The Intelligent Transport System (ITS) services recon upon reliable information dissemination among the heterogeneous vehicular network components along the roads. The information sharing among vehicles, pedestrian units and RoadSide Units (RSUs) is paramount in dynamic vehicular network to provide real-time data services in vehicular networks. Due to distinctive vehicular network characteristics, it is extremely challenging to provide efficient data services in heterogeneous traffic scenario. Firstly, the ITS services have different Quality of Service (QoS) constraints which varies in line with application specification. Secondly, there is a need of coordination among network resources in vehicular network. In this work, a Software Defined Network (SDN) based vehicular network model has been presented to provide reliable data services in heterogeneous traffic in urban environment. The Context Aware Information Scheduling (CAIS) problem has been formulated by analysing the characteristics and QoS requirements of ITS services. A Priority based Context Aware Information (Pr-CAI) scheduling algorithm has been proposed to ensure real time data service scheduling, which assigns priority to requests based on service deadline, request selection priority and traffic dynamics. The Pr-CAI has been simulated for different heterogeneous traffic scenarios to analyse its performance. The comprehensive simulation results prove the ascendancy of proposed algorithm with respect to the existing compared algorithms. The Pr-CAI scheduling algorithm ensures real time data service scheduling for different ITS services that maximizes the service ratio and provides fairness amongst users.

智能运输系统（ITS）服务依赖于沿道路的异构车辆网络组件之间可靠的信息传播。车辆，行人单元和路边单元（RSU）之间的信息共享在动态车载网络中至关重要，以在车载网络中提供实时数据服务。由于具有独特的车载网络特性，在异构流量情况下提供高效的数据服务非常具有挑战性。首先，ITS服务具有不同的服务质量（QoS）约束，这些约束根据应用程序规范而有所不同。其次，需要在车辆网络中的网络资源之间进行协调。在这项工作中，提出了一种基于软件定义网络（SDN）的车辆网络模型，以在城市环境中的异构流量中提供可靠的数据服务。通过分析ITS服务的特征和QoS要求，提出了上下文感知信息调度（CAIS）问题。提出了一种基于优先级的上下文感知信息（Pr-CAI）调度算法，以确保实时数据服务调度，该算法根据服务期限，请求选择优先级和流量动态为请求分配优先级。已针对不同的异构流量场景对Pr-CAI进行了仿真，以分析其性能。全面的仿真结果证明了该算法相对于现有比较算法的优越性。