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Predicting transportation mode choice is a critical component of forecasting travel demand. Recently, machine learning methods have become increasingly more popular in predicting transportation mode choice. Class association rules...
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Predicting transportation mode choice is a critical component of forecasting travel demand. Recently, machine learning methods have become increasingly more popular in predicting transportation mode choice. Class association rules (CARs) have been applied to transportation mode choice, but the application of the imputed rules for prediction remains a longstanding challenge. Based on CARs, this paper proposes a new rule merging approach, called CARM, to improve predictive accuracy. In the suggested approach, first, CARs are imputed from the frequent pattern tree (FP-tree) based on the frequent pattern growth (FP-growth) algorithm. Next, the rules are pruned based on the concept of pessimistic error rate. Finally, the rules are merged to form new rules without increasing predictive error. Using the 2015 Dutch National Travel Survey, the performance of suggested model is compared with the performance of CARIG that uses the information gain statistic to generate new rules, class-based association rules (CBA), decision trees (DT) and the multinomial logit (MNL) model. In addition, the proposed model is assessed using a ten-fold cross validation test. The results show that the accuracy of the proposed model is 91.1%, which outperforms CARIG, CBA, DT and the MNL model.
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The increasing utilization of battery-powered automated guided vehicles in automated container terminals, has an important consequence on terminal cost and efficiency. How to tackle integrated vehicle charging and operation schedu...
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The increasing utilization of battery-powered automated guided vehicles in automated container terminals, has an important consequence on terminal cost and efficiency. How to tackle integrated vehicle charging and operation scheduling problem to maintain high terminal performance is prominent for sustainable port operation. In this paper, fast charging technology is investigated, and a mixed integer programming model for this complicated scheduling problem is constructed, which aims to reduce charging cost and penalty cost related to makespan, and includes sequence-related constraints, time-related constraints and energy-related constraints. A decomposition-iteration algorithm is proposed to solve this problem, and furthermore it is combined with a simulation-based optimization method to address practical-sized instances. Numerical experiments on real-world cases are conducted to verify the efficiency and effectiveness of the proposed solution algorithm. Insightful managerial implications are derived by comparative analysis on charging rules and charging facility locations, and sensitivity analysis on charging power, charging facility configuration and vehicle configuration. Experimental results provide valuable references for terminal managers to make configuration and scheduling decisions for battery-powered vehicle transporting systems.
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In this paper, we explore a stochastic non-autonomous one-predator–two-prey system with Beddington–DeAngelis functional response and impulsive perturbations. First, by using It?’s formula, exponential martingale inequality, Che...
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In this paper, we explore a stochastic non-autonomous one-predator–two-prey system with Beddington–DeAngelis functional response and impulsive perturbations. First, by using It?’s formula, exponential martingale inequality, Chebyshev’s inequality and other mathematical skills, we establish some sufficient conditions for extinction, non-persistence in the mean, weak persistence, persistence in the mean and stochastic permanence of the solution of the stochastic system. Then the limit of the average in time of the sample path of the solution is estimated by two constants. Afterwards, the lower-growth rate and the upper-growth rate of the positive solution are estimated. In addition, sufficient conditions for global attractivity of the system are established. Finally, we carry out some simulations to verify our main results and explain the biological implications: the large stochastic interference is disadvantageous for the persistence of the population and the strong impulsive harvesting can lead to extinct of the population.
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The crew planning problem is a key step in the urban rail transit (URT) planning process and has a critical impact on the operational efficiency of a URT line. In general, the crew planning problem consists of two subproblems, cre...
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The crew planning problem is a key step in the urban rail transit (URT) planning process and has a critical impact on the operational efficiency of a URT line. In general, the crew planning problem consists of two subproblems, crew scheduling and crew rostering, which are usually solved in a sequential manner. Such an approach may, however, lead to a poor-quality crew plan overall. We therefore study the integrated optimization of crew scheduling and crew rostering and propose an effective dual decomposition approach. In particular, we formulate the integrated problem as an integer programming model using a space-time-state network representation, where the objective of the model is to minimize the weighted sum of total travel cost and penalties associated with imbalances in the workloads of the crew members. Then, an Alternating Direction Method of Multipliers (ADMM)-based dual decomposition mechanism that decomposes the model into a set of independent crew member-specific subproblems is introduced, where each of these subproblems is efficiently solved by a tailored time-dependent shortest path algorithm. To improve the performance of ADMM approach, two enhancement strategies are also designed to accelerate convergence. A set of real-life instances based on a rail transit line in Chengdu, China, is used to verify the effectiveness of the proposed model and algorithm. Computational results show that the ADMM-based approach with enhancements significantly outperforms a conventional Lagrangian Relaxation-based approach, yielding improved convergence and significantly smaller optimality gaps. Finally, on a set of real-life instances, the proposed ADMM-based approach with enhancements obtains an optimality gap of, on average, 4.2%. This is substantially better than Lagrangian Relaxation, which provides optimality gaps of, on average, 34.73%.
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In this paper, we construct an infinite series of 9-class association schemes from a refinement of the partition of Delsarte–Goethals codes by their Lee weights. The explicit expressions of the dual schemes are determined through...
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In this paper, we construct an infinite series of 9-class association schemes from a refinement of the partition of Delsarte–Goethals codes by their Lee weights. The explicit expressions of the dual schemes are determined through direct manipulations of complicated exponential sums. As a byproduct, another three infinite families of association schemes are also obtained as fusion schemes and quotient schemes.
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A k-query locally decodable code (LDC) C : ∑~n → T~N encodes each message x into a codeword C(x) such that each symbol of x can be probabilistically recovered by querying only k coordinates of C(x), even after a constant fractio...
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A k-query locally decodable code (LDC) C : ∑~n → T~N encodes each message x into a codeword C(x) such that each symbol of x can be probabilistically recovered by querying only k coordinates of C(x), even after a constant fraction of the coordinates has been corrupted. Yekhanin (in J ACM 55:1-16, 2008) constructed a 3-query LDC of subexponential length, N = exp(exp(O(logn/loglogn))), under the assumption that there are infinitely many Mersenne primes. Efremenko (in Proceedings of the 41st annual ACM symposium on theory of computing, ACM, New York, 2009) constructed a 3-query LDC of length N2 = exp(exp(O(√log n log log n))) with no assumption, and a 2~r-query LDC of length N~r = exp(exp(O(~r√log n(loglogn)~(r-1)))), for every integer r ≥ 2. Itoh and Suzuki (in IEICE Trans Inform Syst E93-D 2:263-270, 2010) gave a composition method in Efremenko's framework and constructed a 3 ? 2~-(r-2)query LDC of length N_r, for every integer r ≥ 4, which improved the query complexity of Efremenko's LDC of the same length by a factor of 3/4. The main ingredient of Efremenko's construction is the Grolmusz construction for super-polynomial size set-systems with restricted intersections, over Z_m, where m possesses a certain "good" algebraic property (related to the "algebraic niceness" property of Yekhanin in J ACM 55:1-16, 2008). Efremenko constructed a 3-query LDC based on m = 511 and left as an open problem to find other numbers that offer the same property for LDC constructions. In this paper, we develop the algebraic theory behind the constructions of Yekhanin (in J ACM 55:1-16, 2008) and Efremenko (in Proceedings of the 41st annual ACM symposium on theory of computing, ACM, New York, 2009), in an attempt to understand the "algebraic niceness" phenomenon in Z_m. We show that every integer m = pq = 2~t — 1, where p, g, and t are prime, possesses the same good algebraic property as m = 511 that allows savings in query complexity. We identify 50 numbers of this form by computer search, which together with 511, are then applied to gain improvements on query complexity via Itoh and Suzuki's composition method. More precisely, we construct a 「3~r/2」-query LDC for every positive integer r < 104 and a「(3/4)~(51)? 2」-query LDC for every integer r ≥ 104, both of length N_r, improving the 2~r queries used by Efremenko (in Proceedings of the 41st annual ACM symposium on theory of computing, ACM, New York, 2009) and 3 ? 2~(r-2) queries used by Itoh and Suzuki (in IEICE Trans Inform Syst E93-D 2:263-270, 2010). We also obtain new efficient private information retrieval (PIR) schemes from the new query-efficient LDCs.
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Predicting transportation mode choice is a classic challenge of travel behavior research. Over the years, different theoretical concepts and modeling approaches have been applied. This paper elaborates the application of class ass...
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Predicting transportation mode choice is a classic challenge of travel behavior research. Over the years, different theoretical concepts and modeling approaches have been applied. This paper elaborates the application of class association rules (CARs) and examines their predictive performance using data extracted from the 2015 National Dutch Travel Survey. To solve the problem how to activate rules that have high confidence but low support, the information gain (IG) concept is introduced in the model building process. The modeling process in this study first involves extracting frequent items from the data using the FP-Growth algorithm and deriving CARs from these frequent items. Next, the IG statistic is used to construct a novel model (named CARIG), which consists of a set of decision rules that formally represent behavioral scripts, for predicting individuals' transportation mode choice. The performance of CARIG is compared with the performance of conventional class-based association rules (CBA), decision trees (DT), a convolutional neural network (CNN) and a logistic regression (LR) model. In addition, a 10-fold cross validation test using a grid search parameter optimization method is conducted to validate the proposed approach. The results show that the proposed method is promising in predicting transportation mode choices observed in the national travel survey data.
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Background Parkinson disease (PD) is a common movement disorder. Patients with PD have multiple gait impairments that result in an increased risk of falls and diminished quality of life. Therefore, gait measurement is important fo...
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Background Parkinson disease (PD) is a common movement disorder. Patients with PD have multiple gait impairments that result in an increased risk of falls and diminished quality of life. Therefore, gait measurement is important for the management of PD. Objective We previously developed a smartphone-based dual-task gait assessment that was validated in healthy adults. The aim of this study was to test the validity of this gait assessment in people with PD, and to examine the association between app-derived gait metrics and the clinical and functional characteristics of PD. Methods Fifty-two participants with clinically diagnosed PD completed assessments of walking, Movement Disorder Society Unified Parkinson Disease Rating Scale III (UPDRS III), Montreal Cognitive Assessment (MoCA), Hamilton Anxiety (HAM-A), and Hamilton Depression (HAM-D) rating scale tests. Participants followed multimedia instructions provided by the app to complete two 20-meter trials each of walking normally (single task) and walking while performing a serial subtraction dual task (dual task). Gait data were simultaneously collected with the app and gold-standard wearable motion sensors. Stride times and stride time variability were derived from the acceleration and angular velocity signal acquired from the internal motion sensor of the phone and from the wearable sensor system. Results High correlations were observed between the stride time and stride time variability derived from the app and from the gold-standard system (r=0.98-0.99, P<.001), revealing excellent validity of the app-based gait assessment in PD. Compared with those from the single-task condition, the stride time (F1,103=14.1, P<.001) and stride time variability (F1,103=6.8, P=.008) in the dual-task condition were significantly greater. Participants who walked with greater stride time variability exhibited a greater UPDRS III total score (single task: β=.39, P<.001; dual task: β=.37, P=.01), HAM-A (single-task: β=.49, P=.007; dual-task: β=.48, P=.009), and HAM-D (single task: β=.44, P=.01; dual task: β=.49, P=.009). Moreover, those with greater dual-task stride time variability (β=.48, P=.001) or dual-task cost of stride time variability (β=.44, P=.004) exhibited lower MoCA scores. Conclusions A smartphone-based gait assessment can be used to provide meaningful metrics of single- and dual-task gait that are associated with disease severity and functional outcomes in individuals with PD.
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To develop effective strategies for the supply of shared parking,
the present study investigates factors influencing the willingness of
private parking space owners to engage in shared parking. Apart
from the attributes of shar...
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To develop effective strategies for the supply of shared parking,
the present study investigates factors influencing the willingness of
private parking space owners to engage in shared parking. Apart
from the attributes of shared parking options, unobserved latent
variables measuring attitudes and personality traits, are assumed to
play a role in the decision-making process. This study estimates a
hybrid prospect theoretic model to investigate the willingness of
parking space owners to share their parking space. The latent variables
include personality traits and attitudes that are incorporated
into a prospect theoretic choice model. Non-linear effects of the
latent variables and the interaction effects between personality traits
and attitudes are examined. Results indicate that non-linear effects
and interactions significantly improve the overall explanatory power
of the model. The research findings may help in developing shared
parking policies and informing companies and governments how to
promote shared parking schemes.
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The advent of sixth-generation (6G) networks brings unmatched speed, reliability, and capacity for massive connections, making it a cornerstone for revolutionary applications. One such application is in vehicular networks, which h...
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The advent of sixth-generation (6G) networks brings unmatched speed, reliability, and capacity for massive connections, making it a cornerstone for revolutionary applications. One such application is in vehicular networks, which have their unique demands and complexities. Specifically, they face the complex issue of packet reordering due to the high-speed movement of vehicles and frequent switching of network connections. This paper examines the impact and causes of packet reordering, its threats to network efficiency, and potential countermeasures, particularly in the context of 6G-enabled vehicular networks. We introduce end-to-end methods and metrics to address packet reordering in 6G, discussing the development trends and application prospects. Our findings highlight the emergence of sophisticated strategies, such as prediction and avoidance, to manage packet reordering. They also reveal potential applications to boost network reliability, emulate traffic distributions, and enhance data security. Furthermore, we anticipate a growing integration of machine learning and data-driven optimization in tackling packet reordering. The insights provided aim to influence the future design and optimization of 6G networks, particularly concerning packet management and performance. This paper aims to assist researchers and practitioners in effectively leveraging packet reordering to promote efficient and secure operations of future 6G networks.
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