A proposed lane change model can be integrated with a car-following model to form a complete microscopic driver model. The model resembles traffic better at a macroscopic level, especially regarding the amount of traffic volume per lane, the traffic speeds in different lanes, and the onset of congestion. In a new approach, lane change incentives are combined for determining a lane change desire. Included incentives are to follow a route, to gain speed, and to keep right. Classification of lane changes is based on behavior that depends on the level of lane change desire. Integration with a car-following model is achieved by influencing car-following behavior for relaxation and synchronization, that is, following vehicles in adjacent lanes. Other improvements of the model are trade-offs between lane change incentives and the use of anticipation speed for the speed gain incentive. Although all these effects are captured, the lane change model has only seven parameters. Loop detector data were used to validate and calibrate the model, and an accurate representation of lane distribution and the onset of congestion was shown.

Word count: nr of words in abstract 241 nr of words (including abstract and references) 5723 nr of figures& tables 7* 250 = 1750 total 7473Please cite this article as: Knoop, V.L., Van Lint, J.W.C., and Hoogendoorn, S.P., (2012) The Macroscopic Fundamental Diagram Used for Control using Subnetwork Accumulation, Transportation Research Records ABSTRACTAn excess number of vehicles in a traffic network will reduce traffic performance. This reduction can be avoided by traffic management. In particular, traffic can be routed such that the bottlenecks are not oversaturated. The macroscopic fundamental diagram provides the relation between the number of vehicles and the network performance. One can apply traffic control on this level, in order to overcome computational complexity of network-wide control using traditional control levels of links or vehicles. Main questions in the paper are: (1) how effective is traffic control using aggregate variables compared to using full information and (2) does the shape of the macroscopic fundamental diagram change under traffic control. A grid network with periodic boundary conditions is used as example, and is split up into several subnetworks.The following routing strategies are compared: (1) the shortest path in distance, (2) the path shortest in time (dynamic due to congestion), (3) an approximation of the path shortest in time, but calculated using only variables aggregated for over a subnetwork, (4) an approximation of the path shortest in time, but calculated using only subnetwork accumulation. For routing strategy 3 and 4 only information aggregated over the subnetwork is used.The results show improved traffic flow using detailed information. Effective control is also possible using aggregated information, but only with the right choice of a subnetwork macroscopic fundamental diagram. Furthermore, when optimizing with detailed informationan hence in a subnetwork -the macroscopic fundamental diagram changes. INTRODUCTIONWhereas research into (and application of) freeway traffic control in the previous century predominantly focussed on local control applications (e.g. ramp metering), in hybrid and hierarchical control of mixed networks is one of the biggest challenges in the coming years.As alternative approach to centralised or fully communicating traffic control systems, one can introduce traffic control at different levels, as for instance argued by Landman et al.(1). Control on the lower level can be detailed with detailed information. However, for control the higher level, only aggregate information of the lower level is used. This way, less data is needed at a central coordination point, and the amount of data is limited. Theoretically, the information needs may be even very moderate. The macroscopic fundamental diagram (MFD) or network fundamental diagram as purported by Daganzo (8) and Geroliminis and Daganzo (9) summarizes the state of an entire traffic network into just two quantities: the accumulation and production of a traffic network. In this paper, we explore...

It is computationally expensive to find out where vulnerable parts in a network are. In literature a variety of methods were introduced that use relatively simple selection criteria (measured in real-life or calculated in a traffic simulator) to pre-determine the seriousness of the delays caused by the blocking of that link and thereafter perform a more detailed analysis. This paper reviews the selection criteria proposed in the literature and assesses the quality of these criteria. Furthermore, a multi-linear fit of the criteria is made to find a better, combined, criterion to rank the links according to their vulnerability. The paper shows that different criteria indicate different links to be vulnerable. Also combined they cannot well predict the vulnerability of a link. Therefore, it is concluded that to find vulnerable links, one has to look further than link-based indicators.

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This paper investigates at an aggregated (macroscopic) scale the effects of route patterns on a road network. Four main variables are considered: the production, the mean speed, the outflow and the mean travel distance. First, a simple network with heterogeneous travel distances between origins and destinations is studied by simulation. It appears that the mean travel distance is not only very sensitive to the changes in the origin-destination (OD) matrix but also to the internal traffic conditions within the network. When this distance is assumed constant as usual in the literature, significant errors may appear when estimating the outflow at the network perimeter. The OD matrix also modifies the shape of the macroscopic fundamental diagram (MFD) to a lesser extend. Second, a new modelling framework is proposed to account for multiple macroscopic routes within reservoirs (spatial aggregates of road network) in the context of MFD simulation. In contrast to existing works, partial accumulations are defined per route and traffic waves are tracked at this level. This leads to a better representation of wave propagation between the reservoir frontiers. A Godunov scheme is combined to a HLL Riemann approximate solver in order to derive the model numerical solutions. The accuracy of the resulting scheme is assessed for several simple cases. The new framework is similar to some multiclass models that have been elaborated in the context of link traffic dynamics.

An increasing amount of vehicles are equipped with driver assistance systems; many of the vehicles currently on the market can be optionally equipped with adaptive cruise control and lane centering systems. Using both systems at the same time brings the vehicle to SAE level-2 automation . This means a driver does not need to perform longitudinal and lateral operational driving, although the driver should be ready to intervene at any time. While this can provide comfort, the interaction between vehicles operated by these systems might cause some undesired effects. This becomes particularly relevant with increasing market penetration rates. This paper describes an experiment with seven SAE level-2 vehicles driven as a platoon on public roads for a trip of almost 500 km. The paper discusses how the experiment was organized and the equipment of the vehicles. It also discusses the interaction of the platoon in traffic, as well as, in basic terms, the interaction between the automated vehicles. The experiences can be useful for other studies setting up field tests. The conclusion from this platoon test is: intentionally creating platoons on public roads is difficult in busy traffic conditions. Moreover, interactions between the vehicles in the platoon show that the current SAE level-2 systems are not suitable for driving as platoons of more than typically three to four vehicles, because of instabilities in the car-following behavior.