Temporal and Spatial Variations of Real-Time Traffic Data in Urban Areas

Abstract

Variations in traffic flow patterns can be very useful in helping intelligent transportation systems (ITS) provide drivers with useful and accurate route and travel-time information, in examining the potential benefits of flexible work hours, and in assessing the environmental effects of traffic congestion. Much of the work done toward examining temporal and spatial variations in traffic flow has concentrated on freeways, largely ignoring urban areas, where ITS strategies can have the most important effects. Addressed are the issues of temporal and spatial distributions and variations in real-time traffic flow by using data from 140 loop detectors from the greater Athens, Greece, urban area. The results indicate that the temporal distributions of traffic flows are nonnormal, and they vary by direction (toward, in, or away from the central business district) and time of day. This result has important implications in correctly calibrating the real-time simulation models used for volume and travel-time predictions. The results also indicate that traffic in Athens exhibits low seasonality, with lower traffic during the summer months, although traffic conditions are very similar for all weekdays but vary relative to the weekends and by time of day.

Vehicle traffic shapes grizzly bear behaviour on a multiple‐use landscape

“…). However, traffic patterns can be complex (Stathopoulos & Karlaftis ; Wilson ) and usually vary temporally among roads of similar type. As a result, traffic can be difficult to quantify and is often simplified in analyses of road effects by examining a subset of roads with known traffic, or using relative indices (e.g.…”

“…(Sawyer, Kauffman & Nielson 2009), caribou Rangifer tarandus L (Dyer et al 2001), elk Cervus elaphus L (Rowland et al 2000), wolves Canis lupus L (Whittington, St. Clair & Mercer 2004) and grizzly bears Ursus arctos L (Mace et al 1996;Wielgus, Vernier & Schivatcheva 2002;Apps et al 2004). However, traffic patterns can be complex (Stathopoulos & Karlaftis 2001;Wilson 2008) and usually vary temporally among roads of similar type. As a result, traffic can be difficult to quantify and is often simplified in analyses of road effects by examining a subset of roads with known traffic, or using relative indices (e.g.…”

Lifetime maximum load effects on short-span bridges subject to growing traffic volumes

“…The Weibull distribution has been used to model the temporal variation in traffic flows [36] and gives a very good fit for the measured daily traffic volumes. It is used to generate a variable number of trucks for each day of the simulation.…”

DLOREAN: Dynamic Location-Aware Reconstruction of Multiway Networks

“…It is simple to design a kernel focussing on weekdays, weekends or monthly trends [37] by simply choosing l β (·) appropriately.…”

“…RELATED WORK a) Short-Term Traffic Forecasting: Short-term traffic forecasting methods provide traffic flow forecasts typically for next 5 − 10 minutes up to an hour into the future [9], [7]. Karlaftis and Stathopoulos [37] investigate spatial variation and different time resolutions (daily, monthly, yearly); and observe that traffic flow exhibits spatial variation but does not show much variation during most months of the year as well as during weekdays.…”