Data Quality Review for GIS-based Crash and Work Zone Data Integration

Transcription

1 Cheng, Parker, Ran and Noyce Data Quality Review for GIS-based Crash and Work Zone Data Integration Yang Cheng, Ph.D. (corresponding author) Assistant Researcher TOPS Laboratory, Department of Civil and Environment Engineering University of Wisconsin Madison cheng@ wisc.edu Steven T. Parker, Ph.D. IT Program Manager TOPS Laboratory, Department of Civil and Environment Engineering University of Wisconsin Madison sparker@engr.wisc.edu Bin Ran, Ph.D. Professor TOPS Laboratory, Department of Civil and Environment Engineering University of Wisconsin - Madison Madison, WI 0, USA bran@wisc.edu David A. Noyce, Ph.D., P.E. Arthur F. Hawnn Professor and Chair Director, Traffic Operations and Safety (TOPS) Laboratory University of Wisconsin-Madison Department of Civil and Environment Engineering 0 Engineering Hall, Engineering Drive Madison, WI 0 noyce@engr.wisc.edu Word Count: + Tables * 0 + Figures * 0 =

2 Cheng, Parker, Ran and Noyce 0 0 ABSTRACT Effective work zone safety analysis requires comprehensive data on crashes, work zones, and other environmental factors. Fundamental to such knowledge is the ability to correlate crash data with corresponding work zones, with the knowledge of the data quality. Previous research demonstrated the data integration and analysis capabilities based on a common GIS roadway network. However, there are cases where the construction zone flag in the crash reports contradicts with the output of the data integration. This study is to review the overall accuracy and completeness of a previously developed crash and work zone data integration algorithm. By examining the randomly selected crashes through the original crash report and the work zone records, the results confirmed the overall accuracy of the GIS-based integration algorithm. The further analysis implies that there are data quality issues in the crash report and work zone records, which can also be identified using this algorithm. The findings can help improve work zone operations system data quality, and develop guidance for determining work zone crashes for police officers. System Acronyms MV000: Wisconsin Motor Vehicle Crash Database STN: Wisconsin State Trunk Network, which is the WisDOT GIS-based linear referencing system (LRS) for state and federal highways in Wisconsin WisLCS: Wisconsin Lane Closure System

3 INTRODUCTION Although necessary to maintain the transportation infrastructure, work zones have been one of the major roadway bottlenecks by reducing the road capacity, increasing traffic delays, and imposing safety risks (, ). Researchers have been investigating the risk factors influencing the occurrence of work zone crashes (-). All those efforts reply on the availability and completeness of the information regarding the crashes and the work zones (,, ). With the deployment of information technologies (IT) and intelligent transportation systems (ITS), states and agencies have been using modern information and data management systems for transportation applications, such as work zone planning/operations and crash data archiving (). Our capabilities to manage and retrieve historical transportation data have been greatly improved. However, those data sources are often managed in different formats and by different systems, since these data are typically oriented towards specific application areas. Therefore, data integration and system interoperability - and data quality control across different data sets - are usually not an easy task; the capability to integrate and cross reference different data sets, such as work zones and crashes, is essential to the task (0). Our previous studies (-) presented and used a matching algorithm to integrate work zone records and crash data from a work zone reporting system and a crash data archival system, respectively. That algorithm enable systematical work zone crash analysis to cover multiple years for the whole state of Wisconsin; results indicate several findings of significant potential for the approach. Despite its success, there are some issues remaining unsolved. The first issue is that there are around 0% of studied work zone crashes as noted in the police crash report whose corresponding work zone records cannot be found. The second issue is that applying this integration method to all the crashes regardless of whether a crash is flagged on the police report as work zone related or not, the algorithm identifies approximately % non-work zone crashes (according to the police report) that occurred in a work zone. That is, one of the objectives of this study is to investigate the aforementioned discrepancies between the crash reports and the output of the data integration algorithm, which are defined as the Type I and II errors in statistics (), as shown in Table. In the crash report, there is a checkbox as construction zone. If it is checked, we consider this crash is a work zone crash. The algorithm uses the time and location of the crash to search any concurrent work zones. If a crash occurred near a work zone, the algorithm consider there is a match.

4 TABLE Definitions of Types of Errors By Crash Report WZ Crash Non-WZ Crashes 0 0 By Algorithm WZ Crash Non-WZ Crashes True Positive (Matching Set) False Negative False Positive True Negative In summary, this study is to further evaluate the accuracy of the data integration method, and expand our previous research to review the overall accuracy and completeness of the crash report construction zone flag by: () Quantitatively evaluating the accuracy of the True Positive/ matching set () Finding out the factors leading to the false negative set. () Finding out the factors leading to the false positive set. The rest of this paper is organized into four sections. Section presents an introduction to the data systems, data sources and data format used in this study. Section presents the work zone and crash data integration algorithm, the matching algorithm. Section shows the results of the matching algorithm and the manual examination of different categories of crashes. Section concludes the report and making recommendations for future research.. INTRODUCTION TO THE DATA AND INTEGRATION The work zone records and crash data were pulled from two systems: the Wisconsin Lane Closure System (WisLCS) () and Wisconsin MV000 crash database (). Both systems are available through the WisTransPortal (), a comprehensive transportation data warehouse, at the Wisconsin Traffic Operations and Safety (TOPS) Laboratory at the University of Wisconsin- Madison. The Wisconsin Lane Closure System The WisLCS is a central acceptance and reporting system for all highway lane closures and restrictions in the state of Wisconsin, including construction, maintenance, utility, and other planned or unplanned closures. The WisLCS has been operational since April 00, and it facilitates work zone activity monitoring at the WisDOT Statewide Traffic Operation Center (STOC) and regional transportation offices. The WisLCS also provides real-time lane closure The crash report has a checked construction zone flag, and the algorithm is able to find this crash occur in a work zone. The crash report has a checked construction zone flag, but the algorithm cannot find any work zone at that time and location. The crash report has an unchecked construction zone flag, but the algorithm is able to find this crash occur in a work zone.

5 information to the Wisconsin traveler information system, and supports Oversize / Overweight Permitting activities of the Wisconsin Department of Transportation (WisDOT). Figure shows a representative interface of the WisLCS and an exemplary work zone showing on the Wisconsin web page. 0 Figure The WisLCS and Work Zones on Wisconsin. All WisLCS records are archived in the WisTransPortal database for future analysis, research and planning purposes. This archive includes detailed work zone information for each closure. In addition to locations and start/end times, other work zone attributes are also available, as shown in Table.

6 0 TABLE Work Zone Details in WislCS () Attributes Values Closure Type Construction, Maintenance, Permit *, Special Event, Emergency ** Duration *** Long Term, Continuous, Weekly, Daily/Nightly Facility Type Bridge, Mainline, Ramp, System Interchange Restriction Weight, Height, Width, Speed Lane Details Full Closure, Left Lanes Closed, Right Lanes Closed, Left Lanes Closed, Right Lanes Closed, Flagging Operation, Lane Restriction, Left Lane Closed, Left Shoulder Closed, Median Turn Lane Closed, Moving Full Closure, Moving Lane Closure, Off Roadway Left, Off Roadway Right, Passing Lane Closed, Right Lane Closed, Right Shoulder Closed, Single Lane Closed, Various Lanes Closed * Permit closures refer to utility work ** Emergency closures refer to unplanned infrastructure repair caused by incidents such as bridge hits. *** Duration: Daily/Nightly: operation on a daily/nightly basis, specified by the start/end times each day within the start/end date range. Weekly: operation on a weekly basis, specified by start/end weekday and time. Continuous: continuous operation within the start/end date/time range, but less than two weeks. Long Term: continuous operation longer than two weeks.. The Wisconsin MV000 Crash Database The WisTransPortal contains a complete database of Wisconsin MV000 police reported crashes from through the present (). This database includes information on all policereported crashes in Wisconsin, including the locations, times, vehicles involved, and other general crash attributes. Highway crashes in MV000 are geocoded to support further analysis. Figure shows an example of report copy and a GIS based map interface provided by the MV000 tool.

7 0 0 Figure The MV000 Crash Reports and GIS Crash Map. Location Coding of WisLCS and MV000 One of the major base maps used by the WisTransPortal is the Wisconsin State Trunk Network (STN) (, ), which is the WisDOT GIS roadway network and linear referencing system for all the state and federal highways in Wisconsin (0). Although the MV000 and WisLCS use different linear referencing methods (LRMs), the locations of the work zone and crashes are translated through the LRM process to align to the STN. By using the common GIS base map, it allows for efficient integration of different datasets, the work zone and crash records, and calculating spatial distances between those entities.. Data Integration of WisLCS and MV000 The purpose of the data integration is to find the candidate work zones associated with a given crash, based on the time and location attributes. Because of the aforementioned common location coding based on the STN, comparing the locations of crashes and work zones becomes possible. There is a gray area about the impacts of a work zone during its inactive period, such as the time outside of the Daily/Nightly or Weekly work periods. It is unclear whether all the devices and equipment of the work zone were cleared from the site. In this study, we decided to include the inactive periods into the analysis for the automatic integration using the algorithm, and address this issue in the following manual examination. It is worth noting that this algorithm can be applied to the integration of other data sources. For example, it can be used to integrate traffic data from roadside detectors with work zone and crash data (, ), as long as the detectors are geocoded to the STN or other linear referencing systems. For details about this data integration algorithm, please refer to our previous studies (, ).

8 0 0. RESULTS AND ANALYSIS This section provides the output from the integration algorithm and results from the manual examination of the output.. Matching Results Table shows the results of the integration algorithm. The total number of crashes happened on Interstate or US highway in Wisconsin from 00 to 0 is,. Among them, there are, crashes flagged as Construction Zone crashes according to the crash report. The integration algorithm was able to retrieve corresponding LCS records for, crashes among those Construction Zone crashes. That makes the matching rate as.%. On the other hand, the matching algorithm found that there are, Non-Construction Zone crashes occurred within a work zone during its scheduled period, which is about.% of all the Non-Construction Zone crashes. TABLE Stats of Matching Output Category Number of Crashes Percentage Matching Set (True Positive),.% WZ Crashes without LCS Record(s) (False Positive),0.% Total WZ Crashes, 00.00% Non-WZ Crashes with LCS Record(s) (False Negative),.% Total Crashes, 00.00% Table shows the overall performance of the integration algorithm in terms of True Positive (.%), False Positive (.%), False Negative (.%) and True Negative (.%). TABLE Statistics of The Integration Algorithm By Crash Report Non-WZ WZ Crash Crashes By Algorithm. Matching Set Evaluation WZ Crash Non-WZ Crashes.%.%.%.% A total of 0 crashes in the matching set were randomly selected to examine the accuracy of the data integration algorithm. Copies of the police crash reports and the retrieved LCS records were

9 0 0 0 pulled and manually examined. We did not find any evidence in the crash reports suggesting that it not be a work zone related crashes. However, crash reports (.%) do not explicitly mention whether the work zones were directly related to the crashes in the narratives of the crash report. In summary, the manual inspection verified the accuracy of the matching set.. Work Zone Crashes without matched WisLCS Work Zones (False Negative) From the category of work zone crashes that do not match a WisLCS work zone record, 00 crashes were randomly picked and examined. Table shows the details of the results. Table Results for the False Negative Set Category Number of Crashes Percentage Outside of the planning range.0% WZ not mentioned.0% Unknown causes.0% Total Checked Crashes % It is found that more than half () of the checked crash reports do not mention the existence of a construction zone, although the construction flag is checked. In addition, there are crashes that turn out to occur upstream or downstream of the work zone, or before/after the recorded start and end time of the construction period, per the work zone details in the LCS. Possible causes would include but not limited to inaccurate work zone records or work zone activities did not fully comply with the planned schedule and area entered to the WisLCS. Our previous study also found similar situations (). For the remaining crashes, the crash reports confirm the existence of work zone, but neither the algorithm or manual inspection were able to find the corresponding work zone records. The actual causes are still unclear and should be investigated in the future work.. None Work Zone Crashes with Matched WisLCS Work Zones (False Positive) For the category of crashes that match a WisLCS record but have not been marked with the construction zone flag, 00 crashes were examined. Table shows the details of the results. Twenty-five of them are animal hits; of them are related to work zones based on the crash description in the report, and the of them do not have any information about the existence of a work zone. Animal Hit crashes occurred within work zone can be safely excluded in further studies. Among the crashes without information about the existence of a work zone, took place during the inactive periods of a work zone. It is an open question about whether restrictions from inactive equipment in the field may have contributed to the crashes, even if not explicitly flagged on the crash report form. It is worth noting that in six cases, the report mentioned the existence of a work zone but the Construction Zone is not checked. It is very like to be a data

10 entry errors when filing the report. For the categories of within active WZ and within inactive WZ, the actual causes should be investigated in the future work. TABLE Results for the False Positive Set Category Number of Crashes Percentage Animal Hit.0% WZ related but not flagged.0% within inactive WZ.0% within active WZ.0% Total Checked Crashes %. Discussion about the Findings The findings confirm the reliability about the work zones records retrieved by the data integration algorithm for work zone crashes. There are also data quality issues in the crash and work zone datasets. The study found cases in which the crash reports do not provide explicit information about the work zones, or the Construction Zone flag is likely to be wrong. Therefore, there would be a room for improvement in terms of providing better information for work zone crash data collection. This study can provide empirical data support for developing guidance related to training and work zone data collection with respect to the new Wisconsin crash report, which will provide better adherence to the national Model Minimum Uniform Crash Criteria Guideline (MMUCC) standards for crash data collection (). The current version of the MMUCC th Edition includes an expanded section on work zone crash data collection. The matching algorithm was not able to find the corresponding work zone records for a small portion of the confirmed work zone crashes (work zone factors explicitly included in the crash report). Future work would including finding the causes and improving the algorithm. The data integration algorithm shows a satisfying accuracy in the matching set, which could serve as a core module in the future automatic system to support real-time and proactive work zone planning and operations applications. One example is the real-time monitoring and notification of crashes in major work zones. It is desirable to monitor the safety level of work zones during their life cycle, to provide more time for measure implantation to eliminate potential safety hazards as quickly as possible. Such proactive approach can save more lives and reduce the crash loss than the traditional way, which analyzes the crashes occurred in the work zone long after the work zone finished. Investigating these issues would help validate and improve the integration algorithm, and enhance the ability to track work zone crashes on the systematic level as well. It could also lead to important process improvements in how police officers fill out work zone related information on the crash

11 0 0 0 report. Therefore, this study is expected to have cross-departmental benefit regarding safety program planning, work zone operations, and law enforcement awareness.. CONCLUSIONS AND FUTURE WORK Effective work zone operations, management, and safety analysis require comprehensive work zone planning and management information that is accessible and easy-to-be-integrated with related data sets. The Wisconsin Lane Closure System (WisLCS), which serves as WisDOT's statewide scheduling and reporting system for all highway lane closures, provides a new opportunity to match crashes to specific work zones on a system-wide scale. Initial research conducted by TOPS Lab demonstrated the data integration and analysis capabilities based on a common GIS roadway network. The objectives of this study were to expand this research, further evaluate the performance of the algorithm, and address two unanswered questions identified in previous studies. The first problem is that there are around 0% of work zone crashes whose corresponding work zone records cannot be found. The second problem is that, when applying this integration method to all crashes regardless of whether a crash is flagged on the police report as work zone related or not, the algorithm implies that there is about % non-work zone crashes (according to the police report) that occurred in a work zone. Investigating these issues would help validate and improve the matching algorithm and, therefore, the ability to track work zone crashes on a systematic level. This study examined 0 crashes, and their related LCS records. The investigation confirmed the overall accuracy of the algorithm when retrieving work zone records for work zone crashes, and no evidence in the crash reports were found to suggest that the matching results were incorrect. There were also quite a number of cases that did not mention the existence of a work zone, even when the construction flag was checked in the crash report. For six crashes, the reports mentioned the existence of a work zone, but the Construction Zone flag is not checked, which was very likely to be a data entry error. There were also crashes which occurred outside of the work zone or before/after the recorded start and end time of the construction period. Future work would include guidance related to training and work zone data collection with respect to new crash reports, and further improvements and extension to the data integration algorithm for work zone planning and engineering improvements, such as real-time monitoring and notification of crashes in major work zones. ACKNOWLEDGEMENT

12 The Wisconsin Lane Closure System, and WisTransPortal MV000 Crash Database were developed through sponsorship and collaboration with the Wisconsin Department of Transportation. REFERENCES. USDOT, FHWA. Developing and Implementing Transportation Management Plans for Work Zones Yang, Hong, Kaan Ozbay, Ozgur Ozturk, and Kun Xie. Work Zone Safety Analysis and Modeling: A State-of-the-Art Review. Traffic Injury Prevention, Vol., No., 0, pp. -.. Wolfe, Arthur C. The Concept of Exposure to the Risk of a Road Traffic Accident and an Overview of Exposure Data Collection Methods. Accident Analysis & Prevention, Vol., No.,, pp Li, Yingfeng and Yong Bai. Highway Work Zone Risk Factors and Their Impact on Crash Severity. Journal of Transportation Engineering, Vol., No. 0, 00, pp Harb, Rami, Essam Radwan, Xuedong Yan, Anurag Pande, and Mohamed Abdel-Aty. Freeway Work-Zone Crash Analysis and Risk Identification Using Multiple and Conditional Logistic Regression. Journal of Transportation Engineering, Vol., No., 00, pp Wang, Jun, Warren Hughes, Forrest Council, and Jeffrey Paniati. Investigation of Highway Work Zone Crashes: What We Know and What We Don't Know. Transportation Research Record: Journal of the Transportation Research Board, Vol., No. -,, pp. -.. Gregoriades, Andreas and Kyriacos C. Mouskos. Black Spots Identification through a Bayesian Networks Quantification of Accident Risk Index. Transportation Research Part C: Emerging Technologies, Vol., No. 0, 0, pp. -.. Qin, Xiao, John N. Ivan, Nalini Ravishanker, Junfeng Liu, and Donald Tepas. Bayesian Estimation of Hourly Exposure Functions by Crash Type and Time of Day. Accident Analysis & Prevention, Vol., No., 00, pp Lee, Eul-Bum and Changmo Kim. Automated Work Zone Information System on Urban Freeway Rehabilitation: California Implementation. Transportation Research Record: Journal of the Transportation Research Board, Vol., No. -, 00, pp Administration, Federal Highway. Highway Safety Improvement Program Manual Cheng, Yang, Steven Parker, Bin Ran, and David Noyce. Enhanced Analysis of Work Zone Safety through Integration of Statewide Crash and Lane Closure System Data. Transportation Research Record: Journal of the Transportation Research Board, Vol., No. -, 0, pp. -.. Cheng, Yang, Steven Parker, Bin Ran, David Noyce, and Rebecca Szymkowski. Enhanced Analysis of Crashes in the Proximity of Work Zones through Integration of Statewide Crash Data with Lane Closure System Data. In Transportation Research Board nd Annual Meeting, 0. Cheng, Yang, Steven Parker, Bin Ran, and David Noyce. Integrating Crash, Real-Time Traffic, and Lane Closure Data for Statewide Highway Work Zone Safety Analysis. In Transportation Research Board th Annual Meeting, 0

13 0 0. Sheskin, David. Handbook of Parametric and Nonparametric Statistical Procedures, CRC Press, 00.. Wisconsin DOT, TOPS Laboratory UW-Madison. Lane Closure System Accessed July, 00.. Wisconsin DOT and TOPS Laboratory UW-Madison. Mv000 Crash Database Query Tools 00 Accessed July, 0.. TOPS, Wisconsin Traffic Operations and Safety Laboratory The Wistransportal Project Accessed, 0.. WisDOT. Wisconsin State Trunck Network Location Control Management Data Dictionary., Wisconsin Department of Transportation, Geographic Information Services Unit.. WisDOT. Location Control Management Manual., Wisconsin Department of Transportation, Geographic Information Services Unit. 0. Erdman, Scott. State Trunk Network Data Collection Primer... Cheng, Yang, Steven Parker, Bin Ran, and David Noyce. Work Zone Crash Cost Prediction Using a Least Median Squares Linear Regression Model. In Transportation Research Board th Annual Meeting, 0. Chen, Xiaoxuan, Yang Cheng, Steven Parker, Bin Ran, and David Noyce. Enhanced Its Data Quality Issue Identification by Incorporating Geospatial Information and Planning Data. In Transportation Research Board th Annual Meeting, 0. Model Minimum Uniform Crash Criteria. 0.