基于轨迹大数据的动态最优路径规划

以轨迹大数据为基础，结合城市交通状态与用户个性化需求，提出一种基于改进Viterbi算法的动态最优路径规划算法。首先融合交通状态和真实路网拓扑结构，构建基于有向多重加权复杂网络的交通网络模型。采用基于层次分析法和熵权法相结合的综合赋权法对交通网络模型的多权重属性进行权重分配，得到新的有向加权复杂网络模型。进一步采用改进的Viterbi算法求解最优路径。最后，以兰州市为例，对最优路径规划进行分析，并将该算法与静态规划方法进行比较，验证城市最优路径规划算法的有效性与实时性。实验结果表明，结合城市交通状态与用户偏向的路径规划更加科学合理，能够为兰州市驾车出行、交通管理部门决策提供决策支持和参考。

Dynamic Optimal Path Planning Based on Trajectory Big Data

Based on trajectory big data, combined with urban traffic status and user personalized needs, a dynamic optimal path planning algorithm is proposed based on improved Viterbi algorithm. First, a traffic network model based on directed complex network with multi-weights is constructed by combining the traffic state with the real road network topology. The multi-weight attributes of the transportation network model are assigned by using the comprehensive weighting method based on the coalition of the analytic hierarchy process and the entropy weight method. Then, a new directed weighted complex network model is obtained. Further, the optimal path is solved by the improved Viterbi algorithm. Finally, taking Lanzhou as an example to analyze the optimal path planning, the effectiveness of the urban optimal path planning algorithm is verified by comparing the proposed algorithm with the static planning method. The experimental results show that path planning that combines urban traffic conditions with user preferences is more scientific and reasonable, and can provide decision-making support and reference for drivers and traffic management departments.