摘要
基于蜂窝无线定位的交通信息采集近年来得到了广泛关注,与传统的道路嵌入式传感器、路面视频采集器以及基于GPS浮动车等交通采集方式相比,具有开发维护成本低、覆盖范围广、部署快捷简单、适应性强等特点,因此研究基于蜂窝定位的交通信息采集具有重要的理论意义和应用价值。本文从以下几方面展开了研究：
1.针对有约束的多元函数极小值求解时随机初始化估计位置可能收敛得到局部最优解的问题,提出一种带约束的二次规划方法,得到了与实际移动台和散射点位置接近的初始位置估计,改善了定位精度。
2.提出利用移动台出发角来代替散射点的位置坐标,建立了定位优化模型,采用改进的粒子群算法求解,从而减少了需求解的个数又在移动台估计位置范围内寻找最优解,提高了定位的精度。
3.提出了移动台识别和移动台聚类的改进算法以确定采集车辆的位置,为了减少位置误差,设计了一种地图匹配算法,通过匹配前对车辆轨迹进行平滑,并提取车辆轨迹的多源特征信息,然后进行多特征的相似性模糊综合评判,提高了匹配的可靠性。
4.针对浮动车在路网中运行具有不确定性,无法按照具有不同的交通几何条件和交通状态的道路来采集交通数据的局限,提出了基于蜂窝小区采集交通信息的方法,给出了采样参数的设置和交通速度、行程时间和交通流估计的方法。
5.提出了一种在预测中充分考虑相似交通状态和交通条件的交通流预测方法,利用回归混合曲线聚类的方法分类不同的交通状态,实现了不同交通状态和交通条件下的交通流的有效预测。
此外,构建了基于蜂窝定位的交通参数采集的仿真环境,验证了所提方法的有效性,最后是总结并指出了需进一步研究的问题。
In recent years, the Cellular location-based traffic information collection technology has been received worldwide extensive attention, compared with conventional embedded road sensors, video collector and GPS-based floating vehicles and other traffic acquisition techniques, the traffic information collection technology based on cellular location has characteristics of low developing and maintaining cost, wide coverage, quick and easy deployment and good adaptability. So the research on the cellular localization-based traffic information collection has important theoretical and practical value. This article was studied the following questions:
1. To the question of random initialization location in constrained minimum estimate which might result in locally optimal solutions, a quadratic programming method with constraints was proposed, we obtained initial position estimation close to the actual location of the mobile station and scatterers, the positioning accuracy is improved.
2.Using the bear angles of the scatterers with respect to the MS replace the scatterers coordinates, a new nonlinear constrained objective function was constructed, and using the improved particle swarm algorithm searching the global optimal solution, the positioning accuracy is further improved.
3.The improved mobile station identification and mobile station clustering methods were proposed to determine the vehicles location; By smoothing the vehicle trajectory before matching, and extracting multiple characteristics of the vehicle trajectory, we adopt fuzzy preference relations to make decision of the multiple characteristics. the map matching reliability is improved.
4.Because of the floating vehicles running randomly in the road network, which resulted in collecting road traffic data not in accordance with the different geometric conditions and traffic conditions, we proposed a traffic information collection method based on cellular size, the sampling parameters were given and traffic speed, travel time and traffic flow were estimated.
5.In traffic flow forecasting, similar traffic conditions and traffic status were considered by using the mixed regression curve clustering method to classify the different traffic status, the improved algorithm achieves better traffic flow forecasting.
In addition, the traffic information gathering simulation environment based on cellular location data was constructed and followed by concluding remarks and the need for further research.
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