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![Fundamental diagram and corresponding space-time regions with phase fronts and front propagation speeds (compare to [15])](profile/Felix-Rempe/publication/319052189/figure/fig1/AS:827283044577281@1574251056665/Fundamental-diagram-and-corresponding-space-time-regions-with-phase-fronts-and-front.png)
Fundamental diagram and corresponding space-time regions with phase fronts and front propagation speeds (compare to [15])
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... the other hand, the dynamically changing inflow Q up i (t, t p ) definitely impacts the propagation of the front. If the inflow is great, the front propagates upstream faster and the congested regime grows. With greater flow in free flow conditions also the density increases, though only slightly (compare Fig. (1)). Compared to relatively slow shockwaves in congested traffic, shockwaves in free flow propagate with a greater velocity of v F ≈ 80km/h downstream [20] [16] [14] [15]. With respect to these empirical observations and traffic theories based on a fundamental diagram (FD), in the proposed method flow and density quantities are propagated ...
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... between the prediction of the first order and higher order fronts is made. The reason for this distinction is the effect that influences the front propagation: the first order front is mostly influenced by the prediction of the upstream flow, while the inflow of higher order fronts is given by the outflow of the neighboring fronts (compare Fig. ...
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... is a sensitive component of the method, which K-DET is not able to estimate correctly. Third, for higher order fronts the naive algorithm is the most accurate one. The reason is that a transition from a congested into a free traffic state and back into a congested traffic state without any additional in-or outflow can be explained well with a FD (Fig. (1)). In this case, the front propagation can be deduced directly from the FD. Potentially erroneous measurements effectively reduce the accuracy of the prediction. As a conclusion a mixed model should be considered for application: The first order front is predicted using the K-MAX variation, while for higher order fronts a naive ...
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Citations
... A method combining stationary detector data and probe vehicle data to predict freeway congestion fronts was presented, as highlighted by Rempe et al. (2017). In Rempe and Bogenberger (2019), a forecast algorithm was applied to urban road networks with farther links taken into account. ...
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... The retrospective analysis often focuses on average vehicle speeds per time and space interval on a road since this provides benefits such as enabling the deduction of travel times for road users, providing jam tail warnings (Rempe et al., 2017b) aiming at the reduction of rear-end collisions at jam tails, etc. However, using current sensor technology, average vehicle speeds are not measured for all times and places on a road stretch. ...
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... In [17], an approach to network-wide traffic state estimation combining loop detector and floating car data is presented. The authors of [1] developed a model to fuse FCD and loop detector data to forecast congestion fronts on a freeway. A comparison of two model-based approaches on filtering methods is conducted in [18]. ...
This paper studies the joint reconstruction of traffic speeds and travel times by fusing sparse sensor data. Raw speed data from inductive loop detectors and floating cars as well as travel time measurements are combined using different fusion techniques. A novel fusion approach is developed which extends existing speed reconstruction methods to integrate low-resolution travel time data. Several state-of-the-art methods and the novel approach are evaluated on their performance in reconstructing traffic speeds and travel times using various combinations of sensor data. Algorithms and sensor setups are evaluated with real loop detector, floating car and Bluetooth data collected during severe congestion on German freeway A9. Two main aspects are examined: (i) which algorithm provides the most accurate result depending on the used data and (ii) which type of sensor and which combination of sensors yields higher estimation accuracies. Results show that, overall, the novel approach applied to a combination of floating-car data and loop data provides the best speed and travel time accuracy. Furthermore, a fusion of sources improves the reconstruction quality in many, but not all cases. In particular, Bluetooth data only provide a benefit for reconstruction purposes if integrated distinctively.
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... They solve an undetermined system of equations using orthogonal matching pursuit assuming a triangular fundamental diagram. Also, Rempe et al. (2017) forecast the position of shockwave fronts in a short time horizon (10 min) by estimating it using Lighthill-Whitham-Richards theory. They estimate the flow as a weighted average of the measures, like ASM. ...
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... On the other hand, traffic speed can be detected by high-resolution location and time measurements of floating vehicles, so-called floating-car data (FCD). This technique performs well compared to stationary sensors [1], [2] or in addition to them [3]. ...
Based on an empirical study of floating car data (probe vehicle data), we have found the following microscopic features of empirical synchronized flow that are relevant for jam warning methods: (i) In accordance with Kerner’s three-phase traffic theory, the empirical speed decrease (speed drop) denoted by Δv that occurs due to vehicle deceleration at the upstream front of synchronized flow is a complex time function. (ii) The empirical function Δv can be considered a “jam wall” at which different vehicles exhibit sometimes very different speed drops Δv occurring at different road locations. Because each of the vehicles decelerates within the “jam wall” at different road locations, the “jam wall” moves in space and in time. (iii) The spatiotemporal structure of the empirical “jam wall” is determined by alternations of the synchronized flow and wide moving jam traffic phases at the upstream front of a congested traffic pattern. (iv) During some time intervals, the “jam wall” can disappear and then it can appear once more, and so on; during “jam wall” disappearance, one or a few vehicles can pass a bottleneck while moving in free flow. These empirical findings can be important for the development of reliable jam warning methods as well as other ITS-applications.
