Skylar Knickerbocker

Reliability of Probe Speed Data for Detecting Congestion Trends

This paper presents a framework for evaluating the reliability of probe-sourced traffic speed data for congestion detection and general infrastructure performance assessment. The methodology outlined employs pattern recognition and time-series analysis to accurately quantify the similarity and dissimilarities between probe-sourced and benchmarked local sensor data. First, an adaptive and multiscale pattern recognition algorithm called Empirical Mode Decomposition (EMD) is used to define short, medium and long-term trends for the probe-sourced and infrastructure mounted local sensor datasets. The reliability of the probe data is then estimated based on the similarity or synchrony between corresponding trends. The synchrony between long-term trends are used as a measure of accuracy for general performance assessment, whereas short and medium term trends are used for testing the accuracy of congestion detection with probe-sourced data. Using one-month of high-resolution speed data, the authors were able to use probe data to detect on average 74% and 63% of the short-term events (events lasting for at most 30 minutes), 95% and 68% of the medium-term events (events lasting between 1 and 3 hours) on freeways and non - freeways respectively. Significant latencies do however exist between both datasets. On non - freeways, the benchmarked data detected events, on average, 12 minutes earlier than the probe data. On freeways, the latency between the datasets was reduced to 8 minutes. The resulting framework can serve as a guide for state DOTs when outsourcing or supplementing traffic data collection to probe-based services.

Measurement and Analysis of Heterogenous Vehicle Following Behavior on Urban Freeways: Time Headways and Standstill Distances

Microscopic traffic modelling is a popular tool in the transportation field, but using such models comes with significant data needs in order to properly calibrate them. Two important driver behavior parameters in these models are the preferred time headways and standstill distances. In this paper, an economical method for collecting headways and standstill distances is presented and applied to urban freeways in Iowa, USA. The following time headways and standstill distances were categorized into four combinations of car and truck pairs. It was found that headway values largely depend on the following vehicle type-when a car was following the average headway was around 2 seconds, compared to around 3 seconds when a truck was following. Additionally, the car-car combination leaves much less space when stopped than when a pair involves trucks. In particular, the average standstill distance of a car following a car was found to be around 9 feet, while the average standstill distances are around 12 feet when a truck is involved. However, both headways and standstill distances follow fairly disperse distributions, due to the heterogeneity in driver behavior. Thus, microsimulation software should be improved to allow these parameters to follow distributions.

Framework for Evaluating the Reliability of Wide-Area Probe Data

This paper presents a framework for evaluating the reliability of probe-sourced traffic speed data for detection of congestion and assessment of roadway performance. The methodology outlined uses pattern recognition to quantify accurately the similarities and dissimilarities of probe-sourced and benchmarked local sensor data. First, a pattern recognition algorithm called empirical mode decomposition was used to define short-, medium-, and long-term trends for the probe-sourced and infrastructure-mounted local sensor data sets. The reliability of the probe data was then estimated on the basis of the similarity or synchrony between corresponding trends. The synchrony between long-term trends was used as a measure of accuracy for general performance assessment, whereas short- and medium-term trends were used for testing the accuracy of congestion detection with probe-sourced data. By using 1 month of high-resolution speed data, the authors were able to use probe data to detect, on average, 74% and 63% of the short-term events (events lasting for at most 30 min) and 95% and 68% of the medium-term events (events lasting between 1 and 3 h) on freeways and nonfreeways, respectively. Significant latencies do, however, exist between the data sets. On nonfreeways, the benchmarked data detected events, on average, 12 min earlier than the probe data. On freeways, the latency between the data sets was reduced to 8 min. The resulting framework can serve as a guide for state departments of transportation when they outsource collection of traffic data to probe-based services or supplement their data with data from such services.

Use of DSFS as a Speed Transition Zone Countermeasure in Small, Rural Communities

Small rural communities in the United States are often located along major state or county roadways and as a result, most of the traffic along their main thoroughfare is pass-through rather than local traffic. Unfortunately, drivers passing through these communities often enter at high rates of speeds which are sometimes significantly higher than the posted speed of the local segment. High speeds create a safety issue for rural communities. They also discourage pedestrian activities, such as walking and bicycling, and discourage community interaction and vitality. Rural communities often lack engineering staff and resources to address speed management. As a result, rural communities find it difficult to address speeds and in many cases, utilize stop signs and lower speed limits to address the problem which may lead to disregard for traffic control. Traffic calming in rural areas requires different considerations than urban areas and within the US, rural traffic calming is in its infancy with little experience or guidance for agencies to draw on. This paper summarizes the results of a study which evaluated several different types of dynamic speed feedback signs which were deployed within the speed transition zone for small rural communities in Iowa (USA). DSFS have been used in a number of applications in the US, such as within school zones and work zones. Their effectiveness was less known in rural community applications. Three different types of DSFS were deployed (simple speed feedback sign, alphanumeric sign, and speed limit sign with LED array). All signs were programmed to activate only for drivers traveling at some threshold over the posted speed limit at the community entrance. Overall, the dynamic speed feedback signs were shown to be very effective in reducing vehicle speeds at community entrance. In particular, large reductions were noted in most cases in the fraction of vehicles that were traveling significantly over the posted speed limit at the community entrance.

Evaluation of Intersection Conflict Warning System: A Critical Gap Analysis

Rural intersections account for around 30% of crashes in rural areas and 6% of all fatal crashes. An Intersection Conflict Warning System (ICWS) is a unique solution to address rural intersection safety. ICWS are typically installed at the minor approaches to two-way stop-controlled intersections to reduce the number of fatalities. Studies indicate these systems result in lower intersection approach speeds, reduced conflicts, and improved driver-gap selection. However, some sites have experienced increases in the number of minor crashes. Although there are positive ICWS examples, their overall effectiveness is not well established. The objective of this research was to evaluate driving behavior at stop-controlled approaches with and without ICWS and to evaluate the spillover effect of ICWS on other adjacent control intersections where the treatment has not been applied. The study examined behavior at five intersections (treatment sites) in Minnesota where an ICWS was installed. For comparison, an additional five similar (control sites) were identified in proximity to each treatment intersection. Data were collected using a video camera array at three different time frames: before, one month, and 12 months after the installation of system. The data were analyzed using a critical gap approach. The analysis shows that the ICWS improved driver gap acceptance at the treatment sites, only at the 12-month period, and that there was no “spillover effect” at the adjacent control sites. When gap acceptance was further compared by type of stop, critical gap selection was shown to increase for drivers making both complete and rolling stops.

Quantitative analysis of probe data characteristics: Coverage, speed bias and congestion detection precision

Abstract In recent years, there has been a growing desire for the use of probe vehicle technology for congestion detection and general infrastructure performance assessment. Unlike costly traditional data collection by loop detectors, wide area detection using probe-based traffic data is significantly different in terms of the nature of data collection, measurement technique, coverage, pricing, and so on. Although many researches have studied probe-based data, there remains critical questions such as data coverage and penetration over time, or the influential factors in the accuracy of probe data. This research studied probe-sourced data from INRIX, to profoundly explore some of these questions. First, to explore coverage and penetration, INRIX real-time data was illustrated temporally over the entire state of Iowa, demonstrating the growth in real-time data over a 4-year timespan. Furthermore, the availability of INRIX real-time and historical data based on type of road and time of day, were explored. Second, a comparison was made with Wavetronix smart sensors, commonly used sensors in traffic management, to explore INRIX’s speed data quality. A statistical analysis on the behavior of INRIX speed bias, identified some of the influential factors in defining the magnitude of speed bias. Finally, the accuracy and reliability of INRIX for congestion detection purposes was investigated based on the road segment characteristics and the congestion type. Overall, this work sheds light onto some of the less explored aspects of INRIX probe-based data to help traffic managers and decision makers in better understanding this source of data and any resultant analyses.