Analyzing Travel Time Reliability from Sparse Probe Vehicle Data: A Case Study on the Effects of Spatial and Temporal Aggregation

Abstract

Travel time reliability (TTR) indices have gained considerable attention for evaluating the quality of traffic infrastructure. Whereas TTR measures have been widely explored using data from stationary sensors with high penetration rates, there is a lack of research on calculating TTR from mobile sensors such as probe vehicle data (PVD) which is characterized by low penetration rates. PVD is a relevant data source for analyzing non-highway routes, as they are often not sufficiently covered by stationary sensors. The paper presents a methodology for analyzing TTR on (sub-)urban and rural routes with sparse PVD as the only data source that could be used by road authorities or traffic planners. Especially in the case of sparse data, spatial and temporal aggregations could have great impact, which are investigated on two levels: first, the width of time of day (TOD) intervals and second, the length of road segments. The spatial and temporal aggregation effects on travel time index (TTI) as prominent TTR measure are analyzed within an exemplary case study including three different routes. TTI patterns are calculated from data of one year grouped by different days-of-week (DOW) groups and the TOD. The case study shows that using well-chosen temporal and spatial aggregations, even with sparse PVD, an in-depth analysis of traffic patterns is possible.

Which Aggregation Fits Best? The Use of Linear Regression to Show the Influence of Temporal and Spatial Aggregation of Sparse Probe Vehicle Data on the Explanation of Travel Time Reliability

“…To define the spatial and temporal aggregation levels, we follow an approach by Steinmaßl et al ( 21 ). We analyze our results depending on the TOD in 15-, 30-, and 60-min intervals.…”

“…We assume that the results vary greatly between these three intervals. According to Steinmaßl et al ( 21 ), a coarser level of temporal aggregation means that significant short-term variations of travel times could not be detected, as they might be lost in the aggregation. Thus, the effect of road construction elements or peak hour could not be identified.…”

“…For the spatial aggregation, we use two different segmentations that vary in length depending on the ontology of the map. For the definition of these two types, we adopt the approach of Steinmaßl et al ( 21 ) and define a spatial unit as a road segment between two turn options. The turn options of a road network representation depend on its level of detail.…”

“…The turn options of a road network representation depend on its level of detail. Following Steinmaßl et al ( 21 ), we use two different network representations based on the Functional Road Classes (FRCs) of roads. In our case, FRCs are defined by the Austrian Graph Integration Platform (GIP.AT) and classify roads hierarchically according to their importance from a traffic planning and network logic perspective.…”

Dynamic density and flow reconstruction in large-scale urban networks using heterogeneous data sources

“…Thus, BT measurements provide an unbiased estimate of the TRs. Some authors have reported BT penetration rates of 12% (Cvetek et al (2019)) and up to 30% in Europe (Steinmaßl et al (2021)). Taking into account the daytime hours (6h00 to 20h00) for intersection 2, the turn with the minimum number of counts reported an average of 8 detections per hour, and the turn with the maximum number of counts reported an average of 92 detections per hour.…”