Enabling Smart Transportation Systems: A Parallel Spatio-Temporal Database Approach

Ding, Zhiming; Yang, Bin; Chi, Yuanying; Guo, Limin

doi:10.1109/tc.2015.2479596

Cited by 65 publications

(31 citation statements)

References 44 publications

(63 reference statements)

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“…As the most creative urban form, the smart city has become the strategic choice for cities aiming to achieve development. At present, researchers are using new information technologies to conduct studies of practical applications based on a conceptual framework of the smart city, such as smart healthcare [31,32], smart transportation [33,34], smart tourism [35][36][37] and smart management [38]. However, most previous studies have focused on analysis and smart city construction in for single research field area.…”

Section: Applications For the Smart City And Shortcomingsmentioning

confidence: 99%

Smart City: A Shareable Framework and Its Applications in China

Liu

Dai

et al. 2019

Sustainability

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Smart City is a new concept that uses information and communication technology (ICT) to promote the smartification of urban construction, planning and services. Currently, a number of cities have conducted studies on smart cities, but they have mostly focused on analyzing the conceptual connotations or applications in specific domains and lack a shareable and integrated framework, which has led to significant barriers for individual smart projects. By analyzing the framework and applications of Smart City, this paper proposes a common, shareable and integrated conceptual framework. Then, based on this framework, it further proposes a unified portal platform that can balance multiple stakeholders, including the government, citizens and businesses, as well as for common, custom and other application modes. Finally, the implementation of Smart Weifang based on this platform is discussed. The applications indicate that this shareable platform can effectively eliminate the data and technological barriers between different smart city systems while also avoiding redundant financial investments. The investigation of this proposed framework and platform is highly significant for the unified construction of smart cities and the intensification of the hardware environment, thus representing a true achievement in the transition from 'information islands' to 'information sharing and interconnection' for urban informatization.Sustainability 2019, 11, 4346 2 of 16 and services by using ICT, such as the Internet of things (IoT), cloud computing and geospatial information [9][10][11][12]. Based on research on the smart city conceptual framework and characteristics, practical studies on smart cities have been conducted, such as the smart e-government "I-Japan Strategy" in Japan, the intelligent transportation study in Sweden, the smart grid projects in the US, and the smart community in Germany.However, despite the large amount of research conducted by numerous countries and cities on the smart city and the substantial progress that they represent, they mostly focused on the analysis of the conceptual framework and applications in specific domains (e.g., smart transportation) and lack a common, shareable and integrated framework of understanding. A city is a giant complex system. To meet the ever-changing needs of its citizens, all systems within the city should communicate accurately with each other and share and utilize each other's resources in a timely and rapid manner [13]. Smart projects in specific domains usually have the characteristics of a resource monopoly and mutual independence, which leads to the following issues: (1) The inability to guarantee the consistency of public data and the spatial base for different smart projects in the same city; (2) the inability to achieve the sharing and integration of different smart systems or data in smart cities; (3) the inability to guarantee consistency across different smart project implementations due to different viewpoints of the project implementers or researchers; and (4) ...

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Section: Applications For the Smart City And Shortcomingsmentioning

confidence: 99%

Smart City: A Shareable Framework and Its Applications in China

Liu

Dai

et al. 2019

Sustainability

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“…Next, we apply Sigmoid activation function to each matrix group to produce a 1 × l matrix. This corresponds |X | reconstructed time series, e.g.,X (1) ,X (2) , andX (3) ,…”

Section: Auto Encoder Crnn (Aecrnn)mentioning

confidence: 99%

Correlated Time Series Forecasting using Multi-Task Deep Neural Networks

Cirstea

Micu

Muresan

et al. 2018

Proceedings of the 27th ACM International Conference on Information and Knowledge Management

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Cyber-physical systems often consist of entities that interact with each other over time. Meanwhile, as part of the continued digitization of industrial processes, various sensor technologies are deployed that enable us to record time-varying attributes (a.k.a., time series) of such entities, thus producing correlated time series. To enable accurate forecasting on such correlated time series, this paper proposes two models that combine convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The first model employs a CNN on each individual time series, combines the convoluted features, and then applies an RNN on top of the convoluted features in the end to enable forecasting. The second model adds additional auto-encoders into the individual CNNs, making the second model a multi-task learning model, which provides accurate and robust forecasting. Experiments on two real-world correlated time series data set suggest that the proposed two models are effective and outperform baselines in most settings. This report extends the paper "Correlated Time Series Forecasting using Multi-Task Deep Neural Networks, " to appear in ACM CIKM 2018, by providing additional experimental results.

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“…The road type feature with the largest probability, i.e., arg max x∈{3,4,5}Ŷ [i, x], is used as the final road type feature. In the exampleŶ in Figure 7, this is TP1 for re 3 Fig. 7: Transferring routing preferences for re 4 .…”

Section: B Step 2: Transferring Routing Preferencesmentioning

confidence: 99%

Learning to Route with Sparse Trajectory Sets

Guo

Yang

et al. 2018

2018 IEEE 34th International Conference on Data Engineering (ICDE)

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Motivated by the increasing availability of vehicle trajectory data, we propose learn-to-route, a comprehensive trajectory-based routing solution. Specifically, we first construct a graph-like structure from trajectories as the routing infrastructure. Second, we enable trajectory-based routing given an arbitrary (source, destination) pair.In the first step, given a road network and a collection of trajectories, we propose a trajectory-based clustering method that identifies regions in a road network. If a pair of regions are connected by trajectories, we maintain the paths used by these trajectories and learn a routing preference for travel between the regions. As trajectories are skewed and sparse, many region pairs are not connected by trajectories. We thus transfer routing preferences from region pairs with sufficient trajectories to such region pairs and then use the transferred preferences to identify paths between the regions. In the second step, we exploit the above graph-like structure to achieve a comprehensive trajectorybased routing solution. Empirical studies with two substantial trajectory data sets offer insight into the proposed solution, indicating that it is practical. A comparison with a leading routing service offers evidence that the paper's proposal is able to enhance routing quality. This is an extended version of "Learning to Route with Sparse Trajectory Sets" [1], to appear in IEEE ICDE 2018.

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Enabling Smart Transportation Systems: A Parallel Spatio-Temporal Database Approach

Cited by 65 publications

References 44 publications

Smart City: A Shareable Framework and Its Applications in China

Smart City: A Shareable Framework and Its Applications in China

Correlated Time Series Forecasting using Multi-Task Deep Neural Networks

Learning to Route with Sparse Trajectory Sets

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