Abstract
Flow and aggregated speed data from stationary detectors and trajectories of floating cars allow us to investigate many aspects of traffic breakdown and jam propagation. In the first two sections, we discuss the three main factors of traffic breakdowns: High traffic flow, bottlenecks, and a disturbances in the traffic flow itself. In the next two sections, we summarize the stylized facts of the spatiotemporal evolution of congested traffic patterns, i.e., typical empirical findings that are repeatedly observed on various highways all over the world. In the last section, we apply this knowledge to real-time traffic-state estimation and short-term prediction. While the traffic breakdown as such is a stochastic process and therefore, in principle, only predictable in terms of probabilities, the stylized facts allow a quasi-deterministic forecast of the evolution of already congested traffic. The focus is on highways and major roadways but the contents of this chapter is also applicable to other types of roads.
Real knowledge is to know the extent of one’s ignorance.
Confucius
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Notes
- 1.
In cities, traffic lights are the most common form of (temporary) bottlenecks.
- 2.
Remember the time scales of traffic-flow investigations displayed in Table 1.1. On the scales of months or years, roadworks bottlenecks are temporary as well.
- 3.
This even applies to situations where a rush hour is about to increase the demand above the static capacity \(C_{B}\) of the bottleneck. So a jam is unavoidable whether traffic flow is unstable, nor not (the case described by the LWR models): Even before the static capacity is reached, a disturbance in the flow will activate the bottleneck reducing its capacity to the dynamical capacity \(C_\mathrm{B}^\mathrm{dyn}\).
- 4.
This is similar to forecasting the weather: It is impossible to exactly predict the times and locations of individual thunderstorms/rainfalls while it is standard to predict the probability of thunderstorms/rainfalls in a certain spatiotemporal region.
- 5.
see: www.traffic-simulation.de
- 6.
These are triggered either by initial conditions not perfectly representing steady-state conditions, or, ultimately, by numerical rounding errors.
- 7.
The authors’ website www.traffic-states.com offers a searchable image database of congested traffic patterns.
- 8.
Because details on scales below the distance of two detectors cannot be resolved, the extension of stationary localized jams cannot be determined exactly. For moving localized jams, Stylized Fact 4 allows to infer the extension from the time period between two waves.
- 9.
Moreover, speed variations between “stop and slow” may result from problems in maintaining low speeds (the accelerator and brake pedals are difficult to control in this regime), and thus are different from the collective dynamics at higher speeds.
- 10.
see: www.traffic-states.com
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Further Reading
Further Reading
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Nanthawichit, C., Nakatsuji, T., Suzuki, H.: Application of probe-vehicle data for real-time traffic-state estimation and short-term travel-time prediction on a highway. Transp. Res. Rec. J. Transp. Res. Board 1855, (2003) 49–59
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Kerner, B., Rehborn, H., Aleksic, M., Haug, A.: Recognition and tracking of spatio-temporal congested traffic patterns on freeways. Transp. Res. Part C Emerg. Technol. 12, (2004) 369–400
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Zang, Y., Papageorgiou, M.: Real-time freeway traffic state estimation based on extended kalman filter: a general approach. Transp. Res. Part B Methodol. 39, (2005) 141–167
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Mihaylova, L., Boel, R., Hegyi, A.: Freeway traffic estimation within particle filtering framework. Automatica 43, (2007) 290–300
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Treiber, M., Kesting, A., Helbing, D.: Three-phase traffic theory and two-phase models with a fundamental diagram in the light of empirical stylized facts. Transp. Res. Part B Methodol. 44(8–9), (2010) 983–1000
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Treiber, M., Kesting, A., Wilson, R.E.: Reconstructing the traffic state by fusion of heterogenous data. Comput. Aided Civ. Infrastruct. Eng. 26, (2011) 408–419
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Treiber, M., Kesting, A. (2013). Traffic Flow Breakdown and Traffic-State Recognition. In: Traffic Flow Dynamics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32460-4_18
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DOI: https://doi.org/10.1007/978-3-642-32460-4_18
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