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Short and long night-time sleep and daytime napping in young and middle-aged populations were associated with increased mortality, but it is unclear in very older people. The aim of this prospective study was to assess the associations in people aged >70 years. We examined the data of British Regional Heart Study, which included 1722 men aged 71–92 years and had night-time sleep duration and daytime napping measured at baseline and were followed up for nine years. There were 597 deaths. Compared to night-time sleep at 7-<8 h, age-adjusted hazard ratio of all-cause mortality in participants sleeping <6 h was 1.04 (95% CI 0.80-1.35), 1.07 (0.85-1.34) in 6-<7 h, 1.04 (0.83-1.30) in 8-<9 h and 0.93 (0.65-1.33) in ≥9 h. Further adjustments for other co-variables still showed no association, and neither the association with cardiovascular mortality nor non-cardiovascular mortality. Daytime napping, however, was associated with mortality. After adjustment for age, smoking, physical activity, obesity, cardiovascular diseases, diabetes, frailty, general health, anti-hypertensive medication and C-reactive protein level, hazard ratio of all-cause mortality in participants with daytime napping >1-h versus no napping was 1.62 (1.18-2.22) and hazard ratio of non-cardiovascular mortality was 1.77 (1.22-2.57). The fully adjusted hazard ratio of cardiovascular mortality was not significantly increased 1.26 (0.69-2.28), although age-adjusted hazard ratio was significant 1.94 (1.20-3.16). In the elderly men, daytime napping was independently associated with increased all-cause and non-cardiovascular mortality, while its association with cardiovascular mortality could be explained by cardiovascular risk factors and co-morbidities. Night-time sleep duration was not associated with mortality risk. [ABSTRACT FROM AUTHOR]

This paper presents a continuous time surplus maximization bi-objective user equilibrium (C-TSmaxBUE) model, in which the users' variability toward the time and toll trade-off in a tolled road network is explicitly considered. Different users are assigned with different ratios of the time saved per unit of money (RTSMs), the values of which represent the different expectations to trade more money for less travel time. Infinite indifference curves are then generated by considering continuously distributed RTSMs in the population. Accordingly, we extend the definition of the path-based time surplus for the C-TSmaxBUE model, which can be formulated as a Beckmann-type mathematical program operating in the space of RTSM boundaries. To solve the problem, we develop a path-based single-boundary adjustment (SBA) algorithm that fits the non-additive structure of the model. The algorithm involves a Newton-type flow equilibration procedure that simultaneously adjusts RTSM boundaries and path flows, as well as a column generation scheme that firstly enumerates all efficient paths and then identifies the paths that have the potential to reduce generalized travel times. Numerical results on a small network show the feature of the equilibrium flow pattern, whereas instances of practical size confirm that SBA is an efficient tool to obtain high-quality equilibrium solutions. Compared to the original TSmaxBUE problem, we found that a larger set of efficient paths will be used when the users' heterogeneity is considered. • Propose a C-TSmaxBUE model. • Prove C-TSmaxBUE is a generalization of UE, GUE, and TSmaxBUE. • Develop an efficient path-based algorithm for solving the C-TSmaxBUE model. • Demonstrate features of C-TSmaxBUE and applicability of the algorithm. [ABSTRACT FROM AUTHOR]

• A model to assess the vulnerability envelope under multiple-link disruptions. • Considering both topology and serviceability features in vulnerability assessment. • A constraint-based method to rank the N most important multiple-link combinations. • Investigating network performance buffer between different cases of a disruption. • Flexibility to set different modeling criteria and incorporate additional knowledge. This study proposes an optimization-based approach to rank the importance of link combinations and analyze network vulnerability in extreme and near-extreme cases of disruption under the simultaneous disruption of multiple links. A vulnerability envelope concept is used, which considers the worst and best network performance under multiple-link disruptions. This study goes a step further than previous studies, which have focused on the extreme cases that form the boundary of a vulnerability envelope, to investigate the near-extreme cases inside an envelope and the network performance buffers (i.e., the differences in network performance) between different cases. A flexible framework based on combinatorial optimization modeling is used to determine the most important link combinations and the lower and upper bounds of network performance under their disruptions, which form the vulnerability envelope. A constraint-based method is developed to iteratively identify sub-important link combinations that lead to the formation of buffers of the lower and upper bounds of the vulnerability envelope. Numerical experiments are conducted to illustrate the properties and applicability of the proposed method. The results demonstrate that the consideration of near-extreme cases yields additional valuable information that is not generated by the traditional vulnerability analysis, which is focused on extreme cases. Ranking of the most and sub-most important link combinations enables the identification of non-unique worst/best cases, thereby revealing alternative link combinations to better inform decision-making. Consideration of the network performances in extreme and near-extreme cases affords a less conservative vulnerability assessment and reveals the potential cost of considering only extreme cases in decision-making processes. [ABSTRACT FROM AUTHOR]

Background: Increased fish consumption reduces the risk of dementia. However, it is unknown whether fish consumption reduced all-cause mortality in people with dementia. The purpose of the study is to investigate the association of fish consumption with all-cause mortality in older people with dementia versus those without dementia. Methods: Using a standard method of the Geriatric Mental State, we interviewed 4165 participants aged ≥ 60 years who were randomly recruited from five provinces in China during 2007–2009 to collect the baseline data of socio-demography, disease risk factors, histories of disease, and details of dietary intakes, and diagnosed dementia (n = 406). They were followed up for vital status until 2012. Results: The cohort follow-up documented 329 deaths; 61 were in participants with dementia (55.3 per 1000 person-years) and 224 were those without dementia (22.3). In all participants, the risk of all-cause mortality was reduced with fish intake at " ≥ twice a week" (multivariate-adjusted hazard ratio 0.58, 95% CI 0.34–0.96) and at "once a week or less" (0.79, 0.53–1.18) compared to "never eat" over the past two years. In participants without baseline dementia, the corresponding HRs for all-cause mortality were 0.57 (0.33–0.98) and 0.85 (0.55–1.31), while in participants with dementia were 1.36 (0.28–6.60) and 1.05 (0.30–3.66), respectively. Conclusion: This study reveals that consumption of fish in older age reduced all-cause mortality in older people without dementia, but not in people with dementia. Fish intake should be increased in older people in general, prior to the development of dementia in the hope of preventing dementia and prolonging life. [ABSTRACT FROM AUTHOR]

Background: Obesity classifications vary globally and the impact of older age adiposity on incident diabetes has not been wellstudied. Methods: We examined a random sample of 2,809 participants aged =60 years in China, who were free of diabetes at baseline and were followed up for up to 10 years to document diabetes (n=178). The incidence of diabetes was assessed in relation to different cut-off points of body mass index (BMI) and waist circumference (WC) in multiple adjusted Cox regression models. Results: The diabetic risk in the cohort increased linearly with the continuous and quartile variables of BMI and WC. The BMIWorld Health Organization (WHO) and BMI-China criteria analysis did not show such a linear relationship, however, the BMIAsian/Hong Kong criteria did; adjusted hazards ratio (HR) was 0.42 (95% confidence interval [CI], 0.20 to 0.90) in BMI <20 kg/m2, 1.46 (95% CI, 0.99 to 2.14) in 23-=26 kg/m2, and 1.63 (95% CI, 1.09 to 2.45) in =26 kg/m2. The WC-China criteria revealed a slightly better prediction of diabetes (adjusted HRs were 1.79 [95% CI, 1.21 to 2.66] and 1.87 [95% CI, 1.22 to 2.88] in central obese action levels 1 and 2) than the WC-WHO. The combination of the BMI-Asian/Hong Kong with WC-China demonstrated the strongest prediction. There were no gender differences in the impact of adiposity on diabetes. Conclusion: In older Chinese, BMI-Asian/Hong Kong criteria is a better predictor of diabetes than other BMI criterion. Its combination with WC-China improved the prediction of adiposity to diabetes, which would help manage bodyweight in older age to reduce the risk of diabetes. [ABSTRACT FROM AUTHOR]

• Develop an eUnit-based p-hub location problem for optimal urban air mobility (UAM) network design. • Linearize the eUnit choice probability to create a mixed integer linear program (MILP). • Provide three design schemes, including revenue, profit, and profit with pricing maximization. • Illustrate the impacts of flight ranges and eUnit constraints on the optimal solution. Urban air mobility (UAM) technology has the potential to revolutionize daily commutes. By leveraging low-altitude airspace, electric vertical take-off and landing (eVTOL) vehicles can provide faster urban transportation between skyports than ordinary surface transport modes. This paper develops a multiple allocation incomplete p-hub location problem for determining the optimal UAM network design under the flying range constraint of eVTOL vehicles. The traditional methods used mathematical programming (MP) to analyse either 1) a deterministic allocation with a flight range limitation or 2) a stochastic allocation using a choice model without a range constraint. We combine the two methods by adopting a recently proposed eUnit choice model in a mixed integer linear programming (MILP) formulation to consider the interaction between travel choice behavior and travel cost incurred by skyport locations and their linkages in the random utility maximization framework. Three schemes of the multiple allocation incomplete p-hub location problem are provided, including revenue maximization, profit maximization, and profit maximization with pricing strategy. Numerical examples are provided to investigate the influence of flight range constraint and the eUnit bound on the solutions, which show significant influence on the UAM network topology and demand allocation. [ABSTRACT FROM AUTHOR]

Cutaneous meningiomas are rare tumors of ectopic meningeal tissue in the dermis or subcutis and are most commonly located on the scalp and around the eyes, ears, nose, and mouth. We present a case of cutaneous meningioma with an unusual presentation on the upper arm, but with positivity for epithelial membrane antigen to support the diagnosis of meningioma. Lack of pancytokeratin AE1/3 and p63 allowed us to rule out a keratinocytic tumor, such as squamous cell carcinoma and basal cell carcinoma. The absence of smooth muscle actin, FXIIIa, CD163, and CD34 allowed us to rule out a leiomyoma, dermatofibroma, and vascular neoplasm respectively. Weak staining of S100 allowed us to rule out a perineuroma. The purpose of this case report is to increase awareness of a rare presentation of cutaneous meningioma not located within the scalp/face region. This may broaden its inclusion within differential diagnoses of neoplasms of uncertain behavior and encourage correct clinical diagnosis, thus improving treatment outcomes. [ABSTRACT FROM AUTHOR]

• Develop an equilibrating mechanism for the hyperpath-based transit equilibrium assignment problem (TEAP). • Propose a hyperpath-based gradient projection solution algorithm for solving the TEAP. • Develop the analytical sensitivity analysis approach for the hyperpath-based TEAP. • Develop the sensitivity-based uncertainty analysis approach for estimating the output variations with different sources of uncertainties. • Avoid burdensome computations of simulating the uncertainties from model input/ parameter in uncertainty analysis. Systematic uncertainty analysis can be used to quantitatively evaluate variation in model outputs and identify the critical sources of uncertainty to improve the reliability and stability of a system. To analyze the effects of uncertainties in transit networks that may be caused by probabilistic travel demand, congestion, or vehicle frequencies, this study develops a sensitivity-based uncertainty analysis approach as a post-analysis tool for equilibrium transit systems. The congestion effect is considered in the waiting time and in-vehicle travel time of a passengers' route-choice model. The hyperpath concept is used to manage passengers' riding strategies due to the common-line problem at transit stops. A hyperpath-based gradient projection (GP) solution algorithm is developed for the solution of the variational inequality formulation of the transit equilibrium assignment problem (TEAP). A restricted sensitivity analysis approach originally developed for road networks is re-developed for the TEAP in transit networks. An analytical sensitivity-based approach is derived to conduct uncertainty analysis for the TEAP, which enables the simultaneous propagation of uncertainties from different input sources to the model outputs. Numerical examples are provided for the following purposes. (1) To demonstrate three applications of the sensitivity analysis of the TEAP, namely the perturbed solution estimation problem, the critical parameter identification problem, and the paradox analysis problem. (2) To illustrate the use of uncertainty analysis of the TEAP, such as estimating the variance and confidence level of model outputs with respect to various model inputs/parameters as random variables, and ranking the importance of arcs using sensitivity-based uncertainty analysis. (3) To demonstrate the applicability of the proposed approach to real transit networks. The findings demonstrate not only the importance of the analytical sensitivity analysis development for the TEAP, but also for the practical applications of sensitivity and uncertainty analyses. [ABSTRACT FROM AUTHOR]

In this paper, we study the elastic-demand bi-criteria traffic assignment problem under the continuously distributed value of time, referred to as the ED-CBiTA problem for simplicity. Specifically, the origin and destination (O–D) demand of ED-CBiTA is endogenously guided by the expected generalized travel time aggregated from all efficient paths, and the consideration of user heterogeneity regarding the tradeoff between time and toll is accomplished by incorporating a continuously distributed value of time. We present a variable demand formulation and an equivalent excess demand reformulation for the ED-CBiTA problem. Based on two types of Gauss–Seidel decomposition schemes, we propose a novel two-stage gradient projection (TSGP) algorithm, which implicitly delivers visual interpretations to depict the interplay of supply and demand interactions. The first stage, called demand equilibration, aims to adjust O–D demand and all efficient path flows "vertically upward or downward" based on the level of network congestion. The second stage, namely boundary equilibration, is to perform the boundary movements and adjust adjacent efficient flows "horizontally forward or backward", to achieve exact positions along the Pareto frontier. Numerical results on a small network show TSGP's features and confirm that TSGP significantly outperforms two link-based benchmark algorithms. For instances of practical network size, TSGP consistently promises to obtain high-quality solutions with a rather smaller CPU time. • Propose a novel two-stage gradient projection algorithm for the ED-CBiTA problem. • The first stage of TSGP adjusts path flows vertically upwards or downwards. • The second stage of TSGP adjusts path flows horizontally forwards or backwards. • TSGP outperforms two existing benchmark algorithms in large-scale networks. [ABSTRACT FROM AUTHOR]

• Two vulnerability indicators based on the capacity of a cut are proposed. • Our indicator not only considers the capacity of the cut but also accounts for the demand passing through the cut. • We provide the GomoryHu tree method to generate all the minimum cuts in the network and evaluate them with two indicators. • We apply the proposed methods to the road network of central region in Japan and specify the high-vulnerable cuts. Vulnerability analysis of transportation networks has rapidly become important in recent decades given the increasing numbers of transportation disasters. In this paper, we describe a method for calculating the minimum cuts between all pairs of nodes in a transportation network and develop two indices for analyzing vulnerability; these are derived from topology-based vulnerability/demand-accountable analyses and enable evaluation of cuts without assuming route-choice behaviors. We show that such analyses can be performed using a Gomory–Hu tree to reduce the computational load. This method is efficient, requiring only N − 1 calculations of the maximum flow problem even though the number of node pairs is N 2, where N is the number of nodes. In addition, we show that the total demand passing each minimum cut, which is necessary for demand-accountable analysis, can be obtained by network loading onto the tree using the Gomory–Hu tree features. We apply the proposed method to the central region of Japan to illustrate the applicability of the two indices for identifying vulnerable links in the road network. [ABSTRACT FROM AUTHOR]

We establish a bus market evolution model to explore the implications of subsidy policies in Hong Kong (HK) for the transition to a zero-emission bus (ZEB) fleet using an unsubsidized case as the benchmark scenario. Using the system dynamics (SD) approach, we capture the intricate feedback mechanisms among market share dynamics, cost considerations, consumer preferences, and competitive interactions within the bus market. These interactions are encapsulated within three pivotal feedback loops: scale economy, public acceptance, and bus market competition. We systematically integrate the HK-specific features of hydrogen buses (H-buses), battery electric buses (E-buses), and diesel buses (D-buses) from technical and economic perspectives and compare them to the benchmark scenario. The results show that only 66% of the bus fleet would be ZEBs by 2050, and this underscores the necessity for governmental financial incentives to expedite the transition to a fully zero-emission fleet. Furthermore, we evaluate various ZEB subsidy policy alternatives in terms of timing, intensity, and recipients. The analysis suggests that policies offering long-term and low-intensity subsidies are most beneficial, taking into account the cumulative effects and inherent delays associated with policy implementation. Importantly, the study highlights the critical balance that policymakers must achieve between short- and long-term objectives, especially given the counter-intuitive outcomes observed in the early stages of policies favoring H-buses exclusively. This study provides valuable policy insights for policymakers and upstream energy companies in HK, with potential applicability in wider real-world contexts. • Zero-emission bus alternatives are analyzed from technical and economic aspects. • Various feedback loops are considered for dynamic market analysis. • A counter-intuitive effect is observed in the hydrogen bus subsidy scenario. • A long-term subsidy with low intensity is recommended. • Tradeoff between short- and long-term policy effects is identified and discussed. [ABSTRACT FROM AUTHOR]

Freight transportation network is an essential backbone for supporting the industrial activities and economic developments of the nation and global trade. In this paper, we extend the network-based measures recently developed by Xu et al. (2018) for assessing freight transportation network redundancy – an important component in making freight transportation networks more robust and resilient against disruptions. Redundancy of freight transportation networks is characterized by two network-based measures. The route diversity measure evaluates the existence of multiple efficient routes available for freight users or the degree of connections between a specific origin-destination (O-D) pair, while the network spare capacity measure quantifies the network-wide spare capacity with an explicit consideration of congestion effect and modal split. These two measures can complement each other by providing a two-dimensional characterization of freight transportation network redundancy that can be used to provide information to freight users as well as to assist network planners in making future infrastructure investment decisions to enhance the redundancy of freight transportation networks. A case study of the Utah freight network for transporting coal is provided to demonstrate the features of the two network-based redundancy measures as well as the applicability of assessing the redundancy of freight transportation networks. The analyses reveal that the two measures provide useful results from two perspectives that may be of benefit to freight carriers and transportation system planners. [ABSTRACT FROM AUTHOR]

Cycling is gaining popularity both as a mode of travel in urban communities and as an alternative mode to private motorized vehicles due to its wide range of benefits (health, environmental, and economical). However, this change in modal share is not reflected in current transportation planning and travel demand forecasting modeling processes. The existing practices to model bicycle trips in a network are not sophisticated enough to describe the full cyclist experience in route decision-making. This is evident in the existing practices' methodology: the all-or-nothing assignment uses single attributes such as distance, safety, or a composite measure of safety multiplied by distance. The purpose of this article is to develop a multi-class and multi-criteria bicycle traffic assignment model that not only accounts for multiple user classes by acknowledging that there are different types of cyclists with varying levels of biking experience, but also for relevant factors that may affect each user classes behavior in route choice decisions. The multi-class, multi-criteria bicycle traffic assignment model is developed in a two-stage process. The first stage examines key criteria to generate the set of non-dominated (or efficient) routes for each user class, and the second stage determines the flow allocation to efficient routes by user class. Numerical experiments are then conducted to demonstrate the two-stage approach for the multi-class, multi-criteria bicycle traffic assignment model. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak has necessitated a critical review of urban transportation and its role in society against the backdrop of an exogenous shock. This article extends the transportation literature regarding community responses to the COVID-19 pandemic and what lessons can be obtained from the case of Hong Kong in 2020. Individual behavior and collective responsibility are considered crucial to ensure both personal and community wellbeing in a pandemic context. Trends in government policies, the number of infectious cases, and community mobility are examined using multiple data sources. The mobility changes that occurred during the state of emergency are revealed by a time-series analysis of variables that measure both the epidemiological severity level and government stringency. The results demonstrate a high response capability of the local government, inhabitants, and communities. Communities in Hong Kong are found to have reacted faster than the implementation of health interventions, whereas the government policies effectively reduced the number of infection cases. The ways in which community action are vital to empower flexible and adaptive community responses are also explored. The results indicate that voluntary community involvement constitutes a necessary condition to help inform and reshape future transport policy and response strategies to mitigate the pandemic. • We conduct a case study in Hong Kong to assess the effectiveness in containing COVID-19 epidemic in the first three waves. • We model the relationship between stringency of government responses, mobility patterns, and spread of COVID-19 cases. • Multiple data sources including epidemiological record, performance indictors of government stringency and community mobility are used. • Results from time-series analysis demonstrate a high response capability of local government, inhabitants and communities. • Community involvement likely constitutes the necessary condition to help inform and reshape future transport policy. [ABSTRACT FROM AUTHOR]

This study provides a gradient projection (GP) algorithm to solve the combined modal split and traffic assignment (CMSTA) problem. The nested logit (NL) model is used to consider the mode correlation under the user equilibrium (UE) route choice condition. Specifically, a two-phase GP algorithm is developed to handle the hierarchical structure of the NL model in the CMSTA problem. The Seoul transportation network in Korea is adopted to demonstrate an applicability in a large-scale multimodal transportation network. The results show that the proposed GP solution algorithm outperforms the method of the successive averages (MSA) algorithm and the classical Evan's algorithm. [ABSTRACT FROM AUTHOR]

• Develop a maximum capture problem to determine park-and-ride (P&R) facility locations. • Propose a paired combinatorial weibit (PCW) model to relax the IID assumption. • Linearize the PCW probability using its two-level tree structure to create a MILP. • Demonstrate the impacts of route overlapping and heterogeneous variance on the optimal solution. Park-and-ride (P&R) facilities are key components in encouraging people to use the transit system by allowing them to leave their private vehicles at certain locations. The well-known multinomial logit (MNL) model is often used to develop a random utility maximization–based mathematical programming formulation to determine P&R facility locations. According to the independently and identically distributed (IID) assumption, the MNL model cannot account for the route similarity and user heterogeneity. This study provides a new mixed integer linear programming (MILP) formulation by incorporating a newly developed paired combinatorial weibit (PCW) model to relax the IID assumption for determining the optimal P&R facility location. Specifically, the incorporation of a copula derived from a generalized extreme value (GEV) model addresses the issue of route overlap within the context of the PCW model. In addition, using the Weibull distribution permits the consideration of heterogeneous perception variance. Its two-level tree structure for evaluating the marginal and conditional probabilities allows a linearization scheme to obtain a set of linear constraints. Numerical examples reveal the influence of the IID assumption relaxation on the results. The two probabilities from the tree structure and the binary location variables are combined to present a corresponding PCW model under open/close P&R facility solution. According to the degree of route overlapping and route-specific perception variance, the fare structure, particularly the distance-based scheme, has an impact on the number of P&R users and location at optimum. [ABSTRACT FROM AUTHOR]

• Developed a closed-form paired combinatorial weibit route choice model. • Develop MP formulations for PCW-SUE-FD and PCW-SUE-ED models. • Developed a unified algorithm framework for PCW-SUE-FD and PCW-SUE-ED models. • Demonstrate the features of PCW-SUE-FD and PCW-SUE-ED models. • Show the applicability of the solution algorithm to real-world networks. In the traffic assignment problem for predicting traffic flow patterns in a transportation network, it is important to account for route overlap and non-identical perception variance in route choice analysis. In this study, we establish a novel route choice model, named the paired combinatorial weibit (PCW) model, to capture the route overlap and route-specific perception variance. The PCW model retains a closed-form probability solution, which allows the development of an equivalent mathematical programming (MP) formulation for the PCW-based stochastic user equilibrium (PCW-SUE) model. Specifically, we propose two equivalent MP formulations for modeling the fixed demand (FD) and elastic demand (ED), named PCW-SUE-FD and PCW-SUE-ED, respectively. The PCW-SUE-ED model can address the abovementioned two issues in route choice for the FD scheme, but also can consider the effect level-of-service (LOS) in travel choice for the ED scheme. The equivalency and uniqueness of the PCW-SUE-FD and PCW-SUE-ED models are rigorously proved. In addition, a path-based partial linearization algorithm combined with a self-regulated averaging line search strategy is developed to solve the two SUE models. Numerical results are presented to illustrate the features of the PCW-SUE-FD and PCW-SUE-ED models and applicability of the solution algorithm to a real transportation network. [ABSTRACT FROM AUTHOR]

• Propose a closed-form multinomial weibit model with an oddball alternative (MNW-O). • Model the oddball alternative with unique features to conventional alternatives. • Address heterogeneity of oddball alternative via a flexible variance ratio. • Relax scale heterogeneity among conventional alternatives via non-identical variances. • Demonstrate empirically the superiority of the MNW-O model over the MNL, NL, MNL-O, HEV, and MNW models. Herein, a weibit-based model is proposed as an alternative to Recker's logit choice model with an "oddball" alternative, which explicitly focuses on a single alternative that has unique attributes to other conventional alternatives in the choice set [ Recker, W.W. (1995). Discrete choice with an oddball alternative. Transportation Research, 29B, 201–212 ]. While retaining the closed-form probability expression, the proposed model handles the oddball alternative using a multiplicative random disutility function assuming Weibull distributed random components. The proposed model thus allows disutility-dependent perception variances of both the conventional and oddball alternatives and a flexible variance ratio between them, which are effective to reflect the way how individuals perceive varying magnitudes of travel disutility from different attributes. This gives the proposed model more flexibility to consider various heterogeneity issues, including the heterogeneous perceptions of conventional alternatives, heterogeneous perceptions of unique and common attributes of the oddball alternative, and heterogeneous features of conventional and oddball alternatives. The empirical application of the proposed model is explored to highlight its practical performance. The results demonstrate the superiority of the proposed model in the choice contexts where both oddball effect and heterogeneity issues need to be considered. The proposed model could further provide new behavioral insights into various decision-making scenarios of transportation networks, such as transportation mode choices in the current era of emerging technologies and destination choices in urban agglomerations. [ABSTRACT FROM AUTHOR]

• A multi-modal network equilibrium problem with captive mode travellers. • An equivalent mathematical programming formulation admits unique solution. • A consistent proportional synthetic index for evaluating transport policies. • Mode captivity significantly effects the odds of making different EBL expansion plans. • We implement the model in a real-size multi-modal network. In this paper, we consider captive mode travelers (those who have no other choices but rely on one specific travel mode for daily commuting trips) in a multi-modal network equilibrium (MMNE) problem. Specifically, the dogit model is adopted to account for captive mode travelers in the modal split problem, and the path-size logit (PSL) model is used to capture route overlapping effects in the traffic assignment problem. The dogit-PSL MMNE model is formulated as an equivalent entropy-based mathematical programming (MP) problem, which admits solution existence and uniqueness. Three numerical examples are provided. The first example examines the effects of mode captivity and route overlapping on network performances and observes that accounting for captive mode travelers would produce different equilibrium states and hence the network performance indicators. The second example applies the dogit-PSL MMNE model for evaluating the exclusive bus lane (EBL) expansion plans, in which a consistent synthetic proportional index is proposed. Numerical results show that considering mode captivity may produce substantial impacts on the odds (up to 50 percent of odds in the given scenarios) of making different EBL line expansion decisions. The third example implements the dogit-PSL MMNE model in the Seoul network to show the applicability of the dogit-PSL MMNE model in a real-size multi-modal system. [ABSTRACT FROM AUTHOR]

Due to the increasing energy prices and environmental issues, energy conservation for metro trains has become an important research topic in recent years. The static timetable optimization approach can reduce the trains' energy consumption to a certain degree by scheduling multiple trains' movements. However, this optimization approach is prone to fail when one or some trains are delayed at stations. This paper develops an energy-efficient rescheduling approach under delay perturbations for metro trains, which aims to minimize the net energy consumption under the premise of reducing or eliminating the delay altogether. Firstly, we provide an illustrative example to describe the rescheduling problem. Furthermore, we formulate an integer programming model and design an allocation algorithm to obtain the optimal schedule. Finally, a numerical example based on the Beijing Metro Yizhuang Line shows that the developed approach can reduce the net energy consumption by 8.19% in comparison with the traditional rescheduling approach. [ABSTRACT FROM AUTHOR]

• Propose a spatial–temporal graph conventional network to predict charging demand; • Train the model with a large private electric vehicle trajectory dataset; • Our model outperforms recently popular spatio-temporal prediction methods; • Incorporating the demand and geographic graphs helps improve model performance. Short-term Electric Vehicle (EV) charging demand prediction is an essential task in the fields of smart grid and intelligent transportation systems, as understanding the spatiotemporal distribution of charging demand over the next few hours could help operators of charging stations and the grid to take measures (e.g., dynamic pricing) in response to varying charging demand. This study proposed a heterogeneous spatial–temporal graph convolutional network to predict the EV charging demand at different spatial and temporal resolutions. Specifically, we first learned the spatial correlations between charging regions by constructing heterogeneous graphs, i.e., a geographic graph and a demand graph. Then, we used graph convolutional layers and gated recurrent units to extract spatio-temporal features in the observations. Further, we designed a region-specific prediction module that grouped regions based on graph embedding and point of interest (POI) data for prediction. We used a large real-world GPS dataset which contained over 76,000 private EVs in Beijing for model training and validation. The results showed that, compared with recently popular spatio-temporal prediction methods, the proposed model had superior prediction accuracy and steady performance at different scales of regions. In addition, we conducted ablation studies and hyperparameter sensitivity tests. The results suggested that incorporating the demand graph and geographic graph could help improve model performance. [ABSTRACT FROM AUTHOR]

Travel time is important for both users and planners. Travel time variability widely exists in transportation systems. Positive skew with a long/fat upper tail is a basic characteristic of travel time variability, which poses great challenges to reporting travel time information to the public. There are two main approaches for disseminating travel time variability information: the traditional expected value and the percentile-based value. They are either unreliable when facing highly-skewed travel time variability, difficult to understand, or sensitive to individual risk preference. Motivated by this observation, this paper develops a conservative expected travel time (CET) approach to enhance the information reliability and maintain simplicity, which is formulated as a two-stage problem: (1) reshaping the travel time distribution, and (2) minimizing the expected value of the reshaped distribution. The features of the CET and the relationship between the CET and traditional measures are explored in both theoretical and numerical dimensions. [ABSTRACT FROM AUTHOR]

This paper aims to develop an optimization approach for deriving the upper and lower bounds of transportation network vulnerability under simultaneous disruptions of multiple links without the need to evaluate all possible combinations as in the enumerative approach. Mathematically, we formulate the upper and lower bounds of network vulnerability as a binary integer bi-level program (BLP). The upper-level subprogram maximizes or minimizes the remaining network throughput under a given number of disrupted links, which corresponds to the upper and lower vulnerability bounds. The lower-level subprogram checks the connectivity of each origin-destination (O-D) pair under a network disruption scenario without path enumeration. Two alternative modeling approaches are provided for the lower-level subprogram: the virtual link capacity-based maximum flow problem formulation and the virtual link cost-based shortest path problem formulation. Computationally, the BLP model can be equivalently reformulated as a single-level mixed integer linear program by making use of the optimality conditions of the lower-level subprograms and linearization techniques for the complementarity conditions and bilinear terms. Numerical examples are also provided to systematically demonstrate the validity, capability, and flexibility of the proposed optimization model. The vulnerability envelope constructed by the upper and lower bounds is able to effectively consider all possible combinations without the need to perform a full network scan, thus avoiding the combinatorial complexity of enumerating multi-disruption scenarios. Using the vulnerability envelope as a network performance assessment tool, planners and managers can more cost-effectively plan for system protection against disruptions, and prioritize system improvements to minimize disruption risks with limited resources. [ABSTRACT FROM AUTHOR]

Redundancy is vital for transportation networks to provide utility to users during disastrous events. In this paper, we develop two network-based measures for systematically characterizing the redundancy of transportation networks: travel alternative diversity and network spare capacity. Specifically, the travel alternative diversity dimension is to evaluate the existence of multiple modes and effective routes available for travelers or the number of effective connections between a specific origin-destination pair. The network spare capacity dimension is to quantify the network-wide residual capacity with an explicit consideration of travelers’ mode and route choice behaviors as well as congestion effect. They can address two fundamental questions in the pre-disaster transportation system evaluation and planning, i.e., " how many effective redundant alternatives are there for travelers in the normal or disruptive event ?" and " how much redundant capacity does the network have ?" To implement the two measures in practice, computational methods are provided to evaluate the network redundancy. Numerical examples are also presented to demonstrate the features of the two redundancy measures as well as the applicability of the computational methods. The analysis results reveal that the two measures have different characterizations on network redundancy from different perspectives, and they can complement each other by providing meaningful information to both travelers and planners. [ABSTRACT FROM AUTHOR]

Battery swap stations have become an important alternative to general charging posts. Predicting battery swapping demand at the station level would be helpful for real-time operation of stations. This paper first provided insights into battery swapping demand patterns by analyzing a real-world dataset which contained 2,529 battery swapping events collected from 36 battery swap stations in Beijing from 31st July to 20th August 2019. Further, we developed a series of deep learning methods to predict the EV battery swapping demand, particularly considering temporal demand patterns obtained from the dataset. The deep learning models were Long Short-Term Memory, Bidirectional Long Short-Term Memory, Gated Recurrent Units, and Bidirectional Gated Recurrent Units. The results showed that the four deep learning models outperformed typical machine learning methods (e.g., support vector regression). An ablation study indicated that incorporating temporal battery swapping demand patterns into the deep learning methods could greatly improve model performance. [ABSTRACT FROM AUTHOR]

An efficient synthesis of substituted isocanthines has been achieved using an intramolecular Rh(III)-catalyzed C-H functionalization of alkyne-tethered indoles in the presence of catalytic tris(acetonitrile)pentamethylcyclopentadienylrhodium(III) hexafluoroantimonate and stoichiometric copper(II) acetate. This isocanthine synthesis tolerates a variety of electronically diverse 5- or 6-substituted indoles with N-tethered alkyne coupling partners and can also be extended to pyrrole derivatives for the synthesis of annulated 5-azaindoles. [ABSTRACT FROM AUTHOR]

This paper presents two torque estimation methods for vehicle engines: unknown input observer (UIO) and adaptive parameter estimation. We first propose a novel yet simple unknown input observer based on the crankshaft rotation dynamics only. For this purpose, an invariant manifold is derived by defining auxiliary variables in terms of first-order low-pass filters, where only one constant (filter coefficient) needs to be tuned. These filtered variables are used to calculate the estimated torque. Robustness of this UIO against sensor noise is studied and compared to two other estimators. On the other hand, since the engine torque dynamics can be formulated as a parameterized form with unknown time-varying parameters, we further present several adaptive laws for time-varying parameter estimation. The parameter estimation errors are derived to drive these adaptive laws and time-varying adaptive gains are introduced. The two proposed estimators only use the measured air mass flow rate and engine speed, and thus allow for improved computational efficiency. Both estimators are verified via a dynamic engine simulator built in a commercial software GT-Power, and also practically tested via experimental data collected in a dynamometer test-rig. Both simulations and practical tests show very encouraging results with small estimation errors even in the presence of sensor noise. [ABSTRACT FROM PUBLISHER]

A bstract Reduction of vehicle emissions is a major component of sustainable transportation development. The promotion of green transport modes is a worthwhile and sustainable approach to change transport mode shares and to contribute to healthier travel choices. In this paper, we provide an alternate weibit-based model for the combined modal split and traffic assignment (CMSTA) problem that explicitly considers both similarities and heterogeneous perception variances under congestion. Instead of using the widely-adopted Gumbel distribution, both mode and route choice decisions are derived from random utility theory using the Weibull distributed random errors. At the mode choice level, a nested weibit (NW) model is developed to relax the identical perception variance of the logit model. At the route choice level, the recently developed path-size weibit (PSW) is adopted to handle both route overlapping and route-specific perception variance. Further, an equivalent mathematical programming (MP) formulation is developed for this NW-PSW model as a CMSTA problem under congested networks. Some properties of the proposed models are also rigorously proved. Using this alternate weibit-based NW-PSW model, different go-green strategies are quantitatively evaluated to examine (a) the behavioral modeling of travelers’ mode shift between the private motorized mode and go-green modes and (b) travelers’ route choice with consideration of both non-identical perception variance and route overlapping. The results reveal that mode shares and route choices from the NW-PSW model can better reflect the changes in model parameters and in network characteristics than the traditional logit and extended logit models. [ABSTRACT FROM AUTHOR]

Equity and justice have long been central in transport policy. The emphasise on the importance of individuals' access to social and economic opportunities stresses the link between accessibility and concepts like agency and freedom of choice. It calls for a nuanced and comprehensive understanding of accessibility that considers specific contextual factors that limit people's transport choices, such as the design of public transport networks and services, government interventions, and competition between public transport companies. To promote people's potential mobility and freedom of choice, it is crucial to initially assess the spatial distribution of transport options. In this article, we synthesise the findings from existing scholarship on transport choice, reinforcing the need for (re)considering choice in transport equity. We propose a metric to evaluate the availability of transport choices by considering the trade-off between time and money. By analysing household survey responses on transport choices and transit service datasets, we investigate the spatial disparities in the availability of transport choices within a multimodal public transportation system in Hong Kong and explore how adjusting unappealing choices can potentially enhance transport equity. • Discuss transport equity issues with a relative understudied angle – transport choice. • Propose a metric to assess spatial equity of transport choices. • A realistic case study based on a multi-modal public transport network in Hong Kong. • Implications to transit choice provision and fare adjustment tactics. [ABSTRACT FROM AUTHOR]

It remains unclear whether a total exposure to air pollution (AP) is associated with an increased risk of dementia. Little is known on the association in low- and middle-income countries. Two cohort studies in China (in Anhui cohort 1402 older adults aged ≥ 60 followed up for 10 years; in Zhejiang cohort 6115 older adults followed up for 5 years) were conducted to examine particulate matter - PM 2.5 associated with all dementia and air quality index (AQI) with Alzheimer's disease, respectively. A systematic literature review and meta-analysis was performed following worldwide literature searched until May 20, 2020 to identify 15 population-based cohort studies examining the association of AP with dementia (or any specific type of dementia) through PubMed, MEDLINE, PsycINFO, SocINDEX, CINHAL, and CNKI. The cohort studies in China showed a significantly increased relative risk (RR) of dementia in relation to AP exposure; in Anhui cohort the adjusted RR was 2.14 (95% CI 1.00–4.56) in people with PM 2.5 exposure at ≥ 64.5 μg/m3 versus <63.5 μg/m3 and in Zhejiang cohort the adjusted RR was 2.28 (1.07–4.87) in AQI>90 versus ≤ 80. The systematic review revealed that all 15 studies were undertaken in high income countries/regions, with inconsistent findings. While they had reasonably good overall quality of studies, seven studies did not adjust smoking in analysis and 13 did not account for depression. Pooling all eligible data demonstrated that dementia risk increased with the total AP exposure (1.13, 1.08–1.19). Data analysis of air pollutants showed that the RR significantly increased with PM 2.5 (1.06, 1.03–1.10 in 2nd tertile exposure; 1.13, 1.07–1.19 in 3rd tertile versus 1st tertile), PM 10 (1.05, 0.86–1.29; 1.62, 0.60–4.36), carbon monoxide (1.69, 0.72–3.93; 1.52, 1.35–1.71), nitrogen dioxide (1.06, 1.03–1.09; 1.18, 1.10–1.28) and nitrogen oxides (1.09, 1.04–1.15; 1.26, 1.13–1.41), but not ozone. Controlling air pollution and targeting on specific pollutants would reduce dementia globally. • Two cohort studies were conducted in China to investigate the high PM 2.5 and poor air quality index associated with dementia risk, revealing significant association. • Pooling data from all eligible studies published and new cohorts in China showed dementia risk increased with overall AP exposure, PM 2.5 , PM 10 , CO, NO 2 , NOx, but not O 3. • Controlling air pollution and targeting on specific pollutants would reduce dementia globally. [ABSTRACT FROM AUTHOR]

• Develop a mode choice model for customized bus (CB) services with loyalty subscription scheme. • Adopt a nested webit model for the choice behavior of passengers not loyal to CB services. • Develop an equivalent mathematical programming (MP) model for the mode choice problem with CB services. • Provide solution existence and uniqueness. • Conduct a case study to illustrate properties and applicability of the proposed model in a real-world setting. This study models the equilibrium mode choice of emerging customized bus (CB) services in a multi-modal urban transportation network. Two unique characteristics of CB services are considered: (1) the reservation of a seat for every CB passenger, and (2) the loyalty scheme for long-term CB subscribers. The first characteristic eliminates in-vehicle crowding but imposes tight constraints on CB demand, while the second characteristic encourages passenger loyalty to CB services. An advanced equilibrium mode choice model based on the dogit model, a well-established travel choice model that is widely used to consider captive mode choices, is proposed to account for the choice behavior of passengers loyal to CB services. In contrast, a nested weibit model is used to account for the choice behavior of passengers not loyal to CB services by simultaneously considering the effects of mode similarity and mode heterogeneity on mode choice behavior. The proposed model is formulated as an equivalent mathematical programming problem, which guarantees the existence and uniqueness of solutions. Numerical experiments are conducted to illustrate the properties and applicability of the proposed model in a real-world setting. The results demonstrate the importance of considering passenger loyalty and managing mode similarity and heterogeneity when modeling emerging CB services. The results also show the applicability of the proposed model for evaluating the performance of a network with CB services and the design and pricing of CB operation and subscription schemes. [ABSTRACT FROM AUTHOR]

We report three infants with infantile hemangioma who experienced severe agitation and diarrhea following propranolol administration. Propranolol, a non‐selective β‐adrenergic receptor blocker, is the first‐line treatment for infantile hemangiomas. All three infants were exposed to opioids in utero and experienced neonatal abstinence syndrome at birth. We hypothesize that chronic opioid exposure in utero may cause protracted upregulation of β2‐adrenergic receptors in the central nervous system, resulting in increased susceptibility to adverse reactions to propranolol. [ABSTRACT FROM AUTHOR]

For optimization of timetables in metro systems with regular cyclic operation, this paper develops a bi-objective programming approach addressed to minimization of net energy consumption and total travel time with provision for dwell time uncertainty. Firstly, we formulate the bi-objective timetable optimization problem as an expected value model with speed profile control. Secondly, we use the ɛ -constraint method within a genetic algorithm framework to determine the Pareto optimal solutions. Finally, numerical examples based on the real-life operation data from the Beijing Metro Yizhuang Line are presented in order to illustrate the practicability and effectiveness of the approach developed in the paper. [ABSTRACT FROM AUTHOR]

Traffic equilibrium models under uncertainty characterize travelers’ route choice behaviors under travel time variability. In this paper, we develop a link -based mean-excess traffic equilibrium (L-METE) model by integrating the sub-additivity property and complete travel time variability characterization of mean-excess travel time (METT), and the computationally tractable additive route cost structure of the conventional user equilibrium (UE) problem. Compared to the majority of relevant models formulated in the route domain, the link -based modeling has two desirable features on modeling flexibility and algorithmic development. First, it avoids the normal route travel time distribution assumption (uniformly imposed for all routes) that inherits from the Central Limit Theorem in most route -based models, permitting the use of any suitable link travel time distributions from empirical studies. Second, the additive route cost structure makes the L-METE model solvable by readily adapting existing UE algorithms without the need of storing/enumerating routes while avoiding the computationally demanding nonadditive shortest path problem and route flow allocations in route-based models, which is a significant benefit for large-scale network applications under uncertainty. [ABSTRACT FROM AUTHOR]

We describe herein a diastereoselective approach to access substituted-4-aminopiperidines from pyridine precursors. This methodology has successfully been applied to synthesize 2-alkyl substrates as well as more complex molecular entities of interest to the pharmaceutical industry. [ABSTRACT FROM AUTHOR]

The integrated timetable and speed profile optimization model has recently attracted more attention because of its good achievements on energy conservation in metro systems. However, most previous studies often ignore the spatial and temporal uncertainties of train mass, and the variabilities of tractive force, braking force and basic running resistance on energy consumption in order to simplify the model formulation and solution algorithm. In this paper, we develop an integrated metro timetable and speed profile optimization model to minimize the total tractive energy consumption, where these real-world operating conditions are explicitly considered in the model formulation and solution algorithm. Firstly, we formulate a two-phase stochastic programming model to determine the timetable and speed profile. Given the speed profile, the first phase determines the timetable by scheduling the arrival and departure times for each station, and the second phase determines the speed profile for each inter-station with the scheduled arrival and departure times. Secondly, we design a simulation-based genetic algorithm procedure incorporated with the optimal train control algorithm to find the optimal solution. Finally, we present a simple example and a real-world example based on the operation data from the Beijing Metro Yizhuang Line in Beijing, China. The results of the real-world example show that, during peak hours, off-peak hours and night hours, the total tractive energy consumptions can be reduced by: (1) 10.66%, 9.94% and 9.13% in comparison with the current timetable and speed profile; and (2) 3.35%, 3.12% and 3.04% in comparison with the deterministic model. [ABSTRACT FROM AUTHOR]

The vestibular system encodes head motion in space which is naturally accompanied by other sensory cues. Electrical stimuli, applied across the mastoid processes, selectively activate primary vestibular afferents which has spurred clinical and biomedical applications of electrical vestibular stimulation (EVS). When properly matched to head motion, EVS may also manipulate the closed-loop relationship between actions and vestibular feedback to reveal the mechanisms of sensorimotor recalibration and learning. We designed a portable, low-cost real-time EVS system using an Arduino microcontroller programmed through Simulink that provides electrical currents based on head angular motion. We used well-characterized vestibular afferent physiological responses to head angular velocity and electrical current to compute head-motion equivalent of real-time modulatory EVS currents. We also examined if our system induced recalibration of the vestibular system during human balance control. Our system operated at 199.997 Hz (± 0.005 Hz) and delivered head-motion-equivalent electrical currents with ∼10 ms delay. The output driving the current stimulator matched the implemented linear model for physiological vestibular afferent dynamics with minimal background noise (<0.2% of ± 10 V range). Participants recalibrated to the modulated closed-loop vestibular feedback using visual cues during standing balance, replicating earlier findings. EVS is typically used to impose external perturbations that are independent of one's own movement. We provided a solution using open-source hardware to implement a real-time, physiology based, and task-relevant vestibular modulations using EVS. Our portable, low-cost vestibular modulation system will make physiological closed-loop vestibular manipulations more accessible thus encouraging novel investigations and biomedical applications of EVS. [Display omitted] • Electrical vestibular stimulation (EVS) activates vestibular afferents. • Our system generates EVS based on the physiology of the vestibular system. • Designed a portable system to modulate human vestibular sense in real-time. • Closed-loop manipulation of the vestibular sense induces vestibular recalibration. • Our system opens opportunities to reveal how we learn and interact with the world. [ABSTRACT FROM AUTHOR]

This article presents a proof‐of‐concept designer‐in‐the‐loop schematic map drawing tool, based on the marriage of two approaches—manual and automated, which provides the technical interactivity of drawing tools between the user and the computer. We focus on concentric circle maps as opposed to the commonly used orthogonal mode representation, which is suggested by previous studies that it could promote better network learning. In comparison with existing methods, the proposed method is more compatible with the framework of effective map design from psychological and aesthetic perspectives, and a range of options can be provided in conjunction with users' preferences. We evaluated our approach on a set of iterations with case studies of Hong Kong metro with a group of three co‐authors from the fields of geography, transport engineering, and education. [ABSTRACT FROM AUTHOR]

In this paper, we develop a customized path-based algorithm for solving the stochastic multi-class traffic assignment problem with asymmetric interactions, route overlapping, and vehicle restrictions. The algorithm consists of an iterative balancing scheme to find the search direction, a self-regulated averaging line search scheme to determine a suitable stepsize, and a column generation scheme to generate a universal path set for multiple vehicle classes. These three schemes work together in the customized path-based algorithm to solve the stochastic multi-class traffic assignment problem. The solution algorithm simultaneously considers the asymmetric interactions among different vehicle types through the link travel time functions, various vehicle restrictions in a transportation network, and route overlapping using the path-size logit model for accounting random perceptions of network conditions in a stochastic user equilibrium framework. A real network in the city of Winnipeg, Canada, is used to examine the computational performance of the customized path-based algorithm. In addition, sensitivity analyses are conducted to test the algorithmic effectiveness with respect to several model parameters and percentages of trucks in the transportation network. Numerical results reveal that the path-based algorithm with the self-regulated averaging line search scheme is computationally effective in solving the stochastic multi-class traffic assignment problem with different modeling considerations. The algorithm is also computationally robust against various model parameters in the sensitivity analyses. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper aims to develop a hybrid closed-form route choice model and the corresponding stochastic user equilibrium (SUE) to alleviate the drawbacks of both Logit and Weibit models by simultaneously considering absolute cost difference and relative cost difference in travelers’ route choice decisions. The model development is based on an observation that the issues of absolute and relative cost differences are analogous to the negative exponential and power impedance functions of the trip distribution gravity model. Some theoretical properties of the hybrid model are also examined, such as the probability relationship among the three models, independence from irrelevant alternatives, and direct and indirect elasticities. To consider the congestion effect, we provide a unified modeling framework to formulate the Logit, Weibit and hybrid SUE models with the same entropy maximization objective but with different total cost constraint specifications representing the modelers’ knowledge of the system. With this, there are two ways to interpret the dual variable associated with the cost constraint: shadow price representing the marginal change in the entropy level to a marginal change in the total cost, and dispersion/shape parameter representing the travelers’ perceptions of travel costs. To further consider the route overlapping effect, a path-size factor is incorporated into the hybrid SUE model. Numerical examples are also provided to illustrate the capability of the hybrid model in handling both absolute and relative cost differences as well as the route overlapping problem in travelers’ route choice decisions. [ABSTRACT FROM AUTHOR]

Many individuals with traumatic brain injury (TBI) suffer difficulty regulating fundamental aspects of attention (focus, sustained attention) and may also exhibit hypo- or hyper-states of alertness. Deficits in thestateof attention may underlie or exacerbate higher order executive dysfunction. Recent studies indicate that computerized cognitive training targeting attentional control and alertness can ameliorate attention deficits evident in patients with TBI or acquired brain injury. The current study examined whether improvements inattentional statefollowing training can also influence performance on higher-order executive function and mood in individuals with mild TBI (mTBI). The current study examined five patients with executive control deficits as a result of mTBI, with or without persistent anxiety. Three patients engaged in ~5 hours of an executive control training task targeting inhibitory control and sustained attention; two additional patients were re-tested following the same period of time. Performance on standard neuropsychological measures of attention, executive function, and mood were evaluated pre- and post-training. The results indicate that tonic and phasic alertness training may improve higher-order executive function and mood regulation in individuals with TBI. [ABSTRACT FROM AUTHOR]

Regenerative braking is an energy recovery mechanism that converts the kinetic energy during braking into electricity, also known as regenerative energy. In general, most of the regenerative energy is transmitted backward along the pantograph and fed back into the overhead contact line. To reduce the trains’ energy consumption, this paper develops a scheduling approach to coordinate the arrivals and departures of all trains located in the same electricity supply interval so that the energy regenerated from braking trains can be more effectively utilized to accelerate trains. Firstly, we formulate an integer programming model with real-world speed profiles to minimize the trains’ energy consumption with dwell time control. Secondly, we design a genetic algorithm and an allocation algorithm to find a good solution. Finally, we present numerical examples based on the real-life operation data from the Beijing Metro Yizhuang Line in Beijing, China. The results show that the proposed scheduling approach can reduce energy consumption by 6.97% and save about 1,054,388 CNY (or 169,223 USD) each year in comparison with the current timetable. Compared to the cooperative scheduling (CS) approach, the proposed scheduling approach can improve the utilization of regenerative energy by 36.16% and reduce the total energy consumption by 4.28%. [ABSTRACT FROM AUTHOR]

Various government laws have recently been enacted to alleviate the environmental deterioration of transportation systems. Environmental constraint is a valid means to explicitly reflect various environmental protection requirements imposed by the government. In this paper, we examine the environmentally constrained traffic equilibrium problem (EC-TEP), which is a fundamental tool for modeling and evaluating environmental protection requirements. Specifically, we provide an equivalent reformulation for the EC-TEP. The proposed reformulation adapts the concept ofgap functionto simultaneously reformulate the nonlinear complementarity conditions associated with the generalized user equilibrium conditions, environmental constraints, and conservation constraints as an equivalent unconstrained optimization problem. This gap function reformulation has two desirable features: (1) it can handle a generalenvironmental constraintstructure (linear or nonlinear; link-based or area-based) and a generallink and route coststructure, enhancing the modeling adaptability and flexibility; (2) it is smooth and unconstrained, permitting a number of existing efficient algorithms for its solution. A gradient-based solution algorithm with a self-regulated averaging stepsize scheme is customized to solve the reformulated unconstrained optimization problem. Numerical examples are also provided to demonstrate the modeling flexibility of the proposed EC-TEP reformulation. [ABSTRACT FROM AUTHOR]

• Consider multiple data sources for transit origin–destination (O-D) estimation. • Develop a frequency-based transit path flow estimator (PFE). • Develop a path-based diagonalization algorithm embedded with an iterative balancing scheme. • Conduct case studies to demonstrate the features and applicability of the proposed model and algorithm. A frequency-based path flow estimator is proposed to estimate the transit passenger origin–destination (OD) matrix in an urban congested transit network. The proposed model not only considers the effect of congestion under an equilibrium framework, but also benefits from being formulated as a single-level model with the route-section-based network representation. Multiple transit data sources are incorporated, including automatic passenger count, automatic fare collection and automatic vehicle location data. A path-based diagonalization approach embedded with an iterative balancing scheme is developed to solve the model. Case studies are conducted to demonstrate the features and applicability of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

Recent empirical studies have found widespread inaccuracies in traffic forecasts despite the fact that travel demand forecasting models have been significantly improved over the past few decades. We suspect that an intrinsic selection bias may exist in the competitive project appraisal process, in addition to the many other factors that contribute to inaccurate traffic forecasts. In this paper, we examine the potential for selection bias in the governmental process of Build-Operate-Transfer (BOT) transportation project appraisals. Although the simultaneous consideration of multiple criteria is typically used in practice, traffic flow estimate is usually a key criterion in these appraisals. For the purposes of this paper, we focus on the selection bias associated with the highest flow estimate criterion. We develop two approaches to quantify the level and chance of inaccuracy caused by selection bias: the expected value approach and the probability approach. The expected value approach addresses the question “ to what extent is inaccuracy caused by selection bias? ”. The probability approach addresses the question “ what is the chance of inaccuracy due to selection bias? ”. The results of this analysis confirm the existence of selection bias when a government uses the highest traffic forecast estimate as the priority criterion for BOT project selection. In addition, we offer some insights into the relationship between the extent/chance of inaccuracy and other related factors. We do not argue that selection bias is the only reason for inaccurate traffic forecasts in BOT projects; however, it does appear that it could be an intrinsic factor worthy of further attention and investigation. [ABSTRACT FROM AUTHOR]

This paper presents an alternative planning framework to model and forecast network traffic for planning applications in small communities, where limited resources debilitate the development and applications of the conventional four-step travel demand forecasting model. The core idea is to use the Path Flow Estimator (PFE) to estimate current and forecast future traffic demand while taking into account of various field and planning data as modeling constraints. Specifically, two versions of PFE are developed: a base year PFE for estimating the current network traffic conditions using field data and planning data, if available, and a future year PFE for predicting future network traffic conditions using forecast planning data and the estimated base year origin–destination trip table as constraints. In the absence of travel survey data, the proposed method uses similar data (traffic counts and land use data) as a four-step model for model development and calibration. Since the Institute of Transportation Engineers (ITE) trip generation rates and Highway Capacity Manual (HCM) are both utilized in the modeling process, the analysis scope and results are consistent with those of common traffic impact studies and other short-range, localized transportation improvement programs. Solution algorithms are also developed to solve the two PFE models and integrated into a GIS-based software called Visual PFE. For proof of concept, two case studies in northern California are performed to demonstrate how the tool can be used in practice. The first case study is a small community of St. Helena, where the city’s planning department has neither an existing travel demand model nor the budget for developing a full four-step model. The second case study is in the city of Eureka, where there is a four-step model developed for the Humboldt County that can be used for comparison. The results show that the proposed approach is applicable for small communities with limited resources. [ABSTRACT FROM AUTHOR]

Paradox in the transportation literature is about improving an existing link or adding a new link can actually increase network-wide travel costs or travel costs of each traveler. In this paper, we investigate the stochastic assignment paradox using the multinomial weibit (MNW) model, a new route choice model developed by Castillo et al. (2008), and compare it to the counter-intuitive results of the multinomial logit (MNL) model when an inferior travel alternative is marginally improved. Using a simple two-link network, we derive the conditions for paradoxical phenomenon to occur for both route choice models, and graphically compare and contrast the paradoxical regions. The results show the stochastic assignment paradox depends on how the cost difference is being considered in the route choice model (i.e., absolute cost difference in the MNL model and relative cost difference in the MNW model) to some extent. Hence, the stochastic paradox analysis is extended to a hybrid model that considers both MNW and MNL models (i.e., both relative cost difference and absolute cost difference). The paradox area of the hybrid model is shown to be a combination of the paradox areas of the two models. In addition, the stochastic assignment paradox conditions derived for a simple two-link network are generalized to three cases: (a) one O–D pair with multiple links on a route, (b) multiple O–D pairs, and (c) adding a new link. Analytical solutions, graphical illustrations, and numerical results are provided to demonstrate the stochastic paradox under different conditions. Future research directions are also discussed in the paper. [ABSTRACT FROM AUTHOR]

Traumatic brain injury (TBI) can result in functional deficits that persist long after acute injury. The authors present a case study of an individual who experienced some of the most common debilitating problems that characterize the chronic phase of mild-to-moderate TBI--difficulties with neurobehavioral functions that manifest via complaints of distractibility, poor memory, disorganization, poor frustration tolerance, and feeling easily overwhelmed. They present a rational strategy for management that addresses important domain-general targets likely to have far-ranging benefits. This integrated, longitudinal, and multifaceted approach first addresses approachable targets and provides an important foundation to enhance the success of other, more specific interventions requiring specialty intervention. The overall approach places an emphasis on accomplishing two major categories of clinical objectives: optimizing current functioning and enhancing learning and adaptation to support improvement of functioning in the long-term for individuals living with brain injury. [ABSTRACT FROM AUTHOR]

Highlights: [•] Alternative risk measure to more cost-effectively capture TTT distribution tail. [•] Analytically estimate risk measures without knowing the explicit distribution form. [•] Avoid setting a too conservative reliability level to reduce unacceptable risk. [•] Construction cost saving and unacceptable risk reduction of capacity enhancement. [•] Avoid burdensome computation of simulation and fitting explicit distribution form. [Copyright &y& Elsevier]