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In recent years, with the development of high-speed railway in China, the operating mileage and passenger transport capacity have increased rapidly in transportation industry. Due to the high density of trains in the daytime, we usually set up skylights at night (0:00–6:00 am) on high-speed railway for comprehensive maintenance. However, this arrangement contradicts with the operation demand of D-series overnight high-speed trains (overnight D-trains for short). In order to adjust the operation plan of overnight D-trains with skylights coordinately, it is necessary to predict the passenger demand of newly added overnight D-trains. Therefore, in this paper, a mixed logit model based on nonlinear random utility functions for different transport modes is proposed, in order to predict transfer passenger demand. According to Maximum Simulated Likelihood Method, the likelihood function of this mixed logit model is proposed to maximize the overall utility value of different passenger groups while Metropolis–Hastings algorithm is adopted to iteratively solve the probabilities of discrete random variables in utility functions. After that, the unknown distributions of parameters are estimated and the optimal solution of this model is provided by traditional algorithms, basic heuristic algorithms and improved heuristic algorithms including improved fireworks-simulated annealing algorithm proposed in this paper, respectively. Finally, a real-world instance with related data of Beijing–Shanghai corridor is implemented to demonstrate the performance and effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]

Data fusion is an efficient way to achieve an improved accuracy and more specific inferences by fusing and aggregating data from different sensors. However, due to the increasing complexity of spatial data with massive and multi-source heterogeneous characteristics, the existing methods cannot satisfy quite well the requirement for the integrity of data and the accuracy of fusion results in some specific situations. By considering the geographical properties of spatial data, a multi-source heterogeneous spatial big data fusion method based on multiple similarity and voting decision (SDFSV) is proposed in this paper, which develops a three-step record linking algorithm to improve the quality of entity recognition for the incremental fusion of massive data. Then, a one-time voting algorithm is introduced into the proposed method, so that the data conflicts can be significantly reduced and thus the accuracy of the data fusion can be improved. And a relation deduction method based on rule and entity recognition is presented to enhance the data integrity. In addition, in order to promote traceability and interpretability of fusion results, it is necessary to construct a data traceability mechanism. Experimental results show that SDFSV has an improved performance by using the data of Beijing Medical Institutions collected from 10 data sources. [ABSTRACT FROM AUTHOR]

The accurate prediction of short-term passenger flow is of high importance to efficiently manage the passenger flow of metro systems and adjust timetable accordingly. However, the existing methods of passenger flow prediction cannot achieve adequate accurate results due to its complex nonlinear spatiotemporal characteristics. To improve the accuracy of short-term passenger flow prediction, this paper proposes a deep learning model based on a spatiotemporal framework. Firstly, the graph convolutional network, which incorporates prior domain knowledge (such as travel time and origin–destination demand), is used to extract spatial features of passenger flow. Secondly, the attention mechanism is integrated into the gated recurrent unit to extract the time correlation of passenger flow. Finally, external factors are introduced to capture their impact on passenger flow as well. A case study of the Beijing Subway system is illustrated to verify the performance of the proposed model. The results show that compared with the existing models, the proposed model achieves the highest prediction accuracy and strong robustness. Furthermore, we demonstrate that the adjacency matrix based on travel time outperforms the one based on OD demand, especially during evening peak hours. In addition, it is also verified that the attention mechanism and external factors can improve the prediction performance of the proposed model. [ABSTRACT FROM AUTHOR]

The accurate carbon price prediction is of significance to decrease investment risks, make scientific decisions and improve production efficiency. As matters stand, most studies focusing on carbon price prediction are following the preprocessing models, while the postprocessing models based on the error correction are rarely applied. To enhance the forecasting robustness and provide a relatively comprehensive comparison between the preprocessing and postprocessing model, this research proposes a novel hybrid model KELM-VMD-KELM by combining variational mode decomposition (VMD) and the kernel-based extreme learning machine (KELM), in which the KELM is firstly employed to forecast the daily carbon price series and obtain the initial prediction results, and then the VMD-KELM is utilized to build the predicting models for the residual error series to implement the process of error correction. The particle swarm optimization (PSO) algorithm is made a use to determine the optimal parameters of KELM and VMD. The daily average carbon price from Beijing, Guangdong and Shanghai market are selected to test the validity of the model. The results indicate that there is heterogeneity of the optimal model in different datasets. Both KELM-VMD-KELM and VMD-KELM perform well in the daily carbon price prediction. The postprocessing model can guarantee a high stability in different datasets. [ABSTRACT FROM AUTHOR]

The reliable operation of urban road traffic is the vision of urban development. The reliability measurement method with travel time as a performance index is the starting point of this study. Based on the travel time data, we have studied the shortcomings of existing travel time reliability indicators. Most of them simplify or even ignore the information of traffic performance thresholds. According to the characteristics of the actual urban road network, we proposed measurement of travel time reliability under uncertain random environment, that is, the travel time belief reliability. By extracting the information of traffic service subject and object, this method takes into account the influence of cognitive and random uncertainty on reliability. Next, we established the belief reliability model of travel task under the uncertain random road environment. The model considers the route selection, departure status and road conditions and gives the route selection algorithm under a time-varying road network. In addition, the impact of road objective factors and driving state factors on the travel time threshold is discussed by using the uncertainty regression analysis method. Finally, we took the actual travel task in Beijing as an example to verify the feasibility and practicability of the model and algorithm. [ABSTRACT FROM AUTHOR]