Estimating Uncertainty of Bus Arrival Times and Passenger Occupancies

Travel time reliability and the availability of seating and boarding space are important indicators of bus service quality and strongly influence users’ satisfaction and attitudes towards bus transit systems. With Automated Vehicle Location (AVL) and Automated Passenger Counter (APC) units becoming common on buses, some agencies have begun to provide real-time bus location and passenger occupancy information as a means to improve perceived transit reliability. Travel time prediction models have also been established based on AVL and APC data. However, existing travel time prediction models fail to provide an indication of the uncertainty associated with these estimates. This can cause a false sense of precision, which can lead to experiences associated with unreliable service. Furthermore, no existing models are available to predict individual bus occupancies at downstream stops to help travelers understand if there will be space available to board. The purpose of this project was to develop modeling frameworks to predict travel times (and associated uncertainties) as well as individual bus passenger occupancies. For travel times, accelerated failure-time survival models were used to predict the entire distribution of travel times expected. The survival models were found to be just as accurate as models developed using traditional linear regression techniques. However, the survival models were found to have smaller variances associated with predictions. For passenger occupancies, linear and count regression models were compared. The linear regression models were found to outperform count regression models, perhaps due to the additive nature of the passenger boarding process. Various modeling frameworks were tested and the best frameworks were identified for predictions at near stops (within five stops downstream) and far stops (further than eight stops). Overall, these results can be integrated into existing real-time transit information systems to improve the quality of information provided to passengers

A Putative Homologue of CDC20/CDH1 in the Malaria Parasite Is Essential for Male Gamete Development

Cell-cycle progression is governed by a series of essential regulatory proteins. Two major regulators are cell-division cycle protein 20 (CDC20) and its homologue, CDC20 homologue 1 (CDH1), which activate the anaphase-promoting complex/cyclosome (APC/C) in mitosis, and facilitate degradation of mitotic APC/C substrates. The malaria parasite, Plasmodium, is a haploid organism which, during its life-cycle undergoes two stages of mitosis; one associated with asexual multiplication and the other with male gametogenesis. Cell-cycle regulation and DNA replication in Plasmodium was recently shown to be dependent on the activity of a number of protein kinases. However, the function of cell division cycle proteins that are also involved in this process, such as CDC20 and CDH1 is totally unknown. Here we examine the role of a putative CDC20/CDH1 in the rodent malaria Plasmodium berghei (Pb) using reverse genetics. Phylogenetic analysis identified a single putative Plasmodium CDC20/CDH1 homologue (termed CDC20 for simplicity) suggesting that Plasmodium APC/C has only one regulator. In our genetic approach to delete the endogenous cdc20 gene of P. berghei, we demonstrate that PbCDC20 plays a vital role in male gametogenesis, but is not essential for mitosis in the asexual blood stage. Furthermore, qRT-PCR analysis in parasite lines with deletions of two kinase genes involved in male sexual development (map2 and cdpk4), showed a significant increase in cdc20 transcription in activated gametocytes. DNA replication and ultra structural analyses of cdc20 and map2 mutants showed similar blockage of nuclear division at the nuclear spindle/kinetochore stage. CDC20 was phosphorylated in asexual and sexual stages, but the level of modification was higher in activated gametocytes and ookinetes. Changes in global protein phosphorylation patterns in the Δcdc20 mutant parasites were largely different from those observed in the Δmap2 mutant. This suggests that CDC20 and MAP2 are both likely to play independent but vital roles in male gametogenesis

Dynamic effect of last glacial maximum ice sheet topography on the east asian summer monsoon

The effect of ice sheet topography on the East Asian summer monsoon (EASM) during the Last Glacial Maximum is studied using CCSM3 in a hierarchy of model configurations. It is found that receding ice sheets result in a weakened EASM, with the reduced ice sheet thickness playing a major role. The lower ice sheet topography weakens the EASM through shifting the position of the midlatitude jet, and through altering Northern Hemisphere stationary waves. In the jet shifting mechanism, the lowering of ice sheets shifts the westerly jet northward and decreases the westerly jet over the subtropics in summer, which reduces the advection of dry enthalpy and in turn precipitation over the EASM region. In the stationary wave mechanism, the lowering of ice sheets induces an anomalous stationary wave train along the westerly waveguide that propagates into the EASM region, generating an equivalent-barotropic low response; this leads to reduced lower-tropospheric southerlies, which in turn reduces the dry enthalpy advection into East Asia, and hence the EASM precipitation

Development and evaluation of frameworks for real-time bus passenger occupancy prediction

One critical aspect of bus service quality that influences riders’ attitudes is the availability of seating and/or space to board vehicles. Unfortunately, little attention has been given to short-term passenger occupancy predictions on individual buses. This research examines the use of conventional linear regression models and a machine-learning (random forest) model to predict passenger occupancies on individual buses when they arrive at future stops using data available in real-time from bus operations (e.g., Automatic Passenger Counter (APC) systems) and weather information. Overall, the linear model (LM) and the random forest (RF) model are found to provide close estimates. Three sets of models are developed in this work to model the current and future stop pairs: a next-stop-based model that only models the occupancy at the right next stop and two models that predict the occupancy at any future stop along the bus route (called OD-pair based models). The OD-pair based models are found to predict passenger occupancies more accurately at downstream stops, regardless of whether the LM or RF is used. Examination of the transferability reveals that models can provide reliable estimates of future data when trained with historical information if demand patterns are fairly stable. These models and insights can be used by transit agencies in improving the quality and breadth of information provided to transit system users and even be integrated directly into real-time end-user feeds

Person-Based Optimization of Signal Timing: Accounting for Flexible Cycle Lengths and Uncertain Transit Vehicle Arrival Times

Recent studies have proposed the use of person-based frameworks for the optimization of traffic signal timing to minimize the total passenger delay experienced by passenger cars and buses at signalized intersections. The efficiency and applicability of existing efforts, however, have been limited by an assumption of fixed cycle lengths and deterministic bus arrival times. An existing algorithm for person-based optimization of signal timing for isolated intersections was extended to accommodate flexible cycle lengths and uncertain bus arrival times. To accommodate flexible cycle lengths, the mathematical program was redefined to minimize total passenger delay within a fixed planning horizon that allowed cycle lengths to vary within a feasible range. Two strategies were proposed to accommodate uncertain bus arrival times: (a) a robust optimization approach that conservatively minimized delays experienced in a worst-case scenario and (b) a blended strategy that combined deterministic optimization and rule-based green extensions. The proposed strategies were tested with numerical simulations of an intersection in State College, Pennsylvania. Results revealed that the flexible cycle length algorithm could significantly reduce bus passenger delay and total passenger delay, with negligible increases in car passenger delay. These results were robust to changes in both bus and car flows. For bus arrival times, the robust optimization strategy seemed to be more effective at low levels of uncertainty and the blended strategy at higher levels of uncertainty. The anticipated benefits decreased with increases in the intersection flow ratio because of the lower flexibility of signal timing at the intersection

Using survival models to estimate bus travel times and associated uncertainties

Transit agencies often provide travelers with point estimates of bus travel times to downstream stops to improve the perceived reliability of bus transit systems. Prediction models that can estimate both point estimates and the level of uncertainty associated with these estimates (e.g., travel time variance) might help to further improve reliability by tempering user expectations. In this paper, accelerated failure time survival models are proposed to provide such simultaneous predictions. Data from a headway-based bus route serving the Pennsylvania State University-University Park campus were used to estimate bus travel times using the proposed survival model and traditional linear regression frameworks for comparison. Overall, the accuracy of point estimates from the two approaches, measured using the root-mean-squared errors (RMSEs) and mean absolute errors (MAEs), was similar. This suggests that both methods predict travel times equally well. However, the survival models were found to more accurately describe the uncertainty associated with the predictions. Furthermore, survival model estimates were found to have smaller uncertainties on average, especially when predicted travel times were small. Tests for transferability over time suggested that the models did not over-fit the dataset and validated the predictive ability of models established with historical data. Overall, the survival model approach appears to be a promising method to predict both expected bus travel times and the uncertainty associated with these travel times

Super-hydrophobic magnesium oxychloride cement (MOC): from structural control to self-cleaning property evaluation

In this paper, magnesium oxychloride cement (MOC), which has a needle-like structure, is upgraded with super-hydrophobic surface using a facile method involving immersion in a FAS-ethanol solution. The influence of the molar ratios of the raw materials on the super-hydrophobic property was investigated. The phase compositions, microstructure, compressive strength, water resistance and wetting behaviour are studied in detail by X-Ray diffraction, scanning electron microscopy, a water contact angle measurement instrument, and mechanical testing. The water contact angle of as-prepared MOC reaches 152 ± 1° for the optimal mix design. The variation of the water contact angle of different mixes can be explained by the Cassie–Baxter model. The experiments using rolling off dust on the super hydrophobic surface present excellent self-cleaning ability. Moreover, proposed super hydrophobic surface exhibited excellent UV-durability, indicating a promising potential for the outdoor application