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• 1. [Article] Improved Safety Performance Functions for Signalized Intersections

  For this effort, the research team developed new safety performance functions (SPFs) for signalized intersections in Oregon. The modeling dataset consisted of 964 crashes from a total of 73 intersections ...

  Citation × Citation Citation Abstract Copy Title: Improved Safety Performance Functions for Signalized Intersections Author: Dixon, Karen, Monsere, Christopher, Avelar, Raul, Barnett, Joel Stephen, Escobar, Paty, Kothuri, Sirisha Murthy Year: 2015 For this effort, the research team developed new safety performance functions (SPFs) for signalized intersections in Oregon. The modeling dataset consisted of 964 crashes from a total of 73 intersections that were randomly selected based on the presence of a traffic signal (identified through the crash data records). The SPFs were developed using a Poissonlognormal Generalized Linear Mixed model framework for total crashes and severe injury crashes (coded as KAB). Three SPFs were developed: 1) an SPF for total crashes, which relies on both major and minor AADTs to predict the expected number of crashes; 2) an SPF for KAB crashes, whose predictions derive from both AADTs as well as from the speed limit on the major road; and (3) a severity model to predict the proportion of KAB crashes to be used in combination with the SPF for total crashes. The research analyses determined that the speed limit variable significantly improved the quality of the SPFs and severity model, and as expected, suggests increasing severity with speed differentials. The models were validated spatially and temporally based on additional sites and using an additional year of data. The models all performed well during the validation; however enhanced models to improve model reliability were developed based on the larger dataset. As part of the model development, this research also explored a variety of rules to identify crashes as intersection-related based on the crash geo-location (including the common 250 feet rule). Crashes were manually classified from the combined data available from the geo-location of crashes, the geometric database, and the various fields in the Oregon crash database. These classifications were then compared to a number of rule options for classifying them as intersection crashes. The analysis revealed that the best performing rule is to use crashes that were geo-located within 300 feet of the centerline intersection at signalized locations plus crashes where the crash report indicates that they were associated with a traffic control device (i.e. traffic signal). Finally, this research effort developed models to estimate to estimate minor road AADT for use in safety analysis where this exposure information is not available. These models were developed from data from 66 intersections with known minor and major AADT volumes and validated with data from another 25 intersections. Significant model variables included major AADT, number of approach lanes, functional class, presence of a two-way leftturn lane, and parallel road AADT. Title: Improved Safety Performance Functions for Signalized Intersections Author: Dixon, Karen, Monsere, Christopher, Avelar, Raul, Barnett, Joel Stephen, Escobar, Paty, Kothuri, Sirisha Murthy Year: 2015 For this effort, the research team developed new safety performance functions (SPFs) for signalized intersections in Oregon. The modeling dataset consisted of 964 crashes from a total of 73 intersections that were randomly selected based on the presence of a traffic signal (identified through the crash data records). The SPFs were developed using a Poissonlognormal Generalized Linear Mixed model framework for total crashes and severe injury crashes (coded as KAB). Three SPFs were developed: 1) an SPF for total crashes, which relies on both major and minor AADTs to predict the expected number of crashes; 2) an SPF for KAB crashes, whose predictions derive from both AADTs as well as from the speed limit on the major road; and (3) a severity model to predict the proportion of KAB crashes to be used in combination with the SPF for total crashes. The research analyses determined that the speed limit variable significantly improved the quality of the SPFs and severity model, and as expected, suggests increasing severity with speed differentials. The models were validated spatially and temporally based on additional sites and using an additional year of data. The models all performed well during the validation; however enhanced models to improve model reliability were developed based on the larger dataset. As part of the model development, this research also explored a variety of rules to identify crashes as intersection-related based on the crash geo-location (including the common 250 feet rule). Crashes were manually classified from the combined data available from the geo-location of crashes, the geometric database, and the various fields in the Oregon crash database. These classifications were then compared to a number of rule options for classifying them as intersection crashes. The analysis revealed that the best performing rule is to use crashes that were geo-located within 300 feet of the centerline intersection at signalized locations plus crashes where the crash report indicates that they were associated with a traffic control device (i.e. traffic signal). Finally, this research effort developed models to estimate to estimate minor road AADT for use in safety analysis where this exposure information is not available. These models were developed from data from 66 intersections with known minor and major AADT volumes and validated with data from another 25 intersections. Significant model variables included major AADT, number of approach lanes, functional class, presence of a two-way leftturn lane, and parallel road AADT. Close

• 2. [Article] Bayesian Models for Reidentification of Trucks Over Long Distances on the Basis of Axle Measurement Data

  Vehicle reidentification methods can be used to anonymously match vehicles crossing 2 different locations on the basis of vehicle attribute data. In this article, reidentification methods are developed ...

  Citation × Citation Citation Abstract Copy Title: Bayesian Models for Reidentification of Trucks Over Long Distances on the Basis of Axle Measurement Data Author: Cetin, Mecit, Monsere, Christopher, Nichols, Andrew Year: 2011 Vehicle reidentification methods can be used to anonymously match vehicles crossing 2 different locations on the basis of vehicle attribute data. In this article, reidentification methods are developed to match commercial vehicles that cross 2 weigh-in-motion sites in Oregon that are separated by 145 miles. Using vehicle length and axle data as attributes to characterize vehicles, a Bayesian model is developed that uses probability density functions obtained by fitting Gaussian mixture models to a sample data set of matched vehicles. The reidentification model when applied to a test data set (where each downstream vehicle also crosses the upstream site) matches vehicles with an accuracy of 91% when both axle weight and axle spacings data are used. To account for the fact that not all vehicles in the downstream also cross the upstream site, an additional new step is developed to screen mismatched vehicles produced by the algorithm. For this step, several screening methods are developed that allow the user to trade off the total number of matched vehicles and error rate. For evaluating the effectiveness of the screening methods, 2 scenarios are considered. In the first scenario, only common vehicles that cross both the upstream and downstream sites are considered, whereas in the second scenario all downstream vehicles are considered. It is shown that the mismatch error can be reduced to as low as 1% and 5% at the expense of not matching about 25% of the common vehicles (crossing both sites) for the first and second scenarios, respectively. Title: Bayesian Models for Reidentification of Trucks Over Long Distances on the Basis of Axle Measurement Data Author: Cetin, Mecit, Monsere, Christopher, Nichols, Andrew Year: 2011 Vehicle reidentification methods can be used to anonymously match vehicles crossing 2 different locations on the basis of vehicle attribute data. In this article, reidentification methods are developed to match commercial vehicles that cross 2 weigh-in-motion sites in Oregon that are separated by 145 miles. Using vehicle length and axle data as attributes to characterize vehicles, a Bayesian model is developed that uses probability density functions obtained by fitting Gaussian mixture models to a sample data set of matched vehicles. The reidentification model when applied to a test data set (where each downstream vehicle also crosses the upstream site) matches vehicles with an accuracy of 91% when both axle weight and axle spacings data are used. To account for the fact that not all vehicles in the downstream also cross the upstream site, an additional new step is developed to screen mismatched vehicles produced by the algorithm. For this step, several screening methods are developed that allow the user to trade off the total number of matched vehicles and error rate. For evaluating the effectiveness of the screening methods, 2 scenarios are considered. In the first scenario, only common vehicles that cross both the upstream and downstream sites are considered, whereas in the second scenario all downstream vehicles are considered. It is shown that the mismatch error can be reduced to as low as 1% and 5% at the expense of not matching about 25% of the common vehicles (crossing both sites) for the first and second scenarios, respectively. Close

• 3. [Article] Using Custom Transportation Data Collection Software with Handheld Computers for Education, Research, and Practice

  In an effort to facilitate data collection for research, give students first-hand experience collecting data for course projects, and generate interest in the transportation field through outreach, the ...

  Citation × Citation Citation Abstract Copy Title: Using Custom Transportation Data Collection Software with Handheld Computers for Education, Research, and Practice Author: Bertini, Robert, Monsere, Christopher, Byrd, Andrew, Rose, Michael, El-Seoud, Tarek Year: 2005 In an effort to facilitate data collection for research, give students first-hand experience collecting data for course projects, and generate interest in the transportation field through outreach, the Intelligent Transportation Systems Laboratory of Portland State University, Oregon, has developed custom data collection software for handheld computers using the Palm OS platform. The software is designed to export the collected data to desktop computers in common file formats suitable for analysis in spreadsheet and geographic information systems applications. Data collection problems addressed include recording position over time, recording geographic location of features, and performing cumulative vehicle or pedestrian counts. Title: Using Custom Transportation Data Collection Software with Handheld Computers for Education, Research, and Practice Author: Bertini, Robert, Monsere, Christopher, Byrd, Andrew, Rose, Michael, El-Seoud, Tarek Year: 2005 In an effort to facilitate data collection for research, give students first-hand experience collecting data for course projects, and generate interest in the transportation field through outreach, the Intelligent Transportation Systems Laboratory of Portland State University, Oregon, has developed custom data collection software for handheld computers using the Palm OS platform. The software is designed to export the collected data to desktop computers in common file formats suitable for analysis in spreadsheet and geographic information systems applications. Data collection problems addressed include recording position over time, recording geographic location of features, and performing cumulative vehicle or pedestrian counts. Close

• 4. [Article] Exploratory Methods for Truck Re-identification in a Statewide Network Based on Axle Weight and Axle Spacing Data to Enhance Freight Metrics

  The main objective of this project is to evaluate the feasibility of re-identifying commercial trucks based on vehicle-attribute data automatically collected by sensors installed at traffic data collection ...

  Citation × Citation Citation Abstract Copy Title: Exploratory Methods for Truck Re-identification in a Statewide Network Based on Axle Weight and Axle Spacing Data to Enhance Freight Metrics Author: Monsere, Christopher, Cetin, Mecit, Nichols, Andrew Year: 2011 The main objective of this project is to evaluate the feasibility of re-identifying commercial trucks based on vehicle-attribute data automatically collected by sensors installed at traffic data collection stations. To support this work, archived data from weigh-in-motion (WIM) stations in Oregon are used for developing, calibrating, and testing vehicle re-identification algorithms. The vehicle re-identification methods developed in this research consist of two main stages. In the first stage, each vehicle from the downstream station is matched to the most “similar” upstream vehicle by using a Bayesian model. In the second stage, several methods are introduced to screen out those vehicles that cross the downstream site but not the upstream site and to tradeoff accuracy versus the total number of vehicles being matched. These methods involve calculating both the highest and the second highest similarity measures for each vehicle being matched. It is demonstrated that the proposed screening approach improves the accuracy of the re-identification methods significantly. The models are applied to the truck data collected by WIM sensors at three stations in Oregon, which together create two different “links” that are 125 and 145 miles long, respectively. It is observed that the algorithms can match trucks with approximately 90% accuracy while the total number of trucks being matched at this accuracy level is about 95% of the actual common trucks that cross both upstream and downstream sites. These methods allow the user to trade-off the accuracy vs. total vehicles being matched by adjusting a threshold parameter. For example, trucks can be matched with 98% accuracy if one is willing to match about 40% of all common trucks. It is also found that when travel times of vehicles between the upstream and downstream sites exhibit larger variation, mismatch rate increases. Overall, for estimating travel times and origin-destination flows between two WIM sites, the methods developed in this project can be used to effectively match commercial vehicles crossing two data collection sites that are separated by long distances. Title: Exploratory Methods for Truck Re-identification in a Statewide Network Based on Axle Weight and Axle Spacing Data to Enhance Freight Metrics Author: Monsere, Christopher, Cetin, Mecit, Nichols, Andrew Year: 2011 The main objective of this project is to evaluate the feasibility of re-identifying commercial trucks based on vehicle-attribute data automatically collected by sensors installed at traffic data collection stations. To support this work, archived data from weigh-in-motion (WIM) stations in Oregon are used for developing, calibrating, and testing vehicle re-identification algorithms. The vehicle re-identification methods developed in this research consist of two main stages. In the first stage, each vehicle from the downstream station is matched to the most “similar” upstream vehicle by using a Bayesian model. In the second stage, several methods are introduced to screen out those vehicles that cross the downstream site but not the upstream site and to tradeoff accuracy versus the total number of vehicles being matched. These methods involve calculating both the highest and the second highest similarity measures for each vehicle being matched. It is demonstrated that the proposed screening approach improves the accuracy of the re-identification methods significantly. The models are applied to the truck data collected by WIM sensors at three stations in Oregon, which together create two different “links” that are 125 and 145 miles long, respectively. It is observed that the algorithms can match trucks with approximately 90% accuracy while the total number of trucks being matched at this accuracy level is about 95% of the actual common trucks that cross both upstream and downstream sites. These methods allow the user to trade-off the accuracy vs. total vehicles being matched by adjusting a threshold parameter. For example, trucks can be matched with 98% accuracy if one is willing to match about 40% of all common trucks. It is also found that when travel times of vehicles between the upstream and downstream sites exhibit larger variation, mismatch rate increases. Overall, for estimating travel times and origin-destination flows between two WIM sites, the methods developed in this project can be used to effectively match commercial vehicles crossing two data collection sites that are separated by long distances. Close

• 5. [Article] Calibration of "Highway Safety Manual" Predictive Models for Oregon State Highways

  The recently released "Highway Safety Manual" (HSM) published by AASHTO provides a comprehensive set of tools for evaluating and identifying opportunities to improve safety for highway facilities. Included ...

  Citation × Citation Citation Abstract Copy Title: Calibration of "Highway Safety Manual" Predictive Models for Oregon State Highways Author: Xie, Fei, Gladhill, Kristie, Dixon, Karen, Monsere, Christopher Year: 2011 The recently released "Highway Safety Manual" (HSM) published by AASHTO provides a comprehensive set of tools for evaluating and identifying opportunities to improve safety for highway facilities. Included in the HSM is a quantitative method for predicting crashes on the basis of recently developed scientific approaches. These predictive methods currently exist for three facility types: rural two-lane roads; rural multilane highways; and urban and suburban arterial highways. To enhance precision, each HSM predictive method should be calibrated for location conditions. This paper demonstrates the HSM calibration procedure for total crashes in Oregon. The research identified three critical data collection limitations on information about pedestrian volumes, minor road traffic volumes at rural locations, and minimum sample size for underrepresented crash locations. Most of the calibration factors for Oregon were determined to be considerably lower than the expected value of approximately 1, and this observation was attributed to Oregon crash reporting thresholds and procedures. The paper includes an evaluation of crash severity distribution methods and an assessment of the significance of collision type distributions on the overall predicted crashes. Title: Calibration of "Highway Safety Manual" Predictive Models for Oregon State Highways Author: Xie, Fei, Gladhill, Kristie, Dixon, Karen, Monsere, Christopher Year: 2011 The recently released "Highway Safety Manual" (HSM) published by AASHTO provides a comprehensive set of tools for evaluating and identifying opportunities to improve safety for highway facilities. Included in the HSM is a quantitative method for predicting crashes on the basis of recently developed scientific approaches. These predictive methods currently exist for three facility types: rural two-lane roads; rural multilane highways; and urban and suburban arterial highways. To enhance precision, each HSM predictive method should be calibrated for location conditions. This paper demonstrates the HSM calibration procedure for total crashes in Oregon. The research identified three critical data collection limitations on information about pedestrian volumes, minor road traffic volumes at rural locations, and minimum sample size for underrepresented crash locations. Most of the calibration factors for Oregon were determined to be considerably lower than the expected value of approximately 1, and this observation was attributed to Oregon crash reporting thresholds and procedures. The paper includes an evaluation of crash severity distribution methods and an assessment of the significance of collision type distributions on the overall predicted crashes. Close

• 6. [Article] Online Tool for Delivering Research Results: Update to Oregon Department of Transportation Database of Crash Reduction Factors

  Often, the challenge for the practitioner in disseminating research results is to package and present them in a manner that is accessible and easy to use while still providing depth, guidance, and necessary ...

  Citation × Citation Citation Abstract Copy Title: Online Tool for Delivering Research Results: Update to Oregon Department of Transportation Database of Crash Reduction Factors Author: Monsere, Christopher, Breakstone, Aaron, Bertini, Robert, Bish, Douglas Year: 2007 Often, the challenge for the practitioner in disseminating research results is to package and present them in a manner that is accessible and easy to use while still providing depth, guidance, and necessary information. In most cases, final research reports are made available in printed form and online; however, for some applications, converting a report to a more interactive online report (tool) can produce results that are easier for the user to access. This is particularly true in this age of information overload and as practitioners find themselves needing to "do more with less." This paper describes how research results from an update to the Oregon Department of Transportation database of crash reduction factors were incorporated into an online tool. The paper describes the methodology for cataloging a total of 94 crash countermeasures, reviewing the literature, and developing the interactive online tool. The tool allows users to search on the basis of key parameters for the countermeasures most appropriate to a particular highway safety improvement project and to access directly pertinent citations from the literature review database. A case study describes how a traffic safety professional might use this online tool in practice. Title: Online Tool for Delivering Research Results: Update to Oregon Department of Transportation Database of Crash Reduction Factors Author: Monsere, Christopher, Breakstone, Aaron, Bertini, Robert, Bish, Douglas Year: 2007 Often, the challenge for the practitioner in disseminating research results is to package and present them in a manner that is accessible and easy to use while still providing depth, guidance, and necessary information. In most cases, final research reports are made available in printed form and online; however, for some applications, converting a report to a more interactive online report (tool) can produce results that are easier for the user to access. This is particularly true in this age of information overload and as practitioners find themselves needing to "do more with less." This paper describes how research results from an update to the Oregon Department of Transportation database of crash reduction factors were incorporated into an online tool. The paper describes the methodology for cataloging a total of 94 crash countermeasures, reviewing the literature, and developing the interactive online tool. The tool allows users to search on the basis of key parameters for the countermeasures most appropriate to a particular highway safety improvement project and to access directly pertinent citations from the literature review database. A case study describes how a traffic safety professional might use this online tool in practice. Close

• 7. [Article] Programming Safety Improvements on Pavement Resurfacing, Restoration, and Rehabilitation Projects

  As part of the project planning process, highway agencies must allocate limited funding to a substantial list of projects that exceeds available resources. For preservation projects, a key component of ...

  Citation × Citation Citation Abstract Copy Title: Programming Safety Improvements on Pavement Resurfacing, Restoration, and Rehabilitation Projects Author: Grile, Cameron, Hunter-Zaworski, Katharine, Monsere, Christopher Year: 2005 As part of the project planning process, highway agencies must allocate limited funding to a substantial list of projects that exceeds available resources. For preservation projects, a key component of this decision is to determine which projects receive safety improvements and which are "pave only." Traditionally, this decision has been made project by project, with the possible result of a selection that does not maximize safety benefits. This paper takes a case study approach and applies a new tool developed in NCHRP Report 486, the Resurfacing Safety Resource Allocation Program (RSRAP), to a subset of the Oregon Department of Transportation's (DOT's) highway network. The RSRAP tool maximizes safety improvements for a given set of projects and budget. Thirty-three projects scheduled to receive a new road surface were selected and analyzed with RSRAP. These projects were subdivided into smaller sites to meet the assumptions of RSRAP. Road geometry, traffic volumes, and crash history for each site were collected and input into the program. The type and cost of the safety improvements output by RSRAP were compared with those selected by Oregon DOT. This research determined that RSRAP, which selected more projects for safety improvements than did Oregon DOT, is a tool that could be used by the department to select various safety improvements on pavement preservation projects. It was also determined that the budget used by Oregon DOT was large enough that all cost-effective improvements could be made. Title: Programming Safety Improvements on Pavement Resurfacing, Restoration, and Rehabilitation Projects Author: Grile, Cameron, Hunter-Zaworski, Katharine, Monsere, Christopher Year: 2005 As part of the project planning process, highway agencies must allocate limited funding to a substantial list of projects that exceeds available resources. For preservation projects, a key component of this decision is to determine which projects receive safety improvements and which are "pave only." Traditionally, this decision has been made project by project, with the possible result of a selection that does not maximize safety benefits. This paper takes a case study approach and applies a new tool developed in NCHRP Report 486, the Resurfacing Safety Resource Allocation Program (RSRAP), to a subset of the Oregon Department of Transportation's (DOT's) highway network. The RSRAP tool maximizes safety improvements for a given set of projects and budget. Thirty-three projects scheduled to receive a new road surface were selected and analyzed with RSRAP. These projects were subdivided into smaller sites to meet the assumptions of RSRAP. Road geometry, traffic volumes, and crash history for each site were collected and input into the program. The type and cost of the safety improvements output by RSRAP were compared with those selected by Oregon DOT. This research determined that RSRAP, which selected more projects for safety improvements than did Oregon DOT, is a tool that could be used by the department to select various safety improvements on pavement preservation projects. It was also determined that the budget used by Oregon DOT was large enough that all cost-effective improvements could be made. Close

• 8. [Article] Impact of Traffic Oscillations on Freeway Crash Occurrences

  Traffic oscillations are typical features of congested traffic flow that are characterized by recurring decelerations followed by accelerations (stop-and-go driving). The negative environmental impacts ...

  Citation × Citation Citation Abstract Copy Title: Impact of Traffic Oscillations on Freeway Crash Occurrences Author: Zheng, Zuduo, Ahn, Soyoung, Monsere, Christopher Year: 2010 Traffic oscillations are typical features of congested traffic flow that are characterized by recurring decelerations followed by accelerations (stop-and-go driving). The negative environmental impacts of these oscillations are widely accepted, but their impact on traffic safety has been debated. This paper describes the impact of freeway traffic oscillations on traffic safety. This study employs a matched case-control design using high-resolution traffic and crash data from a freeway segment. Traffic conditions prior to each crash were taken as cases, while traffic conditions during the same periods on days without crashes were taken as controls. These were also matched by presence of congestion, geometry and weather. A total of 82 cases and about 80,000 candidate controls were extracted from more than three years of data from 2004 to 2007. Conditional logistic regression models were developed based on the case-control samples. To verify consistency in the results, 20 different sets of controls were randomly extracted from the candidate pool for varying control-case ratios. The results reveal that the standard deviation of speed (thus, oscillations) is a significant variable, with an average odds ratio of about 1.08. This implies that the likelihood of a (rear-end) crash increases by about 8% with an additional unit increase in the standard deviation of speed. The average traffic states prior to crashes were less significant than the speed variations in congestion. Title: Impact of Traffic Oscillations on Freeway Crash Occurrences Author: Zheng, Zuduo, Ahn, Soyoung, Monsere, Christopher Year: 2010 Traffic oscillations are typical features of congested traffic flow that are characterized by recurring decelerations followed by accelerations (stop-and-go driving). The negative environmental impacts of these oscillations are widely accepted, but their impact on traffic safety has been debated. This paper describes the impact of freeway traffic oscillations on traffic safety. This study employs a matched case-control design using high-resolution traffic and crash data from a freeway segment. Traffic conditions prior to each crash were taken as cases, while traffic conditions during the same periods on days without crashes were taken as controls. These were also matched by presence of congestion, geometry and weather. A total of 82 cases and about 80,000 candidate controls were extracted from more than three years of data from 2004 to 2007. Conditional logistic regression models were developed based on the case-control samples. To verify consistency in the results, 20 different sets of controls were randomly extracted from the candidate pool for varying control-case ratios. The results reveal that the standard deviation of speed (thus, oscillations) is a significant variable, with an average odds ratio of about 1.08. This implies that the likelihood of a (rear-end) crash increases by about 8% with an additional unit increase in the standard deviation of speed. The average traffic states prior to crashes were less significant than the speed variations in congestion. Close