Ziqiang Zeng

A generalized nonlinear model-based mixed multinomial logit approach for crash data analysis.

The mixed multinomial logit (MNL) approach, which can account for unobserved heterogeneity, is a promising unordered model that has been employed in analyzing the effect of factors contributing to crash severity. However, its basic assumption of using a linear function to explore the relationship between the probability of crash severity and its contributing factors can be violated in reality. This paper develops a generalized nonlinear model-based mixed MNL approach which is capable of capturing non-monotonic relationships by developing nonlinear predictors for the contributing factors in the context of unobserved heterogeneity. The crash data on seven Interstate freeways in Washington between January 2011 and December 2014 are collected to develop the nonlinear predictors in the model. Thirteen contributing factors in terms of traffic characteristics, roadway geometric characteristics, and weather conditions are identified to have significant mixed (fixed or random) effects on the crash density in three crash severity levels: fatal, injury, and property damage only. The proposed model is compared with the standard mixed MNL model. The comparison results suggest a slight superiority of the new approach in terms of model fit measured by the Akaike Information Criterion (12.06 percent decrease) and Bayesian Information Criterion (9.11 percent decrease). The predicted crash densities for all three levels of crash severities of the new approach are also closer (on average) to the observations than the ones predicted by the standard mixed MNL model. Finally, the significance and impacts of the contributing factors are analyzed.

Identification of significant factors in fatal-injury highway crashes using genetic algorithm and neural network

Identification of the significant factors of traffic crashes has been a primary concern of the transportation safety research community for many years. A fatal-injury crash is a comprehensive result influenced by multiple variables involved at the moment of the crash scenario, the main idea of this paper is to explore the process of significant factors identification from a multi-objective optimization (MOP) standpoint. It proposes a data-driven model which combines the Non-dominated Sorting Genetic Algorithm (NSGA-II) with the Neural Network (NN) architecture to efficiently search for optimal solutions. This paper also defines the index of Factor Significance (Fs) for quantitative evaluation of the significance of each factor. Based on a set of three year data of crash records collected from three main interstate highways in the Washington State, the proposed method reveals that the top five significant factors for a better Fatal-injury crash identification are 1) Driver Conduct, 2) Vehicle Action, 3) Roadway Surface Condition, 4) Driver Restraint and 5) Driver Age. The most sensitive factors from a spatiotemporal perspective are the Hour of Day, Most Severe Sobriety, and Roadway Characteristics. The method and results in this paper provide new insights into the injury pattern of highway crashes and may be used to improve the understanding of, prevention of, and other enforcement efforts related to injury crashes in the future.

A Crash Counts by Severity Based Hotspot Identification Method and Its Application on a Regional Map Based Analytical Platform

This paper aims to develop a crash counts by severity based hotspot identification method by extending the traditional empirical Bayes method to a generalized nonlinear model-based mixed multinomial logit approach. A new safety performance index and a new potential safety improvement index are developed by introducing the risk weight factor and compared with traditional indexes by employing four hotspot identification evaluating methods. The comparison results reveal that the new safety performance index derived by the generalized nonlinear model-based mixed multinomial logit approach is the most consistent and reliable method for identifying hotspots. Finally, a regional map based analytical platform is developed by expanding the safety performance module with the new safety performance index and potential safety improvement functions.

A Fuzzy Multi-criteria Decision Making Approach for Supplier Selection Under Fuzzy Environment

Supplier selection is crucial and multi-criteria decision making problem in supply chain. Nowadays in competitive business environment, supplier selection is a critical task for purchasing department in every organization. Appropriate supplier helps manufacturer to reduce cost, consistent quality product, and enhance competitiveness in market. In order to select potential supplier, it is essential to tradeoff between tangible and intangible factors. Uncertainty and vagueness of decision makers opinion is an important characteristic of this problem. Fuzzy analytical hierarchical process (AHP) and extent analysis method is used to choose appropriate supplier under fuzzy environment. The application procedure of FAHP and extent analysis method is elaborated through numerical example.

Developing a Clustering-Based Empirical Bayes Analysis Method for Hotspot Identification

Hotspot identification (HSID) is a critical part of network-wide safety evaluations. Typical methods for ranking sites are often rooted in using the Empirical Bayes (EB) method to estimate safety from both observed crash records and predicted crash frequency based on similar sites. The performance of the EB method is highly related to the selection of a reference group of sites (i.e., roadway segments or intersections) similar to the target site from which safety performance functions (SPF) used to predict crash frequency will be developed. As crash data often contain underlying heterogeneity that, in essence, can make them appear to be generated from distinct subpopulations, methods are needed to select similar sites in a principled manner. To overcome this possible heterogeneity problem, EB-based HSID methods that use common clustering methodologies (e.g., mixture models, -means, and hierarchical clustering) to select “similar“ sites for building SPFs are developed. Performance of the clustering-based EB methods is then compared using real crash data. Here, HSID results, when computed on Texas undivided rural highway cash data, suggest that all three clustering-based EB analysis methods are preferred over the conventional statistical methods. Thus, properly classifying the road segments for heterogeneous crash data can further improve HSID accuracy.

AHP AND FUZZY TOPSIS METHODS FOR GREEN SUPPLIER SELECTION AND EVALUATION.

*Adnan Sarwar1, Jiuping Xu1, Ziqiang Zeng2,1 and Muhammad Hashim3. 1. Uncertainty Decision-Making Laboratory, Sichuan University, Chengdu, 610064, People’s Republic of China. 2. Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA. National Textile University, Faisalabad, Pakistan. ...................................................................................................................... Manuscript Info Abstract ......................... ........................................................................ Manuscript History