Short-term traffic volume prediction, which can assist road users in choosing appropriate routes and reducing travel time cost, is a significant topic of intelligent transportation system. To overcome the error magnification phenomena of traditional combination methods and to improve prediction performance, this paper proposes an improved Bayesian combination model with deep learning (IBCM-DL) for traffic flow prediction. First, an IBCM framework is established based on the new BCM framework proposed by Wang. Then, correlation analysis is used to analyze the relevance between the historical traffic flow and the traffic flow within the current interval. Three sub-predictors including the gated recurrent unit neural network (GRUNN), autoregressive integrated moving average (ARIMA), and radial basis function neural network (RBFNN) are incorporated into the IBCM framework to take advantage of each method. The real-world traffic volume data captured by microwave sensors located on the expressways of Beijing was used to validate the proposed model in multiple scenarios. The overall results illustrate that the IBCM-DL model outperforms the other state-of-the-art methods in terms of accuracy and stability.

In this study, we propose a method that combines Bayesian network, structural equation modeling, and importance-performance analyses to evaluate and improve the service quality of crowded metros from the point of service components. First, service components that influence the service satisfaction of crowded metros are investigated, and 7 service components and 27 factors are collected from the perspective of metro daily operation. Correspondingly, 8,011 sample questioners are collected using both online (i.e. microblog and WeChat) and face-to-face survey methods. The usage of these service components is more practical for metro managers to improve service quality than the factor analysis methods used in previous studies. Second, a hybrid method combining Bayesian network and structural equation modeling is proposed to establish the relationship between service quality and these service components and factors. This hybrid method can learn the uncertain relationship directly from the data, explain the psychological element, and pinpoint the crucial service component. It is concluded that security check and waiting for boarding have the greatest effect on the overall satisfaction. With the help of importance-performance analyses, some practical suggestions (e.g., theme stations, women-only carriages, and one-day ticket) are proposed to optimize bottleneck service components and thus improve the service quality of daily metro operation. Further, heterogeneity of service quality satisfaction for various groups of respondents is discussed, which contributes to effective strategies for satisfaction improvement.

Many special events, including sports games and concerts, often cause surges in demand and congestion for transit systems. Therefore, it is important for transit providers to understand their impact on disruptions, delays, and fare revenues. Ridership after large sporting events is distinct from many other ridership patterns due to the high density of ridership localized to a few nearby stations. This paper provides the following novel methodology for long-term planning of large-event, post-game ridership by 1) predicting the total post-game ridership; 2) combining the total prediction with historical trends to forecast the passenger flow curve at nearby stations after the game; and 3) estimating the required train frequencies to serve these customers with minimal passengers left behind by each train. Additionally, this paper proposes a suite of data-driven techniques that together create a data-driven pipeline to exploit Automated Fare Collection (AFC) data for evaluating, anticipating, and managing the performance of transit systems. This paper includes a case study where the proposed pipeline is used to generate an adjusted train schedule for the post-game period and simulated with the rail ridership data from the Metropolitan Atlanta Rapid Transit Authority (MARTA). The simulation results highlight how the proposed schedules based on the estimated required post-game train frequencies could significantly improve post-game congestion and wait time. Furthermore, the results show that the long-term post-game demand forecasts could be an effective tool for tactical planning decisions such as the number of additional trains and operators that are needed during post-game periods compared to the regularly scheduled timetables.