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This study proposes a modelling framework for the demand-driven train timetable and stop pattern cooperative optimization problem on an urban rail transit line. By embedding the train stop pattern into the timetable optimization process, we consider the minimization of total passenger travel time. A binary variable determination (BVD) method, which can transform complicated linear constraints into simple logical constraints, is proposed to calculate the large number of binary variables easily, and a genetic algorithm (GA) based on the BVD method is designed to solve the proposed model. A case study of the Batong line in the Beijing subway network is conducted to test the proposed model and algorithm. This study can provide beneficial advice for the operator to improve the operational service of urban rail transit lines. [ABSTRACT FROM AUTHOR]

Highlights • Passenger flow state estimation framework to integrate Lagrangian and Eulerian observations. • A discretized state definition for the discrete approximation of continuous passenger flow. • A space-time-state hyper network to integrate structurally heterogeneous data sources. • A decomposition solution framework for application in large-scale urban rail transit networks. Abstract In this study, we focus on one of practically important research problems of integrating Lagrangian and Eulerian observations for passenger flow state estimation in an urban rail transit network. The task is accomplished by using a triple of flow, density, and speed to construct a discretized passenger flow state, further constructing a space-time-state (STS) hyper network so that we can utilize a better defined three-dimensional solution space to integrate structurally heterogeneous data sources. The monitoring data include passenger transaction records and identification space-time samples observed over a possible range of a few hours from origins to destinations (Lagrangian observations), and time-dependent passenger counts collected at some key bottleneck locations (Eulerian observations). To describe the complex urban-rail passenger flow evolution, passenger traveling and fixed sensor state transition processes can be unified within a STS path representation. To estimate the consistent system internal states between two different types of observations, we formulate a hyper network-based flow assignment model in a generalized least squares estimation framework. For applications in large-scale transportation networks, we decompose the proposed model into three easy-to-solve sub-problems. The proposed model is applied to a real-world case based on the Beijing subway network with complete smart card data for each passenger at his/her origin and destination and time-dependent passenger counts in several key transfer corridors, while the specific space-time trajectories of all passengers and high-resolution time-dependent congestion levels at platforms, in trains, and in transfer corridors are estimated. This proposed passenger flow state inference method can provide a rich set of state inferences for advanced transit planning and management applications, for instance, passenger flow control, adaptive travel demand management, and real-time train scheduling. [ABSTRACT FROM AUTHOR]

Different clearance methods in traffic accident management lead to varied duration distributions. Apart from investigating the influence of various factors associated with accidents on the duration of such accidents using different clearance methods, this study also examines the cumulative incidence probability. We used traffic accident data obtained for 12 months from the Fourth Ring Expressway main line in Beijing to develop a subdistribution hazard regression model, which can assess the risk factors of two clearance methods. The regression results show that the different factors have statistically significant effects on the duration of two accident groups with different clearance methods; furthermore, opposite effects occur even for some factors that have a strong effect on both accident groups. For example, an accident involving a taxi extends the duration time with clearance method 1; in comparison, the accident is shorter with clearance method 2. The predicted cumulative incidence curves of the two types of clearance methods are shown as examples, with stratification based on the influence factors (taxi involved, season). Finally, the Gray test of the cumulative incidence functions and the log-rank test of the Kaplan-Meier estimates of the survival functions are compared, in order to demonstrate the importance of using proper methods for analyses. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

• A field experiment was conducted to explain the role of initial experience of AV acceptance. • Experience satisfaction was positively associated with trust, attitude, SN, and PBC. • Attitude, PBC, experience satisfaction, and trust mattered to intention to use AV. • Attitude and PBC mediated effect from trust to intention. • Gender and education moderated the effect from experience satisfaction to PBC. The demonstration, showcase, and city studies of autonomous vehicles (AVs) provide valuable opportunities for the public to interact with AV. Understanding the relationship between experiencing and intention to use AV is crucial because first-hand experience shapes an individual's initial impressions on autonomous driving. This study investigated the users' intention to use AVs based on extended theory of planned behavior, which incorporated experience satisfaction and trust. The data were collected through a field experiment from 117 respondents who had experienced a SAE Level 4 autonomous minibus in China. The proposed relationships were examined using partial least squares structural equation modelling. A satisfied experience contributes to a higher degree of trust, attitude, subjective norm (SN), and perceived behavioral control (PBC). Excluding SN, results suggest that attitude, PBC, experience satisfaction, and trust, are statistically proven to affect the intention to use AV. Moreover, an examination of individual difference reflects that the impact of experience satisfaction on PBC varies distinctively in male and female group, as well as in less educated and more educated group. Findings show the theoretical contributions and practical implications on the role of initial experience of AV acceptance. [ABSTRACT FROM AUTHOR]

• Traffic accident and flow data obtained simultaneously from crowdsourcing approach. • Entire traffic accidents post-impact from occurrence to recovery can be predicted. • Machine learning models were applied and good performance was achieved. • Results improved when updated information added into the model sequentially. Traffic accident management is a critical issue for advanced intelligent traffic management. The increasingly abundant crowdsourcing data and floating car data provide new support for improving traffic accident management. This paper investigates the methods to predict the complicated behavior of traffic flow evolution after traffic accidents using crowdsourcing data. Based on the available data source, the traffic condition is divided into four levels by congestion delay index: severely congested, congested, slow moving and uncongested. Four types of accidents are consequently defined based on the occurrence of each level. A hierarchical scheme is designed for identifying the most congested level and sequentially predicting duration of each level. The proposed model is validated using traffic accident data in 2017 from an anonymous source in Beijing, China by embedding three machine learning algorithms, random forest (RF), support vector machine (SVM) and neural network (NN), in the scheme. The results show NN outperforms the other two models when the assessment is conducted in absolute differences. Meanwhile, RF has a slightly better performance than SVM, especially when predicting the short-period congestion of severely congested level at the first time. By continuously updating the traffic condition information, significant improvement in accuracy can be acquired regardless of the exact model used. This study shows that emerging crowdsourcing data can be used in a real-time analysis of traffic accidents and the proposed model is effective to analyze such data. [ABSTRACT FROM AUTHOR]