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Traffic flow prediction plays an important role in intelligent transportation systems and is of great significance in the applications of traffic control and urban planning. Due to the complexity of road traffic flow data, traffic flow prediction has been one of the challenging tasks to fully exploit the spatiotemporal characteristics of roads to improve prediction accuracy. In this study, a combined flow direction level traffic flow prediction graph convolutional network (GCN) and long short-term memory (LSTM) model based on spatiotemporal characteristics is proposed. First, a GCN model is employed to capture the topological structure of the data graph and extract the spatial features of road networks. Additionally, due to the capability to handle long-term dependencies, the longterm memory is used to predict the time series of traffic flow and extract the time features. The proposed model is evaluated using real-world data, which are obtained from the intersection of Liuquan Road and Zhongrun Avenue in the Zibo High-Tech Zone of China. The results show that the developed combined GCNLSTM flow direction level traffic flow prediction model can perform better than the single models of the LSTM model and GCN model, and the combined ARIMA- LSTM model in traffic flow has a strong spatiotemporal correlation. [ABSTRACT FROM AUTHOR]

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Traffic arrival pattern is an indispensable part of the traffic state analysis and optimization of signal control schemes at the urban intersection. In some previous research relevant to the traffic parameters at the intersection, such as the queue length estimation, delay estimation, and vehicle trajectory reconstruction, the vehicle arrival patterns are assumed to obey a Normal or Poisson distribution. However, the actual traffic arrival patterns at the intersection are dynamic and more complex. License plate recognition (LPR) data containing rich vehicle trajectory information are promising in data-driven applications of traffic control and management. We propose a framework based on the LPR data, to infer the dynamic traffic arrival pattern at the intersection. The proposed integrative data-driven framework consists of three main parts, a method for inferring traffic signal cycle parameters, deriving the cycle start time and cycle length from time headway sequences; a link travel time estimation method, extracting the valid travel time at the selected time interval, and an arrival pattern estimation model based on the above modules to perform the second-level estimation at the downstream intersection. The unique characteristics of LPR data are exploited and utilized in this study. To validate the proposed method, the LPR data from two adjacent intersections in the city of Changsha in China are used. Numerical results reveal that the proposed framework can achieve satisfactory estimation under different traffic scenarios. The findings in this study can be extended for supporting efficient traffic control applications. • A statistical framework to infer the fine-grained traffic arrival pattern is proposed. • The signal cycle parameters are inferred by exploiting the features of the time headway sequences. • The valid travel time is extracted at the selected time interval based on the Gaussian mixed model. • The proposed framework could achieve satisfactory performance in different traffic scenarios. [ABSTRACT FROM AUTHOR]

Located in the eastern coastal areas of the Yangtze River estuary, Chongming is the key strategic space for sustainable development in Shanghai. However, in recent years, Chongming Island has been experiencing increasing levels of traffic, taxing its transportation capacity and raising the question of whether it will be able to continue to meets its growing traffic demand. Simply adding infrastructure will not only fail to meet the need of increased levels of transportation availability, it would also increase pollution and damage the environment of Chongming. This paper argues that the construction of the Chongming Island Intelligent Transportation System (ITS) can improve the utilization of land resources in the city, improve the road network capacity and quality of service lines, and effectively control pollution, thus promoting the sustainable economic and environmental development of Chongming Island. [ABSTRACT FROM AUTHOR]

The rapid development of the Chinese economy over the past 30 years has increased demand for passenger and freight transport considerably. Although restrictions on economic development have been alleviated by a major programme of road construction, China has begun to seek more sustainable ways of supporting transport development. Intelligent transport systems (ITS) have been introduced and a series of research has been conducted to take advantage of ITS applications in traffic management and control with significant social, economic and environmental benefits. This paper reviews the current use of ITS in China and presents policies, technologies, typical applications and recent activities in ITS development. The main achievements that reflect the current level of China's ITS development are described, in particular the ITS showcase programme and preparation for the 2008 Olympic Games. Finally, the paper discusses China's future ITS plans. [ABSTRACT FROM AUTHOR]

The article focuses on the research agenda developed by the Chinese Academy of Sciences focusing on the traffic estimation and prediction system (TrEPS) intelligent systems and technology called the dynamic traffic assignment based on complex adaptive systems (DynaCAS) and its counterpart the DynaChina. The agenda was created to develop an urban transportation system for China to operate its intelligent transportation systems (ITSs). The DynaChina was also proposed to assist transportation researchers on the development and deployment of TrEPS. An overview of DynaCAS, along with a diagram model for its individual behaviors, is offered. Meanwhile, both the DynaCAS and DynaChina are essential approaches to urban traffic management.