基于时空等待特征系数的大型活动出行规划研究

高效、合理的综合交通路径规划是成功举办大型活动的前提之一。本文针对观众群体参 与大型活动的出行路径规划问题，引入乘客出行偏好，转换为时空等待优化问题，再根据大型活 动中乘客通过多模式公共交通出行特点，构建多维时间-空间-交通方式网络，以乘客出行时间总 成本最小为目标建立整数线性规划模型。为提高模型的求解效率与质量，提出一种基于拉格朗 日松弛和次梯度优化的算法进行求解，并在求解中提出基于逆向推断的搜索空间约减方法，提高 了算法求解速度。本文以观众从北京市城区前往延庆区高山滑雪中心观赛为案例验证模型与算 法。结果表明，引入时空等待特征系数后，提升了大型活动综合交通出行路径规划方案的合理 性，改善了乘客的出行体验，并有效缓解举办大型活动时的道路拥堵状况。

Routing Problem for Large-scale Events Based on Space-time Waiting Feature Coefficients

Efficient and reasonable integrated traffic path planning is one of the prerequisites for successful large-scale events. In this paper, we introduce passenger travel preferences for a routing problem of mass groups participating in large events and convert it into a space-time waiting optimization problem. A space-time-transport mode network is constructed based on the characteristics of multi-modal public transportation. An integer linear programming model is developed to minimize the total cost of passenger travel time. To improve the efficiency of solving real-scale datasets, an algorithm based on Lagrangian relaxation and sub-gradient optimization is proposed. A search space reduction method based on inverse inference is introduced to improve the algorithm. The proposed model and algorithm are validated with a hypothetical case of audiences going to watch the Olympic game. The numerical results show that the introduction of time-space waiting improves the rationality of the travel path planning scheme for large-scale events, as well as improving the travel experience of passengers. It is also proved that the model alleviates traffic congestion effectively when large-scale events are held.