SMARTPHONE-BASED SYSTEMS FOR DRIVING

EVALUATION

Mehdi Ghatee* Department of Computer Science, Amirkabir University of Technology,

Tehran, Iran

ABSTRACT

Smartphones play important roles in intelligent transportation systems and

driving behavior evaluation contexts. A driver together a smartphone can be

considered uniquely and by data mining on smartphone data, the driving style can

be recognized. Then any incentive or punitive mechanisms can be applied for

drivers to encourage them to drive better. Besides, the insurance and enforcement

departments can easily use smartphone-based systems to check the driving

behaviors gradually. Since, a smartphone consists of many sensors, memory and a

high processing power, it can be used as a black-box for storing the driver statues.

This can be analyzed when an agency needs to understand something about driving,

crashing, etc. However, the data should be collected with low energy consumption.

To this aim, the multiple criteria such as precision, frequency, energy consumption,

data quality, security and privacy are considered to select a reasonable set of sensors

for data collection and data mining. The type of communication has also a great

role for selecting the sensors and for collecting data. The data of the selected sensors

are sent to pre-processing and processing phases and the results are sent to

applications. For pre-processing, different filters are developed to clean data and to

remove noise and outliers. Also to fusion sensors and to model data there are

different attempts by the aid of neural networks or other machine learning

algorithms. Then, the data are compressed by some intervals, Fourier and wavelets

transforms, statistical distribution, stochastic variables or fuzzy quantities. In

addition, the necessary features can be extracted from the raw data by the aid of

matrix decomposition, PCA, deep learning, etc. Sometimes, it is necessary to

reduce the dimension of features by feature selection process. For processing, based

* Email: ghatee@aut.ac.ir, URL: www.aut.ac.ir/ghatee

Mehdi Ghatee 2

on the needs, we can implement data visualizers, predictors, classifiers, frequent

pattern miners or cluster extractors. We discuss on the different concepts of data

mining approaches which can be implemented on smartphone data. Finally, the

application is designed to warn, to define insurance encouragement, to support from

decision or to provide some information for autonomous driving. In what follows,

we present the details.

Keywords: Smartphone Sensors; Data Mining; Sensor Fusion; Feature Extraction;

Feature Selection; Machine Learning; Driving Profiling.

Smartphone-based Systems for Driving Evaluation 3

Contents

SMARTPHONE-BASED SYSTEMS FOR DRIVING EVALUATION 1 ABSTRACT 1 I. INTRODUCTION 7 II. SMARTPHONE SENSORS 9 III. COMMUNICATION INFRASTRUCTURE 14 IV. PREPROCESSING ON SENSORS DATA 17

A. Quality assurance 18 B. Data visualization 18 C. Data Cleaning 21 D. Data Integration 22 E. Data Reduction 23 G. Feature Extraction 24 H. Transportation Feature Extraction 27 I. Feature Selection 30 J. Data Compression 31 K. Data Modelling and Fusion 31 L. Evaluation measures 32

V. PROCESSING ON SMARTPHONE DATA 32 A. Regression 32 B. Supervised Learning Methods 33 C. Unsupervised Learning Methods 34 D. Frequent Patterns Mining 35

VI. DRIVING EVALUATION APPROACHES 36 A. Driving Modelling 37 B. Driving Evaluation 37 C. Driving Scoring 56 D. Privacy and Smartphone-Based Evaluation 57

VII. CONCLUSION 58 VIII. REFERENCES 59

Mehdi Ghatee 4

Table of Figures: Figure 1. Block Diagram of a typical smartphone and its devices and sensors.

................................................................................................................................ 8

Figure 2. The relative distance between two smartphone based on their GPS

data (applying GLONASS, AGPS, BEIDOU) when the real distance is 1 meter

(Darvishvand, Ghatee and Eftekhari 2016). ............................................................ 9

Figure 3. A feature extracted from gyroscope can detect different maneuvers

such as lane changing and turn (Eftekhari and Ghatee, 2019) .............................. 11

Figure 4 Distribution of acceleration values for safe and neurotic behavior

patterns for each type of maneuver (Eftekhari and Ghatee, 2018a) ...................... 17

Figure 5. Values of gyroscope sensor data for different maneuvers (Eftekhari

and Ghatee 2018a) ................................................................................................ 17

Figure 6. Smartphone Data visualization: (a) The effect of car sensitivity on

driving data, (b): the difference between dangerous driving and normal driving, (c):

different maneuvers, (d) The effect of congestion on driving data (Bejani and

Ghatee 2018) ......................................................................................................... 18

Figure 7. The tracking of the GSM antenna that save the location and time of a

smartphone (Faghani Omran 2016) ...................................................................... 18

Figure 8. Estimation of origin-destination demand based on the analysis on

GSM data, where the polygons are defined by Voronoi diagram, (Faghani Omran

2016) ..................................................................................................................... 20

Figure 9. 1D time-series of accelerometer and gyroscope sensors data to detect

the activities by a CNN (Ronao and Cho 2016) . .................................................. 24

Figure 10. The average of energy of the gyroscope data based on the

smartphone dynamics with respect to the vehicle (Wahlström, Skog and Händel

2015). .................................................................................................................... 25

Figure 11. The abilities of the different features to recognize the different

maneuvers (Eftekhari and Ghatee 2018). .............................................................. 27

Figure 12. The left and right graphs show the smartphone sensors signals for

U-turn and left turn maneuvers that are very similar, but fusion of rotation rate

around x-axis, acceleration on the y-axis, and device pitch is applicable to

distinguish driving maneuvers using the DTW algorithm (Johnson and Trivedi

2011). .................................................................................................................... 29

Figure 13. Different approaches of driving behavior analysis. ...................... 34

Figure 14. Flow diagram of smartphone-based system for driver behavior

analysis (Wahlström, Skog and Händel, 2015) ..................................................... 36

Figure 15. Motorized vs. non-motorized mode using IMU sensors on

SAMSUNG GALAXY N8000 (Eftekhari and Ghatee 2016). .............................. 38

Smartphone-based Systems for Driving Evaluation 5

Figure 16. Classification of driving maneuvers by a multi-layer perceptron

(Eftekhari and Ghatee 2019) ................................................................................. 38

Figure 17. Changes in the Z-axis of gyroscope variance in lane changing

(Eftekhari 2017) .................................................................................................... 39

Figure 18. Comparison of acceleration changes in nervous and normal

behaviors (Eftekhari 2017) ................................................................................... 39

Figure 19. Putting the smartphone on the dashboard (Johnson and Trivedi

2011). .................................................................................................................... 40

Figure 20. Difference between accelerometer and gyroscope values for nervous

and normal treats (Eftekhari, 2017). ..................................................................... 41

Figure 21. Decision tree to detect the driving behavior (Eftekhari 2017). ..... 42

Figure 22. An ensemble method for driving evaluation (Bejani and Ghatee

2018) ..................................................................................................................... 43

Figure 23. (a) car axles; (b) smart cell axes; (c) a mobile device placed on the

steering wheel of a smartphone (Liou 2001); (d) the steering wheel of the vehicle

from the side view. ................................................................................................ 44

Figure 24. (a) Smartphone placed on the steering wheel; (b) Car axes and

smartphones axes together .................................................................................... 45

Figure 25. (a) The smartphone placed on the steering wheel; (b) the angle

between the steering axis and the smartphone. ..................................................... 46

Figure 26. The red and blue signals are the acceleration of the smartphone

placed on the steering wheel and the smartphone placed parallel to the car's axes,

respectively. The left and right red signals are before and after applying re-

orientation. (a), (b): Axis x of acceleration for lane changing, (c), (d): Axis y of

acceleration for turn, (e), (f): Axis x of acceleration for U-turn, (g), (h): Axis y of

acceleration for straight movement (Lotfi, Ghatee and Vedaie 2017). ................. 49

Figure 27. Two fuzzy numbers for a safe and a neurotic behavior pattern in lane

changing maneuver (Eftekhari and Ghatee 2019). ................................................ 51

Figure 28. The fuzzy similarity between a driver and a safe behavior (Eftekhari

and Ghatee 2019) .................................................................................................. 51

Figure 29. Architecture of a fuzzy system for driving evaluation (Eftekhari and

Ghatee 2019) ......................................................................................................... 52

Figure 30. The values of extracted features from acceleration in a window with

200 samples. (a) Angular velocity feature (F1(t)). (b) Lateral acceleration feature

(F2(t)). (c) Longitudinal Acceleration feature (F3(t)). (d) Angle variation feature

(F4(t)) (Eftekhari and Ghatee 2018). ..................................................................... 53

Mehdi Ghatee 6

I. INTRODUCTION Data mining in Intelligent Transportation System (ITS) is a famous and

important problem (Zhang, et al. 2011). Because of some difficulty to work on big-

data collected by such systems, some new challenges are also augmented. As some

instances, the data of connected vehicles, data of different counters and sensors, the

images of surveillance systems, smartphone data and network pervasive data need

big-data approaches to extract the useful knowledge. This concept reveals a

revolution in ITS. In vehicle devices (Toledo and Lotan, In-vehicle data recorder

for evaluation of driving behavior and safety 2006), we also need to process on data

to support the driver by some decisions. Similarly, in autonomous vehicles we need

to mine the data to imagine the real world. However, our attention is on the

smartphones data analysis in this paper, but many results can be extended for

assistant driving and autonomous driving systems.

Smartphones have been considered as an integrated framework for different

ITS purposes. Some applications are presented as the following:

Traffic management (Campolo, et al. 2012),

Sensing in vehicles (Engelbrecht, et al. 2015),

Vehicle telematic (Wahlström, Skog and Händel., 2015a),

Automatic navigation (Wahlström, Skog and Händel, 2015b),

Travel deta collection (Rasouli 2014),

Travel information systems (Nitsche, et al. 2014),

Traffic monitoring (Mohan, Padmanabhan and Ramjee, 2008),

Vulnerable support (Zadeh, Ghatee and Eftekhari 2017),

Safety improvement (Bejani and Ghatee 2018).

Driving style evaluation (Kanarachos, Christopoulos and Chroneos 2018) is

also one the encouraging topic. For some reviews on the most recent references, see

(Wahlström, Skog and Händel, 2017) and (Astarita, Festa and Giofrè, 2018). Dula

and Ballard (2003) presented some measures for driving evaluation.

Now we address some references about the important issues for driving

evaluation:

A) Sensors

Engelbrecht et al., (2015) presented a survey on smartphone-based sensing in

vehicles by considering 24 related works through four categories including traffic

information, vehicle information, environmental information and driver behavior

information. Hoseini-Tabatabaei, Gluhak and Tafazolli (2013) studied on context-

Smartphone-based Systems for Driving Evaluation 7

aware researches based on user’s smartphone. In this work, different sensors and

different techniques were noticed to extract the necessary features.

B) Algorithms

As the algorithmic point of view, we see a great set of contributions. As some

instances, Eftekhari and Ghatee (2019) developed a fuzzy inference engine for

driving evaluation under uncertainty. Bejani and Ghatee (2018) used an ensemble

learning to evaluate the driving style based on context-awareness. The data-mining

algorithms are widely used by many researchers. These algorithms can be

categorized into three kinds of algorithms:

Pre-processing algorithms

Really, any data-mining project needs data cleaning part (Han, Pei and Kamber

2011). In this part, the irrelevant and the noisy data should be detected and the

exceptions need to be explained and interpreted. Also for missing data for example

in GPS data, we need some special process. Eftekhari and Ghatee (2016) discussed

on some threshold methods for data pre-processing in smartphone applications. The

different feature extraction methods were also used in this reference. Besides, the

transformation is important to extract the features. For example, discrete wavelet

transformation is useful for extracting useful features for driver evaluation (Lotfi

and Ghatee 2018). For feature selection, when the redundancy is high, we can also

pursue some statistical or heuristic approaches (Abpeykar, Ghatee and Zare, 2019).

Processing algorithms

In this part, knowledge extraction from smartphone data is considered. In these

cases, different machine learning algorithms are the base of different researches.

For example, (Cho 2016) and (Jahangiri and Rakha 2015) used a varity of machine

learning algorithms for transportation data. Alvarez et al. 2014) specialized a neural

network and Eftekhari and Ghatee (2016) applied an inference engine for

smartphon data. Zadeh, Ghatee and Eftekhari (2017) also developed a geometric

based learning system for smartphons. An ensemble algorithm was also stated by

(Bejani and Ghatee 2018). Dabiri and Heaslip (2018) applied a deep learning

algorithm and Eftekhari and Ghatee (2018) used a fuzzy inference system for

smartphone applications. For a good survey on the recent works, see (Bhavsar, et

al. 2017).

Application algorithms

The aim of these algorithms is the usage of the extracted knowledge in the

applications. For example, some algorithms are developed to warn the users. As

some examples, Zadeh, Ghatee and Eftekhari (2017) proposed a new warning

system for pedestrian, based on the smartphone data. Driving assistant systems

(DAS) is another example which can use such data for preventing the accidents

Mehdi Ghatee 8

(Teimouri and Ghatee 2018). These systems support drivers to drive in confortable

and safe status.

For safety purposes, smartphone based systems usually extract the results of

the different sensors that are embeded in different devices of vehicles (Darvishvand,

Ghatee and Eftekhari 2016). Then, by fusion techniques, some information

regarding the vehicle and the context is derived and based on this information some

decision about the driving can be made (Bejani and Ghatee 2018).

Besides, the ability of smartphones to connect to internet and to send messages,

can be considered for making decision in ITS applications. Some alarms and

guidelines can be sent by these abilities, see (Zadeh, Ghatee and Eftekhari 2017),

(Asadi and Ghatee 2015).

In what follows, we firstly give some information about the smartphone

sensors. Then we state some information about communication for safety systems.

Next, we discuss on preprocessing, processing and applications separately. Finally,

we focus on the driver evaluation and summerize the results in a brief conclusion.

Smartphone-based Systems for Driving Evaluation 9

REFERENCES Abbasi, Elham, Mehdi Ghatee, and Mohammad Ebrahim Shiri. 2013. “FRAN and

RBF-PSO as two components of a hyper framework to recognize protein

folds”. Computers in biology and medicine 43, no. 9: 1182-1191.

Abbasi, Elham, Mohammad Ebrahim Shiri, and Mehdi Ghatee. 2016. “Root-quatric

mixture of experts for complex classification problems.” Expert Systems

with Applications 53: 192-203.

Abpeykar, Shadi, and Mehdi Ghatee. 2014. “Supervised and unsupervised learning

DSS for incident management in intelligent tunnel: A case study in Tehran

Niayesh tunnel.” Tunnelling and Underground Space Technology 42

(2014): 293-306.

Abpeykar, Shadi, and Mehdi Ghatee. 2018. “An ensemble of RBF neural networks

in decision tree structure with knowledge transferring to accelerate multi-

classification.” Neural Computing and Applications: 1-21.

Abpeykar, Shadi, and Mehdi Ghatee. 2018. “Decent direction methods on the

feasible region recognized by supervised learning metamodels to solve

unstructured problems.” Journal of Information and Optimization

Sciences 39, no. 6: 1245-1262.

Abpeykar, Shadi, Mehdi Ghatee, and Hadi Zare. 2019. “Ensemble decision forest

of RBF networks via hybrid feature clustering approach for high-

dimensional data classification.” Computational Statistics & Data

Analysis 131 (2019): 12-36.

Ahmad, Amir, and Lipika Dey. 2007. “A k-mean clustering algorithm for mixed

numeric and categorical data.” Data & Knowledge Engineering 63, no. 2:

503-527.

Akyildiz, Ian F., Weilian Su, Yogesh Sankarasubramaniam, and Erdal Cayirci.

2002. “A survey on sensor networks.” IEEE communications

magazine 40, no. 8: 102-114.

Alvarez, Alberto Diaz, Francisco Serradilla Garcia, José Eugenio Naranjo, Jose

Javier Anaya, and Felipe Jimenez. 2014. “Modeling the driving behavior

of electric vehicles using smartphones and neural networks.” IEEE

Intelligent Transportation Systems Magazine 6, no. 3: 44-53.

Anjum, A., M. Ilyas. “Activity recognition using smartphone sensors”, IEEE

Consumer Communications and Networking Conference (CCNC), (2013):

914-919.

Araniti, Giuseppe, Claudia Campolo, Massimo Condoluci, Antonio Iera, and

Antonella Molinaro. 2013. "LTE for vehicular networking: a

survey." IEEE communications magazine 51, no. 5: 148-157.

Mehdi Ghatee 10

Araújo, Rui, Ângela Igreja, Ricardo de Castro, and Rui Esteves Araujo. 2012.

“Driving coach: A smartphone application to evaluate driving efficient

patterns.” In Intelligent Vehicles Symposium (IV), 2012 IEEE, pp. 1005-

1010.

Arróspide, Jon, Luis Salgado, and Marcos Nieto. 2012. “Video analysis-based

vehicle detection and tracking using an MCMC sampling

framework.” EURASIP Journal on Advances in Signal Processing 2012,

no. 1: 2.

Asadi, Reza, and Mehdi Ghatee. 2015. “A rule-based decision support system in

intelligent hazmat transportation system.” IEEE Transactions on

Intelligent Transportation Systems 16, no. 5: 2756-2764.

Astarita, Vittorio, Demetrio Carmine Festa, and Vincenzo Pasquale Giofrè. 2018.

“Mobile Systems applied to Traffic Management and Safety: a state of the

art.” Procedia computer science 134: 407-414.

Bagheri, Mehrdad, Matti Siekkinen, and Jukka K. Nurminen. 2014. “Cellular-based

vehicle to pedestrian (V2P) adaptive communication for collision

avoidance.” In Connected Vehicles and Expo (ICCVE), 2014 International

Conference on, pp. 450-456.

Bandeira, Jorge, Tiago G. Almeida, Asad J. Khattak, Nagui M. Rouphail, and

Margarida C. Coelho. 2013. “Generating emissions information for route

selection: Experimental monitoring and routes characterization.” Journal

of Intelligent Transportation Systems 17, no. 1: 3-17.

Bejani, Mohammad Mahdi, and Mehdi Ghatee. 2018. “A context aware system for

driving style evaluation by an ensemble learning on smartphone sensors

data.” Transportation Research Part C: Emerging Technologies 89: 303-

320.

Bejani, Mohammad Mahdi, and Mehdi Ghatee. 2019. “Convolutional Neural

Network with Adaptive Regularization to Classify Driving Styles on

Smartphones.” IEEE transaction on Intelligent Transportation Systems.

Belkin, Mikhail, and Partha Niyogi. 2003. “Laplacian eigenmaps for

dimensionality reduction and data representation.” Neural computation 15,

no. 6 (2003): 1373-1396.

Bhavsar, Parth, Ilya Safro, Nidhal Bouaynaya, Robi Polikar, and Dimah Dera.

2017. “Machine Learning in Transportation Data Analytics.” In Data

Analytics for Intelligent Transportation Systems, pp. 283-307.

Bierlaire, Michel, Jingmin Chen, and Jeffrey Newman. 2013. “A probabilistic map

matching method for smartphone GPS data.” Transportation Research Part

C: Emerging Technologies 26: 78-98.

Smartphone-based Systems for Driving Evaluation 11

Blachnik, Marcin, and Włodzisław Duch. 2011. “LVQ algorithm with instance

weighting for generation of prototype-based rules.” Neural Networks 24,

no. 8: 824-830.

Bo, Cheng, Xuesi Jian, Taeho Jung, Junze Han, Xiang-Yang Li, Xufei Mao, and

Yu Wang. 2017. “Detecting driver’s smartphone usage via nonintrusively

sensing driving dynamics.” IEEE Internet of Things Journal 4, no. 2: 340-

350.

Burrus, C. Sidney, Ramesh A. Gopinath, Haitao Guo, Jan E. Odegard, and Ivan W.

Selesnick. 1998. Introduction to wavelets and wavelet transforms: a

primer. Vol. 1. New Jersey: Prentice hall.

Byon, Young-Ji, Baher Abdulhai, and Amer Shalaby. 2009. “Real-time

transportation mode detection via tracking global positioning system

mobile devices.” Journal of Intelligent Transportation Systems 13, no. 4:

161-170.

Campolo, Claudia, Antonio Iera, Antonella Molinaro, Stefano Yuri Paratore, and

Giuseppe Ruggeri. 2012. “SMaRTCaR: An integrated smartphone-based

platform to support traffic management applications.” In Vehicular Traffic

Management for Smart Cities (VTM), 2012 First International Workshop

on, pp. 1-6. IEEE.

Castignani, German, Thierry Derrmann, Raphaël Frank, and Thomas Engel. 2015.

“Driver behavior profiling using smartphones: A low-cost platform for

driver monitoring.” IEEE Intelligent Transportation Systems Magazine 7,

no. 1: 91-102.

Chen, Shuyan, and Wei Wang. 2009. “Decision tree learning for freeway automatic

incident detection.” Expert systems with applications 36, no. 2: 4101-

4105.

Chen, Cong, Yanyan Chen, Jianming Ma, Guohui Zhang, and C. Michael Walton.

2018. “Driver behavior formulation in intersection dilemma zones with

phone use distraction via a logit-Bayesian network hybrid

approach.” Journal of Intelligent Transportation Systems 22, no. 4: 311-

324.

Chen, Huizhong, Sam S. Tsai, Georg Schroth, David M. Chen, Radek Grzeszczuk,

and Bernd Girod. 2011. “Robust text detection in natural images with edge-

enhanced maximally stable extremal regions.” In Image Processing (ICIP),

2011 18th IEEE International Conference on, pp. 2609-2612.

Chen, Jingmin, and Michel Bierlaire. 2015. “Probabilistic multimodal map

matching with rich smartphone data.” Journal of Intelligent Transportation

Systems 19, no. 2: 134-148.

Mehdi Ghatee 12

Chen, Po-Han, Heng-Kuang Shen, Chi-Yang Lei, and Luh-Maan Chang. 2012.

“Support-vector-machine-based method for automated steel bridge rust

assessment.” Automation in Construction 23: 9-19.

Chen, Min, Yin Zhang, Yong Li, Shiwen Mao, and Victor CM Leung, 2015a.

“EMC: Emotion-aware mobile cloud computing in 5G.” IEEE Network

29, no. 2: 32-38.

Chen, Zhenghua, Han Zou, Hao Jiang, Qingchang Zhu, Yeng Chai Soh, and Lihua

Xie, 2015b. “Fusion of WiFi, smartphone sensors and landmarks using the

Kalman filter for indoor localization.” Sensors 15, no. 1: 715-732.

Cheng, Hui, and K. C. Gupta. 1989. “An historical note on finite rotations.” Journal

of Applied Mechanics 56, no. 1: 139-145.

Chhabra, Rishu, Seema Verma, and C. Rama Krishna. 2017. “A survey on driver

behavior detection techniques for intelligent transportation systems.”

In Cloud Computing, Data Science & Engineering-Confluence, 2017 7th

International Conference on, pp. 36-41.

Cho, Sung-Bae. 2016. “Exploiting machine learning techniques for location

recognition and prediction with smartphone logs.” Neurocomputing 176:

98-106.

Choi, Sam-Wook, Dai-Jin Kim, Jung-Seok Choi, Heejune Ahn, Eun-Jeung Choi,

Won-Young Song, Seohee Kim, and Hyunchul Youn. 2015. “Comparison

of risk and protective factors associated with smartphone addiction and

Internet addiction.” Journal of behavioral addictions 4, no. 4: 308-314.

Corripio, J. Rosales, DM Arenas González, AL Sandoval Orozco, LJ García

Villalba, Julio Hernandez-Castro, and Stuart James Gibson. 2013. “Source

smartphone identification using sensor pattern noise and wavelet

transform.” 5th International Conference on Imaging for Crime Detection

and Prevention (ICDP 2013): 1-16.

Johnson, Derick A., and Mohan M. Trivedi. 2011. “Driving style recognition using

a smartphone as a sensor platform.” In Intelligent Transportation Systems

(ITSC), 2011 14th International IEEE Conference on, pp. 1609-1615.

Dabiri, Sina, and Kevin Heaslip. 2018. “Inferring transportation modes from GPS

trajectories using a convolutional neural network.” Transportation research

part C: emerging technologies 86: 360-371.

Dai, Jiangpeng, Jin Teng, Xiaole Bai, Zhaohui Shen, and Dong Xuan. 2010.

“Mobile phone based drunk driving detection.” In Pervasive Computing

Technologies for Healthcare (PervasiveHealth), 2010 4th International

Conference on-NO PERMISSIONS, pp. 1-8.

Darvishvand, Fateme G., Mehdi Ghatee, and Hamid Reza Eftekhari. 2016. “Using

3G and 4G/LTE for collision warning systems in vehicular network.” The

Smartphone-based Systems for Driving Evaluation 13

15th International Conference On Traffic And Transportation Engineering.

Tehran.

Daubechies, Ingrid. 1990. “The wavelet transform, time-frequency localization and

signal analysis.” IEEE transactions on information theory 36, no. 5: 961-

1005.

De Vries, Nic J., Marelie H. Davel, Jaco Badenhorst, Willem D. Basson, Febe De

Wet, Etienne Barnard, and Alta De Waal. 2014. “A smartphone-based

ASR data collection tool for under-resourced languages.” Speech

communication 56: 119-131.

Deffenbacher, Jerry L., Eugene R. Oetting, and Rebekah S. Lynch. 1994.

“Development of a driving anger scale.” Psychological reports 74, no. 1

(1994): 83-91.

Derbel, Oussama. 2018. “Belief and fuzzy theories for driving behavior assessment

in case of accident scenarios.” International journal of automotive

technology 19, no. 1: 167-177.

Do, Trinh Minh Tri, and Daniel Gatica-Perez. 2014. “The places of our lives:

Visiting patterns and automatic labeling from longitudinal smartphone

data.” IEEE Transactions on Mobile Computing 13, no. 3: 638-648.

Dou, Adam Ji, Vana Kalogeraki, Dimitrios Gunopulos, Taneli Mielikinen, Ville

Tuulos, Sean Foley, and Curtis Yu. 2011. “Data clustering on a network of

mobile smartphones.” In 2011 IEEE/IPSJ International Symposium on

Applications and the Internet, pp. 118-127.

Dula, Chris S., and Mary E. Ballard. 2003. “Development and Evaluation of a

Measure of Dangerous, Aggressive, Negative Emotional, and Risky

Driving 1.” Journal of Applied Social Psychology 33, no. 2: 263-282.

Eftekhari, Hamid Reza, and Mehdi Ghatee. 2017. "The lower bound for dynamic

parking prices to decrease congestion through CBD." Operational

Research 1-27.

Eftekhari, Hamid Reza, 2017. Driving modeling with respect to the awareness

about vehicle and route. Tehran, Iran: PhD Dissertion, Department of

Computer Science, Amirkabir University of Technology.

Eftekhari, Hamid Reza, and Mehdi Ghatee. 2016. “An inference engine for

smartphones to preprocess data and detect stationary and transportation

modes.” Transportation Research Part C: Emerging Technologies 69: 313-

327.

Eftekhari, Hamid Reza, and Mehdi Ghatee. 2018. “Hybrid of discrete wavelet

transform and adaptive neuro fuzzy inference system for overall driving

behavior recognition.” Transportation Research Part F: Traffic Psychology

and Behaviour 58: 782-796.

Mehdi Ghatee 14

Eftekhari, Hamid Reza, and Mehdi Ghatee. 2019. “A similarity-based neuro-fuzzy

modeling for driving behavior recognition applying fusion of smartphone

sensors.” Journal of Intelligent Transportation Systems: 1-12.

Eftekhari, Hamid Reza, Ali Reza Jalalian, and Mehdi Ghatee. 2016. “Binary

Programing Model to Optimize RSU Placement for Information

Dissemination.” International Conference on Vehicle Technology and

Intelligent Transport Systems (VEHITS 2016). Rome.

Eldén, Lars. 2007. Matrix methods in data mining and pattern recognition. Vol. 4.

SIAM.

Engelbrecht, Jarret, Marthinus Johannes Booysen, Gert-Jan van Rooyen, and

Frederick Johannes Bruwer. 2015. “Survey of smartphone-based sensing

in vehicles for intelligent transportation system applications.” IET

Intelligent Transport Systems 9, no. 10: 924-935.

Eren, Haluk, Semiha Makinist, Erhan Akin, and Alper Yilmaz. 2012. “Estimating

driving behavior by a smartphone.” In Intelligent Vehicles Symposium

(IV), 2012 IEEE, pp. 234-239.

Faghani Omran, Mohammad J. 2016. Cellphone network data processing in

advanced traveller information system ATIS. Tehran, Iran: MSc. thesis,

Department of Computer Science, Amirkabir University of Technology.

Ghatee, Mehdi, and Malihe Niksirat. 2013. “A Hopfield neural network applied to

the fuzzy maximum cut problem under credibility measure.” Information

Sciences 229: 77-93.

Ghatee, Mehdi. 2014. "Solution of the generalized interval linear programming

problems: pessimistic and optimistic approaches." Journal of Computing

and Security 1 (2):159-167.

Guan, Adrian, Steven H. Bayless, and Radha Neelakantan. 2011. “Connected

vehicle insights.” Trends in computer vision. Technology scan series 2012.

Han, Jiawei, Jian Pei, and Micheline Kamber. 2011. Data mining: concepts and

techniques. Elsevier.

Handel, Peter, Isaac Skog, Johan Wahlstrom, Farid Bonawiede, Richard Welch,

Jens Ohlsson, and Martin Ohlsson. 2014. “Insurance telematics:

Opportunities and challenges with the smartphone solution.” IEEE

Intelligent Transportation Systems Magazine 6, no. 4: 57-70.

Hibbeler, Russell Charles. 2001. Engineering mechanics. Pearson education.

Hoseini-Tabatabaei, Seyed Amir, Alexander Gluhak, and Rahim Tafazolli. 2013.

“A survey on smartphone-based systems for opportunistic user context

recognition.” ACM Computing Surveys (CSUR) 45, no. 3: 27.

Hromic, Hugo, Danh Le Phuoc, Martin Serrano, Aleksandar Antonić, Ivana P.

Žarko, Conor Hayes, and Stefan Decker. 2015. “Real time analysis of

Smartphone-based Systems for Driving Evaluation 15

sensor data for the internet of things by means of clustering and event

processing.” In Communications (ICC), 2015 IEEE International

Conference on, pp. 685-691.

Hung, Nguyen Quoc Viet, Hoyoung Jeung, and Karl Aberer. 2013. “An evaluation

of model-based approaches to sensor data compression.” IEEE

Transactions on Knowledge and Data Engineering 25, no. 11: 2434-2447.

Izadi, Davood, Jemal H. Abawajy, Sara Ghanavati, and Tutut Herawan. 2015. “A

data fusion method in wireless sensor networks.” Sensors 15, no. 2: 2964-

2979.

Jahangiri, Arash, and Hesham A. Rakha. 2015. “Applying Machine Learning

Techniques to Transportation Mode Recognition Using Mobile Phone

Sensor Data.” IEEE Trans. Intelligent Transportation Systems 16, no. 5:

2406-2417.

Johnson, Derick A., and Mohan M. Trivedi. 2011. “Driving style recognition using

a smartphone as a sensor platform.” In Intelligent Transportation Systems

(ITSC), 2011 14th International IEEE Conference on, pp. 1609-1615.

Joubert, Johan W., Dirk de Beer, and Nico de Koker. 2016. “Combining

accelerometer data and contextual variables to evaluate the risk of driver

behaviour.” Transportation research part F: traffic psychology and

behaviour 41: 80-96.

Kalra, Nidhi, and Divya Bansal. 2014. “Analyzing driver behavior using

smartphone sensors: a survey.” Int. J. Electron. Electr. Eng 7, no. 7: 697-

702.

Kanarachos, Stratis, Stavros-Richard G. Christopoulos, and Alexander Chroneos.

2018. “Smartphones as an integrated platform for monitoring driver

behaviour: The role of sensor fusion and connectivity.” Transportation

Research Part C: Emerging Technologies 95: 867-882.

Kent, John T. 1983. “Information gain and a general measure of

correlation.” Biometrika 70, no. 1: 163-173.

Kervick, Aoife. 2016. An evaluation of smartphone driver support systems for

young drivers-acceptance, efficacy, and driver distraction. PhD diss.,

School of Psychology, National University of Ireland, Galway.

Ktusek, Adrian, Marcin Kurdziel, Mateusz Paciorekl, Piotr Wawrykal, and

Wojciech Turekl. 2018. “Driver Profiling by Using LSTM Networks with

Kalman Filtering.” In 2018 IEEE Intelligent Vehicles Symposium (IV),

pp. 1983-1988.

Lajunen, Timo, and Dianne Parker. 2001. “Are aggressive people aggressive

drivers? A study of the relationship between self-reported general

Mehdi Ghatee 16

aggressiveness, driver anger and aggressive driving.” Accident Analysis &

Prevention 33, no. 2: 243-255.

Lee, Boon-Giin, and Wan-Young Chung. 2012. “A smartphone-based driver safety

monitoring system using data fusion.” Sensors 12, no. 12: 17536-17552.

Lee, Boon Giin, Jae-Hee Park, Chuan Chin Pu, and Wan-Young Chung. 2016.

“Smartwatch-based driver vigilance indicator with kernel-fuzzy-C-means-

wavelet method.” IEEE Sensors Journal 16, no. 1: 242-253.

Lee, SeokJu, Girma Tewolde, and Jaerock Kwon. 2014. “Design and

implementation of vehicle tracking system using GPS/GSM/GPRS

technology and smartphone application.” In Internet of Things (WF-IoT),

2014 IEEE World Forum on, pp. 353-358.

Lin, Miao, and Wen-Jing Hsu. 2014. “Mining GPS data for mobility patterns: A

survey.” Pervasive and mobile computing 12: 1-16.

Liou, Yi-Chaung. 2001. “Device for holding securely a mobile phone dialing device

on the steering wheel of a motor vehicle.” U.S. Patent 6,209,767, issued

April 3, 2001.

López, Jesús R., Luis C. González, Johan Wahlström, Manuel Montes y Gómez,

Leonardo Trujillo, and Graciela Ramírez-Alonso. 2018. “A Genetic

Programming Approach for Driving Score Calculation in the Context of

Intelligent Transportation Systems.” IEEE Sensors Journal 18, no. 17:

7183-7192.

Lotfi, Roya, and Mehdi Ghatee. 2018. “Smartphone based Driving Style

Classification Using Features Made by Discrete Wavelet

Transform.” arXiv preprint arXiv:1803.06213 (2018).

Lotfi, Roya, Mehdi Ghatee, and Seyed Shakib Vedaie, 2017. “Dangerous Driving

Maneuver Detection Based on the Smartphone on the Steering Wheel

applying physical-trigonometric relationships.” The 16th International

Conference on Traffic and Transportation Engineering. Tehran.

Lu, Dang-Nhac, Duc-Nhan Nguyen, Thi-Hau Nguyen, and Ha-Nam Nguyen. 2018.

“Vehicle Mode and Driving Activity Detection Based on Analyzing

Sensor Data of Smartphones.” Sensors18, no. 4: 1036.

Mazimpaka, Jean Damascène, and Sabine Timpf. 2016. “Trajectory data mining: A

review of methods and applications.” Journal of Spatial Information

Science 2016, no. 13: 61-99.

Mohan, Prashanth, Venkata N. Padmanabhan, and Ramachandran Ramjee. 2008.

“Nericell: rich monitoring of road and traffic conditions using mobile

smartphones.” In Proceedings of the 6th ACM conference on Embedded

network sensor systems, pp. 323-336. ACM.

Smartphone-based Systems for Driving Evaluation 17

Mumtaz, Shahid, Kazi Mohammed Saidul Huq, Muhammad Ikram Ashraf,

Jonathan Rodriguez, Valdemar Monteiro, and Christos Politis. 2015.

“Cognitive vehicular communication for 5G.” IEEE Communications

Magazine 53, no. 7: 109-117.

Murphy, Enda, and Eoin A. King. 2016. “Testing the accuracy of smartphones and

sound level meter applications for measuring environmental

noise.” Applied Acoustics 106: 16-22.

Navidi, Neda, and Rene Jr. Landry. 2017. "Driving Behavior Assessment Using

Fuzzy Inference System and Low-Cost Inertial Sensors." Journal of Traffic

and Transportation Engineering 5: 217-227.

Nasreen, Shamila, Muhammad Awais Azam, Khurram Shehzad, Usman Naeem,

and Mustansar Ali Ghazanfar. 2014. "Frequent pattern mining algorithms

for finding associated frequent patterns for data streams: a survey."

Procedia Computer Science 37: 109-116.

Nitsche, Philippe, Peter Widhalm, Simon Breuss, Norbert Brändle, and Peter

Maurer. 2014. “Supporting large-scale travel surveys with smartphones–a

practical approach.” Transportation Research Part C: Emerging

Technologies 43: 212-221.

Paefgen, Johannes, Flavius Kehr, Yudan Zhai, and Florian Michahelles. 2012.

“Driving behavior analysis with smartphones: insights from a controlled

field study.” In Proceedings of the 11th International Conference on

mobile and ubiquitous multimedia, p. 36. ACM.

Park, Yongtae, Jihun Ha, Seungho Kuk, Hyogon Kim, Chieh-Jan Mike Liang, and

JeongGil Ko. 2014. “A feasibility study and development framework

design for realizing smartphone-based vehicular networking

systems.” IEEE transactions on mobile computing 13, no. 11: 2431-2444.

Peng, Hanchuan, Fuhui Long, and Chris Ding. 2005. “Feature selection based on

mutual information criteria of max-dependency, max-relevance, and min-

redundancy.” IEEE Transactions on pattern analysis and machine

intelligence 27, no. 8: 1226-1238.

Piotr, Blaszczyk, Wojciech Turek, Aleksander Byrski, and Krzysztof Cetnarowicz.

2015. “Towards credible driver behavior modeling.” In Intelligent

Transportation Systems (ITSC), 2015 IEEE 18th International Conference

on, pp. 1557-1562.

Rasouli, Soora, ed. 2014. Mobile technologies for activity-travel data collection and

analysis. IGI global.

Ronao, Charissa Ann, and Sung-Bae Cho. 2016. “Human activity recognition with

smartphone sensors using deep learning neural networks.” Expert Systems

with Applications 59: 235-244.

Mehdi Ghatee 18

Roweis, Sam T., and Lawrence K. Saul. 2000. “Nonlinear dimensionality reduction

by locally linear embedding.” Science 290, no. 5500: 2323-2326.

Saiprasert, Chalermpol, Suttipong Thajchayapong, Thunyasit Pholprasit, and

Chularat Tanprasert. 2014. “Driver behaviour profiling using smartphone

sensory data in a V2I environment.” In Connected Vehicles and Expo

(ICCVE), 2014 International Conference on, pp. 552-557.

Srinivasan, Vijay, Saeed Moghaddam, Abhishek Mukherji, Kiran K. Rachuri,

Chenren Xu, and Emmanuel Munguia Tapia. 2014. “Mobileminer: Mining

your frequent patterns on your phone.” In Proceedings of the 2014 ACM

International Joint Conference on Pervasive and Ubiquitous Computing,

pp. 389-400. ACM.

Teimouri, Fateme, and Mehdi Ghatee. 2018. “A real-time warning system for rear-

end collision based on random forest classifier.” arXiv preprint

arXiv:1803.10988.

Toledo, Tomer. 2007. “Driving behaviour: models and challenges.” Transport

Reviews 27, no. 1: 65-84.

Toledo, Tomer, and Tsippy Lotan. 2006. “In-vehicle data recorder for evaluation

of driving behavior and safety.” Transportation Research Record: Journal

of the Transportation Research Board 1953: 112-119.

Van Der Maaten, Laurens. 2014. “Accelerating t-SNE using tree-based

algorithms.” The Journal of Machine Learning Research 15, no. 1: 3221-

3245.

Van Ly, Minh, Sujitha Martin, and Mohan M. Trivedi. 2013. “Driver classification

and driving style recognition using inertial sensors.” In Intelligent Vehicles

Symposium (IV), 2013 IEEE, pp. 1040-1045.

Wahlström, Johan, Isaac Skog, and Peter Händel. 2015. “IMU alignment for

smartphone-based automotive navigation.” In Information Fusion

(Fusion), 2015 18th International Conference on, pp. 1437-1443.

Wahlström, Johan. 2017. Sensor Fusion for Smartphone-based Vehicle Telematics.

PhD diss., KTH Royal Institute of Technology, 2017.

Wahlström, Johan, Isaac Skog, and Peter Händel. 2017. “Smartphone-based vehicle

telematics: A ten-year anniversary.” IEEE Transactions on Intelligent

Transportation Systems 18, no. 10: 2802-2825.

Wahlström, Johan, Isaac Skog, and Peter Händel. 2015. “Driving behavior analysis

for smartphone-based insurance telematics.” In Proceedings of the 2nd

workshop on Workshop on Physical Analytics, pp. 19-24. ACM.

Wen, Jiahui, Mingyang Zhong, and Zhiying Wang. 2015. “Activity recognition

with weighted frequent patterns mining in smart environments.” Expert

Systems with Applications 42, no. 17-18: 6423-6432.

Smartphone-based Systems for Driving Evaluation 19

Xiong, Hui, Gaurav Pandey, Michael Steinbach, and Vipin Kumar. 2006.

“Enhancing data analysis with noise removal.” IEEE Transactions on

Knowledge and Data Engineering 18, no. 3: 304-319.

Ye, Yang, Yu Zheng, Yukun Chen, Jianhua Feng, and Xing Xie. 2009. “Mining

individual life pattern based on location history.” In Mobile Data

Management: Systems, Services and Middleware, 2009. MDM'09. Tenth

International Conference on, pp. 1-10, IEEE.

Zadeh, Roya Bastani, Mehdi Ghatee, and Hamid Reza Eftekhari. 2018. “Three-

phases smartphone-based warning system to protect vulnerable road users

under fuzzy conditions.” IEEE Transactions on Intelligent Transportation

Systems 19, no. 7: 2086-2098.

Zhang, Junping, Fei-Yue Wang, Kunfeng Wang, Wei-Hua Lin, Xin Xu, and Cheng

Chen. 2011. “Data-driven intelligent transportation systems: A

survey.” IEEE Transactions on Intelligent Transportation Systems 12, no.

4: 1624-1639.

Zhao, Qingwen, Yanmin Zhu, Chao Chen, Hongzi Zhu, and Bo Li. 2013. “When

3G meets VANET: 3G-assisted data delivery in VANETs.” IEEE Sensors

Journal 13, no. 10: 3575-3584.