基于局部连通性的在途动态路径诱导方法

驾车购物已经成为现代城市居民常见的生活出行方式，而驾车购物出行量的不断 增长也引发了严重的道路交通供需矛盾，加重了城市交通拥堵程度.为更好地满足居民驾车购 物出行的实际需要，出行路径诱导已成为一种优先选择，但目前大多数路径诱导方法运用固 定的最优路径搜索算法来规划行驶路线，不能完全自适应交通流的变化，并没有考虑到购物 出行特点.本文提出一种在途动态路径诱导方法，分析实时交通信息对路网连通性的动态影 响，在途中对诱导路径进行局部范围的重新搜索，并及时将更新结果反馈给在途车辆.实验结 果表明，与其他动态路径诱导方法相比，该方法计算量减少了56%以上，具有更强的实时性和 有效性，并具有开放性结构，能够根据需求替换不同路径搜索算法.

Dynamic En-route Guidance Approach Based on Local-connectivity

Shopping trip by cars is common in residents’traveling. The increasing number brings the traffic conflict between supply and demand and more congestion. Route guidance is a suitable choice for shoppers. But most guidance approaches use the fixed path optimization algorithm to plan and do not adapt itself to the variety of urban traffic, which omits the characteristic of the shopping trip and reliefs the traffic congestion. A new route guidance approach is developed for en- route vehicles. The proposed approach analyzes the local road network based on the dynamic connectivity, updates local of the guidance route and feeds back to the vehicle. The experimental results show that the approach has an open structure in which better path optimization algorithms can be applied and it decreases the computation by more than 56%.