大规模路网下中心式动态交通诱导系统关键技术研究
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摘要
智能运输系统作为国际公认解决交通问题的最佳途径,已经被列入我国《国家中长期科学与技术发展纲要(2006-2020)》优先发展主题。交通诱导系统是智能运输系统的核心系统之一,其中,中心式诱导由于能够有效避免分布式诱导所造成的交通拥挤转移问题,是国际公认解决交通拥挤最有效的诱导方式。本文依托国家“863”计划项目,对大规模路网下中心式动态交通诱导系统关键技术进行了研究,文中分析了目前中心式动态交通诱导系统在实际应用过程中存在的问题,指出了本文的研究目的和意义,并给出了系统的研究思路、具体方法、大规模路网下中心式动态交通诱导系统的逻辑框架和物理框架,并对其关键技术理论进行了分析。研究了基于浮动车的路段行程时间估计技术,包括基于模糊逻辑的GPS数据地图匹配、基于单车的路段行程时间估计及基于多车的路段行程时间估计;研究了基于线圈检测器的路段行程时间估计技术,针对单线圈检测器无法直接对速度参数进行检测,建立了基于ACE变换的区间速度平均速度回归估计模型;研究了基于固定型检测器和浮动车的交通信息融合技术,建立了基于TSGA-LSSVM的交通信息融合模型;研究了基于样本分类拟合的交通信息短时预测技术,建立了基于KSOM-BP神经网络的交通信息短时预测模型;并采用实际数据对上述相关模型、算法进行了实例验证,验证结果表明,本文所提出的模型、算法具有较好的准确性、实用性和先进性。同时,基于多级递阶网络分解方法和双端队列最短路径计算方法,提出了基于MLHND-TQQ的路径优化并行计算技术,并搭建了并行计算平台,采用三个城市的实际路网数据对路径优化计算时间进行了测试,测试结果表明,该项技术能够满足大规模路网下中心式动态交通诱导系统诱导实时性要求。
Rapid growth in vehicle ownership is bringing more and more urban traffic congestion problem. The socioeconomic development and people’s daily lives are both seriously affected. Central traffic flow guidance system (CTFGS) is one of the core systems of intelligent transportation systems (ITS), which is recognized internationally as the best way of traffic flow guidance to solve traffic problems. Based on the national high-tech R&D program (863 program), this paper mainly researches on the key technology of centrally dynamic traffic flow guidance system under large-scale network.
The mainly work is as:
1) Achitecture design and key technologies theories analysis of centrally dynamic traffic flow guidance system under large-scale network This paper gives out the logical and physical architecture, reviews and analysizes the key technologies of centrally dynamic traffic flow guidance system under large-scale network.
2) Link travel time estimation technology based on floating car Floating car can obtain travel time data. This paper designs a set of map-matching technology based on fuzzy logic method, which can select different criteria depending on the time interval for GPS data collection. A path travel time dividing method based on vehicles’travelling characteristics is proposed, and the average link travel time is estimated under the condition of sufficient and insufficient sample.
3) Traffic information fusion technology based on fixed detector and floating car Fix detector and floating car are complementary with each other on traffic information collection. This paper presents a speed estimation method based on the regression model with ACE transform, which is used to obtain the link travel time data with cycle detector data. The fusion model based on TSGA-LSSVM is proposed to fuse the cycle detector data and floating car data. The results of model test based on real data show that the fusion model has higher precision than any single method, and TSGA is faster than GA for parameter optimation.
4) Traffic information short-term forecasting technology based on sample classification fitting
Traffic guidance based on the forecasted dynamic traffic information can void the lag problem of guidance. This paper presents a traffic information short-term forecasting technology based on sample classification fitting, which classifies the history sample based on KSOM method, and establishes sevel BP neural network forecasting models. The results of model test with the real data show that KSOM-BP neural network method has higher precision than BP neural network based on the whole history sample being trained
5) Central guidance path optimization parallel computing technology under large scale network
Path optimization under large scale network always is the Gordian knot. This paper presents a fast path optimization method named MLHND-TQQ through the reseach on multilevel hierarchical network decomposition method and TQQ shortest path computing method. The results of test based on real network data show that this technology can fully meet the real-time demands for central guidance.
Finally, work summary in this paper and the suggestion on future work are given. Above the research achievements give theoretical significance as well as guidance value for the research and application on centrally dynamic traffic flow guidance system under large-scale network.
Rapid growth in vehicle ownership is bringing more and more urban traffic congestion problem. The socioeconomic development and people’s daily lives are both seriously affected. Central traffic flow guidance system (CTFGS) is one of the core systems of intelligent transportation systems (ITS), which is recognized internationally as the best way of traffic flow guidance to solve traffic problems. Based on the national high-tech R&D program (863 program), this paper mainly researches on the key technology of centrally dynamic traffic flow guidance system under large-scale network.
The mainly work is as:
1) Achitecture design and key technologies theories analysis of centrally dynamic traffic flow guidance system under large-scale network This paper gives out the logical and physical architecture, reviews and analysizes the key technologies of centrally dynamic traffic flow guidance system under large-scale network.
2) Link travel time estimation technology based on floating car Floating car can obtain travel time data. This paper designs a set of map-matching technology based on fuzzy logic method, which can select different criteria depending on the time interval for GPS data collection. A path travel time dividing method based on vehicles’travelling characteristics is proposed, and the average link travel time is estimated under the condition of sufficient and insufficient sample.
3) Traffic information fusion technology based on fixed detector and floating car Fix detector and floating car are complementary with each other on traffic information collection. This paper presents a speed estimation method based on the regression model with ACE transform, which is used to obtain the link travel time data with cycle detector data. The fusion model based on TSGA-LSSVM is proposed to fuse the cycle detector data and floating car data. The results of model test based on real data show that the fusion model has higher precision than any single method, and TSGA is faster than GA for parameter optimation.
4) Traffic information short-term forecasting technology based on sample classification fitting
Traffic guidance based on the forecasted dynamic traffic information can void the lag problem of guidance. This paper presents a traffic information short-term forecasting technology based on sample classification fitting, which classifies the history sample based on KSOM method, and establishes sevel BP neural network forecasting models. The results of model test with the real data show that KSOM-BP neural network method has higher precision than BP neural network based on the whole history sample being trained
5) Central guidance path optimization parallel computing technology under large scale network
Path optimization under large scale network always is the Gordian knot. This paper presents a fast path optimization method named MLHND-TQQ through the reseach on multilevel hierarchical network decomposition method and TQQ shortest path computing method. The results of test based on real network data show that this technology can fully meet the real-time demands for central guidance.
Finally, work summary in this paper and the suggestion on future work are given. Above the research achievements give theoretical significance as well as guidance value for the research and application on centrally dynamic traffic flow guidance system under large-scale network.
引文
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