电子学报 ›› 2021, Vol. 49 ›› Issue (9): 1818-1829.DOI: 10.12263/DZXB.20200379
于娟1, 杨琼2, 鲁剑锋1, 韩建民1, 彭浩1
收稿日期:
2020-04-20
修回日期:
2021-05-20
出版日期:
2021-10-21
作者简介:
基金资助:
YU Juan1, YANG Qiong2, LU Jian-feng1, HAN Jian-min1, PENG Hao1
Received:
2020-04-20
Revised:
2021-05-20
Online:
2021-10-21
Published:
2021-09-25
Supported by:
摘要:
地图匹配是许多位置服务与轨迹挖掘应用的基础.随着定位技术和位置服务应用的发展,地图匹配研究不断演进,从早期基于高采样率GPS(Global Position System)的实时匹配,到近期基于低采样率GPS轨迹的离线匹配、再到当前非GPS定位数据或高精度地图匹配。迄今已有许多地图匹配算法相继提出,但鲜有研究对这些算法进行全面总结.为此,对近十年提出的地图匹配算法进行调研,归纳出地图匹配算法的统一框架及常用时空特征.从模型或实现技术角度分类发现:现有算法大都采用HMM(Hidden Markov Model)模型,其次是最大权重模型;深度学习技术近期开始用于地图匹配,将是未来高精度地图匹配研究的趋势.
中图分类号:
于娟, 杨琼, 鲁剑锋, 韩建民, 彭浩. 高级地图匹配算法:研究现状和趋势[J]. 电子学报, 2021, 49(9): 1818-1829.
YU Juan, YANG Qiong, LU Jian-feng, HAN Jian-min, PENG Hao. Advanced Map Matching Algorithms: A Survey and Trends[J]. Acta Electronica Sinica, 2021, 49(9): 1818-1829.
算法 | 观测概率 | 转移概率 |
---|---|---|
HMM-Newson [ EnAcq[ Eddy[ | ||
OHMM[ | ||
HMM-IRL[ | ||
HMM-Jagadeesh [ | ||
Feature-based [ INC-RB [ | ||
HMM-DPP [ | ||
EHMM [ | 其中 | 其中当 |
AOMM[ | ||
Hybrid HMM[ |
表1 典型的基于HMM的地图匹配算法
算法 | 观测概率 | 转移概率 |
---|---|---|
HMM-Newson [ EnAcq[ Eddy[ | ||
OHMM[ | ||
HMM-IRL[ | ||
HMM-Jagadeesh [ | ||
Feature-based [ INC-RB [ | ||
HMM-DPP [ | ||
EHMM [ | 其中 | 其中当 |
AOMM[ | ||
Hybrid HMM[ |
算法 | 权重 |
---|---|
ST-Matching[ IVMM[ | |
WI-Matching[ | |
IF-Matching[ | |
FMM[ | |
MSTM[ | |
STD-Matching[ | |
AIVMM[ | |
FP-based[ |
表2 典型的基于最大权重的地图匹配算法
算法 | 权重 |
---|---|
ST-Matching[ IVMM[ | |
WI-Matching[ | |
IF-Matching[ | |
FMM[ | |
MSTM[ | |
STD-Matching[ | |
AIVMM[ | |
FP-based[ |
应用场景 | 算法 | 采样间隔 | 改进方法和目标 |
---|---|---|---|
实时GPS导航 | EnAcq[ | 5~30s | 利用自适应采样,降低采样率 |
OHMM[ | 1~300s | 利用变长滑动窗,降低时延 | |
Eddy[ | 1~120s | 利用自适应滑动窗,降低时延 | |
OLMM[ | / | 利用变长滑动窗,降低时延 | |
HMM-RCM[ | 1~5min | 利用路径选择模型,提高准确率、降低时延 | |
AOMM[ | 30~210s | 优化转移概率、利用自适应滑动窗,降低时延 | |
HMM+PRP[ | 1~20s | 利用概率路径预测,降低时延 | |
INC-RB[ | 60~180s | 利用回滚机制,降低时延 | |
离线WiFi轨迹挖掘 | HTrack[ | / | 优化转移概率,提高准确率 |
PDMatching[ | 1~6m | ||
离线GPS轨迹挖掘 | SIMP[ | 1~60s | 简化轨迹,提高效率 |
OBR-HMM[ | 1~64s | 逐段匹配,提高效率 | |
FMM[ | / | 利用预计算,提高效率 | |
Truncating[ | 30~300s | 优化最短路径计算,提高效率 | |
Feature-based[ | 1~5min | 优化转移概率,提高效率 | |
EHMM[ | 5~150s | 优化观测、转移概率,提高准确率 | |
OM2[ | / | 利用轨迹分割、后处理,提高准确率 | |
HMM-IRL[ | 30~360s | 优化转移概率,提高准确率 | |
HMM-DPP[ | 20~180s | ||
PIF[ | 1~120s | 利用模型改进,提高准确率 | |
CRF-Xu[ | 180~420s | 优化转移概率,提高准确率 | |
ST-CRF[ | 5~120s | ||
CRF-Yang[ | / | 利用 | |
ST-Matching[ | 30~300s | 优化权重度量,提高准确率 | |
LB-MM[ | 2~6min | ||
IF-Matching[ | / | ||
离线GPS轨迹挖掘 | |||
MSTM[ | 5~30min | ||
STD-Matching[ | 1~120s | ||
AIVMM[ | / | ||
FP-based[ | 1~5min | ||
IVMM[ | 2.5~10.5min | 考虑采样点间影响,提高准确率 | |
WI-Matching[ | / | 优化权重度量、插值,提高准确率 | |
HRIS[ | 3~15min | 利用参考轨迹,提高准确率 | |
InferTra[ | 5~15min | 网络移动性建模,提高准确率 | |
SF-Matching[ | 2~8min | 利用路径选择模型,提高准确率 | |
STRS[ | 15~90s | 利用概率局部路径推断,提高准确率 | |
TAMM[ | 20~260s | 利用重力模型,提高准确率 | |
FP-Matching[ | 30~300s | 利用频繁路径,提高准确率 | |
DPMM[ | 30~300s | 引入个性化路径选择偏好,提高准确率 | |
Genetic[ | / | 考虑全局特征,提高准确率 | |
AntMapper[ | 10~300s |
表3 典型地图匹配算法应用场景角度的对比
应用场景 | 算法 | 采样间隔 | 改进方法和目标 |
---|---|---|---|
实时GPS导航 | EnAcq[ | 5~30s | 利用自适应采样,降低采样率 |
OHMM[ | 1~300s | 利用变长滑动窗,降低时延 | |
Eddy[ | 1~120s | 利用自适应滑动窗,降低时延 | |
OLMM[ | / | 利用变长滑动窗,降低时延 | |
HMM-RCM[ | 1~5min | 利用路径选择模型,提高准确率、降低时延 | |
AOMM[ | 30~210s | 优化转移概率、利用自适应滑动窗,降低时延 | |
HMM+PRP[ | 1~20s | 利用概率路径预测,降低时延 | |
INC-RB[ | 60~180s | 利用回滚机制,降低时延 | |
离线WiFi轨迹挖掘 | HTrack[ | / | 优化转移概率,提高准确率 |
PDMatching[ | 1~6m | ||
离线GPS轨迹挖掘 | SIMP[ | 1~60s | 简化轨迹,提高效率 |
OBR-HMM[ | 1~64s | 逐段匹配,提高效率 | |
FMM[ | / | 利用预计算,提高效率 | |
Truncating[ | 30~300s | 优化最短路径计算,提高效率 | |
Feature-based[ | 1~5min | 优化转移概率,提高效率 | |
EHMM[ | 5~150s | 优化观测、转移概率,提高准确率 | |
OM2[ | / | 利用轨迹分割、后处理,提高准确率 | |
HMM-IRL[ | 30~360s | 优化转移概率,提高准确率 | |
HMM-DPP[ | 20~180s | ||
PIF[ | 1~120s | 利用模型改进,提高准确率 | |
CRF-Xu[ | 180~420s | 优化转移概率,提高准确率 | |
ST-CRF[ | 5~120s | ||
CRF-Yang[ | / | 利用 | |
ST-Matching[ | 30~300s | 优化权重度量,提高准确率 | |
LB-MM[ | 2~6min | ||
IF-Matching[ | / | ||
离线GPS轨迹挖掘 | |||
MSTM[ | 5~30min | ||
STD-Matching[ | 1~120s | ||
AIVMM[ | / | ||
FP-based[ | 1~5min | ||
IVMM[ | 2.5~10.5min | 考虑采样点间影响,提高准确率 | |
WI-Matching[ | / | 优化权重度量、插值,提高准确率 | |
HRIS[ | 3~15min | 利用参考轨迹,提高准确率 | |
InferTra[ | 5~15min | 网络移动性建模,提高准确率 | |
SF-Matching[ | 2~8min | 利用路径选择模型,提高准确率 | |
STRS[ | 15~90s | 利用概率局部路径推断,提高准确率 | |
TAMM[ | 20~260s | 利用重力模型,提高准确率 | |
FP-Matching[ | 30~300s | 利用频繁路径,提高准确率 | |
DPMM[ | 30~300s | 引入个性化路径选择偏好,提高准确率 | |
Genetic[ | / | 考虑全局特征,提高准确率 | |
AntMapper[ | 10~300s |
算法 | 轨迹数据包含: | 路网数据包含: | |||||||
---|---|---|---|---|---|---|---|---|---|
时间+经纬度 | 瞬时速度 | 瞬时方向 | 历史轨迹 | 路网拓扑 | 路宽 | 限速 | 历史平均速度 | 自由流速 | |
CRF-Liao[ | |||||||||
WI-Matching[ | |||||||||
OHMM[ | |||||||||
ST-Matching[ | |||||||||
CRF-Xu[ | |||||||||
HMM-Jagadeesh[ | |||||||||
AOMM[ | |||||||||
STD-Matching[ | |||||||||
HRIS[ | |||||||||
Hybrid HMM[ | |||||||||
HMM-DPP[ | |||||||||
IF-Matching[ |
表4 典型地图匹配算法的数据需求对比
算法 | 轨迹数据包含: | 路网数据包含: | |||||||
---|---|---|---|---|---|---|---|---|---|
时间+经纬度 | 瞬时速度 | 瞬时方向 | 历史轨迹 | 路网拓扑 | 路宽 | 限速 | 历史平均速度 | 自由流速 | |
CRF-Liao[ | |||||||||
WI-Matching[ | |||||||||
OHMM[ | |||||||||
ST-Matching[ | |||||||||
CRF-Xu[ | |||||||||
HMM-Jagadeesh[ | |||||||||
AOMM[ | |||||||||
STD-Matching[ | |||||||||
HRIS[ | |||||||||
Hybrid HMM[ | |||||||||
HMM-DPP[ | |||||||||
IF-Matching[ |
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