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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. Civil Engineering | Transportation Engineering CommentsThis proceeding was published as Houchin, Andrew, Jing Dong, Neal Hawkins, and Skylar Knickerbocker. "Measurement and analysis of heterogenous vehicle following behavior on urban freeways: Time headways and standstill distances.Abstract-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.

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.

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