| Citation: | Haixia ZHANG, Tiantian LI, Dongyang LI, Wenjie LIU. Research on Vehicle Behavior Analysis Based Technologies for Intelligent Vehicular Networks[J]. Journal of Electronics & Information Technology, 2020, 42(1): 36-49. doi: 10.11999/JEIT190820 | 
 
	                | 唐琳琳, 邢敏, 徐群杰. 浅谈我国车联网的发展现状及未来挑战[J]. 内燃机与配件, 2018(23): 167–168. doi:  10.19475/j.cnki.issn1674-957x.2018.23.082 TANG Linlin, XING Min, and XU Qunjie. Discussion on the development status and future challenges of Internet of vehicles in China[J]. Internal Combustion Engine &Parts, 2018(23): 167–168. doi:  10.19475/j.cnki.issn1674-957x.2018.23.082 | 
| 钱志华. 面向5G车联网连通性关键理论综述[J]. 信息通信, 2018(5): 219–220. doi:  10.3969/j.issn.1673-1131.2018.05.111 QIAN Zhihua. A summary of key theories for 5G vehicle network connectivity[J]. Information &Communications, 2018(5): 219–220. doi:  10.3969/j.issn.1673-1131.2018.05.111 | 
| 顾文琰. 5G时代车联网发展的机遇与挑战[J]. 科技视界, 2019(19): 1–3. doi:  10.19694/j.cnki.issn2095-2457.2019.19.001 GU Wenyan. Opportunities and challenges for the development of vehicle network in the 5G era[J]. Science &Technology Vision, 2019(19): 1–3. doi:  10.19694/j.cnki.issn2095-2457.2019.19.001 | 
| ANDREWS J G, BUZZI S, CHOI W, et al. What will 5G be?[J]. IEEE Journal on Selected Areas in Communications, 2014, 32(6): 1065–1082. doi:  10.1109/JSAC.2014.2328098 | 
| QIU Tie, CHEN Ning, LI Keqiu, et al. Heterogeneous ad hoc networks: Architectures, advances and challenges[J]. Ad Hoc Networks, 2017, 55: 143–152. doi:  10.1016/j.adhoc.2016.11.001 | 
| LI Rongpeng, ZHAO Zhifeng, ZHOU Xuan, et al. Intelligent 5G: When cellular networks meet artificial intelligence[J]. IEEE Wireless Communications, 2017, 24(5): 175–183. doi:  10.1109/MWC.2017.1600304WC | 
| DUAN Xiaoyu, LIU Yanan, and WANG Xianbin. SDN enabled 5G-VANET: Adaptive vehicle clustering and beamformed transmission for aggregated traffic[J]. IEEE Communications Magazine, 2017, 55(7): 120–127. doi:  10.1109/MCOM.2017.1601160 | 
| LIU Tuo, ZHOU Sheng, and NIU Zhisheng. Joint optimization of cache allocation and content placement in urban vehicular networks[C]. 2018 IEEE Global Communications Conference, Abu Dhabi, United Arab Emirates, 2018: 1–6. doi: 10.1109/GLOCOM.2018.8647913. | 
| OZFATURA E and GÜNDÜZ D. Mobility and popularity-aware coded small-cell caching[J]. IEEE Communications Letters, 2018, 22(2): 288–291. doi:  10.1109/LCOMM.2017.2774799 | 
| HOU Lu, LEI Lei, ZHENG Kan, et al. A Q-learning-based proactive caching strategy for non-safety related services in vehicular networks[J]. IEEE Internet of Things Journal, 2019, 6(3): 4512–4520. doi:  10.1109/JIOT.2018.2883762 | 
| KHELIFI H, LUO Senlin, NOUR B, et al. An optimized proactive caching scheme based on mobility prediction for vehicular networks[C]. 2018 IEEE Global Communications Conference, Abu Dhabi, United Arab Emirates, 2018: 1–6. doi: 10.1109/GLOCOM.2018.8647898. | 
| MIN Wanli and WYNTER L. Real-time road traffic prediction with spatio-temporal correlations[J]. Transportation Research Part C: Emerging Technologies, 2011, 19(4): 606–616. doi:  10.1016/j.trc.2010.10.002 | 
| KUMAR S V and VANAJAKSHI L. Short-term traffic flow prediction using seasonal ARIMA model with limited input data[J]. European Transport Research Review, 2015, 7(3): 21. doi:  10.1007/s12544-015-0170-8 | 
| KOESDWIADY A, SOUA R, and KARRAY F. Improving traffic flow prediction with weather information in connected cars: A deep learning approach[J]. IEEE Transactions on Vehicular Technology, 2016, 65(12): 9508–9517. doi:  10.1109/TVT.2016.2585575 | 
| LV Yisheng, DUAN Yanjie, KANG Wenwen, et al. Traffic flow prediction with big data: A deep learning approach[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(2): 865–873. doi:  10.1109/TITS.2014.2345663 | 
| YANG Haofan, DILLON T S, and CHEN Y P P. Optimized structure of the traffic flow forecasting model with a deep learning approach[J]. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(10): 2371–2381. doi:  10.1109/TNNLS.2016.2574840 | 
| FOULADGAR M, PARCHAMI M, ELMASRI R, et al. Scalable deep traffic flow neural networks for urban traffic congestion prediction[C]. 2017 International Joint Conference on Neural Networks, Anchorage, USA, 2017: 2251–2258. doi: 10.1109/IJCNN.2017.7966128. | 
| POLSON N G and SOKOLOV V O. Deep learning for short-term traffic flow prediction[J]. Transportation Research Part C: Emerging Technologies, 2017, 79: 1–17. doi:  10.1016/j.trc.2017.02.024 | 
| LI Rongpeng, ZHAO Zhifeng, ZHENG Jianchao, et al. The learning and prediction of application-level traffic data in cellular networks[J]. IEEE Transactions on Wireless Communications, 2017, 16(6): 3899–3912. doi:  10.1109/TWC.2017.2689772 | 
| LEE D, ZHOU Sheng, ZHONG Xiaofeng, et al. Spatial modeling of the traffic density in cellular networks[J]. IEEE Wireless Communications, 2014, 21(1): 80–88. doi:  10.1109/MWC.2014.6757900 | 
| QIU Chen, ZHANG Yanyan, FENG Zhiyong, et al. Spatio-temporal wireless traffic prediction with recurrent neural network[J]. IEEE Wireless Communications Letters, 2018, 7(4): 554–557. doi:  10.1109/LWC.2018.2795605 | 
| ZHANG Chuanting, ZHANG Haixia, YUAN Dongfeng, et al. Citywide cellular traffic prediction based on densely connected convolutional neural networks[J]. IEEE Communications Letters, 2018, 22(8): 1656–1659. doi:  10.1109/LCOMM.2018.2841832 | 
| ZHANG Chuanting, ZHANG Haixia, QIAO Jingping, et al. Deep transfer learning for intelligent cellular traffic prediction based on cross-domain big data[J]. IEEE Journal on Selected Areas in Communications, 2019, 37(6): 1389–1401. doi:  10.1109/JSAC.2019.2904363 | 
| CHEN Xiao and WANG Liangmin. A cloud-based trust management framework for vehicular social networks[J]. IEEE Access, 2017, 5: 2967–2980. doi:  10.1109/ACCESS.2017.2670024 | 
| EIZA M H, NI Qiang, and SHI Qi. Secure and privacy-aware cloud-assisted video reporting service in 5G-enabled vehicular networks[J]. IEEE Transactions on Vehicular Technology, 2016, 65(10): 7868–7881. doi:  10.1109/TVT.2016.2541862 | 
| YANG Qing, ZHU Binghai, and WU Shaoen. An architecture of cloud-assisted information dissemination in vehicular networks[J]. IEEE Access, 2016, 4: 2764–2770. doi:  10.1109/ACCESS.2016.2572206 | 
| WISITPONGPHAN N, BAI Fan, MUDALIGE P, et al. Routing in sparse vehicular ad hoc wireless networks[J]. IEEE Journal on Selected Areas in Communications, 2007, 25(8): 1538–1556. doi:  10.1109/JSAC.2007.071005 | 
| HUANG J J. Accurate probability distribution of rehealing delay in sparse VANETs[J]. IEEE Communications Letters, 2015, 19(7): 1193–1196. doi:  10.1109/LCOMM.2015.2426716 | 
| HE Jianping, NI Yuanzhi, CAI Lin, et al. Optimal dropbox deployment algorithm for data dissemination in vehicular networks[J]. IEEE Transactions on Mobile Computing, 2018, 17(3): 632–645. doi:  10.1109/TMC.2017.2733534 | 
| 邱佳慧, 陈祎, 刘珊, 等. 车联网关键技术及演进方案研究[J]. 邮电设计技术, 2017(8): 29–34. doi:  10.12045/j.issn.1007-3043.2017.08.007 QIU Jiahui, CHEN Yi, LIU Shan, et al. Research on key technologies and evolution strategies of IoV[J]. Designing Techniques of Posts and Telecommunications, 2017(8): 29–34. doi:  10.12045/j.issn.1007-3043.2017.08.007 | 
| REIS A B, SARGENTO S, and TONGUZ O K. On the performance of sparse vehicular networks with road side units[C]. The 73rd IEEE Vehicular Technology Conference, Yokohama, Japan, 2011: 1–5. doi: 10.1109/vetecs.2011.5956724. | 
| REIS A B, SARGENTO S, NEVES F, et al. Deploying roadside units in sparse vehicular networks: What really works and what does not[J]. IEEE Transactions on Vehicular Technology, 2014, 63(6): 2794–2806. doi:  10.1109/tvt.2013.2292519 | 
| CALVO J A L, TOKEL H A, and MATHAR R. Environment-based roadside unit deployment for urban scenarios[C]. The 27th IEEE Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, Valencia, Spain, 2016: 1–6. doi: 10.1109/pimrc.2016.7794889. | 
| PATRA M, THAKUR R, and MURTHY C S R. Improving delay and energy efficiency of vehicular networks using mobile femto access points[J]. IEEE Transactions on vehicular Technology, 2017, 66(2): 1496–1505. doi:  10.1109/tvt.2016.2563980 | 
| KIM D, VELASCO Y, WANG Wei, et al. A new comprehensive RSU installation strategy for cost-efficient VANET deployment[J]. IEEE Transactions on Vehicular Technology, 2017, 66(5): 4200–4211. doi:  10.1109/tvt.2016.2598253 | 
| LI Meng, SI Pengbo, and ZHANG Yanhua. Delay-tolerant data traffic to software-defined vehicular networks with mobile edge computing in smart city[J]. IEEE Transactions on Vehicular Technology, 2018, 67(10): 9073–9086. doi:  10.1109/tvt.2018.2865211 | 
| CHEN Jieqiong, MAO Guoqiang, LI Changle, et al. Capacity of cooperative vehicular networks with infrastructure support: Multiuser case[J]. IEEE Transactions on Vehicular Technology, 2018, 67(2): 1546–1560. doi:  10.1109/tvt.2017.2753772 | 
| DA SILVA C M and MEIRA W. Evaluating the performance of heterogeneous vehicular networks[C]. The 82nd IEEE Vehicular Technology Conference, Boston, USA, 2015: 1–5. doi: 10.1109/vtcfall.2015.7390936. | 
| Intelligent Transport Systems (ITS). Framework for public mobile networks in cooperative ITS (C-ITS)[R]. ETSI TR 102 962-2012, 2012. | 
| Intelligent Transport System (ITS). Vehicular communications; basic set of applications; definition[R]. ETSI TR 102 638 V1.1.1, 2009. | 
| ITS JPO. Vehicle safety applications[R]. 2008. | 
| ZHENG Kan, ZHENG Qiang, CHATZIMISIOS P, et al. Heterogeneous vehicular networking: A survey on architecture, challenges, and solutions[J]. IEEE Communications Surveys & Tutorials, 2015, 17(4): 2377–2396. doi:  10.1109/COMST.2015.2440103 | 
| HAN Chong, DIANATI M, TAFAZOLLI R, et al. Analytical study of the IEEE 802.11p MAC sublayer in vehicular networks[J]. IEEE Transactions on Intelligent Transportation Systems, 2012, 13(2): 873–886. doi:  10.1109/TITS.2012.2183366 | 
| KIHL M, BÜR K, MAHANTA P, et al. 3GPP LTE downlink scheduling strategies in vehicle-to-infrastructure communications for traffic safety applications[C]. 2012 IEEE Symposium on Computers and Communications, Cappadocia, Turkey, 2012: 448–453. doi: 10.1109/ISCC.2012.6249337. | 
| 方箭, 冯大权, 段海军, 等. V2X通信研究概述[J]. 电信科学, 2019, 35(6): 102–112. FANG Jian, FENG Daquan, DUAN Haijun, et al. An overview of V2X communications[J]. Telecommunications Science, 2019, 35(6): 102–112. | 
| TIAN Daxin, ZHOU Jianshan, WANG Yunpeng, et al. A dynamic and self-adaptive network selection method for multimode communications in heterogeneous vehicular telematics[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(6): 3033–3049. doi:  10.1109/TITS.2015.2422144 | 
| HUANG C M, CHIANG Mengshu, DAO D T, et al. V2V data offloading for cellular network based on the software defined network (SDN) inside mobile edge computing (MEC) architecture[J]. IEEE Access, 2018, 6: 17741–17755. doi:  10.1109/ACCESS.2018.2820679 | 
| XIONG Kai, LENG Supeng, HU Jie, et al. Smart network slicing for vehicular fog-RANs[J]. IEEE Transactions on Vehicular Technology, 2019, 68(4): 3075–3085. doi:  10.1109/TVT.2019.2900234 | 
| YAN Shi, ZHANG Xinran, XIANG Hongyu, et al. Joint access mode selection and spectrum allocation for fog computing based vehicular networks[J]. IEEE Access, 2019, 7: 17725–17735. doi:  10.1109/ACCESS.2019.2895626 | 
| PIRMEZ L, CARVALHO JR J C, DELICATO F C, et al. SUTIL–Network selection based on utility function and integer linear programming[J]. Computer Networks, 2010, 54(13): 2117–2136. doi:  10.1016/j.comnet.2010.03.007 | 
| TSENG L C, CHIEN F T, ZHANG Daqiang, et al. Network selection in cognitive heterogeneous networks using stochastic learning[J]. IEEE Communications Letters, 2013, 17(12): 2304–2307. doi:  10.1109/LCOMM.2013.102113.131876 | 
| NIYATO D and HOSSAIN E. Dynamics of network selection in heterogeneous wireless networks: An evolutionary game approach[J]. IEEE Transactions on Vehicular Technology, 2009, 58(4): 2008–2017. doi:  10.1109/TVT.2008.2004588 | 
| 熊凯, 冷甦鹏, 张可, 等. 车联雾计算中的异构接入与资源分配算法研究[J]. 物联网学报, 2019, 3(2): 20–27. XIONG Kai, LENG Supeng, ZHANG Ke, et al. Research on heterogeneous radio access and resource allocation algorithm in vehicular fog computing[J]. Chinese Journal on Internet of Things, 2019, 3(2): 20–27. | 
| CHEN Shanzhi, HU Jinling, SHI Yan, et al. LTE-V: A TD-LTE-based V2X solution for future vehicular network[J]. IEEE Internet of Things Journal, 2016, 3(6): 997–1005. doi:  10.1109/JIOT.2016.2611605 | 
| SUN Shaohui, HU Jinling, PENG Ying, et al. Support for vehicle-to-everything services based on LTE[J]. IEEE Wireless Communications, 2016, 23(3): 4–8. doi:  10.1109/MWC.2016.7498068 | 
| PENG Haixia, LI Dazhou, YE Qiang, et al. Resource allocation for cellular-based inter-vehicle communications in autonomous multiplatoons[J]. IEEE Transactions on Vehicular Technology, 2017, 66(12): 11249–11263. doi:  10.1109/TVT.2017.2723430 | 
| MEI Jie, ZHENG Kan, ZHAO Long, et al. A latency and reliability guaranteed resource allocation scheme for LTE V2V communication systems[J]. IEEE Transactions on Wireless Communications, 2018, 17(6): 3850–3860. doi:  10.1109/TWC.2018.2816942 | 
| SHI Jianfeng, YANG Zhaohui, XU Hao, et al. Dynamic resource allocation for LTE-based vehicle-to-infrastructure networks[J]. IEEE Transactions on Vehicular Technology, 2019, 68(5): 5017–5030. doi:  10.1109/TVT.2019.2903822 | 
| LIANG Le, LI G Y, and XU Wei. Resource allocation for D2D-enabled vehicular communications[J]. IEEE Transactions on Communications, 2017, 65(7): 3186–3197. doi:  10.1109/TCOMM.2017.2699194 | 
| 刘自强, 任晨珊, 田辉. 用户行为驱动的自组织网络资源配置研究[J]. 中兴通讯技术, 2015, 21(1): 4–12, 28. doi:  10.3969/j.issn.1009-6868.2015.01.002 LIU Ziqiang, REN Chenshan, and TIAN Hui. User-behavior-driven resource configuration in self-organizing networks[J]. ZTE Technology Journal, 2015, 21(1): 4–12, 28. doi:  10.3969/j.issn.1009-6868.2015.01.002 | 
| WANG Shuo, ZHANG Xing, ZHANG Jiaxin, et al. An approach for spatial-temporal traffic modeling in mobile cellular networks[C]. The 27th International Teletraffic Congress, Ghent, Belgium, 2015: 203–209. doi: 10.1109/ITC.2015.31. | 
| LIN Chuncheng, DENG D J, and YAO C C. Resource allocation in vehicular cloud computing systems with heterogeneous vehicles and roadside units[J]. IEEE Internet of Things Journal, 2018, 5(5): 3692–3700. doi:  10.1109/JIOT.2017.2690961 | 
| ZHENG Kan, MENG Hanlin, CHATZIMISIOS P, et al. An SMDP-based resource allocation in vehicular cloud computing systems[J]. IEEE Transactions on Industrial Electronics, 2015, 62(12): 7920–7928. doi:  10.1109/TIE.2015.2482119 | 
| ZHANG Ke, MAO Yuming, LENG Supeng, et al. Energy-efficient offloading for mobile edge computing in 5G heterogeneous networks[J]. IEEE Access, 2016, 4: 5896–5907. doi:  10.1109/ACCESS.2016.2597169 | 
| YANG Chao, LIU Yi, CHEN Xin, et al. Efficient mobility-aware task offloading for vehicular edge computing networks[J]. IEEE Access, 2019, 7: 26652–26664. doi:  10.1109/ACCESS.2019.2900530 | 
| HOU Xueshi, LI Yong, CHEN Min, et al. Vehicular fog computing: A viewpoint of vehicles as the infrastructures[J]. IEEE Transactions on Vehicular Technology, 2016, 65(6): 3860–3873. doi:  10.1109/TVT.2016.2532863 | 
| LIU Pengju, LI Junluo, and SUN Zhongwei. Matching-based task offloading for vehicular edge computing[J]. IEEE Access, 2019, 7: 27628–27640. doi:  10.1109/ACCESS.2019.2896000 | 
| CUI Yaping, LIANG Yingjie, and WANG Ruyan. Resource allocation algorithm with multi-platform intelligent offloading in D2D-enabled vehicular networks[J]. IEEE Access, 2019, 7: 21246–21253. doi:  10.1109/ACCESS.2018.2882000 | 
| ZHOU Zhenyu, YU Houjian, XU Chen, et al. BEGIN: Big data enabled energy-efficient vehicular edge computing[J]. IEEE Communications Magazine, 2018, 56(12): 82–89. doi:  10.1109/MCOM.2018.1700910 | 
| ABDELHAMID S, HASSANEIN H S, and TAKAHARA G. On-road caching assistance for ubiquitous vehicle-based information services[J]. IEEE Transactions on Vehicular Technology, 2015, 64(12): 5477–5492. doi:  10.1109/TVT.2015.2480711 | 
| ALNAGAR Y, HOSNY S, and EL-SHERIF A A. Towards mobility-aware proactive caching for vehicular Ad hoc networks[C]. 2019 IEEE Wireless Communications and Networking Conference Workshop, Marrakech, Morocco, 2019: 1–6. doi: 10.1109/WCNCW.2019.8902903. | 
| ZHAO Weicheng, QIN Yajuan, GAO Deyun, et al. An efficient cache strategy in information centric networking vehicle-to-vehicle scenario[J]. IEEE Access, 2017, 5: 12657–12667. doi:  10.1109/ACCESS.2017.2714191 | 
| HUANG Wanying, SONG Tian, YANG Yating, et al. Cluster-based cooperative caching with mobility prediction in vehicular named data networking[J]. IEEE Access, 2019, 7: 23442–23458. doi:  10.1109/ACCESS.2019.2897747 | 
| MA Junchao, WANG Jiahuan, LIU Gang, et al. Low latency caching placement policy for cloud-based VANET with both vehicle caches and RSU caches[C]. 2017 IEEE Globecom Workshops, Singapore, 2017: 1–6. doi: 10.1109/GLOCOMW.2017.8269203. | 
| DAI Yueyue, XU Du, MAHARJAN S, et al. Artificial intelligence empowered edge computing and caching for internet of vehicles[J]. IEEE Wireless Communications, 2019, 26(3): 12–18. doi:  10.1109/MWC.2019.1800411 | 
| LIU Hui, CHEN Zhiyong, and QIAN Liang. The three primary colors of mobile systems[J]. IEEE Communications Magazine, 2016, 54(9): 15–21. doi:  10.1109/MCOM.2016.7565182 | 
| WANG Jun, FENG Daquan, ZHANG Shengli, et al. Computation offloading for mobile edge computing enabled vehicular networks[J]. IEEE Access, 2019, 7: 62624–62632. doi:  10.1109/ACCESS.2019.2915959 | 
| TAN L T and HU R Q. Mobility-aware edge caching and computing in vehicle networks: A deep reinforcement learning[J]. IEEE Transactions on Vehicular Technology, 2018, 67(11): 10190–10203. doi:  10.1109/TVT.2018.2867191 | 
| HE Ying, ZHAO Nan, and YIN Hongxi. Integrated networking, caching, and computing for connected vehicles: A deep reinforcement learning approach[J]. IEEE Transactions on Vehicular Technology, 2018, 67(1): 44–55. doi:  10.1109/TVT.2017.2760281 | 
| TAN L T, HU R Q, and HANZO L. Twin-timescale artificial intelligence aided mobility-aware edge caching and computing in vehicular networks[J]. IEEE Transactions on Vehicular Technology, 2019, 68(4): 3086–3099. doi:  10.1109/TVT.2019.2893898 | 
