1,402 research outputs found
Theoretical vs. Empirical Classification and Prediction of Congested Traffic States
Starting from the instability diagram of a traffic flow model, we derive
conditions for the occurrence of congested traffic states, their appearance,
their spreading in space and time, and the related increase in travel times. We
discuss the terminology of traffic phases and give empirical evidence for the
existence of a phase diagram of traffic states. In contrast to previously
presented phase diagrams, it is shown that "widening synchronized patterns" are
possible, if the maximum flow is located inside of a metastable density regime.
Moreover, for various kinds of traffic models with different instability
diagrams it is discussed, how the related phase diagrams are expected to
approximately look like. Apart from this, it is pointed out that combinations
of on- and off-ramps create different patterns than a single, isolated on-ramp.Comment: See http://www.helbing.org for related wor
Assessing the Impact of Game Day Schedule and Opponents on Travel Patterns and Route Choice using Big Data Analytics
The transportation system is crucial for transferring people and goods from point A to point B. However, its reliability can be decreased by unanticipated congestion resulting from planned special events. For example, sporting events collect large crowds of people at specific venues on game days and disrupt normal traffic patterns.
The goal of this study was to understand issues related to road traffic management during major sporting events by using widely available INRIX data to compare travel patterns and behaviors on game days against those on normal days. A comprehensive analysis was conducted on the impact of all Nebraska Cornhuskers football games over five years on traffic congestion on five major routes in Nebraska. We attempted to identify hotspots, the unusually high-risk zones in a spatiotemporal space containing traffic congestion that occur on almost all game days. For hotspot detection, we utilized a method called Multi-EigenSpot, which is able to detect multiple hotspots in a spatiotemporal space. With this algorithm, we were able to detect traffic hotspot clusters on the five chosen routes in Nebraska. After detecting the hotspots, we identified the factors affecting the sizes of hotspots and other parameters. The start time of the game and the Cornhuskers’ opponent for a given game are two important factors affecting the number of people coming to Lincoln, Nebraska, on game days. Finally, the Dynamic Bayesian Networks (DBN) approach was applied to forecast the start times and locations of hotspot clusters in 2018 with a weighted mean absolute percentage error (WMAPE) of 13.8%
Reconstructing the Traffic State by Fusion of Heterogeneous Data
We present an advanced interpolation method for estimating smooth
spatiotemporal profiles for local highway traffic variables such as flow, speed
and density. The method is based on stationary detector data as typically
collected by traffic control centres, and may be augmented by floating car data
or other traffic information. The resulting profiles display transitions
between free and congested traffic in great detail, as well as fine structures
such as stop-and-go waves. We establish the accuracy and robustness of the
method and demonstrate three potential applications: 1. compensation for gaps
in data caused by detector failure; 2. separation of noise from dynamic traffic
information; and 3. the fusion of floating car data with stationary detector
data.Comment: For more information see http://www.mtreiber.de or
http://www.akesting.d
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