Experimental study on driving scenarios and driver behaviours in Malaysia by using machine learning techniques
<p>The increasing number in annual road fatalities has caused a major challenge in many</p><p>countries. Minimising fatalities and improving safety are the top priorities of different</p><p>countries. This study aimed to analyse d...
Saved in:
| Main Author: | |
|---|---|
| Format: | thesis |
| Language: | eng |
| Published: |
2021
|
| Subjects: | |
| Online Access: | https://ir.upsi.edu.my/detailsg.php?det=7052 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
oai:ir.upsi.edu.my:7052 |
|---|---|
| record_format |
uketd_dc |
| institution |
Universiti Pendidikan Sultan Idris |
| collection |
UPSI Digital Repository |
| language |
eng |
| topic |
LC Special aspects of education |
| spellingShingle |
LC Special aspects of education Garfan, Salem Abdullah Salem Experimental study on driving scenarios and driver behaviours in Malaysia by using machine learning techniques |
| description |
<p>The increasing number in annual road fatalities has caused a major challenge in many</p><p>countries. Minimising fatalities and improving safety are the top priorities of different</p><p>countries. This study aimed to analyse driver behaviours in Malaysia and the impacts</p><p>of practising eco-driving to improve safety, reduce fuel consumption and green gas</p><p>emission by using smartphone sensors and OBD2 (ELM327) adapter based on event</p><p>thresholds and machine learning algorithms. In the experimental study, 30 drivers had</p><p>participated, which were 17 novice drivers (7 males and 10 females) and 13 experienced</p><p>drivers (8 males and 5 females). A Honda Civic 2019 car was used in the experiment.</p><p>A specific route was selected for all drivers, which consisted of two types of road</p><p>(highway and urban), with a total distance of 20.6 km. The analysis of driving behaviour</p><p>was based on threshold events and machine learning algorithms. This was to classify</p><p>the different driving scenarios. In the drivers profiling, driving behaviour was</p><p>categorised into three driving behaviours, such as safe, normal, and aggressive driving.</p><p>Random Forest model was selected for the classification after being compared to other</p><p>different machine learning algorithms (Decision Tree, Support Vector Machine, KNearest</p><p>Neighbour, and Nave Bayes models). The results of this experiment showed</p><p>that a remarkable reduction in terms of fuel consumption and CO2 emission of up to</p><p>30% less was achieved when participants followed the eco driving techniques.</p><p>Moreover, aggressive events were notably reduced in eco driving as compared to</p><p>normal driving. Furthermore, the selected machine learning model was able to</p><p>differentiate and classify different driving scenarios with high classification accuracy</p><p>of up to 100 %, such as identifying male and female drivers, novice and experienced</p><p>drivers, and driving in the highway or city.</p> |
| format |
thesis |
| qualification_name |
|
| qualification_level |
Doctorate |
| author |
Garfan, Salem Abdullah Salem |
| author_facet |
Garfan, Salem Abdullah Salem |
| author_sort |
Garfan, Salem Abdullah Salem |
| title |
Experimental study on driving scenarios and driver behaviours in Malaysia by using machine learning techniques |
| title_short |
Experimental study on driving scenarios and driver behaviours in Malaysia by using machine learning techniques |
| title_full |
Experimental study on driving scenarios and driver behaviours in Malaysia by using machine learning techniques |
| title_fullStr |
Experimental study on driving scenarios and driver behaviours in Malaysia by using machine learning techniques |
| title_full_unstemmed |
Experimental study on driving scenarios and driver behaviours in Malaysia by using machine learning techniques |
| title_sort |
experimental study on driving scenarios and driver behaviours in malaysia by using machine learning techniques |
| granting_institution |
Universiti Pendidikan Sultan Idris |
| granting_department |
Fakulti Seni, Komputeran dan Industri Kreatif |
| publishDate |
2021 |
| url |
https://ir.upsi.edu.my/detailsg.php?det=7052 |
| _version_ |
1747833347620470784 |
| spelling |
oai:ir.upsi.edu.my:70522022-04-28 Experimental study on driving scenarios and driver behaviours in Malaysia by using machine learning techniques 2021 Garfan, Salem Abdullah Salem LC Special aspects of education <p>The increasing number in annual road fatalities has caused a major challenge in many</p><p>countries. Minimising fatalities and improving safety are the top priorities of different</p><p>countries. This study aimed to analyse driver behaviours in Malaysia and the impacts</p><p>of practising eco-driving to improve safety, reduce fuel consumption and green gas</p><p>emission by using smartphone sensors and OBD2 (ELM327) adapter based on event</p><p>thresholds and machine learning algorithms. In the experimental study, 30 drivers had</p><p>participated, which were 17 novice drivers (7 males and 10 females) and 13 experienced</p><p>drivers (8 males and 5 females). A Honda Civic 2019 car was used in the experiment.</p><p>A specific route was selected for all drivers, which consisted of two types of road</p><p>(highway and urban), with a total distance of 20.6 km. The analysis of driving behaviour</p><p>was based on threshold events and machine learning algorithms. This was to classify</p><p>the different driving scenarios. In the drivers profiling, driving behaviour was</p><p>categorised into three driving behaviours, such as safe, normal, and aggressive driving.</p><p>Random Forest model was selected for the classification after being compared to other</p><p>different machine learning algorithms (Decision Tree, Support Vector Machine, KNearest</p><p>Neighbour, and Nave Bayes models). The results of this experiment showed</p><p>that a remarkable reduction in terms of fuel consumption and CO2 emission of up to</p><p>30% less was achieved when participants followed the eco driving techniques.</p><p>Moreover, aggressive events were notably reduced in eco driving as compared to</p><p>normal driving. Furthermore, the selected machine learning model was able to</p><p>differentiate and classify different driving scenarios with high classification accuracy</p><p>of up to 100 %, such as identifying male and female drivers, novice and experienced</p><p>drivers, and driving in the highway or city.</p> 2021 thesis https://ir.upsi.edu.my/detailsg.php?det=7052 https://ir.upsi.edu.my/detailsg.php?det=7052 text eng closedAccess Doctoral Universiti Pendidikan Sultan Idris Fakulti Seni, Komputeran dan Industri Kreatif <p>AbuAli, N. (2015). Advanced vehicular sensing of road artifacts and driver behavior.</p><p>Paper presented at the Computers and Communication (ISCC), 2015 IEEE</p><p>Symposium on.</p><p></p><p>AbuAli, N., & Abou-zeid, H. (2016). Driver behavior modeling: Developments and</p><p>future directions. International Journal of Vehicular Technology, 2016.</p><p></p><p>Achirul Nanda, M., Boro Seminar, K., Nandika, D., & Maddu, A. (2018). A</p><p>comparison study of kernel functions in the support vector machine and its</p><p>application for termite detection. Information, 9(1), 5.</p><p></p><p>Ahmed, H., Pierre, S., & Quintero, A. (2017). A flexible testbed architecture for</p><p>VANET. Vehicular Communications, 9, 115-126.</p><p></p><p>Aichinger, C., Nitsche, P., Sttz, R., & Harnisch, M. (2016). Using low-cost</p><p>smartphone sensor data for locating crash risk spots in a road network.</p><p>Transportation Research Procedia, 14, 2015-2024.</p><p></p><p>Al-Sultan, S., Al-Doori, M. M., Al-Bayatti, A. H., & Zedan, H. (2014). A</p><p>comprehensive survey on vehicular ad hoc network. Journal of network and</p><p>computer applications, 37, 380-392.</p><p></p><p>Alam, K. M., Hariz, M. B., Hosseinioun, S. V., Saini, M., & El Saddik, A. (2016).</p><p>MUDVA: A multi-sensory dataset for the vehicular CPS applications. Paper</p><p>presented at the Multimedia Signal Processing (MMSP), 2016 IEEE 18th</p><p>International Workshop on.</p><p></p><p>Alaybeyoglu, A., & Senel, B. C. (2017). A design of fuzzy logic based android</p><p>application for safe driving. Paper presented at the Artificial Intelligence and</p><p>Data Processing Symposium (IDAP), 2017 International.</p><p></p><p>Albert, G., Musicant, O., Oppenheim, I., & Lotan, T. (2016). Which smartphone's</p><p>apps may contribute to road safety? An AHP model to evaluate experts'</p><p>opinions. Transport Policy, 50, 54-62.</p><p></p><p>Alessandroni, G., Carini, A., Lattanzi, E., Freschi, V., & Bogliolo, A. (2017). A study</p><p>on the influence of speed on road roughness sensing: the SmartRoadSense</p><p>case. Sensors, 17(2), 305.</p><p></p><p>Allamehzadeh, A., de la Parra, J. U., Hussein, A., Garcia, F., & Olaverri-Monreal, C.</p><p>(2017). Cost-efficient driver state and road conditions monitoring system for</p><p>conditional automation. Paper presented at the Intelligent Vehicles</p><p>Symposium (IV), 2017 IEEE.</p><p></p><p>Allamehzadeh, A., & Olaverri-Monreal, C. (2016). Automatic and manual driving</p><p>paradigms: Cost-efficient mobile application for the assessment of driver</p><p>inattentiveness and detection of road conditions. Paper presented at the</p><p>Intelligent Vehicles Symposium (IV), 2016 IEEE.</p><p></p><p>Allouch, A., Kouba, A., Abbes, T., & Ammar, A. (2017). RoadSense: Smartphone</p><p>Application to Estimate Road Conditions Using Accelerometer and</p><p>Gyroscope. IEEE Sensors Journal, 17(13), 4231-4238.</p><p></p><p>Aloul, F., Zualkernan, I., Abu-Salma, R., Al-Ali, H., & Al-Merri, M. (2014). iBump:</p><p>Smartphone application to detect car accidents. Paper presented at the</p><p>Industrial Automation, Information and Communications Technology</p><p>(IAICT), 2014 International Conference on.</p><p></p><p>Alvarez, A. D., Garcia, F. S., Naranjo, J. E., Anaya, J. J., & Jimenez, F. (2014).</p><p>Modeling the driving behavior of electric vehicles using smartphones and</p><p>neural networks. IEEE Intelligent Transportation Systems Magazine, 6(3), 44-</p><p>53.</p><p></p><p>Alvear, O., Calafate, C. T., Cano, J.-C., & Manzoni, P. (2015). Validation of a vehicle</p><p>emulation platform supporting OBD-II communications. Paper presented at</p><p>the Consumer communications and networking conference (CCNC), 2015</p><p>12th Annual IEEE.</p><p></p><p>Aly, H., Basalamah, A., & Youssef, M. (2015). Lanequest: An accurate and energyefficient</p><p>lane detection system. Paper presented at the Pervasive Computing</p><p>and Communications (PerCom), 2015 IEEE International Conference on.</p><p></p><p>Aly, H., Basalamah, A., & Youssef, M. (2016). Robust and ubiquitous smartphonebased</p><p>lane detection. Pervasive and Mobile Computing, 26, 35-56.</p><p></p><p>Amarasinghe, M., Kottegoda, S., Arachchi, A. L., Muramudalige, S., Bandara, H. D.,</p><p>& Azeez, A. (2015). Cloud-based driver monitoring and vehicle diagnostic</p><p>with OBD2 telematics. Paper presented at the Advances in ICT for Emerging</p><p>Regions (ICTer), 2015 Fifteenth International Conference on.</p><p></p><p>Amici, R., Bonola, M., Bracciale, L., Rabuffi, A., Loreti, P., & Bianchi, G. (2014).</p><p>Performance assessment of an epidemic protocol in VANET using real traces.</p><p>Procedia Computer Science, 40, 92-99.</p><p></p><p>An, J.-W., Moon, D., & Cho, D.-S. (2015). Design of a Smartphone-Based Driving</p><p>Habit Monitoring System Advances in Computer Science and Ubiquitous</p><p>Computing (pp. 861-866): Springer.</p><p></p><p>Antoniou, C., Gikas, V., Papathanasopoulou, V., Danezis, C., Panagopoulos, A. D.,</p><p>Markou, I., . . . Perakis, H. (2015). Localization and driving behavior</p><p>classification with smartphone sensors in direct absence of global navigation</p><p>satellite systems. Transportation Research Record: Journal of the</p><p>Transportation Research Board(2489), 66-76.</p><p></p><p>Aras, A. C., & Gocer, I. (2016). Driver Rating based on Interval Type-2 Fuzzy Logic</p><p>System. IFAC-PapersOnLine, 49(11), 95-100.</p><p></p><p>Arroyo, C., Bergasa, L. M., & Romera, E. (2016). Adaptive fuzzy classifier to detect</p><p>driving events from the inertial sensors of a smartphone. Paper presented at</p><p>the Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International</p><p>Conference on.</p><p></p><p>Asim, F. (2015). AndroSensor: Fiv Asim. Retrieved from</p><p>https://play.google.com/store/apps/details?id=com.fivasim.androsensor&hl=en</p><p></p><p>Astarita, V., Festa, D. C., Giofr, P., Guido, G., & Mongelli, D. W. E. (2016). Cooperative</p><p>ITS: ESD a smartphone based system for sustainability and</p><p>transportation safety. Procedia Computer Science, 83, 449-456.</p><p></p><p>Azzuhana Roslan, N. S. M. Z., Nur Zarifah Harun, Akmalia Shabadin, Siti Zaharah</p><p>Ishak, Wong Shaw Voon. (2017). Risk of Motorcycle Crashes at Federal</p><p>Road. (MRR 221). Malaysian Institute of Road Safety Research: Perpustakaan</p><p>Negara Malaysia Retrieved from</p><p>http://web.miros.gov.my/1/publications.php?id_page=19&id_event=579.</p><p></p><p>Bahadoor, K., & Hosein, P. (2016). Application for the Detection of Dangerous</p><p>Driving and an Associated Gamification Framework. Paper presented at the</p><p>Future Internet of Things and Cloud Workshops (FiCloudW), IEEE</p><p>International Conference on.</p><p></p><p>Barata, J., Ferro, R., & Ferreira, J. (2014). My Traffic Manager. Procedia</p><p>Technology, 17, 209-216.</p><p></p><p>Bi, C., Huang, J., Xing, G., Jiang, L., Liu, X., & Chen, M. (2017). SafeWatch: A</p><p>Wearable Hand Motion Tracking System for Improving Driving Safety. Paper</p><p>presented at the Internet-of-Things Design and Implementation (IoTDI), 2017</p><p>IEEE/ACM Second International Conference on.</p><p></p><p>Birrell, S. A., & Fowkes, M. (2014). Glance behaviours when using an in-vehicle</p><p>smart driving aid: A real-world, on-road driving study. Transportation</p><p>research part F: traffic psychology and behaviour, 22, 113-125.</p><p></p><p>Birrell, S. A., Fowkes, M., & Jennings, P. A. (2014). Effect of using an in-vehicle</p><p>smart driving aid on real-world driver performance. IEEE Transactions on</p><p>Intelligent Transportation Systems, 15(4), 1801-1810.</p><p></p><p>Botzer, A., Musicant, O., & Perry, A. (2017). Driver behavior with a smartphone</p><p>collision warning applicationA field study. Safety science, 91, 361-372.</p><p></p><p>Brezger, F., & Albers, A. (2013). Evaluation of a New Method for Customer-</p><p>Orientated Rating of Clutch Systems in Conceptual Hybrid Vehicles. Paper</p><p>presented at the ASME 2013 International Mechanical Engineering Congress</p><p>and Exposition.</p><p></p><p>Briante, O., Campolo, C., Iera, A., Molinaro, A., Paratore, S. Y., & Ruggeri, G.</p><p>(2014). Supporting augmented floating car data through smartphone-based</p><p>crowd-sensing. Vehicular Communications, 1(4), 181-196.</p><p></p><p>Bruwer, F. J., & Booysen, M. J. (2015). Comparison of GPS and MEMS support for</p><p>smartphone-based driver behavior monitoring. Paper presented at the</p><p>Computational Intelligence, 2015 IEEE Symposium Series on.</p><p></p><p>Camlica, Z., Hilal, A., & Kuli, D. (2016). Feature abstraction for driver behaviour</p><p>detection with stacked sparse auto-encoders. Paper presented at the Systems,</p><p>Man, and Cybernetics (SMC), 2016 IEEE International Conference on.</p><p></p><p>Carvalho, E., Ferreira, B. V., Ferreira, J., de Souza, C., Carvalho, H. V., Suhara, Y., . .</p><p>. Pessin, G. (2017). Exploiting the use of recurrent neural networks for driver</p><p>behavior profiling. Paper presented at the Neural Networks (IJCNN), 2017</p><p>International Joint Conference on.</p><p></p><p>Castignani, G., Derrmann, T., Frank, R., & Engel, T. (2015a). Driver behavior</p><p>profiling using smartphones: A low-cost platform for driver monitoring. IEEE</p><p>Intelligent Transportation Systems Magazine, 7(1), 91-102.</p><p></p><p>Castignani, G., Derrmann, T., Frank, R., & Engel, T. (2015b). Validation study of</p><p>risky event classification using driving pattern factors. Paper presented at the</p><p>Communications and Vehicular Technology in the Benelux (SCVT), 2015</p><p>IEEE Symposium on.</p><p></p><p>Castignani, G., Derrmann, T., Frank, R., & Engel, T. (2017). Smartphone-based</p><p>adaptive driving maneuver detection: A large-scale evaluation study. IEEE</p><p>Transactions on Intelligent Transportation Systems, 18(9), 2330-2339.</p><p></p><p>Castignani, G., & Frank, R. (2014). SenseFleet: A smartphone-based driver profiling</p><p>platform. Paper presented at the Sensing, Communication, and Networking</p><p>(SECON), 2014 Eleventh Annual IEEE International Conference on.</p><p></p><p>Castignani, G., Frank, R., & Engel, T. (2013a). Driver behavior profiling using</p><p>smartphones. Paper presented at the Intelligent Transportation Systems-</p><p>(ITSC), 2013 16th International IEEE Conference on.</p><p></p><p>Castignani, G., Frank, R., & Engel, T. (2013b). An evaluation study of driver profiling</p><p>fuzzy algorithms using smartphones. Paper presented at the Network Protocols</p><p>(ICNP), 2013 21st IEEE International Conference on.</p><p></p><p>Cervantes-Villanueva, J., Carrillo-Zapata, D., Terroso-Saenz, F., Valdes-Vela, M., &</p><p>Skarmeta, A. F. (2016). Vehicle maneuver detection with accelerometer-based</p><p>classification. Sensors, 16(10), 1618.</p><p></p><p>Chaovalit, P., Saiprasert, C., & Pholprasit, T. (2014). A method for driving event</p><p>detection using SAX with resource usage exploration on smartphone platform.</p><p>EURASIP Journal on Wireless Communications and Networking, 2014(1),</p><p>135.</p><p></p><p>Chen, K.-W., Tsai, H.-M., Hsieh, C.-H., Lin, S.-D., Wang, C.-C., Yang, S.-W., . . .</p><p>Chou, C.-T. (2014). Connected vehicle safety science, system, and framework.</p><p>Paper presented at the Internet of Things (WF-IoT), 2014 IEEE World Forum</p><p>on.</p><p></p><p>Chen, L.-B., Li, H.-Y., Chang, W.-J., Tang, J.-J., & Li, K. S.-M. (2015). An intelligent</p><p>vehicular telematics platform for vehicle driving safety supporting system.</p><p>Paper presented at the Connected Vehicles and Expo (ICCVE), 2015</p><p>International Conference on.</p><p></p><p>Chirawichitchai, N. (2015). Developing term weighting scheme based on term</p><p>occurrence ratio for sentiment analysis Information Science and Applications</p><p>(pp. 737-744): Springer.</p><p></p><p>Chuang, M.-C., Bala, R., Bernal, E. A., Paul, P., & Burry, A. (2014). Estimating Gaze</p><p>Direction of Vehicle Drivers Using a Smartphone Camera. Paper presented at</p><p>the CVPR Workshops.</p><p></p><p>Creaser, J. I., Edwards, C. J., Morris, N. L., & Donath, M. (2015). Are cellular phone</p><p>blocking applications effective for novice teen drivers? Journal of safety</p><p>research, 54, 75. e29-78.</p><p></p><p>Crippa, M., Oreggioni, G., Guizzardi, D., Muntean, M., Schaaf, E., Lo Vullo, E., . . .</p><p>Vignati, E. (2019). Fossil CO2 and GHG emissions of all world countries.</p><p>Luxemburg: Publication Office of the European Union.</p><p></p><p>D'Andrea, E., & Marcelloni, F. (2017). Detection of traffic congestion and incidents</p><p>from GPS trace analysis. Expert Systems with Applications, 73, 43-56.</p><p></p><p>Dang, V.-C., Kubo, M., Sato, H., Yamaguchi, A., & Namatame, A. (2014). A simple</p><p>braking model for detecting incidents locations by smartphones. Paper</p><p>presented at the Computational Intelligence for Security and Defense</p><p>Applications (CISDA), 2014 Seventh IEEE Symposium on.</p><p></p><p>Dange, G. R., Paranthaman, P. K., Bellotti, F., Samaritani, M., Berta, R., & De Gloria,</p><p>A. (2016). Assessment of driver behavior based on Machine learning</p><p>approaches in a social gaming scenario. Paper presented at the International</p><p>Conference on Applications in Electronics Pervading Industry, Environment</p><p>and Society.</p><p></p><p>Daptardar, S., Lakshminarayanan, V., Reddy, S., Nair, S., Sahoo, S., & Sinha, P.</p><p>(2015). Hidden Markov Model based driving event detection and driver</p><p>profiling from mobile inertial sensor data. Paper presented at the SENSORS,</p><p>2015 IEEE.</p><p></p><p>de Oliveira, L. P., Wehrmeister, M. A., & de Oliveira, A. S. (2017). Systematic</p><p>Literature Review on Automotive Diagnostics. Paper presented at the</p><p>Computing Systems Engineering (SBESC), 2017 VII Brazilian Symposium</p><p>on.</p><p></p><p>Diewald, S., Lindemann, P., Mller, A., Stockinger, T., Koelle, M., & Kranz, M.</p><p>(2014). Gamified training for vehicular user interfacesEffects on drivers'</p><p>behavior. Paper presented at the Connected Vehicles and Expo (ICCVE), 2014</p><p>International Conference on.</p><p></p><p>Dobbins, C., & Fairclough, S. (2017). A mobile lifelogging platform to measure</p><p>anxiety and anger during real-life driving. Paper presented at the Pervasive</p><p>Computing and Communications Workshops (PerCom Workshops), 2017</p><p>IEEE International Conference on.</p><p></p><p>Domingos Da Cunha, F., Villas, L., Boukerche, A., Maia, G., Carneiro Viana, A.,</p><p>Mini, R. A. F., & Loureiro, A. A. F. (2016). Data Communication in</p><p>VANETs: Survey, Applications and Challenges. Ad Hoc Networks, 44(C), 90-</p><p>103. doi: 10.1016/j.adhoc.2016.02.017</p><p></p><p>Eboli, L., Guido, G., Mazzulla, G., Pungillo, G., & Pungillo, R. (2017). Investigating</p><p>Car Users Driving Behaviour through Speed Analysis. PROMETTraffic&</p><p>Transportation, 29(2), 193-202.</p><p></p><p>Eboli, L., Mazzulla, G., & Pungillo, G. (2016). Combining speed and acceleration to</p><p>define car users safe or unsafe driving behaviour. Transportation research</p><p>part C: emerging technologies, 68, 113-125.</p><p></p><p>Eboli, L., Mazzulla, G., & Pungillo, G. (2017). How to define the accident risk level</p><p>of car drivers by combining objective and subjective measures of driving style.</p><p>Transportation research part F: traffic psychology and behaviour, 49, 29-38.</p><p></p><p>Eftekhari, H. R., & Ghatee, M. (2016). An inference engine for smartphones to</p><p>preprocess data and detect stationary and transportation modes.</p><p>Transportation research part C: emerging technologies, 69, 313-327.</p><p></p><p>Ekanayake, H. B., Backlund, P., Ziemke, T., Ramberg, R., Hewagamage, K. P., &</p><p>Lebram, M. (2013). Comparing expert driving behavior in real world and</p><p>simulator contexts. International Journal of Computer Games Technology,</p><p>2013.</p><p></p><p>Engelbrecht, J., Booysen, M. J., van Rooyen, G.-J., & Bruwer, F. J. (2015).</p><p>Performance comparison of dynamic time warping (DTW) and a maximum</p><p>likelihood (ML) classifier in measuring driver behavior with smartphones.</p><p>Paper presented at the Computational Intelligence, 2015 IEEE Symposium</p><p>Series on.</p><p></p><p>Engelbrecht, J., Booysen, M. J., van Rooyen, G.-J., & Bruwer, F. J. (2015). Survey of</p><p>smartphone-based sensing in vehicles for intelligent transportation system</p><p>applications. IET Intelligent Transport Systems, 9(10), 924-935.</p><p></p><p>Environment, D. o. (2019). Environmental Quality Report 2018. Malaysia:</p><p>Department of Environment.</p><p></p><p>Fan, X., Liu, J., Wang, Z., Jiang, Y., & Liu, X. (2017a). Crowdsourced Road</p><p>Navigation: Concept, Design, and Implementation. IEEE Communications</p><p>Magazine, 55(6), 126-128.</p><p></p><p>Fan, X., Liu, J., Wang, Z., Jiang, Y., & Liu, X. S. (2017b). CrowdNavi: Demystifying</p><p>Last Mile Navigation With Crowdsourced Driving Information. IEEE</p><p>Transactions on Industrial Informatics, 13(2), 771-781.</p><p></p><p>Fang, S.-H., Fei, Y.-X., Xu, Z., & Tsao, Y. (2017). Learning Transportation Modes</p><p>From Smartphone Sensors Based on Deep Neural Network. IEEE Sensors</p><p>Journal, 17(18), 6111-6118.</p><p></p><p>Feraud, I. S., Lara, M. M., & Naranjo, J. E. (2017). A fuzzy logic model to estimate</p><p>safe driving behavior based on traffic violation. Paper presented at the</p><p>Ecuador Technical Chapters Meeting (ETCM), 2017 IEEE.</p><p></p><p>Ferris, J. S., & Newburn, D. A. (2017). Wireless alerts for extreme weather and the</p><p>impact on hazard mitigating behavior. Journal of Environmental Economics</p><p>and Management, 82, 239-255.</p><p></p><p>Fitz-Walter, Z., Johnson, D., Wyeth, P., Tjondronegoro, D., & Scott-Parker, B.</p><p>(2017). Driven to drive? Investigating the effect of gamification on learner</p><p>driver behavior, perceived motivation and user experience. Computers in</p><p>Human Behavior, 71, 586-595.</p><p></p><p>Fitzpatrick, C. D., McKinnon, I. A., Tainter, F. T., & Knodler Jr, M. A. (2016). The</p><p>application of continuous speed data for setting rational speed limits and</p><p>improving roadway safety. Safety science, 85, 171-178.</p><p></p><p>Geng, Y., & Cassandras, C. G. (2013). New smart parking system based on</p><p>resource allocation and reservations. IEEE Transactions on Intelligent</p><p>Transportation Systems, 14(3), 1129-1139.</p><p></p><p>Gnther, M., Rauh, N., & Krems, J. F. (2017). Conducting a study to investigate ecodriving</p><p>strategies with battery electric vehiclesa multiple method approach.</p><p>Transportation Research Procedia, 25, 2242-2256.</p><p></p><p>Hadiwardoyo, S. A., Patra, S., Calafate, C. T., Cano, J.-C., & Manzoni, P. (2017). An</p><p>Android ITS Driving Safety Application Based on Vehicle-to-Vehicle (V2V)</p><p>Communications. Paper presented at the Computer Communication and</p><p>Networks (ICCCN), 2017 26th International Conference on.</p><p></p><p>Handel, P., Skog, I., Wahlstrom, J., Bonawiede, F., Welch, R., Ohlsson, J., &</p><p>Ohlsson, M. (2014). Insurance telematics: Opportunities and challenges with</p><p>the smartphone solution. IEEE Intelligent Transportation Systems Magazine,</p><p>6(4), 57-70.</p><p></p><p>Hansen, J. H., Busso, C., Zheng, Y., & Sathyanarayana, A. (2017). Driver Modeling</p><p>for Detection and Assessment of Driver Distraction: Examples from the</p><p>UTDrive Test Bed. IEEE Signal Processing Magazine, 34(4), 130-142.</p><p></p><p>Harding, S., Kandlikar, M., & Gulati, S. (2016). Taxi apps, regulation, and the market</p><p>for taxi journeys. Transportation Research Part A: Policy and Practice, 88,</p><p>15-25.</p><p></p><p>Harikrishnan, P., & Gopi, V. P. (2017). Vehicle Vibration Signal Processing for Road</p><p>Surface Monitoring. IEEE Sensors Journal, 17(16), 5192-5197.</p><p></p><p>Hashimoto, N., Okuma, T., Miyakoshi, S., Tomita, K., Matsumoto, O., Smirnov, A., .</p><p>. . Lashkov, I. (2016). Use cases for rider assistant mobile application</p><p>evaluation using travelling simulator. Paper presented at the Open Innovations</p><p>Association (FRUCT), 2016 19th Conference of.</p><p></p><p>Hawkins, I. (2019). Torque Pro (OBD 2 & Car) (Version 1.10.114): Ian J Hawkins.</p><p>Retrieved from</p><p>https://play.google.com/store/apps/details?id=org.prowl.torque&hl=en</p><p></p><p>Hlasny, T., Fanti, M. P., Mangini, A. M., Rotunno, G., & Turchiano, B. (2017).</p><p>Optimal fuel consumption for heavy trucks: A review. Paper presented at the</p><p>Service Operations and Logistics, and Informatics (SOLI), 2017 IEEE</p><p>International Conference on.</p><p></p><p>Hsieh, C.-H., Tsai, H.-M., Yang, S.-W., & Lin, S.-D. (2014). Predict scooter's</p><p>stopping event using smartphone as the sensing device. Paper presented at the</p><p>Internet of Things (iThings), 2014 IEEE International Conference on, and</p><p>Green Computing and Communications (GreenCom), IEEE and Cyber,</p><p>Physical and Social Computing (CPSCom), IEEE.</p><p></p><p>Hu, J., Shao, Y., Sun, Z., Wang, M., Bared, J., & Huang, P. (2016). Integrated optimal</p><p>eco-driving on rolling terrain for hybrid electric vehicle with vehicleinfrastructure</p><p>communication. Transportation research part C: emerging</p><p>technologies, 68, 228-244.</p><p></p><p>Hu, X., Chiu, Y.-C., Ma, Y.-L., & Zhu, L. (2015). Studying driving risk factors using</p><p>multi-source mobile computing data. International journal of transportation</p><p>science and technology, 4(3), 295-312.</p><p></p><p>Huang, K.-S., Chiu, P.-J., Tsai, H.-M., Kuo, C.-C., Lee, H.-Y., & Wang, Y.-C. F.</p><p>(2016). Redeye: preventing collisions caused by red-light running scooters</p><p>with smartphones. IEEE Transactions on Intelligent Transportation Systems,</p><p>17(5), 1243-1257.</p><p></p><p>Husnjak, S., Forenbacher, I., & Bucak, T. (2015). Evaluation of Eco-Driving Using</p><p>Smart Mobile Devices. PROMET-Traffic&Transportation, 27(4), 335-344.</p><p></p><p>Husnjak, S., Perakovi, D., Forenbacher, I., & Mumdziev, M. (2015). Telematics</p><p>system in usage based motor insurance. Procedia Engineering, 100, 816-825.</p><p></p><p>Jahangiri, A., & Rakha, H. A. (2015). Applying machine learning techniques to</p><p>transportation mode recognition using mobile phone sensor data. IEEE</p><p>Transactions on Intelligent Transportation Systems, 16(5), 2406-2417.</p><p></p><p>Jeon, M., Yang, E., Oh, E., Park, J., & Youn, C.-H. (2017). A Deterministic Feedback</p><p>Model for Safe Driving based on Nonlinear Principal Analysis Scheme.</p><p>Procedia Computer Science, 113, 454-459.</p><p></p><p>Ji, Z., Ganchev, I., O'Droma, M., Zhao, L., & Zhang, X. (2014). A cloud-based car</p><p>parking middleware for IoT-based smart cities: Design and implementation.</p><p>Sensors, 14(12), 22372-22393.</p><p></p><p>Jiang, L., Chen, X., & He, W. (2016). SafeCam: Analyzing intersection-related driver</p><p>behaviors using multi-sensor smartphones. Paper presented at the Pervasive</p><p>Computing and Communications (PerCom), 2016 IEEE International</p><p>Conference on.</p><p></p><p>Jo, J., Kim, H., Park, H., & Yoon, D. (2015). A Monitoring System to Understand</p><p>Postal Motorcyclist's Driving Behavior. Paper presented at the Intelligent</p><p>Transportation Systems (ITSC), 2015 IEEE 18th International Conference on.</p><p></p><p>John, G. H., & Langley, P. (2013). Estimating continuous distributions in Bayesian</p><p>classifiers. arXiv preprint arXiv:1302.4964.</p><p></p><p>Jnior, J. F., Carvalho, E., Ferreira, B. V., de Souza, C., Suhara, Y., Pentland, A., &</p><p>Pessin, G. (2017). Driver behavior profiling: An investigation with different</p><p>smartphone sensors and machine learning. PLoS one, 12(4), e0174959.</p><p></p><p>Kaiwartya, O., Abdullah, A. H., Cao, Y., Altameem, A., Prasad, M., Lin, C.-T., &</p><p>Liu, X. (2016). Internet of vehicles: Motivation, layered architecture, network</p><p>model, challenges, and future aspects. IEEE Access, 4, 5356-5373.</p><p></p><p>Kamalanathsharma, R. K., Rakha, H. A., & Zohdy, I. H. (2015). Survey on in-vehicle</p><p>technology use: results and findings. International journal of transportation</p><p>science and technology, 4(2), 135-149.</p><p></p><p>Kaplan, S., Guvensan, M. A., Yavuz, A. G., & Karalurt, Y. (2015). Driver behavior</p><p>analysis for safe driving: A survey. IEEE Transactions on Intelligent</p><p>Transportation Systems, 16(6), 3017-3032.</p><p></p><p>Kar, A., & Corcoran, P. (2017). A Review and Analysis of Eye-Gaze Estimation</p><p>Systems, Algorithms and Performance Evaluation Methods in Consumer</p><p>Platforms. IEEE Access, 5, 16495-16519.</p><p></p><p>Karaduman, M., & Eren, H. (2017a). Classification of road curves and corresponding</p><p>driving profile via smartphone trip data. Paper presented at the Artificial</p><p>Intelligence and Data Processing Symposium (IDAP), 2017 International.</p><p></p><p>Karaduman, M., & Eren, H. (2017b). Deep learning based traffic direction sign</p><p>detection and determining driving style. Paper presented at the Computer</p><p>Science and Engineering (UBMK), 2017 International Conference on.</p><p></p><p>Karaliopoulos, M., Katsikopoulos, K., & Lambrinos, L. (2017). Bounded rationality</p><p>can make parking search more efficient: The power of lexicographic</p><p>heuristics. Transportation Research Part B: Methodological, 101, 28-50.</p><p></p><p>Karatas, C., Liu, L., Li, H., Liu, J., Wang, Y., Tan, S., . . . Martin, R. (2016).</p><p>Leveraging wearables for steering and driver tracking. Paper presented at the</p><p>Computer Communications, IEEE INFOCOM 2016-The 35th Annual IEEE</p><p>International Conference on.</p><p></p><p>Kato, H., Sakajyo, Y., & Kaneda, S. (2017). Visualization Method for Bicycle Rider</p><p>Behavior Analysis Using a Smartphone. Paper presented at the Computer</p><p>Software and Applications Conference (COMPSAC), 2017 IEEE 41st Annual.</p><p></p><p>Khanapuri, A. V., Shastri, A., D'souza, G., & D'souza, S. (2015). On road: A car</p><p>assistant application. Paper presented at the Technologies for Sustainable</p><p>Development (ICTSD), 2015 International Conference on.</p><p></p><p>Khoo, H. L., & Asitha, K. (2016). An impact analysis of traffic image information</p><p>system on driver travel choice. Transportation Research Part A: Policy and</p><p>Practice, 88, 175-194.</p><p></p><p>Kim, H., Hwang, Y., Yoon, D., & Park, C. H. (2013). Short paper: Study of driver</p><p>workload-based smartphone control service. Paper presented at the Vehicular</p><p>Networking Conference (VNC), 2013 IEEE.</p><p></p><p>Koesdwiady, A., Soua, R., Karray, F., & Kamel, M. S. (2017). Recent Trends in</p><p>Driver Safety Monitoring Systems: State of the Art and Challenges. IEEE</p><p>Transactions on Vehicular Technology, 66(6), 4550-4563.</p><p></p><p>Kong, W., Zhou, L., Wang, Y., Zhang, J., Liu, J., & Gao, S. (2015). A system of</p><p>driving fatigue detection based on machine vision and its application on smart</p><p>device. Journal of Sensors, 2015.</p><p></p><p>Kujala, T., Karvonen, H., & Mkel, J. (2016). Context-sensitive distraction</p><p>warningsEffects on drivers visual behavior and acceptance. International</p><p>Journal of Human-Computer Studies, 90, 39-52.</p><p></p><p>Laval, J. A., Toth, C. S., & Zhou, Y. (2014). A parsimonious model for the formation</p><p>of oscillations in car-following models. Transportation Research Part B:</p><p>Methodological, 70, 228-238.</p><p></p><p>Li, F., Zhang, H., Che, H., & Qiu, X. (2016). Dangerous driving behavior detection</p><p>using smartphone sensors. Paper presented at the Intelligent Transportation</p><p>Systems (ITSC), 2016 IEEE 19th International Conference on.</p><p></p><p>Liu, M., Ordez-Hurtado, R. H., Wirth, F., Gu, Y., Crisostomi, E., & Shorten, R.</p><p>(2016). A distributed and privacy-aware speed advisory system for optimizing</p><p>conventional and electric vehicle networks. IEEE Transactions on Intelligent</p><p>Transportation Systems, 17(5), 1308-1318.</p><p></p><p>Ma, C., Dai, X., Zhu, J., Liu, N., Sun, H., & Liu, M. (2017). DrivingSense: Dangerous</p><p>Driving Behavior Identification Based on Smartphone Autocalibration. Mobile</p><p>Information Systems, 2017.</p><p></p><p>Magaa, V. C., & Muoz-Organero, M. (2015). Reducing stress on habitual journeys.</p><p>Paper presented at the Consumer Electronics-Berlin (ICCE-Berlin), 2015</p><p>IEEE 5th International Conference on.</p><p></p><p>Magaa, V. C., & Muoz-Organero, M. (2016). Artemisa: A personal driving</p><p>assistant for fuel saving. IEEE Transactions on Mobile Computing, 15(10),</p><p>2437-2451.</p><p></p><p>Manasseh, C., & Sengupta, R. (2013). Predicting driver destination using machine</p><p>learning techniques. Paper presented at the Intelligent Transportation</p><p>Systems-(ITSC), 2013 16th International IEEE Conference on.</p><p></p><p>Martinez, C. M., Heucke, M., Wang, F.-Y., Gao, B., & Cao, D. (2018). Driving style</p><p>recognition for intelligent vehicle control and advanced driver assistance: A</p><p>survey. IEEE Transactions on Intelligent Transportation Systems, 19(3), 666-</p><p>676.</p><p></p><p>Meiring, G. A. M., & Myburgh, H. C. (2015). A review of intelligent driving style</p><p>analysis systems and related artificial intelligence algorithms. Sensors, 15(12),</p><p>30653-30682.</p><p></p><p>Mekki, T., Jabri, I., Rachedi, A., & ben Jemaa, M. (2017). Vehicular cloud networks:</p><p>Challenges, architectures, and future directions. Vehicular Communications, 9,</p><p>268-280.</p><p></p><p>Melnicuk, V., Birrell, S., Crundall, E., & Jennings, P. (2016). Towards hybrid driver</p><p>state monitoring: Review, future perspectives and the role of consumer</p><p>electronics. Paper presented at the Intelligent Vehicles Symposium (IV), 2016</p><p>IEEE.</p><p></p><p>Meseguer, J. E., Calafate, C. T., Cano, J. C., & Manzoni, P. (2013). Drivingstyles: A</p><p>smartphone application to assess driver behavior. Paper presented at the</p><p>Computers and Communications (ISCC), 2013 IEEE Symposium on.</p><p></p><p>Meseguer, J. E., Calafate, C. T., Cano, J. C., & Manzoni, P. (2015). Assessing the</p><p>impact of driving behavior on instantaneous fuel consumption. Paper</p><p>presented at the Consumer Communications and Networking Conference</p><p>(CCNC), 2015 12th Annual IEEE.</p><p></p><p>Meseguer, J. E., Toh, C. K., Calafate, C. T., Cano, J. C., & Manzoni, P. (2017).</p><p>Drivingstyles: a mobile platform for driving styles and fuel consumption</p><p>characterization. Journal of Communications and Networks, 19(2), 162-168.</p><p></p><p>Mihai, D., Dumitru, A., Postelnicu, C., & Mogan, G. (2015). Video-based evaluation</p><p>of driver's visual attention using smartphones. Paper presented at the</p><p>Information, Intelligence, Systems and Applications (IISA), 2015 6th</p><p>International Conference on.</p><p></p><p>Ming, L. J., Tan, I. K., & Hoong, P. K. (2017). Classifying drivers using electronic</p><p>logging devices. Paper presented at the Information and Communication</p><p>Technology (ICoIC7), 2017 5th International Conference on.</p><p></p><p>Miyajima, C., & Takeda, K. (2016). Driver-behavior modeling using on-road driving</p><p>data: a new application for behavior signal processing. IEEE Signal</p><p>Processing Magazine, 33(6), 14-21.</p><p></p><p>Monzon, A., Garcia-Castro, ., & Valdes, C. (2017). Methodology to Assess the</p><p>Effects of ICT-measures on Emissions. The Case Study of Madrid. Procedia</p><p>Engineering, 178, 13-23.</p><p></p><p>Munoz-Organero, M., & Magaa, V. C. (2013). Validating the impact on reducing</p><p>fuel consumption by using an ecodriving assistant based on traffic sign</p><p>detection and optimal deceleration patterns. IEEE Transactions on Intelligent</p><p>Transportation Systems, 14(2), 1023-1028.</p><p></p><p>MUSICANT, O., & BOTZER, A. (2016). The Safety Benefits Of Collision Warning</p><p>ApplicationsEvidence From On-road Data. International Journal of Safety</p><p>and Security Engineering, 6(2), 362-371.</p><p></p><p>Musicant, O., & Lotan, T. (2016). Can novice drivers be motivated to use a</p><p>smartphone based app that monitors their behavior? Transportation research</p><p>part F: traffic psychology and behaviour, 42, 544-557.</p><p></p><p>Musicant, O., Lotan, T., & Grimberg, E. (2015). Potential of group incentive schemes</p><p>to encourage use of driving safety apps. Transportation Research Record:</p><p>Journal of the Transportation Research Board(2516), 1-7.</p><p></p><p>Narote, S. P., Bhujbal, P. N., Narote, A. S., & Dhane, D. M. (2018). A review of</p><p>recent advances in lane detection and departure warning system. Pattern</p><p>Recognition, 73, 216-234. doi: https://doi.org/10.1016/j.patcog.2017.08.014</p><p></p><p>Natpratan, C., & Cooharojananone, N. (2015). Study of Sound and Haptic Feedback</p><p>in Smart Wearable Devices to Improve Driving Performance of Elders</p><p>Information Science and Applications (pp. 51-58): Springer.</p><p></p><p>Negi, N. S., van Leeuwen, P., & Happee, R. (2019). Differences in driver behaviour</p><p>between novice and experienced drivers: A driving simulator study. Paper</p><p>presented at the Proceedings of the 5th International Conference on Vehicle</p><p>Technology and Intelligent Transport Systems (VEHITS 2019).</p><p></p><p>Nguyen, C., Wang, Y., & Nguyen, H. N. (2013). Random forest classifier combined</p><p>with feature selection for breast cancer diagnosis and prognostic.</p><p></p><p>Nkenyereye, L., & Jang, J.-w. (2016). Integration of Big Data for Connected Cars</p><p>Applications Based on Tethered Connectivity. Procedia Computer Science,</p><p>98, 554-559.</p><p></p><p>Orfila, O., Saint Pierre, G., & Messias, M. (2015). An android based ecodriving</p><p>assistance system to improve safety and efficiency of internal combustion</p><p>engine passenger cars. Transportation research part C: emerging</p><p>technologies, 58, 772-782.</p><p></p><p>Osafune, T., Takahashi, T., Kiyama, N., Sobue, T., Yamaguchi, H., & Higashino, T.</p><p>(2017). Analysis of accident risks from driving behaviors. International</p><p>Journal of Intelligent Transportation Systems Research, 15(3), 192-202.</p><p></p><p>Ouyang, Z., Niu, J., Liu, Y., & Rodrigues, J. (2016). Multiwave: A novel vehicle</p><p>steering pattern detection method based on smartphones. Paper presented at</p><p>the Communications (ICC), 2016 IEEE International Conference on.</p><p></p><p>Perera, S. C., & Dias, N. G. (2017). Applying Intelligent Speed Adaptation to a Road</p><p>Safety Mobile Application-DriverSafeMode. Paper presented at the</p><p>International Conference on Advances in ICT for Emerging Regions (ICTer).</p><p></p><p>Phanama, Y. A., Duthoit, C., & Sari, R. F. (2016). Aware-D: Voice recognition-based</p><p>driving awareness detection. Paper presented at the Communications (APCC),</p><p>2016 22nd Asia-Pacific Conference on.</p><p></p><p>Phumphuang, P., Wuttidittachotti, P., & Saiprasert, C. (2015). Driver identification</p><p>using variance of the acceleration data. Paper presented at the Computer</p><p>Science and Engineering Conference (ICSEC), 2015 International.</p><p></p><p>Pozueco, L., Rionda, A., Paeda, A. G., Snchez, J. A., Paeda, X. G., Garca, R., . . .</p><p>Tuero, A. G. (2017). Impact of on-board tutoring systems to improve driving</p><p>efficiency of non-professional drivers. IET Intelligent Transport Systems,</p><p>11(4), 196-202.</p><p></p><p>Pratama, B. G., Ardiyanto, I., & Adji, T. B. (2017). A review on driver drowsiness</p><p>based on image, bio-signal, and driver behavior. Paper presented at the</p><p>Science and Technology-Computer (ICST), 2017 3rd International Conference</p><p>on.</p><p></p><p>Predic, B., & Stojanovic, D. (2015). Enhancing driver situational awareness through</p><p>crowd intelligence. Expert Systems with Applications, 42(11), 4892-4909.</p><p></p><p>Qiao, F., Rahman, R., Li, Q., & Yu, L. (2017). Safe and Environment-Friendly</p><p>Forward Collision Warning Messages in the Advance Warning Area of a</p><p>Construction Zone. International Journal of Intelligent Transportation</p><p>Systems Research, 15(3), 166-179.</p><p></p><p>Rachburee, N., Punlumjeak, W., Rugtanom, S., Jaithavil, D., & Pracha, M. (2015). A</p><p>prediction of engineering students performance from core engineering course</p><p>using classification Information Science and Applications (pp. 649-656):</p><p>Springer.</p><p></p><p>Rai, K., Devi, M. S., & Guleria, A. (2016). Decision tree based algorithm for intrusion</p><p>detection. International Journal of Advanced Networking and Applications,</p><p>7(4), 2828.</p><p></p><p>Reyes-Muoz, A., Domingo, M. C., Lpez-Trinidad, M. A., & Delgado, J. L. (2016).</p><p>Integration of body sensor networks and vehicular ad-hoc networks for traffic</p><p>safety. Sensors, 16(1), 107.</p><p></p><p>Rionda, A., Paeda, X. G., Garca, R., Daz, G., Martnez, D., Mitre, M., . . . Marn, I.</p><p>(2014). Blended learning system for efficient professional driving. Computers</p><p>& Education, 78, 124-139.</p><p></p><p>Road Facts. from https://www.miros.gov.my/1/page.php?id=17</p><p></p><p>Romera, E., Bergasa, L. M., & Arroyo, R. (2016). Need data for driver behaviour</p><p>analysis? Presenting the public UAH-DriveSet. Paper presented at the</p><p>Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International</p><p>Conference on.</p><p></p><p>Rosolino, V., Teresa, I., Vittorio, A., Carmine, F. D., Antonio, T., Daniele, R., &</p><p>Claudio, Z. (2014). Road safety performance assessment: a new road network</p><p>Risk Index for info mobility. Procedia-Social and Behavioral Sciences, 111,</p><p>624-633.</p><p></p><p>Ruf, M., Ziehn, J., German, L., Rosenhahn, B., Willersinn, D., Beyerer, J., & Gotzig,</p><p>H. (2016). Lightweight, non-invasive collection of steering wheel angles and</p><p>pedal positions. Paper presented at the Instrumentation, Control and</p><p>Automation (ICA), 2016 International Conference on.</p><p></p><p>Ryder, B., Gahr, B., Egolf, P., Dahlinger, A., & Wortmann, F. (2017). Preventing</p><p>traffic accidents with in-vehicle decision support systems-The impact of</p><p>accident hotspot warnings on driver behaviour. Decision support systems, 99,</p><p>64-74.</p><p></p><p>Saiprasert, C., Pholprasit, T., & Thajchayapong, S. (2017). Detection of driving</p><p>events using sensory data on smartphone. International Journal of Intelligent</p><p>Transportation Systems Research, 15(1), 17-28.</p><p></p><p>Saiprasert, C., Thajchayapong, S., Pholprasit, T., & Tanprasert, C. (2014). Driver</p><p>behaviour profiling using smartphone sensory data in a V2I environment.</p><p>Paper presented at the Connected Vehicles and Expo (ICCVE), 2014</p><p>International Conference on.</p><p></p><p>Seraj, F., Havinga, P. J., & Meratnia, N. (2016). Spinsafe: An unsupervised</p><p>smartphone-based wheelchair path monitoring system. Paper presented at the</p><p>Pervasive Computing and Communication Workshops (PerCom Workshops),</p><p>2016 IEEE International Conference on.</p><p></p><p>Seraj, F., Meratnia, N., & Havinga, P. J. (2017a). An aggregation and visualization</p><p>technique for crowd-sourced continuous monitoring of transport</p><p>infrastructures. Paper presented at the Pervasive Computing and</p><p>Communications Workshops (PerCom Workshops), 2017 IEEE International</p><p>Conference on.</p><p></p><p>Seraj, F., Meratnia, N., & Havinga, P. J. (2017b). RoVi: Continuous transport</p><p>infrastructure monitoring framework for preventive maintenance. Paper</p><p>presented at the Pervasive Computing and Communications (PerCom), 2017</p><p>IEEE International Conference on.</p><p></p><p>Simmons, S. M., Caird, J. K., & Steel, P. (2017). A meta-analysis of in-vehicle and</p><p>nomadic voice-recognition system interaction and driving performance.</p><p>Accident Analysis & Prevention, 106, 31-43.</p><p></p><p>Simonyi, E., Fazekas, Z., & Gspr, P. (2014). Smartphone application for assessing</p><p>various aspects of urban public transport. Transportation Research Procedia,</p><p>3, 185-194.</p><p></p><p>Singh, G., Bansal, D., & Sofat, S. (2017). A smartphone based technique to monitor</p><p>driving behavior using DTW and crowdsensing. Pervasive and Mobile</p><p>Computing, 40, 56-70.</p><p></p><p>Smirnov, A., Kashevnik, A., & Lashkov, I. (2016). Human-Smartphone Interaction</p><p>for Dangerous Situation Detection and Recommendation Generation While</p><p>Driving. Paper presented at the International Conference on Speech and</p><p>Computer.</p><p></p><p>Smirnov, A., Kashevnik, A., Lashkov, I., Baraniuc, O., & Parfenov, V. (2016).</p><p>Smartphone-based identification of dangerous driving situations: Algorithms</p><p>and implementation. Paper presented at the Proceedings of the 18th</p><p>Conference of Open Innovations Association FRUCT.</p><p></p><p>Smirnov, A., Kashevnik, A., Lashkov, I., Hashimoto, N., & Boyali, A. (2015).</p><p>Smartphone-based two-wheeled self-balancing vehicles rider assistant. Paper</p><p>presented at the Open Innovations Association (FRUCT), 2015 17th</p><p>Conference of.</p><p></p><p>Soriguera, F., & Miralles, E. (2016). Driver feedback mobile app. Transportation</p><p>Research Procedia, 18, 264-271.</p><p></p><p>Stamatiadis, N., Pappalardo, G., & Cafiso, S. (2017). Use of technology to improve</p><p>bicycle mobility in smart cities. Paper presented at the Models and</p><p>Technologies for Intelligent Transportation Systems (MT-ITS), 2017 5th IEEE</p><p>International Conference on.</p><p></p><p>Statistics on Causes of Death, Malaysia. (2017). from</p><p>https://www.dosm.gov.my/v1/index.php?r=column/cthemeByCat&cat=401&b</p><p>ul_id=Y3psYUI2VjU0ZzRhZU1kcVFMMThGUT09&menu_id=L0pheU43N</p><p>WJwRWVSZklWdzQ4TlhUUT09</p><p></p><p>Suzdaleva, E., & Nagy, I. (2014). Data-based speed-limit-respecting eco-driving</p><p>system. Transportation research part C: emerging technologies, 44, 253-264.</p><p></p><p>Sysoev, M., Kos, A., Guna, J., & Poganik, M. (2017). Estimation of the Driving</p><p>Style Based on the Users Activity and Environment Influence. Sensors,</p><p>17(10), 2404.</p><p></p><p>Taha, A.-E. M., & Nasser, N. (2015). Utilizing CAN-Bus and smartphones to enforce</p><p>safe and responsible driving. Paper presented at the Computers and</p><p>Communication (ISCC), 2015 IEEE Symposium on.</p><p></p><p>Tak, S., Woo, S., & Yeo, H. (2016). Study on the framework of hybrid collision</p><p>warning system using loop detectors and vehicle information. Transportation</p><p>research part C: emerging technologies, 73, 202-218.</p><p></p><p>Tal, I., Olaru, A., & Muntean, G.-M. (2013). eWARPE-Energy-efficient weatheraware</p><p>route planner for electric bicycles. Paper presented at the Network</p><p>Protocols (ICNP), 2013 21st IEEE International Conference on.</p><p></p><p>Thill, S., & Riveiro, M. (2015). Situation awareness in eco-driving. Paper presented</p><p>at the Cognitive Methods in Situation Awareness and Decision Support</p><p>(CogSIMA), 2015 IEEE International Inter-Disciplinary Conference on.</p><p></p><p>Tselentis, D. I., Yannis, G., & Vlahogianni, E. I. (2017). Innovative motor insurance</p><p>schemes: A review of current practices and emerging challenges. Accident</p><p>Analysis & Prevention, 98, 139-148.</p><p></p><p>Turkes, O., Seraj, F., Scholten, H., Meratnia, N., & Havinga, P. J. (2015). An ad-hoc</p><p>opportunistic dissemination protocol for smartphone-based participatory</p><p>traffic monitoring. Paper presented at the Vehicular Technology Conference</p><p>(VTC Fall), 2015 IEEE 82nd.</p><p></p><p>Vacca, A., & Meloni, I. (2015). Understanding route switch behavior: An analysis</p><p>using gps based data. Transportation Research Procedia, 5, 56-65.</p><p></p><p>Vaezipour, A., Rakotonirainy, A., Haworth, N., & Delhomme, P. (2017). Enhancing</p><p>eco-safe driving behaviour through the use of in-vehicle human-machine</p><p>interface: A qualitative study. Transportation Research Part A: Policy and</p><p>Practice, 100, 247-263.</p><p></p><p>Vahdat-Nejad, H., Ramazani, A., Mohammadi, T., & Mansoor, W. (2016). A survey</p><p>on context-aware vehicular network applications. Vehicular Communications,</p><p>3, 43-57.</p><p></p><p>Vaiana, R., Iuele, T., Astarita, V., Caruso, M. V., Tassitani, A., Zaffino, C., & Giofr,</p><p>V. P. (2014). Driving behavior and traffic safety: an acceleration-based safety</p><p>evaluation procedure for smartphones. Modern Applied Science, 8(1), 88.</p><p></p><p>Valdemars, A., Atstaja, D., & Vasiljeva, Z. (2015). RESPONSIBLE CHANGE OF</p><p>VEHICLE DRIVERS DRIVING BEHAVIOURS. ECONOMIC SCIENCE</p><p>FOR RURAL DEVELOPMENT, 132.</p><p></p><p>Vasconcelos, I., Vasconcelos, R. O., Olivieri, B., Roriz, M., Endler, M., & Junior, M.</p><p>C. (2017). Smartphone-based outlier detection: a complex event processing</p><p>approach for driving behavior detection. Journal of Internet Services and</p><p>Applications, 8(1), 13.</p><p></p><p>Vieira, P., Costeira, J. P., Brando, S., & Marques, M. (2016). SMARTcycling:</p><p>Assessing cyclists' driving experience. Paper presented at the Intelligent</p><p>Vehicles Symposium (IV), 2016 IEEE.</p><p></p><p>Vilaa, R. D., Arajo, R., & Arajo, R. E. (2017). A System for Driver Analysis Using</p><p>Smartphone as Smart Sensor. Paper presented at the Doctoral Conference on</p><p>Computing, Electrical and Industrial Systems.</p><p></p><p>Vlahogianni, E. I., & Barmpounakis, E. N. (2017). Driving analytics using</p><p>smartphones: Algorithms, comparisons and challenges. Transportation</p><p>research part C: emerging technologies, 79, 196-206.</p><p></p><p>Wahlstrm, J., Skog, I., & Hndel, P. (2015). Detection of dangerous cornering in</p><p>GNSS-data-driven insurance telematics. IEEE Transactions on Intelligent</p><p>Transportation Systems, 16(6), 3073-3083.</p><p></p><p>Wahlstrm, J., Skog, I., & Hndel, P. (2017). Smartphone-Based Vehicle Telematics:</p><p>A Ten-Year Anniversary. IEEE Transactions on Intelligent Transportation</p><p>Systems, 18(10), 2802-2825.</p><p></p><p>Walcott-Bryant, A., Tatsubori, M., Bryant, R. E., Oduor, E., Omondi, S., Osebe, S., . .</p><p>. Bent, O. (2016). Harsh brakes at potholes in Nairobi: Context-based driver</p><p>behavior in developing cities. Paper presented at the Intelligent Transportation</p><p>Systems (ITSC), 2016 IEEE 19th International Conference on.</p><p></p><p>Wallace, B., Rockwood, M., Goubran, R., Knoefel, F., Marshall, S., & Porter, M.</p><p>(2015). Measurement of vehicle acceleration in studies of older drivers from</p><p>GPS position and OBDII velocity sensors. Paper presented at the Medical</p><p>Measurements and Applications (MeMeA), 2015 IEEE International</p><p>Symposium on.</p><p></p><p>Wang, L., & Ju, D. Y. (2015). Concurrent use of an in-vehicle navigation system and</p><p>a smartphone navigation application. Social Behavior and Personality: an</p><p>international journal, 43(10), 1629-1640.</p><p></p><p>Whaiduzzaman, M., Sookhak, M., Gani, A., & Buyya, R. (2014). A survey on</p><p>vehicular cloud computing. Journal of network and computer applications, 40,</p><p>325-344.</p><p></p><p>Wilhelem, T., Okuda, H., Kawashima, A., & Suzuki, T. (2016). Identification of timevarying</p><p>parameters in Gipps model for driving behavior analysis. Paper</p><p>presented at the Systems, Man, and Cybernetics (SMC), 2016 IEEE</p><p>International Conference on.</p><p></p><p>Wilhelem, T., Okuda, H., Levedahl, B., & Suzuki, T. (2017). Energy Consumption</p><p>Evaluation Based on a Personalized DriverVehicle Model. IEEE</p><p>Transactions on Intelligent Transportation Systems, 18(6), 1468-1477.</p><p></p><p>Woo, C., & Kuli, D. (2016). Manoeuvre segmentation using smartphone sensors.</p><p>Paper presented at the Intelligent Vehicles Symposium (IV), 2016 IEEE.</p><p></p><p>Wu, X., Freese, D., Cabrera, A., & Kitch, W. A. (2015). Electric vehicles energy</p><p>consumption measurement and estimation. Transportation Research Part D:</p><p>Transport and Environment, 34, 52-67.</p><p></p><p>Xiao, D., & Feng, C. (2016). Detection of drivers visual attention using smartphone.</p><p>Paper presented at the Natural Computation, Fuzzy Systems and Knowledge</p><p>Discovery (ICNC-FSKD), 2016 12th International Conference on.</p><p></p><p>Xie, J., Hilal, A. R., & Kuli, D. (2017). Driving Maneuver Classification: A</p><p>Comparison of Feature Extraction Methods. IEEE Sensors Journal.</p><p></p><p>Xu, X., Gao, H., Yu, J., Chen, Y., Zhu, Y., Xue, G., & Li, M. (2017). ER: Early</p><p>recognition of inattentive driving leveraging audio devices on smartphones.</p><p>Paper presented at the INFOCOM 2017-IEEE Conference on Computer</p><p>Communications, IEEE.</p><p></p><p>Xu, X., Yin, S., & Ouyang, P. (2017). Fast and low-power behavior analysis on</p><p>vehicles using smartphones. Paper presented at the Next Generation</p><p>Electronics (ISNE), 2017 6th International Symposium on.</p><p></p><p>Xu, Y., Li, H., Liu, H., Rodgers, M. O., & Guensler, R. L. (2017). Eco-driving for</p><p>transit: An effective strategy to conserve fuel and emissions. Applied energy,</p><p>194, 784-797.</p><p></p><p>Yen, Y.-H., Huo, C.-L., & Sun, T.-Y. (2014). Adaptive lane departure warning</p><p>system on Android smartphone. Paper presented at the Consumer Electronics-</p><p>Taiwan (ICCE-TW), 2014 IEEE International Conference on.</p><p></p><p>Yu, J., Chen, Z., Zhu, Y., Chen, Y. J., Kong, L., & Li, M. (2017). Fine-grained</p><p>abnormal driving behaviors detection and identification with smartphones.</p><p>IEEE Transactions on Mobile Computing, 16(8), 2198-2212.</p><p></p><p>Zadeh, R. B., Ghatee, M., & Eftekhari, H. R. (2017). Three-Phases Smartphone-Based</p><p>Warning System to Protect Vulnerable Road Users Under Fuzzy Conditions.</p><p>IEEE Transactions on Intelligent Transportation Systems.</p><p></p><p>Zaid, S. M., Myeda, N. E., Mahyuddin, N., & Sulaiman, R. (2015). Malaysias rising</p><p>GHG emissions and carbon lock-inrisk: A review of Malaysian building</p><p>sector legislation and policy. Journal of Surveying, Construction and</p><p>Property, 6(1), 1-13.</p><p></p><p>Zeeman, A. S., & Booysen, M. J. (2013). Combining speed and acceleration to detect</p><p>reckless driving in the informal public transport industry. Paper presented at</p><p>the 16th International IEEE Conference on Intelligent Transportation Systems</p><p>(ITSC 2013).</p><p></p><p>Zfnebi, K., Souissi, N., & Tikito, K. (2017). Driver behavior quantitative models:</p><p>Identification and classification of variables. Paper presented at the Networks,</p><p>Computers and Communications (ISNCC), 2017 International Symposium on.</p><p></p><p>Zhang, C., Patel, M., Buthpitiya, S., Lyons, K., Harrison, B., & Abowd, G. D. (2016).</p><p>Driver classification based on driving behaviors. Paper presented at the</p><p>Proceedings of the 21st International Conference on Intelligent User</p><p>Interfaces.</p><p></p><p>Zhao, X., Wu, Y., Rong, J., & Zhang, Y. (2015). Development of a driving simulator</p><p>based eco-driving support system. Transportation research part C: emerging</p><p>technologies, 58, 631-641.</p><p></p><p>Zhao, Y., Li, S., Hu, S., Su, L., Yao, S., Shao, H., . . . Abdelzaher, T. (2017).</p><p>Greendrive: A smartphone-based intelligent speed adaptation system with</p><p>real-time traffic signal prediction. Paper presented at the Cyber-Physical</p><p>Systems (ICCPS), 2017 ACM/IEEE 8th International Conference on.</p><p></p><p>Zheng, Y., & Hansen, J. H. (2016). Unsupervised driving performance assessment</p><p>using free-positioned smartphones in vehicles. Paper presented at the</p><p>Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International</p><p>Conference on.</p><p></p><p>Zhu, X., Hu, X., & Chiu, Y.-C. (2013). Design of Driving Behavior Pattern</p><p>Measurements Using Smartphone Global Positioning System Data.</p><p>International journal of transportation science and technology, 2(4), 269-288.</p><p></p><p>Zhu, X., Yuan, Y., Hu, X., Chiu, Y.-C., & Ma, Y.-L. (2017). A Bayesian Network</p><p>model for contextual versus non-contextual driving behavior assessment.</p><p>Transportation research part C: emerging technologies, 81, 172-187.</p><p></p><p></p><p></p><p></p><p></p><p></p><p></p> |