Abstract
By means of simulations of microscopic traffic flow models, we investigate measures to increase the efficiency and stability of traffic flow when the infrastructure and the traffic demand are fixed. Road-based measures include variable message signs (for traffic-adaptive speed limits and dynamic routing), ramp metering, and selective overtaking bans for certain vehicle classes (trucks) in certain situations (gradients). Another class of optimization measures is vehicle-based rather than road-based. At present, these measures have entered the market and are expected to have a significant market penetration (and influence) in the near future. They include semi-automated driving (adaptive cruise control), individual traffic-adaptive navigation, traffic-light assistants, and other driver-assistance systems.
When it is obvious that the goals cannot be reached, don’t adjust the goals, adjust the action steps.
Confucius
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Notes
- 1.
The effects may be counter-intuitive. In an extreme case known as Braess’s paradox, the construction of a new link may lead to longer travel times on all routes from a given origin to a given destination after the new user equilibrium has settled.
- 2.
Nevertheless, this belongs to the category of static measures: Because roadworks have to be planned in advance, they can only take into account historical demand profiles, without feedback from the actual situation.
- 3.
In fact, Fig. 4.6 shows that the sharp “truck peak” of the speed distribution is at 89 km/h rather than 80 km/h. Obviously, no consequences threaten truck drivers up to this speed in Germany so that speed limiters are set accordingly.
- 4.
- 5.
As a matter of fact, there are other reasons for speed limits, e.g., traffic safety or noise pollution. This will not be considered here.
- 6.
see: www.movsim.org.
- 7.
see: www.movsim.org.
- 8.
The ACC model (Sect. 11.3.8) has actually been implemented into the ACC systems of test cars to investigate the traffic-adaptive strategy to be described below.
- 9.
Vehicles pass their information via short-range communication to a road-side unit (V2I) which transmits them to a further road-side unit in the upstream direction (I2I). The latter, in turn, passes the information back to equipped vehicles (I2V).
- 10.
In ACC systems, drivers can not only set the maximum speed but also the time gap in car-following mode. Some car manufacturers also offer more “sportive” or more comfort-oriented overall settings.
- 11.
This must not be confused with the reaction time which is insignificant in modern ACC systems, and not contained in the ACC model.
- 12.
As a general rule, the relative savings in travel time are about three times greater than that of fuel.
- 13.
Of course, safety comes always first, so the time gap should never be lower than a critical value which is necessary to avoid rear-end collisions (cf. Sect. 4.3). Furthermore, drivers should be particularly attentive in such situations.
- 14.
As a welcome side effect for the individual driver on lanes to be closed, he or she often passes several vehicles on the neighboring lanes (in accordance with traffic regulations of most countries) when this rule is adopted.
- 15.
Strictly speaking, objective functions map functions (such as the spatiotemporal local speed) onto a number, so they are, mathematically speaking, functionals rather than functions. Agreeing with the common usage, we will nevertheless speak of objective “functions”.
- 16.
In the simplest case, the routes are statically prescribed by a succession of nodes \(\{k\}\) with \(i\) being the first and \(j\) the last node. When defining route choices dynamically at each node according to the driver’s subjective impression of the traffic state, things begin to get really complicated.
- 17.
In case of traffic jams, information flow propagates upstream, so downstream boundary conditions are needed.
- 18.
To simulate the coffeemeter, you do not need to apply the full three-dimensional hydrodynamics of hot coffee with mixed Dirichlet and free boundary conditions for the cup and the free coffee surface, respectively. It suffices to phenomenologically model the two orthogonal lowest-order modes (corresponding to in-phase motions of the whole surface) by a two-dimensional pendulum driven by the longitudinal and lateral accelerations. The eigenfrequencies are determined by the cup geometry, and the modes are phenomenologically damped to yield the observed decay of the oscillations inside the cup (time constant \(\approx \) 5 s).
- 19.
Meaning exponentially sensitive dependence of the output on the initial data and model parameters.
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Further Reading
Further Reading
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Papageorgiou, M., Hadj-Salem, H., Blosseville, J.: ALINEA: A local feedback control law for on-ramp metering. Transportation Research Record 1320 (1991) 58–64
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Treiber, M., Helbing, D.: Microsimulations of freeway traffic including control measures. Automatisierungstechnik 49 (2001) 478–484
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Chatterjee, K., McDonald, M.: Effectiveness of using variable message signs to disseminate dynamic traffic information: Evidence from field trails in European cities. Transport Reviews 24 (2004) 559–585
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Hegyi, A., De Schutter, B., Hellendoorn, H.: Model predictive control for optimal coordination of ramp metering and variable speed limits. Transportation Research Part C: Emerging Technologies 13 (2005) 185–209
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Kesting, A., Treiber, M., Schönhof, M., Helbing, D.: Adaptive cruise control design for active congestion avoidance. Transportation Research Part C: Emerging Technologies 16 (2008) 668–683
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Papageorgiou, M., Kosmatopoulos, E., Papamichail, I.: Effects of variable speed limits on motorway traffic flow. Transportation Research Record: Journal of the Transportation Research Board 2047 (2008) 37–48
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Hartenstein, H., Laberteaux, K.: A tutorial survey on vehicular ad hoc networks. Communications Magazine, IEEE 46 (2008) 164–171
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Kesting, A., Treiber, M., Helbing, D.: Connectivity statistics of store-and-forward intervehicle communication. IEEE Transactions on Intelligent Transportation Systems 11 (2010) 172–181
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Treiber, M., Kesting, A.: An open-source microscopic traffic simulator. Intelligent Transportation Systems Magazine 2 (2010) 6–13
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Treiber, M., Kesting, A. (2013). Model-Based Traffic Flow Optimization. In: Traffic Flow Dynamics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32460-4_21
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DOI: https://doi.org/10.1007/978-3-642-32460-4_21
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