海量低频浮动车数据道路匹配及行程时间估算
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摘要
道路交通信息的全面、准确、快速获取是城市交通管理、交通规划的基础,对缓解大城市交通拥堵,提供有效的大众出行指导具有重要的意义。浮动车是一种安装有全球定位设备并通过无线通讯系统将车辆状态和信息发送出的车辆,浮动车数据能及时准确的反映车辆所行驶道路的交通状况,是全面、快速获取道路交通信息的重要途径。本文以武汉市中上万辆出租车为载体的低频浮动车数据为研究对象,以准确、快速浮动车数据道路匹配和路段行程时间准确估算为目标,对大城市环境下海量低频浮动车数据的处理进行了讨论和研究,并通过真实、海量的浮动车数据和城市路网数据对研究的成果进行了验证。本文的主要的研究工作包括以下四个方面：
1、海量低频浮动车数据的分析与预处理。对武汉市一万二千多辆出租车获取的浮动车数据的格式、瞬时速度及航向、不同时段的数据量、采样时间间隔以及载客状态相关的数据和信息进行了分析。对于导航道路地图加密产生道路偏移的问题提出了一种道路地图栅格化的道路坐数据标系与浮动车数据坐标系的标定方法,提高了二种坐标系之间标定的精度,为海量浮动车数据的有效处理奠定了基础。
2、海量浮动车数据快速道路初匹配算法的研究海量浮动车数据道路匹配的算法效率是影响此类数据应用的重要因素。道路初匹配是根据匹配度计算将小于阈值的道路作为浮动车数据的候选匹配道路,由于一天的浮动车数据就有约一万四千个,并且道路的数量有二万六千多条,算法的效率对浮动车处理的时间影响极大。本文首先对基于地图格网化的浮动车数据道路匹配算法进行了分析和讨论,给出了一种面向计算效率的地图格网划分最佳参数；还提出了一种基于道路地图栅格化的海量浮动车数据地图初匹配的算法,使得海量浮动车数据完成道路初匹配计算的时间更短。
3、基于序列低频浮动车数据路径计算的研究首先研究了基于序列浮动车数据路径计算中一次路径计算选取浮动车数据点数量的问题,指出了在城市复杂路网条件下路径计算点只有达到或超过3个才可能保障路径重建正确,同时路径计算点越多,重建结果可靠性越大,但计算的复杂度也越大。然后针对相关的浮动车数据质量不稳定,载客状态发生变化等情况进行了研究,并对算法进行了优化改进,提高了计算结果的准确性。还研究了路径算法中路径搜索区域道路端点的甄选方法,的提高路径计算效率的方法,减少了路径计算的时间。
4、基于低频浮动车数据路段行程时间估算的研究路段行程时间是道路交通中的一项最重要的参数。在分析已有利用低频浮动车数据进行路段行程时间计算算法的基础上提出了一种基于道路交叉口下游路段浮动车数据的路段行程时间估算方法,在分析低频浮动车数据在道路交叉口附近路段上的位置、速度信息,结合车辆在道路交叉口附近路段行驶的特点,给出了一个车辆通过道路交叉口时刻的计算模型。基于这个模型,在获得道路交叉口下游路段上低频浮动车数据的位置和速度信息后,能较准确地计算出浮动车通过路口的时刻,通过对路段上下游二个路口浮动车通过时刻的计算,从而能较准确地估算出路段的通行时间。
The accurate and timely acquisition of traffic information is vital to urban transport management and planning. The collected traffic information can not only mitigate traffic congestions in the road network but also provide real-time route guidance services to public users. In recent years, the floating car system becomes increasing popular for collecting traffic information. This floating car system utilizes a large fleet of vehicles equipped with global positioning systems (GPS) and wireless communication devices. The collected trajectories of these floating cars can be a very useful data source for generating traffic information due to its low cost and large spatial coverage. This study focuses on low-frequent floating car data (FCD) generated by ten thousands of taxis in Wuhan city. In this study, an efficient and accurate algorithm is developed for matching huge-volume low-frequent FCD onto the road network. Then, a method is proposed to estimate accurately link travel time information based on low-frequent FCD. A real world case study is carried out to demonstrate the applicability of the proposed map matching algorithm and travel time estimation method. This study contributes to the literature in following four aspects:
1. The pre-process technique of huge-volume low-frequent floating car data. Using FCD generated by12,000taxis in Wuhan, the characteristics of FCD are analyzed, including data format, instantaneous travel speed and direction, data volume collected in different time periods, data sampling frequency, and passenger loading status. For solving the network deviation error due to the encryption of road maps, a method is proposed to calibrate rasterized road network coordinate system and floating car coordinate system. The proposed method improves the calibration accuracy between these two coordinate systems, and thus facilitates the effective processing of huge-volume FCD.
2. A primary map matching algorithm for huge-volume low-frequent FCD. Computational performance of matching huge-volume FCD onto the road network is one of critical factors for the FCD applications. The primary map matching is to match GPS points to the network links with a matching score less than a given threshold. Because FCD have a huge data volume (e.g. more than12,000taxis in Wuhan) and the road network contains ten thousands of links (e.g. more than 26,000links in Wuhan), computational efficiency of map matching algorithm can have a significant impact on the process of FCD. This study first analyzes the proposed map matching algorithm built on the rasterized road network, and discusses how to determine the optimal raster size of the road network. Then, a primary map matching algorithm based on rasterized road network is proposed in order to enhance the computational efficiency of matching huge-volume low frequent FCD.
3. The study of low-frequent FCD trajectory recovery The effects of the number of GPS points in the trajectory recovery process are discussed. It is found that at least three GPS points are required for correctly recovering the trajectory of a floating car moving in the complex road network. The more number of GPS points we have, the more robust results will be obtained but the more computational resources are required. Then, the proposed map matching is optimized for scenarios when passenger loading status changes and the quality of FCD are unstable. The optimal strategy for selecting candidate nodes on the links within the route search area is developed to further enhance the computational efficiency of the proposed algorithm.
4. Travel time estimation method using low-frequent FCD Travel time information is a key factor for evaluating the network performance. Based on the comprehensive review of existing methods, a new travel time estimation method is proposed by fully using the travel statuses of floating cars at downstream and upstream of road junctions. In the proposed method, turn delays at road junctions can be estimated accurately by using the vehicle location and instantaneous travel speed information respectively collected at downstream and upstream of road junctions.
1、海量低频浮动车数据的分析与预处理。对武汉市一万二千多辆出租车获取的浮动车数据的格式、瞬时速度及航向、不同时段的数据量、采样时间间隔以及载客状态相关的数据和信息进行了分析。对于导航道路地图加密产生道路偏移的问题提出了一种道路地图栅格化的道路坐数据标系与浮动车数据坐标系的标定方法,提高了二种坐标系之间标定的精度,为海量浮动车数据的有效处理奠定了基础。
2、海量浮动车数据快速道路初匹配算法的研究海量浮动车数据道路匹配的算法效率是影响此类数据应用的重要因素。道路初匹配是根据匹配度计算将小于阈值的道路作为浮动车数据的候选匹配道路,由于一天的浮动车数据就有约一万四千个,并且道路的数量有二万六千多条,算法的效率对浮动车处理的时间影响极大。本文首先对基于地图格网化的浮动车数据道路匹配算法进行了分析和讨论,给出了一种面向计算效率的地图格网划分最佳参数；还提出了一种基于道路地图栅格化的海量浮动车数据地图初匹配的算法,使得海量浮动车数据完成道路初匹配计算的时间更短。
3、基于序列低频浮动车数据路径计算的研究首先研究了基于序列浮动车数据路径计算中一次路径计算选取浮动车数据点数量的问题,指出了在城市复杂路网条件下路径计算点只有达到或超过3个才可能保障路径重建正确,同时路径计算点越多,重建结果可靠性越大,但计算的复杂度也越大。然后针对相关的浮动车数据质量不稳定,载客状态发生变化等情况进行了研究,并对算法进行了优化改进,提高了计算结果的准确性。还研究了路径算法中路径搜索区域道路端点的甄选方法,的提高路径计算效率的方法,减少了路径计算的时间。
4、基于低频浮动车数据路段行程时间估算的研究路段行程时间是道路交通中的一项最重要的参数。在分析已有利用低频浮动车数据进行路段行程时间计算算法的基础上提出了一种基于道路交叉口下游路段浮动车数据的路段行程时间估算方法,在分析低频浮动车数据在道路交叉口附近路段上的位置、速度信息,结合车辆在道路交叉口附近路段行驶的特点,给出了一个车辆通过道路交叉口时刻的计算模型。基于这个模型,在获得道路交叉口下游路段上低频浮动车数据的位置和速度信息后,能较准确地计算出浮动车通过路口的时刻,通过对路段上下游二个路口浮动车通过时刻的计算,从而能较准确地估算出路段的通行时间。
The accurate and timely acquisition of traffic information is vital to urban transport management and planning. The collected traffic information can not only mitigate traffic congestions in the road network but also provide real-time route guidance services to public users. In recent years, the floating car system becomes increasing popular for collecting traffic information. This floating car system utilizes a large fleet of vehicles equipped with global positioning systems (GPS) and wireless communication devices. The collected trajectories of these floating cars can be a very useful data source for generating traffic information due to its low cost and large spatial coverage. This study focuses on low-frequent floating car data (FCD) generated by ten thousands of taxis in Wuhan city. In this study, an efficient and accurate algorithm is developed for matching huge-volume low-frequent FCD onto the road network. Then, a method is proposed to estimate accurately link travel time information based on low-frequent FCD. A real world case study is carried out to demonstrate the applicability of the proposed map matching algorithm and travel time estimation method. This study contributes to the literature in following four aspects:
1. The pre-process technique of huge-volume low-frequent floating car data. Using FCD generated by12,000taxis in Wuhan, the characteristics of FCD are analyzed, including data format, instantaneous travel speed and direction, data volume collected in different time periods, data sampling frequency, and passenger loading status. For solving the network deviation error due to the encryption of road maps, a method is proposed to calibrate rasterized road network coordinate system and floating car coordinate system. The proposed method improves the calibration accuracy between these two coordinate systems, and thus facilitates the effective processing of huge-volume FCD.
2. A primary map matching algorithm for huge-volume low-frequent FCD. Computational performance of matching huge-volume FCD onto the road network is one of critical factors for the FCD applications. The primary map matching is to match GPS points to the network links with a matching score less than a given threshold. Because FCD have a huge data volume (e.g. more than12,000taxis in Wuhan) and the road network contains ten thousands of links (e.g. more than 26,000links in Wuhan), computational efficiency of map matching algorithm can have a significant impact on the process of FCD. This study first analyzes the proposed map matching algorithm built on the rasterized road network, and discusses how to determine the optimal raster size of the road network. Then, a primary map matching algorithm based on rasterized road network is proposed in order to enhance the computational efficiency of matching huge-volume low frequent FCD.
3. The study of low-frequent FCD trajectory recovery The effects of the number of GPS points in the trajectory recovery process are discussed. It is found that at least three GPS points are required for correctly recovering the trajectory of a floating car moving in the complex road network. The more number of GPS points we have, the more robust results will be obtained but the more computational resources are required. Then, the proposed map matching is optimized for scenarios when passenger loading status changes and the quality of FCD are unstable. The optimal strategy for selecting candidate nodes on the links within the route search area is developed to further enhance the computational efficiency of the proposed algorithm.
4. Travel time estimation method using low-frequent FCD Travel time information is a key factor for evaluating the network performance. Based on the comprehensive review of existing methods, a new travel time estimation method is proposed by fully using the travel statuses of floating cars at downstream and upstream of road junctions. In the proposed method, turn delays at road junctions can be estimated accurately by using the vehicle location and instantaneous travel speed information respectively collected at downstream and upstream of road junctions.
引文
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