Significant spatial co-distribution pattern discovery

Given instances (spatial points) of different spatial features (categories), significant spatial co-distribution pattern discovery aims to find subsets of spatial features whose spatial distributions are statistically significantly similar to each other. Discovering significant spatial co-distribution patterns is important for many application domains such as identifying spatial associations between diseases and risk factors in spatial epidemiology. Previous methods mostly associated spatial features whose instances are frequently located together; however, this does not necessarily indicate a similarity in the spatial distributions between different features. Thus, this paper defines the significant spatial co-distribution pattern discovery problem and subsequently develops a novel method to solve it effectively. First, we propose a new measure, dissimilarity index, to quantify the difference between spatial distributions of different features under the spatial neighbor relation and then employ it in a distribution clustering method to detect candidate spatial co-distribution patterns. To further remove spurious patterns that occur accidentally, the validity of each candidate spatial co-distribution pattern is verified through a significance test under the null hypothesis that spatial distributions of different features are independent of each other. To model the null hypothesis, a distribution shift-correction method is presented by randomizing the relationships between different features and maintaining spatial structure of each feature (e.g., spatial auto-correlation). Comparisons with baseline methods using synthetic datasets demonstrate the effectiveness of the proposed method. A case study identifying co-morbidities in central Colorado is also presented to illustrate the real-world applicability of the proposed method.     

Transit smart card data mining for passenger origin information extraction

The automated fare collection(AFC) system,also known as the transit smart card(SC) system,has gained more and more popularity among transit agencies worldwide.Compared with the conventional manual fare collection system,an AFC system has its inherent advantages in low labor cost and high efficiency for fare collection and transaction data archival.Although it is possible to collect highly valuable data from transit SC transactions,substantial efforts and methodologies are needed for extracting such data because most AFC systems are not initially designed for data collection.This is true especially for the Beijing AFC system,where a passenger’s boarding stop(origin) on a flat-rate bus is not recorded on the check-in scan.To extract passengers’ origin data from recorded SC transaction information,a Markov chain based Bayesian decision tree algorithm is developed in this study.Using the time invariance property of the Markov chain,the algorithm is further optimized and simplified to have a linear computational complexity.This algorithm is verified with transit vehicles equipped with global positioning system(GPS) data loggers.Our verification results demonstrated that the proposed algorithm is effective in extracting transit passengers’ origin information from SC transactions with a relatively high accuracy.Such transit origin data are highly valuable for transit system planning and route optimization.     

Cooperation and profit allocation in two-echelon logistics joint distribution network optimization

Collaborative two-echelon logistics joint distribution network can be organized through a negotiation process via logistics service providers or participants existing in the logistics system, which can effectively reduce the crisscross transportation phenomenon and improve the efficiency of the urban freight transportation system. This study establishes a linear optimization model to minimize the total cost of two-echelon logistics joint distribution network. An improved ant colony optimization algorithm integrated with genetic algorithm is presented to serve customer clustering units and resolve the model formulation by assigning logistics facilities. A two-dimensional colony encoding method is adopted to generate the initial ant colonies. Improved ant colony optimization combines the merits of ant colony optimization algorithm and genetic algorithm with both global and local search capabilities. Finally, an improved Shapley value model based on cooperative game theory and a cooperative mechanism strategy are presented to obtain the optimal profit allocation scheme and sequential coalitions respectively in two-echelon logistics joint distribution network. An empirical study in Guiyang City, China, reveals that the improved ant colony optimization algorithm is superior to the other three methods in terms of the total cost. The improved Shapley value model and monotonic path selection strategy are applied to calculate the best sequential coalition selection strategy. The proposed cooperation and profit allocation approaches provide an effective paradigm for logistics companies to share benefit, achieve win–win situations through the horizontal cooperation, and improve the negotiation power for logistics network optimization.     

Transit smart card data mining for passenger origin information extraction

The automated fare collection (AFC) system,also known as the transit smart card (SC) system,has gained more and more popularity among transit agencies worldwide.Compared with the conventional manual fa...     

Extending the Fatigue Life of Riveted Coped Stringer Connections

Fatigue cracking occurs at the copes of stringer–floorbeam connections of older, riveted steel bridges. Some cracks are quite long and raise serious questions regarding the remaining fatigue life of the subject bridges. Damage limitation methods (DLMs) have been used to increase the fatigue life of these stringers, but the effectiveness of the DLMs for these riveted connections had never been evaluated by tests. Therefore, tests were conducted to evaluate the fatigue life of coped, riveted stringer–floorbeam connections, and the effectiveness of DLMs. Fatigue cracks in the coped stringer–floorbeam connection were initially developed to establish crack initiation requirements and the rate and extent of crack growth. Once a significant crack was noted, one of several DLMs was applied, and the specimens were retested to determine the effectiveness of the DLM in controlling cracking. These DLMs included the drilled hole, the inserted bolt and the removed rivet methods. The relative effectiveness of the methods is described, and a design procedure is proposed for improving their performance.     

Application of a random effects negative binomial model to examine tram-involved crash frequency on route sections in Melbourne,Australia

Safety is a key concern in the design, operation and development of light rail systems including trams or streetcars as they impose crash risks on road users in terms of crash frequency and severity. The aim of this study is to identify key traffic, transit and route factors that influence tram-involved crash frequencies along tram route sections in Melbourne. A random effects negative binomial (RENB) regression model was developed to analyze crash frequency data obtained from Yarra Trams, the tram operator in Melbourne. The RENB modelling approach can account for spatial and temporal variations within observation groups in panel count data structures by assuming that group specific effects are randomly distributed across locations. The results identify many significant factors effecting tram-involved crash frequency including tram service frequency (2.71), tram stop spacing (−0.42), tram route section length (0.31), tram signal priority (−0.25), general traffic volume (0.18), tram lane priority (−0.15) and ratio of platform tram stops (−0.09). Findings provide useful insights on route section level tram-involved crashes in an urban tram or streetcar operating environment. The method described represents a useful planning tool for transit agencies hoping to improve safety performance.     

A combined M5P tree and hazard-based duration model for predicting urban freeway traffic accident durations

The duration of freeway traffic accidents duration is an important factor, which affects traffic congestion, environmental pollution, and secondary accidents. Among previous studies, the M5P algorithm has been shown to be an effective tool for predicting incident duration. M5P builds a tree-based model, like the traditional classification and regression tree (CART) method, but with multiple linear regression models as its leaves. The problem with M5P for accident duration prediction, however, is that whereas linear regression assumes that the conditional distribution of accident durations is normally distributed, the distribution for a “time-to-an-event” is almost certainly nonsymmetrical. A hazard-based duration model (HBDM) is a better choice for this kind of a “time-to-event” modeling scenario, and given this, HBDMs have been previously applied to analyze and predict traffic accidents duration. Previous research, however, has not yet applied HBDMs for accident duration prediction, in association with clustering or classification of the dataset to minimize data heterogeneity. The current paper proposes a novel approach for accident duration prediction, which improves on the original M5P tree algorithm through the construction of a M5P-HBDM model, in which the leaves of the M5P tree model are HBDMs instead of linear regression models. Such a model offers the advantage of minimizing data heterogeneity through dataset classification, and avoids the need for the incorrect assumption of normality for traffic accident durations. The proposed model was then tested on two freeway accident datasets. For each dataset, the first 500 records were used to train the following three models: (1) an M5P tree; (2) a HBDM; and (3) the proposed M5P-HBDM, and the remainder of data were used for testing. The results show that the proposed M5P-HBDM managed to identify more significant and meaningful variables than either M5P or HBDMs. Moreover, the M5P-HBDM had the lowest overall mean absolute percentage error (MAPE).     

Effects of NOM properties on copper release from model solid phases

This study examined impacts of concentrations and properties of natural organic matter (NOM) on copper release from characteristic copper solid model phases such as tenorite CuO and malachite Cu₂(OH)₂CO₃. Unaltered Aldrich humic acid (AHA) and standard Suwannee River fulvic acid (SRFA) strongly increased copper release from the model phases but NOM alteration by chlorination or ozonation gradually suppressed or, at higher oxidant doses, eliminated these effects. The nature of NOM changes induced by chlorination and ozonation was examined using differential absorbance spectroscopy (DAS) and high-performance size-exclusion chromatography (HPSEC). The data of these methods show that NOM molecules with higher apparent molecular weight (AMW), higher aromaticities and contributions of protonation-active phenolic and carboxylic groups play a key role in adsorption and colloidal dispersion of the model solids. The data also show that metal release from model phases was well correlated with a number of spectroscopic parameters characterizing NOM properties, notably SUVA₂₅₄, spectral slopes of NOM absorbance, and differential absorbance at wavelength of 280 nm and 350 nm that is indicative of the contributions of carboxylic and phenolic functional groups. Changes of ζ-potential of the model solid phases were the strongest predictor of the enhancement of copper release especially in the system controlled by malachite. While effects of NOM on the ζ-potential of tenorite and malachite were prominent for unaltered NOM, its oxidation by chlorine and ozone was accompanied by a gradual decrease and ultimately disappearance of its surface activity.     

Large-Bar Connection for Precast Bridge Bents in Seismic Regions

A new beam-to-column connection has been developed for assembling precast concrete bridge bents in regions of high seismicity. The connection is made with a small number of large column bars, which are grouted into large corrugated-metal ducts embedded in the cap beam. Bents built with these connections can be erected quickly and permit generous construction tolerances. To evaluate the seismic performance of the proposed connection, lateral-load tests were performed on three manifestations of the connection, as well as on a comparable cast-in-place connection. The tests demonstrated that the force-displacement response and damage progression in the precast connection are similar to those of typical cast-in-place concrete connections. Deliberate partial debonding of the longitudinal reinforcement only slightly affected the force-displacement response and observed damage.