Detecting, classifying and rating roadway pavement ...

Detecting, classifying and rating roadway pavement anomalies using smartphones C. Kyriakou, S. E. Christodoulou & L. Dimitriou

Dept. of Civil and Environmental Engineering, University of Cyprus, Nicosia, Cyprus

ABSTRACT: Pavements are major roadway infrastructure assets, and pavement maintenance to the preferred level of serviceability comprises one of the most challenging problems faced by civil and transportation engineers. Presented herein is a study on the utilization of low-cost technology for the data collection and classification of roadway pavement anomalies, by using sensors from smartphones and from automobiles’ on-board diagnostic (OBD-II) devices while vehicles are in movement. The smartphone-based data collection is complimented with artificial neural network techniques, various algorithms and classification models for the classification of detected roadway anomalies. The proposed system architecture and methodology utilize nine metrics in the analysis, are checked against three types of roadway anomalies, and are validated against hundreds of roadway runs (relating to several thousands of data points) with an accuracy rate of about 90%. The study’s results confirm the value of smartphone sensors in the low-cost (and eventually crowd-sourced) detection of roadway anomalies. 1 INTRODUCTION In recent years, the area of interest for transportation authorities, researchers and practitioners has shifted from the construction of new roads to the management of existing ones (Panagopoupou & Chassiakos 2012). One of the most significant indicators for road quality is the pavement surface condition, which is identified by the anomalies in the pavement surface that have an effect on the ride quality of a vehicle. Road anomalies can cause unpleasant driving, increase fuel consumption, damage vehicles and in many cases be the reason for traffic accidents, injuries and/or fatalities. Pavement surface can deteriorate in time from causes related to location, materials used, traffic, weather, etc. Identifying road anomalies related to transverse defects, longitudinal defects, potholes and cracking on the pavement surface can collaborate to surveying road condition quality. Pavement surface condition monitoring systems could raise the value of road surface, protect vehicles from damage as a result of bad roads and improve traffic safety. As an outcome of the fast and powerful development of smartphones in current years, connected vehicle technology has obtained noteworthy consideration within the transportation, infrastructure, and automotive industries. This paper examines the use of smartphones and connected vehicle applications, in the interest of improving the condition evaluation and management of roadway pavement surfaces, and by extension it investigates the possibility that connected vehicle data may contribute to monitoring the condition of transport infrastructure. Modern smartphones can be utilized to capture vehicle sensor data without integration with built-in vehicle systems, since they come with a range of built-in sensors, such as accelerometer, gyroscope and GPS sensors. Further, vehicle system (Controller Area

Network, CAN, bus) data can be collected through on-board diagnosis (OBD) Bluetooth connectors (ELM 327 Bluetooth Car Diagnostic Scanner) to a smartphone (e.g. the DashCommandTM software application). This combination of hardware and software components enables the monitoring of, among others, forward, lateral and vertical acceleration, vehicle roll and pitch, GPS latitude and longitude, GPS vehicle speed, engine RPM and current acceleration based on the last two speed readings. The vision for roadway anomaly detection by use of smartphone technology is set in parallel with the premise that such technology can be utilized for crowd-sourced data collection and analysis in GISbased pavement management systems (PMS) (fig.1). Up-to-date and future primary datasets will be different in form commencing traditional pavement surface condition data, and in order to overtake this barrier, it is prudent that crowd-sourced data be collected from a statistically significant number of probe-vehicles (Dennis et al. 2014). A number of vehicles collecting this data could be used in order to highlight rough pavement and potholes within a roadway network. Further, crowd-sourced data would create a geocoded event at points where the car develops abnormal behaviour. Populating a database with crowd-sourced event points from a number of connected vehicles will allow engineers and PMS program managers to identify where vehicles are experiencing rough riding conditions.

Figure 1. GIS – PMS.

The work included herein presents ongoing research on road anomaly detection and classification utilizing smartphones, artificial neural networks, various algorithms and classification models. Further to this short introduction, a literature review section provides a brief outline of existing work concerned to roadway anomaly detection utilizing PMS and pothole detection using smartphones. The section on methodology setup section outlines the developed data collection structure and methods, while the results and discussion section exhibits the methods and tools used to classify the data and experiment results. The paper concludes with key findings and an outline of future research directions.

2 LITERATURE REVIEW The American Association of Highway Transportation Officials (AASHTO, 1993) states that the “…function of a PMS is to improve the efficiency of decision making, expand its scope, provide feedback on the consequences of decisions, facilitate the coordination of activities within the agency, and ensure the consistency of decisions made at different management levels within the same organization.” Unquestionably, the investment in a PMS is worthwhile as it provides the tools an agency demands for the balanced resource allocation, ideal use of funds, pavement treatment selection, pavement treatment cost reductions and enhanced credibility with stakeholders (Washington Department of Transportation, 1994). It is essential to understand the benefits and related cost of any expenses in pavement management before starting the process (Khattak et al. 2008). In developed countries, PMS are specialized platforms equipped with expensive equipment built-in specialized Pavement Evaluation Vehicles (Seraj et al. 2014). The costs related to a PMS include software (purchase and installation), data collection, database setting up and system maintenance, as well as, updates, consultant services, employee training, personnel time and actual expenditures on the pavement and rehabilitation (AASHTO, 1990).

The first step in developing a pavement management procedure is to define the roadway network. The second step in pavement management design process is to verify the survey methodology for collecting the distress data. At present a PMS utilizes surveys, such as the Pavement Surface Evaluation and Rating (PASER) rating system, which is built on a series of photographs and descriptions for each of the individual rating categories (Walker et al. 2002). A rater uses this series of photographs to evaluate the overall condition of an individual pavement surface segment (Wolters et al. 2011). After verifying the survey methodology for collecting the distress data, an agency must opt between the two main approaches of collecting road surface condition data, i.e. manual or automated data collection (McQueen & Timm 2005). In manual surveys, pavement surface condition data are collected from a moving vehicle (windshield surveys) or by “walking” the pavement. Automated surveys are performed using vehicles being fitted with specialized camera that collect images and other sensing devices that collect sensor data relevant to the pavement being under examination (AASHTO, 2006). The third step concerns the prediction of the pavement surface condition. Pavement network conditions can be evaluated using either average deterioration rates or prediction models via statistical modelling like regression analysis. Furthermore, some systems use probabilistic type models which have mostly been based on Markovian theory (Wolters et al. 2011). The fourth step of PMS design is to select the suitable treatments for the pavement network. The recommended treatments are arranged using cyclical treatment selection, ranking or benefit/cost/analysis. The fifth step refers to the development of reports comprising the results obtain from the previous steps. The sixth step is to select pavement management tool such as a pavement management software, customized spreadsheets and/or GIS software. The selection of a pavement management tool being used varies according to the requirements of the agency and user needs. The final step is keeping the process updated. Notably, pavement management is a dynamic process that requires regular updates (Washington Department of Transportation, 1994). De Zoysa et al. (2007) proposed a public transport system called “BusNet” in order to monitor environmental pollution and pavement surface condition by adding acceleration sensors boards to the system. BusNet implements a sensor network, placed on top of public buses. The acceleration sensors identify potholes through changes in the vertical acceleration and determine the car speed modification using the horizontal acceleration. Erikson et al. (2008) used seven taxis running in the Boston area and developed a mobile sensor system called “Pothole Patrol”. Each taxi needed a computer running the Linux operating system, a WiFi card for transmitting collect-

ed data, an external GPS and a 3-axis accelerometer. In general, there are three main problems concerning the above systems. First, there is a large number of events (such as doors being knocked, unexpected swerves) and road anomalies (such as road expansion joints) that are difficult to distinguish from potholes. The second problem is that the systems cannot identify between a pothole that merits fixing and a bump in the road. The third problem is that the values reported by the sensors depend on a car’s speed and how the sensors are mounted on the car. Tai et al. (2010) used a mobile phone with triaxial accelerometer to collect acceleration data while riding a motorcycle. Strazdins et al. (2011) proposed a method requiring an Android smartphone with GPS, 3-axis accelerometer and a communication channel (cellular or Wifi). The system consists of two application components, one for the Android device and one for a data server. Seraj et al. (2014) proposed a system that detects road anomalies using mobile equipped with inertial accelerometers and gyroscopes sensors. They applied a method to remove the effects of speed, slopes and drifts from sensor signals. For future work they aim to apply this method for road anomalies detection in participatory sensing, using clustering by geo-coordinates. Alessandroni et al. (2014) described a system which included a combination of a custom mobile application and a georeferenced database system. The roughness values computed and stored into a back-end geographic information system enable visualization of road conditions. This proposed approach introduced an integrated system for monitoring applications in a scalable, crowdsourcing collaborative sensing environment. Mohamed et al. (2015) suggested the gyroscope around gravity rotation as the primary indicator for road anomalies, in addition to the accelerometer sensor, in order to avoid false-positive indications; especially when there is a sudden stop or sudden change in motion acceleration. The above systems, despite hardware differences in terms of GPS accuracy and accelerometer sampling rate and noise, they show that pothole detection is possible. The 2014 Mercedes-Benz S-Class used a LightDetection-and-Ranging (lidar) scanner to measure pavement roughness as a component of an active suspension system. Recently, the Jaguar Land Rover automaker announced that is examining a new connected vehicle technology which permits a vehicle to point dangerous potholes in the road and then allocate this data in real time with other vehicles and road authorities (Nick O'Donnell, 2015).

3 METHODOLOGICAL SETUP As aforementioned, the work presented herein investigates the utilization of smartphone technology for the detection of roadway anomalies and for their classification. Vehicle and smartphone data can be selected by both state-operated fleet vehicles and privately owned vehicles, and smartphones operated by the general public. Vehicle data are collected by sensors already installed on typical vehicles and smartphones. Further, the smartphones can perform data screening, fusion, and transmission of collected data for further data processing, analysis, or storage. The technology required to implement crowdsourced pavement condition monitoring from smartphones is already established and has been proven workable. The power of crowd-sourced data is that large data sets, which are collected through multiple data sources, negate the limitations in generalizability of data collected from a single data source. Even though multiple vehicles might provide conflicting data relating to pavement condition, the total effect and ‘knowledge’ inherent in the data provides an accurate model of the roadway condition in relation to how an average user experiences the pavement condition. The study focuses on three types of common roadway anomalies (transverse depressions Fig. 2a; longitudinal depressions, Fig. 2b; and potholes or manholes, Fig. 2c), which it examines first individually and then in tandem. Data on these types of roadway anomalies is collected in-situ by use of a car fitted with a smartphone (mounted on the car’s windshield) with its GPS, accelerometers and gyroscopes activated, and with an on-board diagnosis (OBD-II) reader connected to it. Further to the sensors, the smartphone had its video camera working as well for recording the routes travelled and consequently for visually validating the existence of roadway anomalies (as detected by the data analysis and ANN classification). The smartphone was also fitted with the DashCommand application for recording (and exporting) sensor readings, date/time stamps, and GPS locations of taken data. Vehicle system (CAN) data can be relayed through the OBD-II reader to the smart device and then transmitted for processing or storage via digital cellular connection or other means.

(a)

(b) Figure 3. Smartphone’s (a) roll and pitch directions (Physics Forums, 2015) and (b) relation to car’s wheels’ differential (White-Smoke, 2010).

(b)

(c) Figure 2. Roadway anomaly types examined for detection and classification: (a) transverse defect/anomaly; (b) longitudinal defect/anomaly; (c) potholes/manholes.

The collected case-study data are of high spatial resolution (at intervals of 0.1 seconds) and pertain to both uni-dimensional (e.g. X, Y, Z accelerations, speed, etc.) and two-dimensional indicators (e.g. the smartphone’s roll and pitch values, Fig. 3a, which can be related to the traveling host car’s roll and pitch values). In essence, the roll relates to a car’s acceleration difference between its left and right front wheels, while the pitch relates to a car’s acceleration difference between its front and rear wheels. In tandem, roll and pitch point out in what manner the host car is off balance, sideways and front/back

(a)

The field investigation included the collection of sensor data for a total of nine parameters (as shown in Table 1), which were also presumed to influence the accuracy of detection and classification of the roadway anomalies examined. Most of the nine factors examined were varied during the field investigation (e.g. speed, acceleration, engine RPM) and some were left constant (e.g. type of car/engine and fuel type). Two variables (VAR_3, VAR_4) were calculated posterior the data collection, as they relate to the difference in roll and pitch, respectively, between two data points (0.1 seconds in difference between the two data points). Table 1. Data collected and variables used for classifying roadway anomalies. Variable

Variable Name

Variable Description

VAR _2

Aux.Accel.Lateral (Gs)

Lateral Acceleration

VAR _1 VAR _3

Aux.Accel.Forward (Gs) Roll 2 - Roll 1(°)

VAR _4

Pitch 2 - Pitch 1(°)

VAR _5

Aux.Gps.Latitude (°)

VAR _8 VAR _9

Sae.Rpm (rpm) Calc.Acceleration (m/s²)

VAR _6 VAR _7

Aux.Gps.Longitude (°) Aux.Gps.Speed (Km/Hr)

Forward Acceleration Numerical Difference Between Two Successive Roll Values Numerical Difference Between Two Successive Pitch Values GPS Latitude GPS Longitude GPS Vehicle Speed

Engine RPM Current acceleration based on the last two speed readings

4 RESULTS AND DISCUSSION A first look at the collected raw data brings to light the complexity of the problem, as variables such as the X/Y/Z acceleration and vehicular speed thought to point out roadway anomalies are not as conclusive as originally thought. A plot of acceleration in one direction over time, such as the vertical acceleration (Fig. 3), helps demonstrate the lack of any pattern in the taken readings. The vertical variability is random, and even at a point of known roadway anomaly the variability in the acceleration is not definite of the existence of the anomaly. A related situation displays itself for various vehicular speeds (20, 40, 60, 80, 100 km/hr)

Time (s)

Figure 4. Vertical acceleration over time.

Roll (°)

The situation can be improved, should one weigh in the vehicular roll and pitch values over time (Fig. 4a, 4b), but even the aforementioned indicators fail to safely point out locations of roadway anomalies. Despite the fact these plots indicate areas of suspicion (highs and lows, away from the running average values), they are not reliable indicators. Further, the plots fail to provide information on other running parameters which could affect the accuracy of the data (e.g. the vehicle’s speed at the time of data sensing). The difference in values between subsequent locations (Fig. 4c, 4d) appears to be a better predictor (points of high peaks indicate a roadway anomaly), but still that is not fail-proof in the absence of other complimentary parameters.

Time (s)

Time (s) Figure 5. Raw and processed sensor data: (a) roll, over time; (b) pitch, over time; (c) point-to-point roll variation, over time; (d) point-to-point pitch variation, over time.

4.1 ANN Clasification For that reason, the datasets are then fed into an artificial neural network (ANN) consisting of 9 inputs (I1, I2, …, I9), 8 hidden neurons (H1, H2, …, H10) and 2 outputs (I1, I2). The ANN’s architecture (shown in Fig. 6) was implemented in MATLABTM. The ANN inputs are the parameters listed in Table 1, while the outputs are binary in nature (‘0’ for no defect, ‘1’ for defect) and they are used to classify data readings into classes of roadway anomalies. The ANN is first trained for each case of roadway anomaly (as given by Fig. 1), and then trained with all three roadway anomalies in tandem (ClassType 0, ClassType 1, ClassType 2, ClassType 3). ‘ClassType 0’ refers to the no-defect case, ‘ ClassType 1’ refers to transverse defect/anomaly (Fig. 1a), ‘ClassType 2’ refers to longitudinal defect/anomaly (Fig. 1b) and ‘ClassType 3’ refers to potholes/manholes (Fig. 1c). Each case 70% of the data is used for training, 15% for testing and 15% for validating the ANN, with a synopsis of the obtained classification results shown in (Table 2).

Pitch (°)

(a)

PITCH 2 - PITCH 1 (°)

Gz (m/s²)

Known location of roadway anomaly

Time (s)

Roll 2 – Roll1 (â°)

(b)

Time (s)

Figure 6. ANN model schematic architecture.

Table 2. Ann training and validation statistics for the various roadway anomaly cases examined. Classes 0:Type_0 1:Type_1 0:Type_0 1:Type_2 0:Type_0 1:Type_3 0:Type_0 1:Type_1 2:Type_2 3:Type_3

Train. Accuracy 100% 100% 84% 95% 100% 100% 100% 99.1% 100% 100%

Valid. Accuracy 100% 96% 83% 98% 100% 100% 97.6% 100% 100% 100%

Test. Accuracy 100% 100% 79% 94% 100% 100% 100% 100% 100% 100%

Over. Accuracy 100% 99% 83% 95% 100% 100% 99.7% 99.3% 100% 100%

Total Accuracy 99.8% 91 % 100% 99.8%

1 2 3 4 Over all

Output Class

Target Class 1 2 3 4 Overall 201 0 0 0 99.5% 23.9% 0.0% 0.0% 0.0% 0.5% 0 110 0 0 100.0% 0.0% 13.0% 0.0% 0.0% 0.0% 0 0 223 0 100.0% 0.0% 0.0% 26.5% 0.0% 0.0% 0 0 0 305 100.0% 0.0% 0.0% 0.0% 36.3% 0.0% 100% 99.1% 100.0% 100.0% 99.9% 0.0% 0.9% 0.0% 0.0% 0.1% Class 1: No defect Class 2: Transverse defect/anomaly (Fig. 1a) Class 3: Longitudinal defect/anomaly (Fig. 1b) Class 4: Potholes/manholes (Fig. 1c)

2 3 4 Overall

Table 5. Test Confusion Matrix.

Target Class 1 2 3 4 Overall 48 0 0 0 100.0% 26.7% 0.0% 0.0% 0.0% 0.0% 0 20 0 0 100.0% 0.0% 11.1% 0.0% 0.0% 0.0% 0 0 51 0 100.0% 0.0% 0.0% 28.3% 0.0% 0.0% 0 0 0 61 100.0% 0.0% 0.0% 0.0% 33.9% 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% Class 1: No defect Class 2: Transverse defect/anomaly (Fig. 1a) Class 3: Longitudinal defect/anomaly (Fig. 1b) Class 4: Potholes/manholes (Fig. 1c)

1 2 3 4 Overall

Table 6. All Confusion Matrix.

Target Class 1 2 3 4 289 1 0 0 24.1% 0.1% 0.0% 0.0% 1 152 0 0 0.1% 12.7% 0.0% 0.0% 0 0 329 0 0.0% 0.0% 27.4% 0.0% 0 0 0 428 0.0% 0.0% 0.0% 35.7% 99.7% 99.3% 100.0% 100.0% 0.3% 0.7% 0.0% 0.0% Class 1: No defect Class 2: Transverse defect/anomaly (Fig. 1a) Class 3: Longitudinal defect/anomaly (Fig. 1b) Class 4: Potholes/manholes (Fig. 1c)

Overall 99.7% 0.3% 99.3% 0.7% 100.0% 0.0% 100.0% 0.0% 99.8% 0.2%

1 2 3

Output Class

Table 3. Training Confusion Matrix.

1

Output Class

The intent was to first train the ANN to detect each defect in isolation of the others, and then train the ANN to distinguish defects between the three defect classes in examination. As can be seen the ANN classification arrives at a high degree of accuracy, not only when examining for the existence of a specific roadway anomaly but also when examining for all roadway anomalies at once (last case in Table 2). This high degree of accuracy is also evident when the ANN results are examined closer, by means of the produced ANN confusion matrices for training, testing and validating the classification of defects. The ANN confusion matrices enable us to investigate the numbers (and percentages) of not only the accurate classifications (i.e. perfect matches between target and output classes) but also of erroneous and of false-positive classifications. The horizontal axis in each confusion matrix indicates the target class (what the ANN classification should result to) and the vertical axis indicates the output class (what the ANN classification actually results to). As a backdrop, let us note that a good classifier yields a confusion matrix that will look dominantly diagonal, and that all off-diagonal elements on the confusion matrix represent misclassified data.

Target Class 1 2 3 4 Overall 40 0 0 0 100.0% 22.2% 0.0% 0.0% 0.0% 0.0% 1 22 0 0 95.7% 0.6% 12.2% 0.0% 0.0% 4.3% 0 0 55 0 100.0% 0.0% 0.0% 30.6% 0.0% 0.0% 0 0 0 62 100.0% 0.0% 0.0% 0.0% 34.4% 0.0% 97.6% 100.0% 100.0% 100.0% 99.4% 2.4% 0.0% 0.0% 0.0% 0.6% Class 1: No defect Class 2: Transverse defect/anomaly (Fig. 1a) Class 3: Longitudinal defect/anomaly (Fig. 1b) Class 4: Potholes/manholes (Fig. 1c)

Output Class

Refers to Fig. 1a Fig. 1b Fig. 1c Fig. 1a,1 b,1c

Table 4. Validation Confusion Matrix.

4 Over all

In essence, the ANN classifier detects and precisely categorizes the three roadway anomalies (target classes ‘2’, ‘3’ and ‘4’) while also distinguishing the ‘no defect’ condition (target class ‘1’), thus separating normal and abnormal roadway pavement conditions (Table 3-6).

4.2 Classification Models

In addition, classification can be performed with supervised machine learning in which an algorithm "learns" to classify new observations from examples of labeled data. In order to classify and validate the data, supervised learning algorithms were used for multiclass problems. A test was performed between various algorithms to train and cross validate classification models for binary or multiclass problems. A training was performed to search for the best classification model type, including decision trees using various classifiers, discriminant analysis, support vector machines, logistic regression, nearest neighbors, and ensemble classification. After cross validating multiple models, compare their crossvalidation errors side-by-side, the best model was selected (bagged trees). Supervised machine learning was done by supplying a known set of input data (observations or examples) and known responses to the data (i.e., labels or classes). The aforementioned datasets were fed into classification models consisting of 9 observations and 4 responses. The classification models observations are the parameters listed in Table 1, while the responses are ‘1’ for no defect, ‘2’ for transverse defects, ‘3’ for longitudinal defects and ‘4’ for potholes. The data used to train a model that generates predictions for the response to new data. The classification models architecture (shown in Fig. 7) was implemented in MATLABTM.

Figure 7. Classification models schematic architecture.

Bagged trees use Breiman's 'random forest' algorithm. ‘Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them’(Breiman 2001). Table 7. Bagged Trees training, validation and prediction statistics for the various roadway anomaly cases examined. Classifier Type Bagged Trees Bagged Trees Bagged Trees

Accuracy Train Validation Prediction

(%) 99.8 99.65 100

Figure 8. Bagged Trees Confusion Matrices.

As shown in (Fig. 8), the ANN classification rightly classifies roadway anomalies (diagonal elements for each of the confusion matrices) with approximately absolute accuracy. In essence, the classification model (bagged trees) distinguishes and accurately categorizes the three roadway anomalies (target classes ‘2’, ‘3’ and ‘4’) while also differentiating the ‘no defect’ condition (target class ‘1’), hence separating normal and abnormal roadway pavement conditions. Each case the data is used for training, for validating and for predicting the classification model (bagged trees), with a synopsis of the obtained classification results shown in (Table 7).

5 CONLCLUSIONS AND FUTURE WORK Transportation agencies can improve the condition and operation of their transportation networks by implementing a pavement management system (PMS) that utilizes vehicle-based data collection, OBD connections and decision support software. The popularity of smartphone technology in vehicles provides an opportunity to efficiently collect vehicle data and process it by use of connected and distributed systems. Even though connected vehicle data is not likely to directly provide us with traditional assessment metrics (such as IRI and PCI), new metrics might supplement and eventually supplant traditional metrics. The paper presented a study on the utilization of smartphones for the detection of roadway anomalies and on the utilization of artificial neural networks and classification models for the classification of such defects. The applied methodology is instantly available, low-cost and precise, and can be utilized in crowd-sourced applications leading to roadway assessment and pavement management systems. The presented study documents the detection and classification of three types of roadway anomalies, exhibiting accuracy levels higher than 90%. The proposed methodology is currently field-tested with larger datasets and a higher number of roadway defect types, with links created to GIS mapping and database management systems for the use of the proposed methodology in PMS.

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