WisTransPortal: A Wisconsin Traffic Operations Data Hub

Parker, Steven; Yang, Tao

doi:10.1061/40799(213)97

Cited by 8 publications

(10 citation statements)

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“…Data for segment-related crashes that occurred on the Wisconsin state trunk network system between 2013 and 2015 were collected, excluding deer-related crashes (26).…”

Section: Data Descriptionmentioning

confidence: 99%

Exploration of Contributing Factors Related to Driver Errors on Highway Segments

Shaon

Qin

Chen

et al. 2018

Transportation Research Record

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A significant portion of crashes occurred on highway segments, with more than 90% of crashes associated with driving errors. To avoid a crash, a driver needs to detect a hazard, decide the safest driving maneuvers, and execute them properly. Driver errors at any of these sequential phases may lead to a crash; therefore, it is necessary to identify the contributing factors and assess their influence on driver behavior. To assist this investigation, a multinomial probit model was employed to study driver errors reported in crashes in rural and urban areas. The modeling results identified many highway geometric features, traffic conditions, roadway events, and driver characteristics as statistically correlated to different types of driver error. Following the extensive list, the impacts of error-contributing factors were discussed within each error category. This exercise helps to gain a better understanding of similar or varying effects of explanatory variables across different error categories. The broad and insightful information will help researchers and safety professionals to better understand when, where, and how the driver error may lead to a crash and to develop cost-effective preventive countermeasures.

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“…Data for segment-related crashes that occurred on the Wisconsin state trunk network system between 2013 and 2015 were collected, excluding deer-related crashes (26).…”

Section: Data Descriptionmentioning

confidence: 99%

Exploration of Contributing Factors Related to Driver Errors on Highway Segments

Shaon

Qin

Chen

et al. 2018

Transportation Research Record

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show abstract

“…It is a common practice to consolidate KABCO into three levels-major injury (K + A), minor injury (B + C), and no injury (O) to ensure that a sufficient number of observations is available in each injury severity level (10,14,38). Specific driver errors were extracted from the MV4000 database, in which the investigating police officers documented detailed accident information (37). There is a list of 14 driver-related factors from which the reporting police officer identifies the driver-related factor(s) associated with a crash.…”

Section: Data Description and Exploratory Analysismentioning

confidence: 99%

“…Segment-related crashes that occurred on the Wisconsin state trunk network system between 2011 and 2015 were collected from WisTransportal data hub maintained by TOPSLAB ( 37 ). Deer-related and hit-and-run crashes were removed from the crash dataset as driver-related information for these crashes are not available.…”

Section: Data Description and Exploratory Analysismentioning

confidence: 99%

Crash Data-Based Investigation into How Injury Severity Is Affected by Driver Errors

Shaon

Qin

2020

Transportation Research Record: Journal of the Transportation R

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Unsafe driving behaviors, driver limitations, and conditions that lead to a crash are usually referred to as driver errors. Even though driver errors are widely cited as a critical reason for crash occurrence in crash reports and safety literature, the discussion on their consequences is limited. This study aims to quantify the effect of driver errors on crash injury severity. To assist this investigation, driver errors were categorized as sequential events in a driving task. Possible combinations of driver error categories were created and ranked based on statistical dependences between error combinations and injury severity levels. Binary logit models were then developed to show that typical variables used to model injury severity such as driver characteristics, roadway characteristics, environmental factors, and crash characteristics are inadequate to explain driver errors, especially the complicated ones. Next, ordinal probit models were applied to quantify the effect of driver errors on injury severity for rural crashes. Superior model performance is observed when driver error combinations were modeled along with typical crash variables to predict the injury outcome. Modeling results also illustrate that more severe crashes tend to occur when the driver makes multiple mistakes. Therefore, incorporating driver errors in crash injury severity prediction not only improves prediction accuracy but also enhances our understanding of what error(s) may lead to more severe injuries so that safety interventions can be recommended accordingly.

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“…The basic objective of the WisTransPortal is to develop capabilities for a statewide ITS data hub to support multiple applications in traffic operations and safety. Those capabilities include integration, management, analysis, and dissemination of real-time and historical ITS operations data through a centralized database and communications infrastructure (7). The data archiving component of the WisTransPortal consists of a collection of automated services that connect to various WisDOT and other ITS data sources and prepare the data for archiving in a common relational database.…”

Section: Wisconsin Its Traffic Detector Data Wisconsin Traffic Data Smentioning

confidence: 99%

Large Scale Intelligent Transportation System Traffic Detector Data Archiving

Qu¹,

Parker²

2015

Cictp 2015

Self Cite

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Archived traffic data can be used in transportation planning, administration, and research by various entities and agencies. During the past two decades, considerable effort has been dedicated to developing and implementing large-scale traffic data archives. The Wisconsin Traffic Operation and Safety (TOPS) Laboratory at the University of Wisconsin-Madison maintains a statewide traffic detector data archiving and retrieving system, which is developed to enable centralized management of statewide ITS detector and configuration data, optimizing the utilization of massive data on a systematic level, and improving the interactivity and accessibility for integration with other transportation data sources such as lane closure data or incident data. This data archive is currently being enhanced to incorporate higher resolution traffic data by migrating from 5-minute to 1-minute and even 20-second sampling intervals. At the same time, there is a desire to generate aggregated datasets such as hourly, monthly, and annual average values from the raw data. As the traffic data requirements continue to grow, the management of the traffic data archive becomes a complex big data problem. This paper describes a proposed redesign of the TOPS Lab traffic detector archived data management system to improve storage, performance, access, and integration capabilities. Particular detail is given to the data archiving process, including data validation, and support for spatial attributes and GIS data integration.

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WisTransPortal: A Wisconsin Traffic Operations Data Hub

Cited by 8 publications

References 0 publications

Exploration of Contributing Factors Related to Driver Errors on Highway Segments

Exploration of Contributing Factors Related to Driver Errors on Highway Segments

Crash Data-Based Investigation into How Injury Severity Is Affected by Driver Errors

Large Scale Intelligent Transportation System Traffic Detector Data Archiving

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