车载自组网使用先进的无线技术，将具有智能的车辆之间（V2V）以及车辆与路边基础设施之间（V2I）互联互通，是解决城市交通问题以及实现智慧城市的一项重要技术。高效的数据通信技术是车载自组网迈向实际应用的基础，也是研究的难点。本课题主要研究以下三个方面：首先，以大规模车辆真实数据分析结果为依据，拟分析路网拓扑、交通控制等因素对车辆运动的影响，构建真实的车辆运动模型，为研究车载自组网奠定理论基础；其次，拟对车辆节点间通信机会进行两个层次的挖掘：1）通信机会的时空相关性；2）节点间通信关系的社会性，并利用以上性质设计数据转发协议，以达到缩短传输延迟、降低网络开销、提高数据送达率的设计目标；最后，针对同时包含V2V和V2I通信的异构网络模型，拟提出基于最大化网络容量的带宽分配算法，为上层应用提供可扩展的、保障服务质量的数据传输服务。本课题研究成果将为车载自组网的应用发展提供坚实的理论基础和技术支撑。

With the advancement of wireless technology, vehicular ad hoc networks (VANETs) are emerging as a promising approach to realizing "smart cities" and addressing many important transportation problems such as road safety, efficiency, and convenience. Efficient data communications are the cornerstone for advanced VANET applications, which keep receiving research attention. This project will develop a new set of solutions for efficient data communications in VANETs, via three following research thrusts. First, studying the GPS (Global Positioning System) trace data of about tens of thousand experimental vehicles in Shanghai and Shenzhen, we have observed that the tail distribution of the inter-contact time, that is the time gap separating two contacts of the same pair of taxies, exhibits an exponential decay. Based on this observation, we will investigate the key factors in urban settings that cause the exponential distribution. In addition, we will study realistic urban vehicular mobility models, taking into account the impact of the underlying road network topology and traffic control strategy. Our results will provide fundamental guidelines on design of new data forwarding protocols and their performance analysis. Second, the mobility characteristics of vehicles are central to forwarding algorithms design especially when only inter-vehicle communications are considered. We study mobility in two different levels, i.e., contact-level and social-level. Leveraging contact-level mobility, data forwarding can be well conducted between familiar nodes but very costly when the source node has little knowledge about the destination node. In contrast, leveraging social-level mobility, nodes have better understanding about the whole network structure and therefore data can be forwarded to "strangers" vehicles faster but less efficient for "familiar" vehicles. We will study new data forwarding algorithms, integrating both levels of mobility, which can greatly reduce the end-to-end delay as well as the network cost and increase the delivery ratio at the same time. Last, we will further examine the data forwarding performance under a more complex network model, where both inter-vehicle and vehicle-to-infrastructure communications are considered. Given the QoS (Quality of Service) constraints and the diversity of data communication methods, we will propose new bandwidth allocation strategy which guarantees the QoS and maximizes the network capacity. The research outcomes will make a significant contribution to the development of future smart vehicles, and will provide the automotive industry with critical knowledge for driving the deployment of intelligent transportation systems.