•Develop a subway operation incident delay model using parametric accelerated time failure (AFT) approaches.•The log-logistic AFT model with random parameters is most suitable for estimating the ...subway incident delay.•A longer subway delay is related with power cable failure, turnout communication disruption and crashes involving casualty.•Temporal transferability test results show that the developed model is stable over time.
This study aims to develop a subway operational incident delay model using the parametric accelerated time failure (AFT) approach. Six parametric AFT models including the log-logistic, lognormal and Weibull models, with fixed and random parameters are built based on the Hong Kong subway operation incident data from 2005 to 2012, respectively. In addition, the Weibull model with gamma heterogeneity is also considered to compare the model performance. The goodness-of-fit test results show that the log-logistic AFT model with random parameters is most suitable for estimating the subway incident delay. First, the results show that a longer subway operation incident delay is highly correlated with the following factors: power cable failure, signal cable failure, turnout communication disruption and crashes involving a casualty. Vehicle failure makes the least impact on the increment of subway operation incident delay. According to these results, several possible measures, such as the use of short-distance and wireless communication technology (e.g., Wifi and Zigbee) are suggested to shorten the delay caused by subway operation incidents. Finally, the temporal transferability test results show that the developed log-logistic AFT model with random parameters is stable over time.
A dramatic increase in talking on the phone whilst driving has been seen over the past decades, which posed a significant safety threat on the whole society consequently. Studies on the topic ...regarding the effect of phone conversations on drivers’ driving performances have never come to a cease, especially on the studies of drivers’ brake response times. However, few studies focus on the relationship between situation criticality and the effect of cognitive load on drivers’ brake responses. To better understand it, a driving simulator experiment with two braking scenarios corresponding to two levels of situation criticality was conducted in this study. Participants were asked to follow a lead vehicle as they normally did and answer arithmetic problems (simple and complex) in three phone modes (baseline, hands-free, and handheld) in the meantime. Drivers’ brake response times to the lead vehicle under five conditions were collected and fitted in accumulator models, in which visual looming and brake lights onset were included as the sensory cues. Results demonstrated that the previously proposed mechanistically explicit simulation model was able to predict drivers’ brake response times on different levels of cognitive load and the increased effect of cognitive load on drivers’ brake response times in less critical situations was demonstrated in this paper as well.
In recent decades, studies on short-term traffic speed forecasting of the large-scale road are a new challenge for researchers and engineers. Especially based on deep learning neural networks, ...studies on short-term traffic forecasting have achieved mush-room growth. This study proposes a stacked Bidirectional Gated Recurrent Unit neural network model to predict the traffic speed of the expressway over different estimation time intervals in an effective manner. By building a multiscale-grid model, it can take less time to derive a set of key traffic parameters of different scales to predict traffic speed of the various-scale road. The speed prediction of small-scale sections can cover more detailed road spatial features preparing for Vehicle Navigation System, and the speed prediction of large-scale sections can establish the real-time traffic control strategies. In order to validate the effectiveness of the proposed model, we use the floating car data, with an updating frequency of 1 minute from the urban freeway of Beijing, for model training and testing. The experimental results show that the stacked BiGRU network with the multiscale-grid model enables to capture the spatial-temporal characteristics of traffic speed efficiently. Furthermore, the BiGRU with two layers (BiGRU-2L) outperforms benchmark models in the prediction of the traffic speed, which presents a significant advantage in reducing the overfitting problem, decreasing the excessive time-consuming and improving the effective use of limited computation resources.
The intersection field of view (IFOV) indicates an extent that the visual information can be observed by drivers. It has been found that further enhancing IFOV can significantly improve emergent ...collision avoidance performance at intersections, such as faster brake reaction time, smaller deceleration rate, and lower traffic crash involvement risk. However, it is not known how IFOV affects drivers' eye movements, visual attention and the relationship between visual searching and traffic safety. In this study, a driving simulation experiment was conducted to uncover the changes in drivers' visual performance during the collision avoidance process as a function of different field of views at an intersection by using an eye tracking system. The experimental results showed that drivers' ability in identifying the potential hazard in terms of visual searching was significantly affected by different IFOV conditions. As the IFOVs increased, drivers had longer gaze duration (GD) and more number of gazes (NG) in the intersection surrounding areas and paid more visual attention to capture critical visual information on the emerging conflict vehicle, thus leading to a better collision avoidance performance and a lower crash risk. It was also found that female drivers had a better visual performance and a lower crash rate than male drivers. From the perspective of drivers' visual performance, the results strengthened the evidence that further increasing intersection sight distance standards should be encouraged for enhancing traffic safety.
Resistance to platinum drugs has emerged as a major obstacle in the treatment of ovarian cancers. Through proteomic analysis, we have found that the expression of annexin A3, a member of the Ca(2+) ...and phospholipid-binding annexin family, is significantly increased in platinum-resistant ovarian cell lines. Anti-annexin A3 immunostaining indicated that cancers from platinum-resistant patients also possess higher levels of annexin A3 than those from platinum-sensitive patients. Although expression of annexin A3 made susceptible ovarian cancer cells more resistant to platinum, expression of antisense annexin A3 downregulated its expression and rendered the resistant cells more sensitive to platinum. In athymic mice, the growth of tumors from inoculated SKOV3 cells was inhibited by the administration of platinum, whereas tumors from annexin A3-expressing SKOV3/Ann were resistant to platinum treatment. Interestingly, the intracellular platinum concentration and platinum-DNA binding are significantly lower in annexin A3-overexpressing cells than those in parental cells. The lower cisplatin concentration was also accompanied by reduced induction of p53, which could be restored by downregulation of annexin A3. These results indicate that increased expression of annexin A3 is a mechanism of platinum resistance in ovarian cancer. It seems to act by preventing uptake or accumulation of platinum in cells. Therefore, it is conceivable that annexin A3 could be a target for therapeutic intervention and may also serve as a biomarker for drug resistance in ovarian cancer patients.
As a sustainable transportation mode, high-speed railway (HSR) has been developing rapidly during the past decade in China. With the formation of dense HSR network, how to improve the utilization ...efficiency of train-sets (the carrying tools of HSR) has been a new research hotspot. Moreover, the emergence of railway transportation hubs has brought great challenges to the traditional train-sets’ utilization mode. Thus, in this paper, we address the issue of train-sets’ utilization problem with the consideration of railway transportation hubs, which consists of finding an optimal Train-set Circulation Plan (TCP) to complete trip tasks in a given Train Diagram (TD). An integer programming TCP model is established to optimize the train-set utilization scheme, aiming to obtain the one-to-one correspondence relationship among sets of train-sets, trip tasks, and maintenances. A genetic algorithm (GA) is designed to solve the model. A case study based on Nanjing and Shanghai HSR transportation hubs is made to demonstrate the practical significance of the proposed method. The results show that a more efficient TCP can be formulated by introducing train-sets being dispatched among different stations in the same hub.
With the rapid increase in vehicle population, solving difficult-to-park and inconvenient-to-park problems has become a necessity for sustainable transportation in major cities. This study ...investigated the parking resource allocation optimization problem based on a two-level grid model. To address this problem, first users’ travel data were matched to the map to obtain the distribution of both parking resources and demands. Second, key demand-supply imbalanced grids were identified. Subsequently, the parking resource allocation optimization problem was formulated as an integer linear programming (ILP) problem aiming to minimize the total cost, including the parking facility construction cost, users’ total walking distance, and the penalty for unserved users. Finally, a case study based on real-world data in Cangzhou was conducted to verify the feasibility and effectiveness of the proposed method. The results show that the imbalance between the supply and demand of parking resources can be effectively alleviated.
Urban road intersection bottleneck has become an important factor in causing traffic delay and restricting traffic efficiency. It is essential to explore the prediction of the operating performance ...at intersections in real-time and formulate corresponding strategies to alleviate intersection delay. However, because of the sophisticated intersection traffic condition, it is difficult to capture the intersection traffic Spatio-temporal features by the traditional data and prediction methods. The development of big data technology and the deep learning model provides us a good chance to address this challenge. Therefore, this paper proposes a multi-task fusion deep learning (MFDL) model based on massive floating car data to effectively predict the passing time and speed at intersections over different estimation time granularity. Moreover, the grid model and the fuzzy C-means (FCM) clustering method are developed to identify the intersection area and derive a set of key Spatio-temporal traffic parameters from floating car data. In order to validate the effectiveness of the proposed model, the floating car data from ten intersections of Beijing with a sampling rate of 3s are adopted for the training and test process. The experiment result shows that the MFDL model enables us to capture the Spatio-temporal and topology feature of the traffic state efficiently. Compared with the traditional prediction method, the proposed model has the best prediction performance. The interplay between these two targeted prediction variables can significantly improve prediction accuracy and efficiency. Thereby, this method predicts the intersection operation performance in real-time and can provide valuable insights for traffic managers to improve the intersection’s operation efficiency.
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