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Project Report: Road Pothole Classification and Reporting with
Data Quality Estimates
Ayush Rajesh Vora | [email protected]
Project Advisor : Prof. Leonid Reznik | [email protected]
Table of Contents:
Sections Description Page No.
1. Abstract 2
2. Introduction 2
3. Background a. Real-time detection using smartphone accelerometers b. Pothole Detection and Warning System using Wireless Sensor
Networks c. Existing Smartphone Apps
3-5
4. Requirement Analysis 6 5. Proposed Solution 7
6. Approach and System Explained a. Literature Review b. Android Smartphone Apllication – Pothole Reporter
i. User Interfaces ii. Output
c. Intelligent System - Pothole Priority Rule Engine. d. Intelligent System – Data Quality Rule Engine. e. Purpose and Implementation of the Database connection
8-17
7. Results 18
8. Observations 19
9. Conclusion 20 10. Future Work 21
11. References 22
. 2
1. Abstract:
This research discusses a solution to report, record and classify potholes based on factors
that influence the destructiveness of the pothole, by using a widely popular platform in the
current generation. Harnessing the power of the mobile computing platform has opened up
newer possibilities of gathering and classifying data by leveraging the use of crowd-
sourcing. In the current generation that is being dominated by the mobile computing
platform namely smartphones, crowd-sourcing can be achieved in a relatively hassle-free
yet effective means of collecting data from a large source-pool, viz, people. Tapping into
this source-pool however has a drawback of data reliability. The approach used in this
project aims to realize the most influential factors related to destructiveness of potholes
that are encountered on roadways, while supplementing the data with quality estimates
derived from the completeness of the data and certain factors of the input device itself that
is involved in the data collection process, thereby addressing the drawback of data
reliability.
2. Introduction
One of the most notable aspects of the development of human civilizations involve the
advent of roadways. Roadways have allowed civilizations to exchange goods and services
and have provided civilizations with the necessary mobility required for survival. The
problem of maintenance of these roadways has persisted to this date and as time has
progressed this problem has only become more complex and significant.
The problem of road-maintenance has particularly gained immense significance with the
advent of vehicular transportation. The roadways have become highly complex road-
networks spanning larger and larger areas. Invention of the internal combustion engine
. 3
boosted human civilization exponentially, but with it the matter of road maintenance
became extremely vital to minimize the threat of vehicular accidents.
One particular problem of road maintenance involves the gradual emergence of deep
cavities on pavements and roads due to natural processes. These deep cavities and
depressions are potholes which have been a recurrent and inevitable problem for road
maintenance. They are the results of general wear and tear and the weather cycle. In the
modern era potholes generally tend to be bowl-shaped openings in the road and their
depths can go up to and above 10 inches in the most severely ignored cases. Modern roads
have a concrete base and a top layer made of asphalt and the top asphalt layer can wear
quickly exposing the concrete base. This aggravates due to rain-water, snow etc
accumulating in these cavities and results into big potholes that may eventually cause
damage and car accidents.
Poor maintenance can lead to numerous damages that range from vehicular damage to loss
of human lives. Since this is an inevitable problem and they are difficult to tackle
preemptively, there is an urgent requirement of having a classification scheme that would
prioritize potholes as per their destructiveness. Once prioritized their repair can take place
in a systematic manner, thereby reducing the damage they can cause.
3. Background:
Using smartphones for reporting general infrastructure problems is recently emerging and
mostly within the stages of its infancy. A wide range of solutions exist that involve Real-
time detection using smartphone accelerometers[1], Pothole Detection and Warning System
using Wireless Sensor Networks[2], smartphone apps which record the geo-location of
potholes along with some physical characteristics[4] etc.. The project research included here
will highlight some of the existing solutions, with the advantages they offer and the
drawbacks that exist for those particular solutions.
. 4
a. Real-time detection using smartphone accelerometers:
This research tries to harness the embedded accelerometers within smartphones to
detect potholes in real time, rather than using external accelerometers and external
gps devices. They aim to provide a solution which utilizes more advanced heuristic
real-time event detection using limited hardware and software resources. They
followed the approach from previous researches like PotholePatrol[3] which
threshold the acceleration amplitudes at Z-axis. It uses values exceeding specific
thresholds that identify the type of pothole( i.e small, medium and large) as features
that classify the measurements. Instead of using virtual re-orientation of the
accelerometer, they follow a simpler approach by fixing the placement of the
accelerometer and eliminating the need for virtual re-orientation. They use various
algorithms like Z-Thresh and Z-diff to detect pothole events.
Advantages
o Potholes are detected in real-time without user-intervention.
o System could detect larger potholes at medium vehicular speeds with a fairly
decent true-positive rate.
o System can run on a popular platform like Android.
Disadvantages
o Continuous use of accelerometers tend to drain a lot of battery power on
smartphone devices.
o Does not provide a detailed description of a pothole other than some loose
physical aspects.
o Lacks the contextual information that can put the significance of the pothole
within a different perspective.
o System does not have a relatively considerable true-positive rate with smaller
potholes and/or at low vehicular speeds.
. 5
b. Pothole Detection and Warning System using Wireless Sensor Networks
This research tries to propose the use of devices like embedded sensors like
accelerometers within the vehicle, embedded communication subsystem and
external Wi-Fi Access Points, to detect and warn vehicular commuters about
impending potholes. It proposes to use a Mobile Node (MN) within the vehicle
which does the job of sensing the pothole, transmitting certain pothole data to a Wi-
Fi Access Point (AP) and receiving localized information on a pothole from an AP.
Advantages
o Dedicated sensors, and communication devices make detection and warning a
lot more accurate.
o Can work well as a good detection and warning system.
o Does not involve user-intervention.
Disadvantages
o Expensive to install dedicated sensors and communication devices
o Does nothing to address the problem of classification of potholes for scheduling
repair.
o Lacks the contextual information that can put the significance of the pothole
within a different perspective.
c. Existing Smartphone Apps
There are existing smartphone apps like Commonwealth Connect and iReport, which
also harnesses crowd-sourcing. They address the problem of reporting potholes to
authorities. However they are reported in the context of general infrastructure
problems and don't do much to provide a detailed description or any contextual
information of the pothole.
. 6
4. Requirement Analysis:
After analyzing some of the drawbacks of existing solutions, we realize the requirement of
having accurate and descriptive data related to potholes. This project aims at realizing some
general and universal policies related to classifying potholes via crowd-sourcing. Some of
the requirements to achieve this were recognized below.
a. Determining the most appropriate influencing factors that would be able to signify the
destructiveness of a pothole so it can be assigned a priority.
b. Providing vehicle commuters or concerned citizens a quick and efficient way to report
pothole data related to factors that influence its priority and at the same time extracting
some contextual information if possible from users so as to assist the effort of
scheduling the repair of said pothole.
c. After extracting the information, using a progressive intelligent system to determine a
calculated priority for the for the pothole.
d. Estimating the accuracy of the data that is generated using some factors which are
derived from the completeness of the data and certain factors of the input device itself
that is involved in the data collection process.
e. Sending the information to a database where, it can be analyzed and used for
scheduling pothole repair.
These requirements address the drawbacks of, lack of descriptive data related to potholes,
provides information on accuracy of the extracted information, provides some contextual
information related to the pothole, ensures that proposed solution does not drain a
significant amount of smartphone resources like battery life etc. It does this, while
sacrificing on the required level of user-intervention, which needs to be high as per nature
of crowd-sourcing applications.
. 7
5. Proposed Solution
The proposed solution follows the approach below to address the requirements.
a. Performing a literature review to gather domain knowledge on roadway maintenance
systems, by going through various roadway maintenance policies, protocols and
recognizing the most significant factors related to the pothole, the patterns and rules to
establish a general rule-base for classifying a pothole based on its destructiveness,
described by the most influential factors.
b. Designing and implementing a smartphone application on the popular platform Android.
Establishing a user-friendly UI which assists the user to recognize the qualities of the
pothole and requesting the user to provide some contextual information using visual
cues and a survey-type activity.
c. Designing and implementing the intelligent system which involves balancing the
significance of various factors that influence the priority of the pothole and calculating
the priority while taking into consideration the contextual information provided by the
user.
d. Calculate the completeness ratio (i.e amount of information that is received through the
user input / amount of total information requested), determine camera quality of the
android smartphone, and the current security characteristics of the android smartphone
to estimate the data quality.
e. Use the Android-PHP-MySQL approach to store and record the data in a MySQL
database. This MySQL database is maintained by a separate project which will provide
additional features like lookup and analysis of potholes and identify trends.
This project is iterative in nature i.e, it is made extensible to incorporate newer factors and
context influencing the potholes priority which is based on its destructiveness. All the above
steps can be repeated to improve the system.
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6. Approach and System Explained:
a. Literature Review: After reading through many online road maintenance policy
and protocol documents from various counties a common pattern was observed.
Factors like the type of street, weather, depth and location were mentioned
within all protocols. The main purpose of the literature review was to gather
information on how different road maintenance authorities and offices react to
various contextual conditions and physical characteristics of potholes. Some of
these factors are taken into consideration for the intelligent system to assign a
priority to the pothole. The research arrived and finalizes the following factors.
. 9
Pothole Data reflecting the significantly influential factors
i. Type Of Street
ii. Current Weather Conditions
iii. Depth of pothole
iv. Traffic Volume
v. Area of pothole
vi. Average Speed
vii. Safety Zone Rating
viii. Type of Junction
ix. Estimated location(PPRRW)
x. Road Material
xi. Existing Repair Conditions
xii. Type of pothole
xiii. GPS location
xiv. Image
xv. Priority
To determine an estimate of the accuracy of the data, the research arrived to the
following factors.
Data reflecting the quality of input
i. Camera Quality
ii. Data Completeness
iii. Security Characteristics
b. Android Smartphone Application:
. 10
i. User Interface :-
The smartphone application was designed keeping in mind the willingness
of users to input the requested information. Since this is based on a crowd-
sourcing approach, this solution works on the assumption that a
considerable amount of users participate. In order to make users
participate in surveying the potholes, the UI needed to be made as hassle-
free as possible.
Figure 2.a. Picture & Location Extraction
Capturing the image and enabling locations services is encouraged and is
the first suggestion made to the user, since this is one of the most
important information for an official that might use this data in the
database to decide on scheduling repair of potholes.
. 11
Figure 2.b. Dimension Extraction
The next important information that needed to be captured is the
dimensions of the potholes. The area, depth is captured using an Android
Number picker, the limits are set to values that are practically observed
and keeping in mind the average width of roads in the US.
. 12
Figure 2.c. Pothole Type Extraction
Another physical characteristic that needed to be captured was the type of
pothole. Visual cues are used to get this information by using a custom UI
component that uses a "SelectOneImage" approach, to help users choose
which type is most applicable to the pothole they want to report. This
information may help officials identify what approach they could follow in
performing repair ( For example, what tools, materials may be necessary
and help to cut costs.)
. 13
Figure 2.c. Survey Activity
The next section of the activities comprises of a survey for gathering
contextual information that helps the intelligent system to assess the
priority of the pothole being reported.
This activity and the models that back it are kept generic so as to
incorporate newer contextual factors.
The survey activity is unique, follows a fixed choice question style survey
and uses a generic framework, wherein new questions and new choices
can be easily defined and gets automatically incorporated into the UI.
The choices are fixed, so as to avoid inaccurate information being recorded.
. 14
ii. Output :-
i. Once the survey activity is complete, the pothole data rule engine
assesses the priority of the pothole being reported and assigns a
priority score and the pothole data quality rule engine assesses
the quality of the input and assigns a quality score.
ii. All the physical characteristics, contextual information that
influence the priority of the pothole along with the pothole
priority score as well as the data quality score is sent to a separate
database project that maintains this data and officials can use this
database for scheduling repair or analyzing the reported data.
iii. The app is divided into different types of activities. The
architecture allows different aspects of pothole related data to be
extracted through different types of UI elements because the
nature of the information may differ, while at the same time be
necessary for arriving at accurate results.
iv. The app does not force the user to input all the information,
respecting different levels of participation. However they are
encouraged to do so. Depending on the level of participation the
data quality rule engine determines data completeness which
plays the biggest role in calculating data quality score.
. 15
c. Intelligent System - Pothole Priority Rule Engine:
Figure 3.a. Pothole Priority Rule Engine
o Priority is scored within 0-100 pts to determine a suitable priority class.
o All fields given a fixed weight and together sum to a 100 pts.
o All values of each field assigned a numeric value between (0-1).
o Rule Engine is a forward chained rule-based hierarchical tree and
evaluates each prioritized field in order and determines if the next field’s
weight needs to be adjusted based on its current value.
o If weight is adjusted then weights of all fields down the hierarchy are re-
adjusted accordingly, so that maximum priority sum does not change.
o It is important to note that, some fields which are partially dependent on
the previous fields require a weight adjustment, while some others don’t.
. 16
d. Intelligent System - Data Quality Rule Engine:
Figure 3.b. Data Quality Rule Engine
Data quality is determined in a similar way as priority, taking into consideration the
data completeness, camera quality and security in that order. No dependencies
between the factors seem to be obvious hence weight adjustments are not
considered in this model.
o Quality scored between 0-100.
o All factors given fixed weights.
o Completeness rating determined based on the number of questions answered by
the user to the number of answers requested by the app.
o Camera quality rating determined through existing android API's
o Security rating determined using the security rule engine from a previously
developed application called Android Security Evaluator.
o After determining rating for each factor it is multiplied with each factor's weight
and final weighted sum gives the calculated data quality.
. 17
e. Purpose and Implementation of the Database connection:
Once all the information is acquired from the user, it needed to be stored into a
database so that it can be used by the officials for scheduling repair of potholes and
even for further analysis of the recorded data.
The intention is to analyze the data and detect patterns such as regions which are
extremely prone to specific types of potholes, or specific sized potholes, or how the
weather conditions affect potholes in a specific region as opposed to how similar
weather conditions affect pothole severity in a different regions.
Once adequate amount of data gets populated in the database it can exponentially
increase the ability to answer many interesting questions and ultimately help in the
effort of pre-emptive action against pothole related maintenance.
An Android-PHP-MySQL approach is used for the implementation. The Android
smartphone application converts all the data related to the pothole into a JSON
object and sends it to the Apache Web Server where a PHP file receives the JSON
object and inserts it into the MySQL database. This database is maintained by a
separate project.
. 18
7. Result:
Table 1. Pothole Instances Table
Factors Instance 1 Instance 2 Instance 3 Instance 4
Weather Type Snowy Sunny Sunny Rainy
Street Type Arterial Commercial Alleyway Commercial
Depth 16 cm 12 cm 4 cm 8 cm
Area 20 sq.ft 12 sq.ft 1 sq.ft 12 sq.ft
Traffic Volume Extremely High Medium Extremely Low Medium
Average Speed Extremely High High Extremely Low High
Safety Zone Type School Zone None None School Zone
Junction Intersection T-Junction None Intersection
Relative Position Center Side-Center Sides Side-Center
Road Material Asphalt Asphalt Concrete Asphalt
Repair Permanent/None Temporary Temporary Permanent/None
Priority Extremely High Medium Extremely Low High
A lot of different sets of inputs have been tested and compared. To better contrast the
results three input instances have been isolated to better help explain the priority score
results and draw interesting observations.
Interpretation of Priority Score :-
Instance 1: The priority score for this set of inputs was 98.3%. The priority class assigned
to this instance evaluates to Extremely High, indicating that this is a one of the most
significant potholes in the region. It must be immediately looked at and either requires a
temporary repair (depending on weather conditions) or a permanent repair (if the
weather permits and if temporary repair may have already been performed). Neglecting
this pothole may even result in the loss of lives.
Instance 2: The priority score for this set of inputs was 64.31%, The priority class
assigned to this instance evaluates to Medium, indicating that although this pothole
. 19
may be a nuisance, it is not likely to cause an enormous amount of damage. Pothole
must be repaired soon to provide commuters a good experience.
Instance 3: The priority score for this set of input was 24.23%. The priority class assigned
to this instance evaluates to Extremely Low, indicating that this pothole is not in any
way an immediate danger, it may be within an Alleyway or parking space, where vehicle
speeds are slow enough to not cause any considerable damage to either people or
vehicles. It must be repaired after all important potholes in the region have been
attended to.
Instance 4: The priority score for this set of inputs was 73.59%. The priority class
assigned to this instance evaluates to High, indicating that this pothole may be a
dangerous pothole owing either to its size or owing to the contextual significance of this
pothole. It should be repaired as soon as possible and shouldn't be neglected and has
the potential to cause some serious damage
8. Observations:
The observations that were made after looking at the results of each pothole instance are
as follows
Instance 1: We see that if weather conditions are snowy and the type of the street where
the pothole was observed is an Arterial street, where a considerably size pothole has
appeared, with extremely high volumes of traffic and extremely high speed traffic, is almost
certainly a very high priority pothole and the system answers this obvious question by
giving an obvious answer based on the significance of its physical characteristics as well as
the contextual information.
Instance 2: We see that if the general weather conditions are sunny and clear, the type of
the street is commercial , with a fairly deep and big pothole, with medium volume of traffic
. 20
and high speed traffic, indicates a fairly common pothole scenario and prioritized to be a
pothole of medium significance.
Instance 3: We see that if general weather conditions are sunny and clear, the type of street
may be an alleyway or parking space, with a very small pothole, with extremely low volume
of traffic and extremely low speed traffic, indicates the least significant kind of potholes but
this shouldn't diminish the effort to report these since they are still a nuisance.
Instance 4: This is the instance where we can clearly see the contextual information at work
in raising the significance of a pothole on characteristics other than physical. We see that
the difference between Instance 2 and Instance 4 is the weather being rainy, safety zone
type being a school zone, and the road being an intersection, which raises its priority score
and evaluates to a High priority pothole even though Instance 4 reflects a smaller pothole
with regards to its physical dimensions like depth.
9. Conclusion:
There were a lot of things to consider for making this smartphone application to be viable.
o Some dependencies of the fields needed to be adjusted so as to reflect a more
accurate model.
o Field Weights were calibrated adequately so that outliers were not produced.
o Tests showed that system prioritized potholes as expected for a lot of instances.
o We see that contextual information can play an important role in determining a
potholes significance, this is important because it has been consistently observed
that context plays an important role in understanding what amount of damage
the pothole in question maybe able to inflict.
o The system manages to weigh contextual information in a fairly balanced
manner.
. 21
o Adaptive and relative weights help to address the issue of devising a fair
and balanced intelligent system where each field is given required
attention
Some other conclusions that are important to note are :-
o The Android Smartphone application UI made it significantly easier to gather
data.
o There is no universally accepted guide or policy existing to prioritize
potholes and this project tries to realize and establish this.
o Baseline research and literature review was extremely beneficial for the
intelligent system as it provided lot of insight in developing a balanced IS.
10. Future Work:
Since this project is based on an iterative process, improving the amount of involved
fields, the intelligent system by tweaking the field weights and updating the database
will improve the contextual accuracy of the system.
Essentially in the future there should be more user types and notifications for users
when the pothole gets repaired because of their report.
One of the major intention of this research project is to analyze the data and detect
patterns such as regions which are extremely prone to specific types of potholes, or
specific sized potholes, or how the weather conditions affect potholes in a specific
region as opposed to how similar weather conditions affect pothole severity in a
different regions.
Also as the database populates, the comparison between the determined priority and
the actually assigned priority should be used for improving the IS.
. 22
Once adequate amount of data gets populated in the database it can exponentially
increase the ability to answer many interesting questions and ultimately help in the
effort of pre-emptive action against pothole related maintenance.
11. References:
[1] Artis Mednis, Girts Strazdins, Reinholds Zviedris, Leo Selavo, Real-time detection using smartphone
accelerometers, Conference: 2011 7th IEEE International Conference on Distributed Computing in Sensor
Systems and Workshops (DCOSS).
[2] Sudarshan S Rode, Shonil Vijay, Prakhar Goyal, Purushottam Kulkarni, Pothole Detection and Warning
System using Wireless Sensor Networks,Kavi Arya Embedded Real-Time Systems Laboratory Indian Institute of
Technology Bombay.
[3] Jakob Eriksson, Lewis Girod, Bret Hull, Ryan Newton, Samuel Madden, Hari Balakrishnan, The Pothole
Patrol: Using a Mobile Sensor Network for Road Surface Monitoring, MobiSys’08, June 17–20, 2008,
Breckenridge, Colorado, USA.
[4] http://commonwealthconnect.io/
[5] http://www.krdo.com/news/local-news/pothole-patrol-how-does-the-city-prioritize-pothole-
repairs_20160715115016223/35612995
[6] http://stewartvillemn.com/media/Policy-Pothole-Repair.pdf
[7] http://www.johnsoncitytn.com/publicworks/street/pothole/
[8] http://www.canoncity.org/Departments/Engineering/StreetsProjectList/2016_Street_Info/Pothole%2
0Repair%20Policy.pdf
[9] https://www.southampton.gov.uk/roads-parking/road-maintenance/
[10] http://blog.nola.com/potholepatrol/2008/07/know_your_potholes.html
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