ABSTRACT
Traffic survey was carried out to obtain data for traffic study and evaluation. The surveys were conducted from 7am to 7pm each day using an automatic traffic recorder (Trax 1 Plus) Pneumatic tube, and manual counting for comparison. The Survey was conducted on Ogoja (Mbok) Mfum Road in Cross River State at the following six junctions namely Ogoja-Mbok junction , EdorJunction, Four Corner Junction, Ikom-Cameroun , Effraya Junction, and lastly at Mfum Bridge. The tendency of the mechanical counting machine to withstand non-stop 24hr-continuous counting was noted during the exercise compared to manual counting, since security at night cannot be guaranteed when using the manual method. The design ESAL generated from the comparative analysis shows that Ikom-Cameroon Junction has the highest value with a difference of 0.369917 x10^6 ESAL between the mechanical and manual count this value, implies a downward effect when using the manual data to design the pavement structural thickness; this can further degenerate to structural failure, tensional fatigue and cracks at the earlier stage. Hence, the relevance of mechanical counting as against the manual cannot be overemphasized. This program enables you to quickly and easily analyze your data and produce comprehensive reports and has become an alternative source of data gathering that can provide accurate real-time information over a large road network while overcoming some problems related to fixed detectors. Furthermore, a prediction of traffic generated for the period at 2015 to 2035 along the section of the corridor and pavement thickness shows that the automatic counter machine is more accurate. A regression equation y = 1.016 x + 11643 was generated, which can be used where the mechanical machine is not readily available; the square root of the regression coefficient R2 = 0.999. The mechanical counter is therefore more advantageous when compared to the manual counter and the mechanical counter should therefore be employed during raw data traffic survey.
TABLE OF CONTENTS
Title Page . . . . . . . . . . . i
Certification . . . . . . . . . . . ii
Approval Page . . . . . . . . . . iii
Dedication . . . . . . . . . . . iv
Acknowledgements . . . . . . . . . v
Abstract . . . . . . . . . . . vi
Table of Content . . . . . . . . . vii
List of Tables . . . . . . . . . ix
List of Plates . . . . . . . . . . x
List of Figures . . . . . . . . . . xi
CHAPTER ONE: INTRODUCTION . . . . . . 13
- Background of the Study . . . . . . . . 13
- Aim and Objectives . . . . . . . . . 14
- Definition of Terms . . . . . . . . . 14
- Scope . . . . . . . . . . 17
- CHAPTER TWO: LITERATURE REVIEW . . . . 18
2.1 Traffic Survey / Count . . . . . . . . 18
2.1.1 Variability in Traffic Count . . . . . . 20
2.2 Method of Conducting Traffic Count . . . . . . 25
2.2.1 Manual Counts . . . . . . . . 25
2.2.2 Automatic Counts . . . . . . . . 26
2.3 Summary of Review . . . . . . . . 30
3.0 CHAPTER THREE: METHODOLOGY . . . . 31
3.1 Material and Methods . . . . . . . . 31
3.2 Description of Study Location . . . . . . . 31
3.3 Traffic Volume Study Analysis and Forecasts . . . . . 31
- Methodology Adopted in The Study . . . . . 34
- Volume Study . . . . . . . . 35
- Axle Classification Study . . . . . . . 35
3.4 Trax 1 Plus Mode of Operation . . . . . . . 35
- Sub Grade Investigation and Pavement Condition Survey . . . 36
- Field Investigations , Results and Analyses . . . . 37
3.5.2 Dynamic Cone Penetration Test Conducted along Road . . 37
4.0 CHAPTER FOUR: RESULTS AND DISCUSSION . . . 39
4.1 Tabulation of Results . . . . . . . . 39
4.2 Discussion of Results . . . . . . . . 49
4.3 Adjustment of Average Daily Traffic . . . . . . 49
4.4 Estimation of Traffic . . . . . . . . . 50
4.5 Projected Traffic . . . . . . . . . 50
4.6 Using Regression Analysis To Determine an Equation for Mechanical and
Manual Mode of Traffic Survey . . . . . . . 59
5.0 CHAPTER FIVE: CONCLUSION . . . . . . 64
5.1 RECOMMENDATIONS . . . . . . . . 64
REFERENCES . . . . . . . . . 65
APPENDICES . .
CHAPTER ONE
INTRODUCTION
1.1 BACKGROUND OF THE STUDY
Traffic volume studies help agencies make sound decision on traffic safety, traffic control and geometric improvements. Such decisions are based on data collected with high degree of accuracy using mechanical or manual devices. Basically, traffic volume studies determine the number and type of vehicles, the number of vehicles at Intersections during rush hours, pedestrian counts, average daily traffic, and annual average daily traffic (Ahmed Abdel-Rahim, 2012).
There are two methods of collecting traffic volume data; automatic and manual. Automatic counting methods are used to gather large amounts of traffic data over an extended period of time. Counts are generally collected at one (1) – hour intervals over 24-hour periods. Automatic counting methods are generally used to determine traffic patterns and trends. The following information can be determined using automatic counts:
- hourly traffic patterns
- daily or seasonal variations
- growth trends
- annual traffic estimates
Manual counting methods, involve the use of observers that collect data at location for a specific time interval (generally measured with a stopwatch).
Manual counts is used when
- small data samples are required.
- automatic equipment is not available, or the effort and expense of using automated equipment are not justified.
- the count period is less than a day.
Manual counts are typically used to gather data on the following:
- vehicle types
- turning movements
- direction of travel
- pedestrian movements
- vehicle occupancy
During the colonial era, the Southern Cameroon Road segment was an integral part of the Eastern Region of Nigeria. The road became neglected and the condition of the Nigerian section was in a very deplorable condition.
Following the International Court of Justice ruling on Bakassi, the Nigeria-Cameroon Mixed Commission recommended the rehabilitation of the road as a confidence reassurance measure between Nigeria and Cameroon Republic. Endorsed by the United Nations (UN) Secretary General, the recommendation was accepted by the Nigeria and the Cameroon Republics. The African Development Bank (AfDB) has also accorded major priority rating to the Project.
Thus there is a need to accurately and safely collect the traffic data that will be used for the said rehabilitation. We shall therefore in collecting the data also compare the performance of the mechanical and manual methods.
1.2 AIM AND OBJECTIVES
The objectives of this research work are:-
- To determine the actual number of vehicles using the road, with an automatic recorder and at
the same time counting manually to enable comparison between the mechanical means and manual counting.
- To estimate the present and projected future traffic volumes spanning a period of 20 years
using road traffic data obtained from the count.
- To determine the accuracy of data collected using different statistical methods.
- DEFINITION OF TERMS
Basic Data: This refers to any data collected in a time-stamp format. In this format, the traffic recorder marks every sensor activation (road tube hit) with a time stamp of when it occurs.
Per Vehicle Data: This stands for data stored on a vehicle-by-vehicle basis. In this format the program stores a table of the data of every individual vehicle recorded during the study. There are two ways to produce this type of data in the program. One, by collecting the data in this format in a traffic counter, or by processing Basic Data into this format.
Binned: This stands for data that have been sorted into pre-defined categories. This includes classification, speed and gap data. In this format, the data is not stored for each vehicle, but rather for each category. For example, in a speed study a vehicle traveling at 100km/hr would be added to the 90-120 speed category (or bin) of the time period it was recorded. There are two ways to produce this type of data in the program. One, by collecting the data in this format in a traffic counter, or by processing Per Vehicle Data into this format.
Volume: This is data that has been collected with the purpose of determining the amount of vehicles traveling over the study site for a given period of time. In this format, vehicles can be counted in several ways, such as a vehicle-by-vehicle count, a divide-by-two count, or an axle count.
Classification: This is data that has been collected with the purpose of determining the different types of vehicles traveling over the study site for a given period of time. Classifications are based on the spacing and number of axles a vehicle possess. The most commonly used scheme for classifying vehicles is the Federal Highway Administration’s Scheme F. However, other custom schemes can be created using the Scheme Editor. This report focuses on this method of analysis.
Speed: This is data that has been collected with the purpose of determining how fast vehicles are traveling over the study site for a given period of time.
Gap: This is data that has been collected with the purpose of determining when no traffic (or gaps) occurs at the study site. Once a gap occurs, this format also records how long the gap lasts. This data is generally used to help determine whether pedestrians have adequate time to cross a street, or if cross traffic has adequate time to turn onto or cross a street.
Interval: This is a pre-defined time period that the data in a study is divided into. The most commonly used interval times are 15 minutes and 60 minutes.
Site Code: This is a number, or combination of number and letters, used to help identify where a specific study was done. This is for optional use. You do not have to enter a site code if you do not use them.
Group Description: Also known as Direction Description, this is the name assigned to a specific portion of the data. In most cases this is used for speed, class or gap data that has been collected in more than one lane.
Volume/Flow: Volume is defined as the number of vehicles (or persons) that pass a point or section of a lane or roadway on a transportation facility during a specified time period. In traffic engineering studies there are many volumes such as daily volume, hourly volume, peak hour volume. In addition volumes of a day or an hour can vary greatly, depending on the different days of the week or different time periods of a day.
Rate of Flow: The equivalent hourly rate at which vehicles pass over a given point or section of a lane or roadway during a given time interval, less than 1 hour, usually 15minutes, is known as rate of flow.
Demand: In traffic volume studies the demand is not always measured but in many cases it is required. Demand is the number of vehicles (or persons) desiring to travel past a point during a specified time period (usually an hour).
Capacity: The maximum rate at which vehicles can traverse a point or short segment during a specified time period, is known as capacity.
Average Daily Traffic (ADT): ADT is defined as the average 24-hour volume at a given
location for some period of time less than a year. It is expressed in terms veh/day or vpd.
Average Annual Daily Traffic (AADT): AADT is the average of 24-hour traffic volume at a given location over a full year. It is expressed in terms of vpd.
Peak hour factor: Traffic engineers focus on the peak-hour traffic volume in evaluating capacity and other parameters because it represents the most critical time period. And, as any motorist who travels during the morning or evening rush hours knows, it’s the period during which traffic volume is at its highest. The analysis of level of service is based on peak rates of flow occurring within the peak hour because substantial short-term fluctuations typically occur during an hour. Common practice is to use a peak 15-minute rate of flow. Flow rates are usually expressed in vehicles per hour, not vehicles per 15 minutes. The relationship between the peak 15-minute flow rate and the full hourly volume is given by the peak-hour factor (PHF) as shown in the following equation:
(1.1)
If 15-minute periods are used, the PHF is computed as:
(1.2)
Where
V = peak-hour volume (vph)
V15 = volume during the peak 15 minutes of flow (veh/15 minutes)
Typical peak-hour factors for freeways range between 0.80 and 0.95. Lower factors are more typical for rural freeways or off-peak conditions. Higher factors are typical of urban and suburban peak-hour conditions.
1.4 SCOPE
This study is centered on the collection of traffic volume data analysis, and projection to determine axle load distributions with derivation of equation using regression analysis for comparison using Mbok-Ogoja (Mfum) road. The research is highly expensive, and time consuming due to nature of equipment involves.
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