Incident Occurrence Models for Freeway Incident Management

2003 ◽  
Vol 1856 (1) ◽  
pp. 125-135 ◽  
Author(s):  
Sravanthi Konduri ◽  
Samuel Labi ◽  
Kumares C. Sinha
Keyword(s):  
Negative Binomial ◽  
Prediction Models ◽  
Negative Binomial Regression ◽  
Decision Support Tool ◽  
Cost Effective ◽  
Traffic Volume ◽  
Incident Management ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Support Tool

Incident prediction models are presented for the Interstate 80/Interstate 94 (Borman Expressway in northwestern Indiana) and Interstate 465 (northeastern Indianapolis, Indiana) freeway sections developed as a function of traffic volume, truck percentage, and weather. Separate models were developed for all incidents and noncrash incidents. Three model types were considered (Poisson regression, negative binomial regression, and nonlinear regression), and the results were compared based on magnitudes and signs of model parameter estimates and t-statistics. Least-squares estimation and maximum-likelihood methods were used to estimate the model parameters. Data from the Indiana Department of Transportation and the Indiana Climatology Database were used to establish the relationships. For a given session and incident category, the results from the Poisson and negative binomial models were found to be consistent. It was observed that, unlike section length, traffic volume is nonlinearly related to incidents, and therefore these two variables have to be considered as separate terms in the modeling process. Truck percentage was found to be a statistically significant factor affecting incident occurrence. It was also found that the weather variable (rain and snow) was negatively correlated to incidents. The freeway incident models developed constitute a useful decision support tool for implementation of new freeway patrol systems or for expansion of existing ones. They are also useful for simulating incident occurrences with a view to identifying elements of cost-effective freeway patrol strategies (patrol deployment policies, fleet size, crew size, and beat routes).

scholarly journals Identifying Temporal Aggregation Effect on Crash-Frequency Modeling

2021 ◽  
Vol 13 (11) ◽  
pp. 6214
Author(s):  
Bumjoon Bae ◽  
Changju Lee ◽  
Tae-Young Pak ◽  
Sunghoon Lee
Keyword(s):  
Traffic Safety ◽  
Negative Binomial ◽  
Random Effect ◽  
Temporal Correlation ◽  
Negative Binomial Regression ◽  
Spatiotemporal Analysis ◽  
Parameter Estimates ◽  
Crash Frequency ◽  
Crash Frequency Modeling ◽  
Using Data

Aggregation of spatiotemporal data can encounter potential information loss or distort attributes via individual observation, which would influence modeling results and lead to an erroneous inference, named the ecological fallacy. Therefore, deciding spatial and temporal resolution is a fundamental consideration in a spatiotemporal analysis. The modifiable temporal unit problem (MTUP) occurs when using data that is temporally aggregated. While consideration of the spatial dimension has been increasingly studied, the counterpart, a temporal unit, is rarely considered, particularly in the traffic safety modeling field. The purpose of this research is to identify the MTUP effect in crash-frequency modeling using data with various temporal scales. A sensitivity analysis framework is adopted with four negative binomial regression models and four random effect negative binomial models having yearly, quarterly, monthly, and weekly temporal units. As the different temporal unit was applied, the result of the model estimation also changed in terms of the mean and significance of the parameter estimates. Increasing temporal correlation due to using the small temporal unit can be handled with the random effect models.

Evaluation of Strategies to Mitigate Culvert-Involved Crashes

2021 ◽  
pp. 036119812199207
Author(s):  
Hitesh Chawla ◽  
Megat-Usamah Megat-Johari ◽  
Peter T. Savolainen ◽  
Christopher M. Day
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Cost Effective ◽  
Negative Binomial Regression Model ◽  
Promising Alternative ◽  
Roadside Safety ◽  
Adverse Conditions ◽  
Traffic Characteristics ◽  
Annual Average Daily Traffic ◽  
Binomial Regression

The objectives of this study were to assess the in-service safety performance of roadside culverts and evaluate the potential impacts of installing various safety treatments to mitigate the severity of culvert-involved crashes. Such crashes were identified using standard fields on police crash report forms, as well as through a review of pertinent keywords from the narrative section of these forms. These crashes were then linked to the nearest cross-drainage culvert, which was associated with the nearest road segment. A negative binomial regression model was then estimated to discern how the risk of culvert-involved crashes varied as a function of annual average daily traffic, speed limit, number of travel lanes, and culvert size and offset. The second stage of the analysis involved the use of the Roadside Safety Analysis Program to estimate the expected crash costs associated with various design contexts. A series of scenarios were evaluated, culminating in guidance as to the most cost-effective treatments for different combinations of roadway geometric and traffic characteristics. The results of this study provide an empirical model that can be used to predict the risk of culvert-involved crashes under various scenarios. The findings also suggest that the installation of safety grates on culvert openings provides a promising alternative for most of the cases where the culvert is located within the clear zone. In general, a guardrail is recommended when adverse conditions are present or when other treatments are not feasible at a specific location.

A structured model for evaluating countermeasures at highway–railway grade crossings

2005 ◽  
Vol 32 (4) ◽  
pp. 627-635 ◽  
Author(s):  
Young-Jin Park ◽  
Frank F Saccomanno
Keyword(s):  
Data Mining ◽  
Statistical Models ◽  
Decision Support Tool ◽  
Cost Effective ◽  
Expected Number ◽  
Structured Model ◽  
Support Tool ◽  
Data Mining Techniques ◽  
Use Of Data ◽  
Grade Crossings

Various countermeasures can be introduced to reduce collisions at highway–railway grade crossings. These countermeasures may take different forms, such as passive and (or) active driver warning devices, supplementary traffic controls (four quadrant barriers, wayside horn, closed circuit television (CCTV) monitoring, etc.), illumination, signage and highway speed limit, etc. In this research, we present a structured model that makes use of data mining techniques to estimate the effect of changes in countermeasures on the expected number of collisions at a given crossing. This model serves as a decision-support tool for the evaluation and development of cost-effective and practicable safety program at highway–railway grade crossings. The use of data mining techniques helps to resolve many of the problems associated with conventional statistical models used to predict the expected number of collisions for a given type of crossing. Statistical models introduce biases that limit their ability to fully represent the relationship between selected countermeasures and resultant collisions for a mix of crossing attributes. This paper makes use of Canadian inventory and collision data to illustrate the potential merits of the proposed model to provide decision support.Key words: highway–railway grade crossing, collision prediction model, countermeasures, Poisson regression.

scholarly journals Cost-effective surveillance of invasive species using info-gap theory

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yang Liu ◽  
Penghao Wang ◽  
Melissa L. Thomas ◽  
Dan Zheng ◽  
Simon J. McKirdy
Keyword(s):  
Invasive Species ◽  
Native Species ◽  
Cost Effective ◽  
Expert Assessment ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Surveillance Systems ◽  
Effective Manner ◽  
Novel Method ◽  
Population Threshold

AbstractInvasive species can lead to community-level damage to the invaded ecosystem and extinction of native species. Most surveillance systems for the detection of invasive species are developed based on expert assessment, inherently coming with a level of uncertainty. In this research, info-gap decision theory (IGDT) is applied to model and manage such uncertainty. Surveillance of the Asian House Gecko, Hemidactylus frenatus Duméril and Bibron, 1836 on Barrow Island, is used as a case study. Our research provides a novel method for applying IGDT to determine the population threshold ($$K$$ K ) so that the decision can be robust to the deep uncertainty present in model parameters. We further robust-optimize surveillance costs rather than minimize surveillance costs. We demonstrate that increasing the population threshold for detection increases both robustness to the errors in the model parameter estimates, and opportuneness to lower surveillance costs than the accepted maximum budget. This paper provides guidance for decision makers to balance robustness and required surveillance expenditure. IGDT offers a novel method to model and manage the uncertainty prevalent in biodiversity conservation practices and modelling. The method outlined here can be used to design robust surveillance systems for invasive species in a wider context, and to better tackle uncertainty in protection of biodiversity and native species in a cost-effective manner.

A Fuzzy-Based Decision Support Tool for Appraisement of Supplier's Quality Assurance Practices

2016 ◽  
pp. 255-290
Author(s):  
Chitrasen Samantra ◽  
Saurav Datta ◽  
Siba Sankar Mahapatra
Keyword(s):  
Quality Assurance ◽  
Evaluation Model ◽  
Decision Support Tool ◽  
Cost Effective ◽  
Working Relationships ◽  
Global Marketplace ◽  
Support Tool ◽  
Effective Components ◽  
Present Context ◽  
Interactive Relationship

Recently competition in the global marketplace has stimulated immense attention being paid by the enterprises towards securing highest quality, cost effective components and materials, consistently delivered on time. This objective can only be achieved by establishing long term, close working relationships with suppliers, who adopt a proper quality philosophy. Supplier Quality Assurance is the confidence in a supplier's ability to deliver a commodity or service towards satisfying customer's needs. Supplier Quality Assurance can be achieved through interactive relationship between the customer and the supplier; it aims at ensuring the product's ‘suitably fit' to the customer's requirements with little or no adjustment or inspection. In the present context, the study develops a decision-making framework to assure as well as to assess suppliers' existing quality philosophy, current policy and related practices. An Interval-Valued Fuzzy Set (IVFS) theory has been adopted to develop such an evaluation model.

Choosing cost-effective locations for conservation fences in the local landscape

2012 ◽  
Vol 39 (3) ◽  
pp. 192 ◽  
Author(s):  
Michael Bode ◽  
Karl E. C. Brennan ◽  
Keith Morris ◽  
Neil Burrows ◽  
Neville Hague
Keyword(s):  
Systematic Approach ◽  
Decision Support Tool ◽  
Cost Effective ◽  
Small Scale ◽  
Decision Making Process ◽  
Large Set ◽  
Expected Return ◽  
Support Tool ◽  
Funding Sources ◽  
Predator Exclusion

Context Exclosure fences are widely used to reintroduce locally extinct animals. These fences function either as permanent landscape-scale areas free from most predators, or as small-scale temporary acclimatisation areas for newly translocated individuals to be ‘soft released’ into the wider landscape. Existing research can help managers identify the best design for their exclosure fence, but there are currently no methods available to help identify the optimal location for these exclosures in the local landscape (e.g. within a property). Aims We outline a flexible decision-support tool that can help managers choose the best location for a proposed exclosure fence. We applied this method to choose the site of a predator-exclusion fence within the proposed Lorna Glen (Matuwa) Conservation Park in the rangelands of central Western Australia. Methods The decision was subject to a set of economic, ecological and political constraints that were applied sequentially. The final exclosure fence location, chosen from among those sites that satisfied the constraints, optimised conservation outcomes by maximising the area enclosed. Key results From a prohibitively large set of potential exclosure locations, the series of constraints reduced the number of candidates down to 32. When ranked by the total area enclosed, one exclosure location was clearly superior. Conclusions By describing the decision-making process explicitly and quantitatively, and systematically considering each of the candidate solutions, our approach identifies an efficient exclosure fence location via a repeatable and transparent process. Implications The construction of an exclusion fence is an expensive management option, and therefore needs to convincingly demonstrate a high expected return-on-investment. A systematic approach for choosing the location of an exclosure fence provides managers with a decision that can be justified to funding sources and stakeholders.

Traffic accident modeling: some statistical issues

2006 ◽  
Vol 33 (9) ◽  
pp. 1115-1124 ◽  
Author(s):  
Z Sawalha ◽  
T Sayed
Keyword(s):  
Road Safety ◽  
Regression Models ◽  
Model Building ◽  
Negative Binomial ◽  
Prediction Models ◽  
Negative Binomial Regression ◽  
Outlier Analysis ◽  
Accident Prediction ◽  
Binomial Regression ◽  
Statistical Issues

Accident prediction models are invaluable tools that have many applications in road safety analysis. However, there are certain statistical issues related to accident modeling that either deserve further attention or have not been dealt with adequately in the road safety literature. This paper discusses and illustrates how to deal with two statistical issues related to modeling accidents using Poisson and negative binomial regression. The first issue is that of model building or deciding which explanatory variables to include in an accident prediction model. The study differentiates between applications for which it is advisable to avoid model over-fitting and other applications for which it is desirable to fit the model to the data as closely as possible. It then suggests procedures for developing parsimonious models, i.e., models that are not over-fitted, and best-fit models. The second issue discussed in the paper is that of outlier analysis. The study suggests a procedure for the identification and exclusion of extremely influential outliers from the development of Poisson and negative binomial regression models. The procedures suggested for model building and conducting outlier analysis are more straightforward to apply in the case of Poisson regression models because of an added complexity presented by the shape parameter of the negative binomial distribution. The paper, therefore, presents flowcharts detailing the application of the procedures when modeling is carried out using negative binomial regression. The described procedures are then applied in the development of negative binomial accident prediction models for the urban arterials of the cities of Vancouver and Richmond located in the province of British Columbia, Canada. Key words: accident prediction models, overfitting, parsimony, outlier analysis, Poisson regression, negative binomial regression.

Effect of Median Design on Rural Freeway Safety

2008 ◽  
Vol 2060 (1) ◽  
pp. 29-37 ◽  
Author(s):  
Andrew P. Tarko ◽  
Natalie M. Villwock ◽  
Nicolas Blond
Keyword(s):  
Opposite Direction ◽  
Negative Binomial ◽  
Prediction Models ◽  
Negative Binomial Regression ◽  
Well Being ◽  
Crash Severity ◽  
Before And After ◽  
Single Vehicle ◽  
Median Width ◽  
The Impact

Although median barriers are an absolute means of preventing drivers from crossing road medians and colliding with vehicles moving in the opposite direction, they may cause additional crashes. This perhaps complex safety effect of median barriers has not been investigated well. Being able to predict the safety impact of most types of median barriers on rural freeways is becoming more desirable because some state departments of transportation plan to expand many of their four-lane rural freeways to six lanes to accommodate increases in traffic volume. Realistic crash prediction models sensitive to the median design would provide the needed guidance useful in designing adequate median treatments on widened freeways. The impact of median designs on crash frequency was investigated in this study through negative binomial regression and before-and-after studies based on data collected in eight participating states. The impact on crash severity was investigated with a logit model. The separate effects of changes in median geometry were quantified for single-vehicle, multiple-vehicle same direction, and multiple-vehicle opposite direction crashes. The results were significantly different and indicated that reducing the median width without adding barriers (the remaining median width is still reasonably wide) increases the severity of crashes, particularly opposite direction crashes. Further, reducing the median and installing concrete barriers eliminates opposite direction crashes but doubles the frequency of single-vehicle crashes and tends to lessen the frequency of same direction crashes. The crash severity also tends to increase.

scholarly journals Predicting the Amputation Risk for Patients with Diabetic Foot Ulceration – A Bayesian Decision Support Tool

2020 ◽  
Author(s):  
Jens Hüsers ◽  
Guido Hafer ◽  
Jan Heggemann ◽  
Stefan Wiemeyer ◽  
Swen Malte John ◽  
...  
Keyword(s):  
Decision Support ◽  
Diabetic Foot ◽  
Prediction Models ◽  
A Priori ◽  
Decision Support Tool ◽  
Major Amputation ◽  
Statistical Prediction ◽  
Support Tool ◽  
Cohen’S D ◽  
Cohen's D

Abstract Background: Diabetes mellitus is a major global health issue with a growing prevalence. In this context, the number of diabetic complications is also on the rise, such as diabetic foot ulcers (DFU), which are closely linked to the risk of lower extremity amputation (LEA). Statistical prediction tools may support clinicians to initiate early tertiary LEA prevention for DFU patients. Thus, we designed Bayesian prediction models, as they produce transparent decision rules, quantify uncertainty intuitively and acknowledge prior available scientific knowledge.Method: A logistic regression using observational collected according to the standardised PEDIS classification was utilised to compute the six-month amputation risk of DFU patients for two types of LEA: 1.) any-amputation and 2.) major-amputation. Being able to incorporate information which is available before the analysis, the Bayesian models were fitted following a twofold strategy. First, the designed prediction models waive the available information and, second, we incorporated the a priori available scientific knowledge into our models. Then, we evaluated each model with respect to the effect of the predictors and validity of the models. Next, we compared the performance of both models with respect to the incorporation of prior knowledge.Results: This study included 237 patients. The mean age was 65.9 (SD 12.3), and 83.5 per cent were male. Concerning the outcome, 31.6% underwent any- and 12.2% underwent a major-amputation procedure. The risk factors of perfusion, ulcer extent and depth revealed an impact on the outcomes, whereas the infection status and sensation did not. The major-amputation model using prior information outperformed the uninformed counterpart (AUC 0.765 vs AUC 0.790, Cohen’s d 2.21). In contrast, the models predicting any-amputation performed similarly (0.793 vs 0.790, Cohen’s d 0.22).Conclusions: Both of the Bayesian amputation risk models showed acceptable prognostic values, and the major-amputation model benefitted from incorporating a priori information from a previous study. Thus, PEDIS serves as a valid foundation for a clinical decision support tool for the prediction of the amputation risk in DFU patients. Furthermore, we demonstrated the use of the available prior scientific information within a Bayesian framework to establish chains of knowledge.

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