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MMUCC Model Minimum Uniform Crash Criteria. Crash Vehicle Person Roadway. Impact of threshold adjustments. Sketch and narrative. http:// www.nhtsa-tsis.net/crashforms /. Storage/retrieval. <500 annually may be filed (paper) with summary tables
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MMUCCModel Minimum Uniform Crash Criteria Crash Vehicle Person Roadway
Storage/retrieval • <500 annually may be filed (paper) with summary tables • Increasingly, all data are input into a database (and forms scanned) • Feeds state and national databases
Data Collection Technologies • TraCS: Traffic and Criminal Software
Easy Street Draw & Visio Florida TraCS show
Data limitations Ogden) • Systematic reporting bias • Database not truly reflective of crash situation • Random bias • Under-reporting can result in distorted picture of road crash situation • Numerically • Nature of the crashes • Not recording particular factor, means it was not present • Factor was present, but police officer did not think that it is not important
Data limitations (continued) • Coding errors • Location errors • Discontinuities • Data from one time period can not be compared to another time period • Delays • Takes too long to have data available for analysis, so countermeasures development is responding to historical crashes which may be out of date
Data limitations (continued) • Hidden problems • It is assumed that database is good indicator of road safety problems • There might some masked problems • Pedestrians avoid using an area because perceived safety problem • This kind of problems need to be tackled through a road safety audit or identified through community consultation
Use and Abuse of Crash Data in Roadway Access Management A Workshop at the National Access Management Conference Baltimore, Maryland July 13, 2008
Data-Driven Access Management • Access management treatments and plans should be directly tied to measurable objectives such as crash rate or crash cost reduction • Access management treatments proposed should be appropriate given the types of crashes and pattern of crashes being experienced in a corridor • Access management treatment costs need to be justifiable based upon the expected benefits of crash reductions and other objectives • Stakeholders and decision-makers must be convinced that the “gain” of access management is worth the “pain” • Confidence in both past (“before treatment”) and expected future crash rates (“after treatment”) should be high • You want to be very sure that any treatments will produce a noticeable and positive result
Access Management and Safety • Most access-management related crashes occur on urban and suburban arterial roadways at speeds of 35 to 55 miles per hour • Up to half of all crashes in urban areas are related to issues of access (minor public road intersections, traffic signal spacing, driveways) • Although most access-related crashes occur in urban or suburban areas, access-related crashes in rural areas tend to be severe crashes due to higher travel speeds • Access-related crashes occur at conflict points • The diagram represents one crash data point
What’s On Your Table … Traffic over time Crash data tables and charts Corridor photos Land Use 27 Laminated base map Crash data stack map
Crash Data Allow Better … • Problem Identification • Understanding of the problem before jumping into exploring and designing solutions • Focus on severe crashes rather than all (minor) crashes However …
You Need Good Quality Data The Ingredients Matter: Quality Control
Crash Data Quality: Timeliness • Sometimes crash data are not available for months or even years • Varying timeliness of different jurisdictions can cause issues for comparative analysis • Time itself is important – did something change during the analysis period? • Also – the time period is important … one year of data are probably not enough!
Crash Data Quality: Accuracy Considering functional area Original • Spatial Location • Attributes, e.g., severity, crash type, roadway info 1ST Road SOUTH ANKENY BOULEVARD v
Crash Data Quality: Completeness • Missing data can lead to a misleading picture and erroneous conclusions • Some crash records have “unknown” or “other” fields • Some crash records are missing altogether • Variations between jurisdictions (county level, state level) can lead to inaccuracies in comparative analysis
Crash Data Quality: Consistency/Uniformity • Across jurisdictions • Across time • Consistent severities
Consistency • Although the need for data is universally recognized, there is little consistency in collected data (Ogden) • Comparative study of eleven European countries found that • Only two variables (date & hour) were collected in all eleven countries • 7 percent of items were recorded in three countries • 70 percent recorded in only one country • There is no nationwide crash data reporting system in US • Little consistency within states for recorded data elements
Crash Data Quality: Integration • Integration provides a ‘richer’, more complete source of information (e.g., integration with roadway features) • Double check on accuracy (including severity) • Privacy is a tough issue • Another tough issue is multiple offices and even agencies being in charge of various parts of safety data
Crash Data Quality: Accessibility • How can you get crash data? • How easy is it to get? • What form do you want it in? • Liability and perception is an issue. • Continuum: not available … special request w/delay … regular updates … service … instant web access
Typical Crash Data IssuesThese may not be apparent to the data user
Changes in Crash Forms Collision Type Before After • Content • Addition/elimination of attributes collected • Change in definitions (values)
Changes in Crash Forms, cont. Change in crash form Crash Rate Crash Rate Year Year Statewide Site #1 Impacts: Difficult to perform direct comparisons over analysis period. May result in systematic change in apparent crash performance, e.g. crash reduction.
Cartographic (Base Map) Changes • Shift, update to reference road network Impact: Challenging to systematically assign crash location.
Location Accuracy • How are the crashes located? • GPS (where?) • Manually derived, based on literal description • LRS, Link-node, other? • What reference networks are used? • GIS • LRS • Link-node
X Location Accuracy, cont. • How do accuracies vary among location methods and reference networks? • Ex. GPS ±5m v. GIS-based road network ±10m Crash may be located anywhere within this area. X Roadway may be presented anywhere within this area. GIS road network Actual crash location Geocoded crash location Impact: type I or type II errors – you’d not know
Changes in Statute • Reportable crash definition • Property damage threshold, e.g. $500 v. $1000 • Injury crash • Reporting requirements • Driver report “…is not required when the accident is investigated by a law enforcement agency.” Impact: May result in systematic change in apparent crash performance, e.g. crash reduction.
Reporting Extent & Completeness • All public roads • Private property • State-maintained roads only • Jurisdiction, agency dependent Impacts: • Incomplete crash history skews findings. • Difficult to compare different locations.
Multiple Data Sources • Local law enforcement • State DOT • Other agencies, e.g. taxi authority Impact: Difficult to access and integrate all crash data, i.e. difficult to create a comprehensive, useable data set.
Limited Frame of Reference • Limited, no comparison to similar locations. • No comparison to “expected” conditions (comparables). Impact: • What may appear to be a problem site, in isolation, may be performing as well as, or better than, similar locations. • However, this does not imply that a location is performing well and/or can not be improved.
