are made to enhance safety and efficiency of the intersection.

1. There is clearly a need for stop-bar for all-approaches in addition to the existing pedestrian crossing. This can prevent rolling-stop behavior and inching.

2. From the observations, it is clear that the following vehicles are less likely to stop completely when vehicles arrive in platoon than the lead vehicle. The given intersection is found to have many platoons than individual vehicles feeding. Hence, it is advisable to add an exclusive left-turn bay on the eastbound approach so that the platoon, if consists of left-turners and through vehicles, will split.

3. Also, an additional stop-sign on the left median in advisable for east-bound approach since it is fed from the interstate ramp.

REFERENCES 1. E. Hauer and J Lovell, The safety effect of conversion to all-way stop control, Transportation

Research Record, Vol 1068, Transportation Research Board, Washington, DC (1986), pp 103-107.

2. U.S. Department of Transportation, Traffic Safety Facts, 2000 Report No. DOT HS-809-337, Washington, DC (2002).

3. R. Van Houten and R.A Retting, Increasing motorist compliance and caution at stop-signs, Journal of Applied Behavior Analysis 34 (2001), pp. 185-193.

4. R. A Retting, H. B. Weinstein and M.G. Solomon, Analysis of motor-vehicle crashes at stop-signs in four U.S. cities, Journal of Safety Research, Vol 34, Issue 5 (2003), pp 485-489.

5. E. Rumano, R Vaos and S Tippetts, Stop-sign violations: The role of race and ethnicity on fatal crashes, Journal of Safety Research, Vol 37, Issue 1 (2006), pp 1-7.

6. D. F. Preusser, A. F. Williams, S. A. Ferguson, R. G. Ulmer and H. B Weinstein, Fatal crash risk for older drivers at intersections, Accident Analysis and Prevention, Vol 30(2), Mar 1998, pp 151-159.

7. M. Pietrucha, T. Opiela, K. Knoblauch and R. L. Crigler, Motorist compliance with standard safety control devices, Public Roads 53 (1990) (4).

8. J.S. Harper, W.M. Marine, C.J. Garret, D. Lezotte and S.R. Lowenstein, Motor vehicle crash fatalities: A comparison of Hispanic and Non-Hispanic motorists in Colorado, Annals of Emergency Medicine 36 (2000) (6), pp. 589-596.

9. National Highway Traffic Safety Administration, Fatality Analysis and Reporting System (FARS), http://www.fars.nhtsa.dot.gov, U.S. Department of Transportation.


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10. Federal Highway Administration, Manual on Uniform Traffic Control Devices (MUTCD), 2003 Edition, U. S. Department of Transportation.

11. Caliper Corporation, TransModeler Version 2.0 Brochure (2008), Retrieved from the URL: http://www.caliper.com/PDFs/TransModeler%20Brochure.pdf


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APPENDIX A

EXAMPLES OF DATASHEET

Sl No

Gender (M/F)

Age (TN/AD/EL)

Occupancy

(1,2,+)

Vehicle Class

(PC,SUV, BUS)

Movement(LT/TH/R

T)

Pedestrians (YES/NO)

Vehicle on other appr. (YES/NO)

Arrival Sequence (SINGLE,

LEAD, FOLLOW)

Stop (NO/ROLL/

COMP)


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APPENDIX B

TYPES OF CONFLICT Vehicle-to-Pedestrian Conflict

Type SP1: Southbound Right-turn Vehicle vs. Pedestrians

Type SP2: Southbound Left-turn Vehicle vs.

Pedestrians

Type EP1: Eastbound Left-turn Vehicle vs. Pedestrians

Type EP2: Eastbound Through Vehicle vs.

Pedestrians


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Type WP1: Westbound Through Vehicle vs. Pedestrians

Type WP2: Westbound Left-turn Vehicle vs.

Pedestrians Vehicle-to-Vehicle Conflict

Type SV1: Southbound Right-turn Vehicle vs. Westbound Through Vehicle

Type SV2: Southbound Left-turn Vehicle vs.

Westbound Through Vehicle


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Type SV3: Southbound Left-turn Vehicle vs. Eastbound Through Vehicle

Type EV1: Eastbound Left-turn Vehicle vs.

Westbound Through Vehicle

Type EV2: Eastbound Through Vehicle vs. Southbound Left-Turn Vehicle

Type WV1: Westbound Through Vehicle vs.

Eastbound Left-Turn Vehicle


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Type WV2: Westbound Through Vehicle vs. Southbound Left-Turn Vehicle

Type WV3: Westbound Left-turn Vehicle vs.

Eastbound Right-Turn Vehicle


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APPENDIX C

EXAMPLES OF PHOTOS

Figure B-1: Eastbound

Figure B-2: Southbound


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Figure B-3: Westbound


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APPENDIX D

TIME SCHEDULE OF THE STUDY Steps Date Milestone 1 Oct 23 Reconnaissance 2 Oct 26 Defining Experiment 3 Nov 2 Literature Review 4 Nov 2 Defining Operation Definitions 5 Nov 16 Complete field experiment 6 Nov 23 Accumulate and Analyze Data 7 Nov 30 Technical Report with conclusions

observational study for determining the percentage of vehicles completely stopping at an all-way...

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