| 引用本文: | 王璞,鲁恒宇,谭倩,熊雨沙,毛应萍,李琳.手机信令与出租车GPS数据融合车源定位方法[J].哈尔滨工业大学学报,2018,50(9):96.DOI:10.11918/j.issn.0367-6234.201708042 |
| WANG Pu,LU Hengyu,TAN Qian,XIONG Yusha,MAO Yingping,LI Lin.A data fusion approach for locating driver sources using mobile phone signaling data and taxi GPS data[J].Journal of Harbin Institute of Technology,2018,50(9):96.DOI:10.11918/j.issn.0367-6234.201708042 |
|
| 摘要: |
| 为揭示居民出行行为与城市交通拥堵的内在关联，并为缓解城市交通拥堵提供技术支持，利用高覆盖率、低精度的手机数据和低覆盖率、高精度的出租车GPS数据，构建了数据驱动的车源定位方法.利用手机数据获取出行需求信息，利用出租车GPS数据获取交通状态信息；提出基于数据融合的出行OD估计方法，进行交通流分配，对城市道路车流来源及城市拥堵源进行动态定位.结果表明：道路车流主要来自于少量车源小区，且拥堵状态下这些小区更加集中；同时受居民通勤行为的影响，城市全局拥堵源在早晚高峰表现出不同的特征.利用数据融合的车源定位可以用于揭示拥堵形成的内在机理及演化规律，辅助制定有针对性的拥堵缓解策略.
|
| 关键词: 城市交通 车源定位 数据融合 手机数据 出租车GPS |
| DOI：10.11918/j.issn.0367-6234.201708042 |
| 分类号:U491 |
| 文献标识码:A |
| 基金项目:国家自然科学基金 (0,0)； 湖南省科技计划(2015RS4011) |
|
| A data fusion approach for locating driver sources using mobile phone signaling data and taxi GPS data |
|
WANG Pu1,LU Hengyu1,TAN Qian1,XIONG Yusha1,MAO Yingping2,LI Lin2
|
|
(1. School of Traffic and Engineering, Central South University, Changsha 410000, China; 2. Shenzhen Urban Transport Planning Center, Shenzhen 518021, Guangdong, China)
|
| Abstract: |
| To investigate the relation between people's travel behavior and urban traffic congestion, and provide technical support to alleviate urban traffic congestion, a data-driven source localization approach was constructed based on high-coverage-rate low-precision mobile phone data and high-precision taxi GPS data. Travel demand and traffic condition information was obtained by personal mobile phone data and taxi GPS data, respectively. Mobile phone data and taxi GPS data were combined to estimate origin-destination matrix, conduct traffic assignment, and dynamically locate traffic sources of road segments and the congestion sources of the city. Result showed that a majority of traffic flow was generated by a few sources, and the sources became more concentrated when traffic jam occurred. Urban congested sources were affected by commuting behaviors, and exhibited different characteristics during morning and evening peak hours. Traffic source location by data fusion can uncover the internal mechanism and evolution law of traffic congestion, and help in making target policies dealing with traffic congestion.
|
| Key words: urban transportation traffic source location data fusion mobile phone data taxi GPS data |
