Segment Of Interstate Research Articles

Quantifying Operational Impacts of Variations in Work Zone Setups, Traffic Demand, and Traffic Composition: A Case Study

The presence of work zones due to pavement repair and rehabilitation is very common in highway facilities. Lane closures associated with work zones result in capacity reduction, which, in turn, often leads to increased congestion at such locations. This paper documents findings from a study that investigated the performance of freeway facilities in the presence of work zones under various Temporary Traffic Control (TTC) and lane closure scenarios while taking under consideration traffic composition and driving behaviors. The study site was an approximately 10-mile freeway segment of Interstate 65 (I-65) located in Birmingham, AL. The testbed was coded in PTV VISSIM, a microscopic simulation analysis platform, for: 1) baseline conditions (i.e., no work zone presence) and 2) work zone conditions with single lane closure (i.e., 3-to-2 lane closure). Work zone scenarios were coded for two TTC strategies, namely, early merge and late merge control and for three different positions of the lane closure (i.e., left, right, and center lane closures). The length of the work zones varied from 1000 to 2000, and 3000 ft. Sensitivity analysis was performed to document the operational impacts of varying heavy vehicle percentages, changes in drivers’ aggressiveness, and projected traffic demand changes. The impacts were quantified using linked-based measures of effectiveness (MOEs) such as travel time, and travel time index. The study results show that there is no significant change in travel time index due to the variation of work zone length across the study corridor. Under similar traffic control and demand conditions, a center lane closure consistently results in significantly higher travel time index than a left or right lane closure and should be avoided. Consideration of operational impacts of changes in truck percentage indicates that the corridor can absorb an increase in truck percentage from 10% to 15%, while performance rapidly deteriorates when a higher percentage of trucks is present in the traffic stream. The study findings can be used to guide transportation agencies in their future efforts to develop strategic lane closure plans that minimize congestion.

An Automatic Incident Detection Method for a Vehicle-to-Infrastructure Communication Environment: Case Study of Interstate 64 in Missouri

Transportation agencies continuously and consistently work to improve the processes and systems for mitigating the impacts of roadway incidents. Such efforts include utilizing emerging technologies to reduce the detection and response time to roadway incidents. Vehicle-to-infrastructure (V2I) communication is an emerging transportation technology that enables communication between a vehicle and the infrastructure. This paper proposes an algorithm that utilizes V2I probe data to automatically detect roadway incidents. A simulation testbed was developed for a segment of Interstate 64 in St. Louis, Missouri to evaluate the performance of the V2I-based automatic incident detection algorithm. The proposed algorithm was assessed during peak and off-peak periods with various incident durations, under several market penetration rates for V2I technology, and with different spatial resolutions for incident detection. The performance of the proposed algorithm was assessed on the basis of the detection rate, time to detect, detection accuracy, and false alarm rate. The performance measures obtained for the V2I-based automatic incident detection algorithm were compared with California #7 algorithm performance measures. The California #7 algorithm is a traditional automatic incident detection algorithm that utilizes traffic sensors data, such as inductive loop detectors, to identify roadway events. The California #7 algorithm was implemented in the Interstate 64 simulation testbed. The case study results indicated that the proposed V2I-based algorithm outperformed the California #7 algorithm. The detection rate for the proposed V2I-based incident detection algorithm was 100% in market penetrations of 50%, 80%, and 100%. However, the California #7 algorithm's detection rate was 71%.

Impact of High Resolution Radar-Obtained Weather Data on Spatio-Temporal Prediction of Freeway Speed

Inclement weather and environmental factors impact traffic operations resulting in travel delays and a reduction in travel time reliability. Precipitation is an example of an environmental factor that affects travel conditions, including traffic speed. While Intelligent Transportation Systems services aim to proactively mitigate congestion on roadways, these services are often not sensitive to weather conditions. This paper investigates the application of high-resolution weather data in improving the performance of proactive transportation management models and proposes short-term speed prediction models that fuse real-time high-resolution weather surveillance radar data with traffic stream data to conduct spatial and temporal prediction of the speed of roadway segments. Extreme gradient boosting weather-aware speed prediction models were developed for a 7-km segment of Interstate 270 in St. Louis, MO, USA. The performance of the weather-aware models was compared with the performance of weather-insensitive speed prediction models that did not take precipitation into account. The results indicated that in the majority of instances, the weather-aware models outperformed the weather-insensitive models. The extreme gradient boosting models were compared with the K-nearest neighbors algorithm and feed-forward neural network models. The extreme gradient boosting model consistently outperformed the other two methods. In addition to speed prediction models, van Aerde speed-flow traffic stream models were developed for rain and no-rain conditions to study the impact of precipitation on the traffic stream across the corridor. Results indicated that the impact of precipitation is not identical across the corridor, which was mirrored in the results obtained from weather-aware speed prediction models.

Prediction of Travel Time Reliability on Interstates Using Linear Quantile Mixed Models

Under the Moving Ahead for Progress in the 21st Century Act (MAP-21), state Departments of Transportation (DOTs) are responsible for reporting travel time reliability and also setting targets and showing progress toward those targets. To know how to improve travel time reliability and what to expect from investments in transportation infrastructure, state DOTs need a better understanding of the factors that affect travel time reliability and methods to predict future travel time reliability. This paper proposes linear quantile mixed models (LQMMs) to quantify travel time reliability impact factors and predict selected reliability measures (level of travel time reliability [LOTTR] and the 90th percentile) to address these needs. The method was demonstrated using probe vehicle data from interstate segments in Virginia that had been partitioned into approximately homogeneous clusters based on the similarity of their cumulative distribution functions (CDFs) of travel times. Using clustered data meant that LQMMs were only necessary for a limited number of clusters rather than for hundreds of individual segments, thus making the process more efficient and manageable. The LQMMs showed that frequencies of non-recurrent events, such as incidents and weather, were correlated with higher travel time percentiles. The prediction performance of LQMMs was compared with trend line predictions, a common method used in practice. The results showed that LQMMs significantly improved prediction accuracy.

Simulation of mixed traffic with cooperative lane changes

AbstractThis study proposes a mixed traffic simulation framework that integrates vehicle car‐following (CF) and lane‐changing (LC) with connected and automated vehicles (CAVs) of different cooperation behaviors. This framework is centered at a CAV LC model incorporated with CF dynamics in mixed traffic. The model was calibrated and validated using data collected from small‐scale field experiments in a previous study. To demonstrate a large‐scale application of this framework, PTV Vissim was used to implement the framework on a segment of Interstate 75 highway. Sensitivity analyses were conducted to investigate the impacts of key parameters on traffic mobility and stability performance. The results show that traffic performance degraded as the traffic demand and vehicle diverging rate increased. As the CAV penetration rate increased, traffic performance fluctuated when CAVs were more conservative. As the CAV cooperation rate, incentive criterion threshold, and incentive criterion bias increased, mobility and stability performance first improved and then degraded. When CAV platooning was considered, traffic performance was enhanced. These findings shed light on mixed traffic management from the perspectives of both transportation operators (e.g., facilities and policies to promote vehicle cooperation) and automakers (e.g., tuning parameters in their LC models) to achieve the best traffic performance.

Evaluation of techniques for mitigating snowmelt infiltration-induced landsliding in a highway embankment

Infiltration-induced landslides threaten transportation infrastructure around the world, and impose both direct costs through repair and remediation work and indirect costs through lost economic activity. Therefore, finding the most cost-effective techniques to mitigate slope failures that can impact critical infrastructure links is desirable. The Straight Creek landslide, which affects a segment of Interstate 70 in Summit County, Colorado (USA), has experienced seasonal failure driven by rapid springtime snowmelt infiltration since the early 1970s, allowing changes in its stability to be studied. Past studies have established that seasonal failure is driven by pore-water pressure increase caused by the rapid infiltration of snowmelt and the hydraulic conductivity contrast between upper slope materials and the highway embankment. Two remediation designs have been applied to the site, including lightweight caissons beneath the highway surface in 2011 and 2012, and horizontal drains near the slide toe in 2012. The effects of the lightweight caissons and horizontal drains, as well as an alternative drain design that would extend into the hillslope above the highway embankment, are evaluated within a rigorous hydro-mechanical simulation framework along with a method to generate a field of local factor of safety. Model results show that the effect of the lightweight caissons on the factor of safety is no more than 1% during times of critical instability, as they do not affect the seasonal changes in hydrology that cause destabilizing decreases in effective stress along the failure surface. Horizontal drains are intended to reduce pore-water pressures, but the location of existing drains limit their efficacy due to the low hydraulic conductivity of subsurface materials underneath the highway. Model results indicate that these drains are only partially responsible for a reduction in movement rate since their installation, which is also due to lower annual cumulative snowmelt infiltration levels since 2012. Results also show that an alternative drain design could result in increased stability during critical periods by intercepting downslope subsurface flow before it arrives at the hydraulic conductivity contrast at the embankment.

Exploratory Analysis of the Relationships between Congestion, Travel Time Reliability, and Freight-Related Performance Management Measures and Their Associativity with the Roadway Attributes

The purpose of this study is to investigate the performance management measures related to highway system reliability, freight, and traffic congestion in light of the federal rulemaking that establishes these performance measures. The study conducts an exploratory analysis to understand their inter-relationships and examines their (un)common underlying attributes to discover their associativity with each of the performance measures. In doing so several traffic and roadway related characteristics of each reporting segment of the National Highway System (NHS) of South Carolina were processed and modeled for the travel time reliability and peak hour excessive delay using generalized linear models with a log-link function. The results from the study indicate that the unreliable Interstate segments contributed to about 87% of the excessive delays on the entire Interstate. It was also found that more than half of the non-Interstate NHS segments that experienced excessive delay, were reliable and they contributed to approximately 52% of the entire peak hour excessive delay of the non-Interstate NHS. The results from the model indicate that the directional annual average daily traffic (AADT) and the urban areas are the two most important attributes positively associated with all three performance measures, while the number of through lanes was found to be negatively associated with all three performance measures. The length of the reporting segments was positively associated with the excessive delays but negatively associated with the travel time reliabilities. The percentage of single trucks was unique to the Interstate delays and positively associated.

Evaluating Advancements in Bluetooth Technology for Travel Time and Segment Speed Studies

Application of Bluetooth technology has become ubiquitous in the current age of transportation research. The purpose of this article is to explore advancements of Bluetooth technology, applied to transportation studies, using performance metrics such as match rate, travel time, and segment speed through analyzing collected data from several Bluetooth devices. Specifically, the study explores the performance of two advanced Bluetooth devices coupled with classic Bluetooth technology: the demodulator (BT DM), and the low-energy Bluetooth signal additional component (BLE). Data were collected in two locations/phases: (1) along a 0.59-mi segment of interstate and (2) along a 0.52-mi segment of an urban arterial road in Baton Rouge, LA. The data collected were compared with benchmark data sets, gathered during the same period, using manual counts from video footage, radar data, and floating car data (FCD). Comparative analysis showed that BLE produced significantly higher matched rates than BT DM. Furthermore, BLE was able to maintain higher accuracy with increased levels of detection. Results of a Kruskal–Wallis test showed BT DM to have a statistically significant difference with FCD during only one out of three peak periods along the interstate segment. However, BLE matched closest with FCD along the interstate, but shared a significant difference with the benchmark data set during two peak periods along the urban arterial roadway. Considering the level of detection with the accuracy of travel times and segment speeds when compared with the benchmark data, it was evident that the BLE performed better than the BT DM.

Effectiveness of Seasonal Deer Advisories on Changeable Message Signs as a Deer Crash Mitigation Tool

The Virginia Department of Transportation began posting deer advisory messages on changeable message signs (CMSs) along a 16.7-mi segment of Interstate 64 in October 2015. The posting of these messages during the peak of deer activity, from October to November from 5 p.m. to 9 a.m., was intended to raise driver awareness and reduce the high number of deer–vehicle collisions (DVCs) in the area. The effectiveness of deer advisory messages with regard to DVC reduction is not well known. Effectiveness in this study was determined by evaluating deer carcass removal data from three October and November deer advisory posting periods. Vehicle speed evaluations were also conducted to determine whether drivers reduced speed in response to the advisories. Carcass removals were lower when the deer advisories were posted than when they were not posted, and this difference was statistically significant. During the deer advisory postings, there were statistically significant speed reductions of up to 2.8 mph at four of the five vehicle sensor stations. Speed reductions were greater when deer advisories were posted during periods of lower traffic volumes. Seasonal deer advisory messages on an interstate appear to be an effective form of DVC mitigation. Posting seasonal deer advisories every other day on the five existing CMSs in this study’s project area is expected to save approximately $700,000 to $1.4 million over the service life of the CMSs.

A Dynamic Bayesian Network Based Merge Mechanism for Autonomous Vehicles

This work explores the design of a central collaborative driving strategy between connected cars with the objective of improving road safety in case of highway on-ramp merging scenario. Based on a suitable method for predicting vehicle motion and behavior for a central collaborative strategy, a dynamic Bayesian network method that predicts the intention of drivers in highway on-ramp is proposed. The method was validated using real data of detailed vehicle trajectories on a segment of interstate 80 in Emeryville, California.

Shortage of Commercial Vehicle Parking and Truck-Related Interstate Ramp Crashes in Tennessee

Interstate ramps are the location of more crashes per mile than any other segment of interstate, yet commercial vehicle (CMV) drivers are often left with the last option of parking along these segments for off-duty rest to meet federal hours-of-service regulations. Past literature has evaluated the shortage of CMV parking facilities and examined characteristics of freeway ramp crashes. However, relatively little research has studied the influence of CMV parking deficiency on crash frequency involving parked CMVs along the ramps. This study examined 179 police-reported illegally parked CMV-involved crashes on 1,221 rural and urban ramps in the State of Tennessee, United States. Three Bayesian binary logit models using varying prior distributions were used to analyze the odds of a CMV crash occurring based on CMV parking shortage and ramp attributes. The results illustrated the higher frequency crashes (N = 4 or 5) occurred on entrance ramps of shorter length and often were adjacent to public parking facilities. Approximately one-third of the collisions on interchange ramps had a parking facility utilization rate of 90% or higher, and 23 ramps (15.8%) were adjacent to parking facilities that were at or over-capacity. Several ramp characteristics were significantly associated with an increase in the occurrence of an illegally parked CMV-involved crash and include the presence of a parking facility on exit, ramps with illegally parked CMVs, diamond-shaped ramp configurations, larger ramp shoulder widths, and exit ramps. This study illustrates the impact of the CMV parking shortage on the safety of ramps and highlights the need for more parking facilities.

Highway Design and Safety Consequences: A Case Study of Interstate Highway Vertical Grades

Vertical alignment, which includes vertical grades and lengths, is a critical aspect of highway design policy that influences safety. A full understanding of the effect of vertical grade and segment length on highway safety can help agencies to evaluate or adjust their design policies regarding vertical alignment design features (grade and length). For this reason, it is useful to assess the current relationships between design policy and safety performance. To address this task, this paper uses data from interstate segments to first establish the relationship between these design features and safety. Safety is expressed in terms of the three different levels of crash severity (fatal, injury, and property damage only). In its analysis, the paper departs from the traditional univariate models (where each crash severity is modeled separately) and instead uses a seemingly unrelated negative binomial (SUNB) technique, a multivariate model that duly accounts for the unobserved shared effects between the different levels of crash severity. In addition, the paper’s models duly recognize and account for the holistic nature of the grade and tangent length effects: the effect of the sum (interaction) of the vertical grade and length is different from the sum of their individual effects. The paper investigates the relationships for rural and urban interstate highway segments. Against the background of the developed models, the paper evaluates current design policies (specifications on vertical alignment grade and length) for similar classes of highways at a number of countries and presents a set of nomograms that feature lines representing points of equal safety performance. These charts can be used by the highway agencies to evaluate and compare their current or possible future highway design policies.

Understanding the Highway Safety Benefits of Different Approaches of Connected Vehicles in Reduced Visibility Conditions

This study evaluated the effectiveness of connected vehicle (CV) technologies in adverse visibility conditions using microscopic traffic simulation. Traffic flow characteristics deteriorate significantly in reduced visibility conditions resulting in high crash risks. This study applied CV technologies on a segment of Interstate I-4 in Florida to improve traffic safety under fog conditions. Two types of CV approaches (i.e., connected vehicles without platooning (CVWPL) and connected vehicles with platooning (CVPL) were applied to reduce the crash risk in terms of three surrogate measures of safety: the standard deviation of speed, the standard deviation of headway, and rear-end crash risk index (RCRI). This study implemented vehicle-to-vehicle (V2V) communication technologies of CVs to acquire real-time traffic data using the microsimulation software VISSIM. A car-following model for both CV approaches was used with an assumption that the CVs would follow this car-following behavior in fog conditions. The model performances were evaluated under different CV market penetration rates (MPRs). The results showed that both CV approaches improved safety significantly in fog conditions as MPRs increase. To be more specific, the minimum MPR should be 30% to provide significant safety benefits in terms of surrogate measures of safety for both CV approaches over the base scenario (non-CV scenario). In terms of surrogate safety measures, CVPL significantly outperformed CVWPL when MPRs were equal to or higher than 50%. The results also indicated a significant improvement in the traffic operation characteristics in terms of average speed.

Hybrid short-term freeway speed prediction methods based on periodic analysis

Short-term traffic speed forecasting is an important issue for developing Intelligent Transportation Systems applications. So far, a number of short-term speed prediction approaches have been developed. Recently, some multivariate approaches have been proposed to consider the spatial and temporal correlation of traffic data. However, as traffic data often demonstrates periodic patterns, the existing methodologies often fail to take into account spatial and temporal information as well as the periodic features of traffic data simultaneously in the multi-step prediction. This paper comprehensively evaluated the multi-step prediction performance of space time (ST) model, vector autoregression (VAR), and autoregressive integrated moving average (ARIMA) models using the 5 minute freeway speed data collected from five loop detectors located on an eastbound segment of Interstate 394 freeway, in Minnesota. To further consider the cyclical characteristics of freeway speed data, hybrid prediction approaches were proposed to decompose speed into two different components: a periodic trend and a residual part. A trigonometric regression function is introduced to capture the periodic component and the residual part is modeled by the ST, VAR, and ARIMA models. The prediction results suggest that for multi-step freeway speed prediction, as the time step increases, the ST model demonstrates advantages over the VAR and ARIMA models. Comparisons among the ST, VAR, ARIMA, and hybrid models demonstrated that modeling the periodicity and the residual part separately can better interpret the underlining structure of the speed data. The proposed hybrid prediction approach can accommodate the periodic trends and provide more accurate prediction results when the forecasting horizon is greater than 30 min.

Vehicle density estimation of freeway traffic with unknown boundary demand–supply: an interacting multiple model approach

As distributed parameter systems, dynamics of freeway traffic are dominated by the current traffic parameter and boundary fluxes from upstream/downstream sections or on/off ramps. The difference between traffic demand-supply and boundary fluxes actually reflects the congestion level of freeway travel. This paper investigates simultaneous traffic density and boundary flux estimation with data extracted from on-road detectors. The existing studies for traffic estimation mainly focus on the traffic parameters (density, velocity, etc.) of mainline traffic and ignore flux fluctuations at boundary sections of the freeway. We propose a stochastic hybrid traffic flow model by extending the cell transmission model (CTM) with Markovian multi-mode switching. A novel interacting multiple model (IMM) filtering for simultaneous input and state estimation is developed for discrete-time Markovian switching systems with unknown input. A freeway segment of Interstate 80 East (I-80E) in Berkeley, Northern California, is chosen to investigate the performance of the developed approach. Traffic data is obtained from the Performance Measurement System (PeMS).

Impact of speed cameras on trauma centers

While studies, mostly from Europe and Australia, have examined the effect of speed cameras on motor vehicle collisions, limited data exist regarding their impact on charges and number of patients taken to Level 1 trauma centers (L1TCs). Because of conflicting perceptions and data on their value, speed cameras were implemented along select Arizona highways in 2008 but then removed in 2010. The hypotheses of our study were twofold. (1) Speed cameras reduce admissions to L1TCs, and (2) speed cameras reduce crash kinetic energy, resulting in lower Injury Severity Score (ISS), mortality, hospital costs, and length of stay (LOS). A retrospective review of all patients admitted to L1TCs who were injured in motor vehicle crashes along a 26-mile segment of interstate I-10 in urban Phoenix was performed. Patients were identified using both the Arizona State Trauma Registry and the Arizona Department of Transportation collision data for 2009 to 2011. This specific 26-mile segment of I-10 was selected because it contained at least one speed camera within 1 mile along its entire length from October 2008 to October 2010. Two time frames were evaluated: January 1 to December 31, 2009, when cameras were in place (2009 camera group) and January 1 to December 31, 2011, when no cameras were in place (2011 no-camera group). Variables analyzed include number of collisions, number of injuries, on-scene mortality, trauma center admissions, number of collisions with admissions, in-hospital mortality, ISS, hospital charges, LOS, age, sex, race, and ethnicity. Five confounding variables were eliminated. Analysis was performed using Fisher's exact test and linear regression. Camera removal was associated with a twofold increase in L1TC admissions as well as increased resource use. There were no significant differences between the two time frames for ISS, mortality, median charges, or median LOS. In this study, removal of speed cameras resulted in increased trauma center admissions and resource use. Care management study, level IV.

Impact of Dynamic Message Signs on Speeds Observed on a Rural Interstate

Dynamic message signs (DMSs) are a component of intelligent transportation systems (ITSs) used to convey real-time travel information to motorists, enabling them to make better decisions in response to real-time roadway conditions such as congestion, crashes, and adverse weather. The purpose of this paper is to analyze the existing DMS system implemented by the Wyoming Department of Transportation (WYDOT) along a rural segment of Interstate 80 in southeastern Wyoming between the cities of Laramie and Cheyenne. The research uses speed, weather, and DMS message data collected from the winter season of 2009 to 2010 along the corridor to determine how driver speed behavior is affected by DMS signs. A linear regression model utilizing four different weather severity categories was estimated and the results indicate that the DMS message signs are effective at reducing drivers’ speeds along rural interstate corridors from 8 km/h to 32 km/h (5 mi/h to 20 mi/h) above the speed reductions that can be accounted for by weather conditions alone.

Nonlinear Acceleration and Deceleration Response Behavior in Stimulus-Response Car-Following Models

This study developed a nonlinear family of car-following models that emulate driving behavior in congested freeway traffic conditions. The study developed separate sub-models for acceleration and deceleration responses. The study calibrated these models using individual vehicle trajectory data for following automobile collected on a segment of Interstate 101 in Los Angeles, California. The study used nonlinear regression with robust standard errors to estimate the model parameters and to obtain their distributions across drivers. The stimulus response thresholds that delimited the acceleration and deceleration responses were determined based on Signal Detection Theory. The results indicated that the average driver's response time lag was lower for the deceleration response than for the acceleration response. This result was expected, since deceleration response is related to safety, therefore, drivers tend to respond faster than for acceleration response. The acceleration response is related to drivers' desire to attain maximum speed, which is a less critical need than deceleration response. Due to similar reasons, the results also showed that the average stimulus response threshold was lower for deceleration response than acceleration response. Furthermore, the deceleration response had higher magnitude of parameters than the acceleration response, which further indicated that, on the average, drivers were more aggressive when required to decelerate than when they wanted to accelerate. Additionally, drivers' response to negative stimuli is sometimes further aided by the activation of brake lights for a leading vehicle that is braking.

Analysis of vehicle-following heterogeneity using self-organizing feature maps.

A self-organizing feature map (SOM) was used to represent vehicle-following and to analyze the heterogeneities in vehicle-following behavior. The SOM was constructed in such a way that the prototype vectors represented vehicle-following stimuli (the follower's velocity, relative velocity, and gap) while the output signals represented the response (the follower's acceleration). Vehicle trajectories collected at a northbound segment of Interstate 80 Freeway at Emeryville, CA, were used to train the SOM. The trajectory information of two selected pairs of passenger cars was then fed into the trained SOM to identify similar stimuli experienced by the followers. The observed responses, when the stimuli were classified by the SOM into the same category, were compared to discover the interdriver heterogeneity. The acceleration profile of another passenger car was analyzed in the same fashion to observe the interdriver heterogeneity. The distribution of responses derived from data sets of car-following-car and car-following-truck, respectively, was compared to ascertain inter-vehicle-type heterogeneity.

Probe Vehicle–Based Statewide Mobility Performance Measures for Decision Makers

Decision makers in state transportation agencies typically manage budgets approaching or exceeding $1 billion. Historically, the data used to make investment decisions have been quite coarse and have been typically based on short-term volume counts fed into models to forecast performance. As a result, it is not uncommon for construction projects to address needs that were forecast to be a priority 5 to 10 years earlier, while more pressing congestion challenges go unmet. It is essential that long-term planning begins to be supplemented by more current performance measures. The emerging private-sector probe vehicle data obtained from commercial providers offer an opportunity to augment traditional forward-looking planning models with performance measures that reflect the conditions motorists are experiencing today. This paper proposes scalable, analytical probe data–reduction techniques to create technically sound, yet visually intuitive, system-performance measures of current freeway conditions. These types of performance measure are increasingly used by high-level agency management to identify locations at which customers experience congestion, and the magnitude of that congestion, and to compare congestion on various highway corridors. These proposed performance measures can be used for policy-oriented decisions, such as the prioritization of capital program investments, the management of snow removal, and the scheduling of lane closures. The techniques are applied to seven Indiana Interstate highways, comprised of 1,886 directional miles. The Interstates span rural and urban sections that experience varying levels of recurring and nonrecurring congestion as a result of winter weather and construction activity. Specific examples adjacent to the Indianapolis, Indiana; Louisville, Kentucky; and Chicago, Illinois, metropolitan areas are presented, along with the 10 most congested Interstate segments.