Bowen Du

Missing Data Compensation Model in Real-Time Traffic Information Service System

Nowadays the floating car data (FCD) is playing a more and more important role in the route guidance, because it can collect more accurate travel time information in traffic service systems. But the problem is that the congested traffic performs to be dynamic and quite complex, and therepsilas great data amount fluctuation at different time of day. Therefore, the stability of the data canpsilat be guaranteed, and the OD travel time calculated in dynamic route guidance will be seriously affected. In this paper, a multipattern compensating model for blank-data links which is based on the optimized road network is proposed. According to the classified historical data, the missing real-time traffic data is filled up. Finally the experiment which involved the data of 15,000 taxies for 6 months was carried out in several ways. The result suggests that this method raised the road coverage while guaranteed the accuracy and it can be applied in real-time systems to manage large amount of data.

Understand Group Travel Behaviors in an Urban Area Using Mobility Pattern Mining

With the development of cities, especially in developing countries, public transport is a major choice for millions city dwellers, which can ease the traffic pressure, such as crowdedness. However, for cities, in particular cities of developing countries, the continuous development of urban construction leads to the function of regions are changed, and then the collective that travel to specific locations in the city are redistributed, such as a new shopping mall in operation or a new-built community comes into service to meet travel demands of citizens. Currently, Automated Fare Collection Systems (AFCS) are widely used in cities around the world and large amounts of data from AFCS have been acquired. In this paper, we present a new framework to use the data through AFCS to discovering regions with high passenger gathering intensity and classify points in these regions with similar passenger gathering feature varying with time in dynamic way, which is called spark region. Furthermore, the novel definition group mobility pattern (GMP) is proposed to mine the regular group behavior among these spark regions. A series of analysis is employed by using large-scale and real-world data, which consists of nearly 17million peoples daily public transit records, bus trajectories generated by over 14,854 buses organizations in Beijing at 20seconds interval. The actual application indicates group mobility pattern is helpful for diagnosis and understanding residence of each region with their demand for public transportation in a significant way.

An effective method to estimate urban link travel time in real-time traffic information system

Nowadays the Floating Car technology is playing a more and more important role in the dynamic route guidance, congestion management, traffic incidents detection, because it can collect more accurate travel time information in real-time traffic service systems. However the key problem of using it is that the moving direction and driving behaviors of floating car at low speed are dynamic and quite complex, disregarded these influence factors will seriously affect the accuracy of the evaluation of the Average Link Travel Time. In this paper, we proposed an effective Average Link Travel Time evolution method via combining trajectories with a new road network structure. Finally, we carried out our experiments on real data of 15,000 taxies for 13 months in several ways.

An FCD Compensation Model Based on Traffic Condition Trends Matching

As an advanced means of collecting information of traffic condition of roads, floating car technology has drawn increasing attention from many countries. Whereas, Floating Car Data(FCD) collected by floating car technology often could not cover all the roads of a city. Therefore, in order to increase the integrality and usability of FCD which reflects the traffic condition of roads, a model based on History FCD(HFCD) is proposed to compensate the information of traffic condition of roads which are not covered by real-time FCD. The model corrects the abnormal data in HFCD and extracts all the changing modes of driving speed of all roads from HFCD, and by matching the changing trend of driving speed in real-time FCD with these modes, the vacant real-time data could be derived. In the end, 20 floating car are arranged to testify the effectiveness of the model, the results shows that the model compensates the vacant real-time data accurately and increases the coverage of road networks.

An FCD Information Processing Model under Traffic Signal Control

Nowadays Float Car Data (FCD) is playing an important role in real-time traffic information systems. However, traffic signal control in urban road network will cause random delay on float cars, and this kind of delay will result in considerable fluctuation of travel time. Thus, the accuracy of FCD system is seriously affected. In this paper, float car refining models are proposed to calculate the stopped-time delay by means of low-sampling-rate FCD. And then, the classification of controlled delay and non-controlled delay is performed in order to remove traffic signal controls affection, and to obtain the data which can truly reflect the traffic flow characteristics. The contrast experiments indicate that the accuracy of the FCD system has achieved significant improvement after applying the new processing model.

An evolvable and transparent data as a service framework for multisource data integration and fusion

Combining data from multiple sources is a means of enabling unified and comprehensive description of objects in high-dimensional space and helping unlock the potential value of such data. In recent years, more and more studies have focused on this field of research. However, challenges posed by separately stored data and comprehension barriers about different systems hinder the integration of data from different sources. To overcome these problems, this paper proposes a Transparent Data as a Service framework, a novel approach combining Transparent Computing and Representational State Transfer (REST) Web Services based on Linked Data. This framework is capable of integrating data from different sources and offering data services in a transparent way. That is, consumers use data services without the need to know details of where or how the data are stored. Our framework is transparent on three levels: transparent data resource integration, transparent data fusion and transparent data service provision. The Data Model Pool and Data Resource Pool are able to evolve as new data models and datasets are generated in the provision of data services. Finally, we demonstrate the feasibility of the framework by implementing a prototype system.

Applied research of data sensing and service to ubiquitous intelligent transportation system

High-efficiency transportation systems in urban environments are not only solutions for the growing public travel demands, but are also the premise for enlarging transportation capacity and narrowing the gap between urban and rural areas. Such transportation systems should have characteristics such as mobility, convenience and being accident-free. Ubiquitous-intelligent transportation systems (U-ITS) are next generation of intelligent transportation system (ITS). The key issue of U-ITS is providing better and more efficient services by providing vehicle to vehicle (V2V) or vehicle to infrastructure (V2I) interconnection. The emergence of cyber physical systems (CPS), which focus on information awareness technologies, provides technical assurance for the rapid development of U-ITS. This paper introduces the ongoing Beijing U-ITS project, which utilizes mobile sensors. Realization of universal interconnection between real-time information systems and large-scale detectors allows the system to maximize equipment efficiency and improve transportation efficiency through information services.

Active CTDaaS: A Data Service Framework Based on Transparent IoD in City Traffic

Transport infrastructure generates a huge amount of city transportation data due to the significant increasing of advanced devices, such as sensing devices, mobile devices and real-time monitors. However, transportation big data cannot be fully analyzed and utilized by urban traffic data services currently. This paper proposes a novel City Traffic Data-as-a-Service (CTDaaS), which fuses data from distributed providers. Initially, we build an Internet of Traffic Data Service (IoTDS) model to identify associations and relationships among data resources. Then a CTDaaS agent is developed under Transparent Computing paradigm and service oriented architecture. It receives user requests, fuses knowledge from a variety of data sources according to different computing models, and responses differentiated Quality of Data (QoD). Finally, an application scenario, named Park and Ride (P+R), is implemented and evaluated to demonstrate how the service works using existing dynamic city traffic data.

Context-Aware Answer Sentence Selection With Hierarchical Gated Recurrent Neural Networks

In this paper, we study the task of reading comprehension style answer sentence selection that aims to select the best sentence from a given passage to answer a question. Unlike most previous works that match the question and each candidate sentence separately, we observe that the context information among sentences in the same passage plays a vital role in this task. We propose modeling context information with hierarchical gated recurrent neural networks. Specifically, we first apply a word level recurrent neural network to model the context independent matching between the question and each candidate sentence. We then employ a sentence level recurrent neural network to incorporate the context information among all candidate sentences. Moreover, we introduce the gate mechanism to select matching information before feeding into recurrent neural networks at both word and sentence level. Experiments on the WikiQA and SQuAD datasets show that our model outperforms state-of-the-art methods.