Comparison of Di ff erent Approaches and Future Research

6 Numerical Results 110 real-world scenario do not exactly match with those from the simulations of greedy-cruise control. Also, the number of optimizations per guided car is quite low for all scenarios – and especially for the simulations with longer cycle time. This means that a lot of equipped cars did not manage to enable the greedy-cruise-control due to close preceding cars. Probably, the threshold of 40 meters is chosen too large.

Regarding the four-intersections network, we assess noticeable differences between the particular equipment ratios. Due to the loose character of traffic, the amount of equipped cars whose greedy-cruise-control is not disabled is higher as in the small network. Besides the development of travel time and waiting time when changing the equipment rate, the values for the environmental parameters also improve when the equipment rate is increased. Nonetheless, there is also a slight deterioration in terms of traffic flow and environmental parameters for the longer cycle time. Reasons might be the same as for the single-intersection network. On both networks, traffic flow benefits from shorter green times, which is consistent with the experiments for the RACC.

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real-world RACC greedy-algorithm global-MILP

Waitingtimeinseconds

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Figure 6.18: The average waiting time of cars for real-world traffic and the different methods on the single-intersection network with the shorter cycle-time if present.

The different colors depict different equipment rates.

Regarding fuel consumption we can derive analogous outcomes: for the RACC, a decrease of up to 19 % and 12 % is measured during the experiments on the single- and four-intersections network, respectively. The outcomes for the greedy-algorithm and the solutions of the global-MILP are again on the same level.

Improvements of up to 54 % for fuel consumption on the single-intersection net-work and 41 % on the four-intersections netnet-work can be observed.

The outcome of the experiments suggests a high potential for enhancements of traffic in different dimensions using cooperative systems. A major takeaway-message of this thesis is that calculating optimal solutions concerning the behavior of cars and traffic-lights from an individual point of view in contrast to a global one can result in a massive reduction of the problem’s complexity. In parallel, the benefit for traffic as a whole is surprisingly close to what can be achieved by global optimization – at least for the greedy-cruise-control. Additionally, applications which consider each car individually seem to be more attractive for a contemporary introduction: participants who do not run the respective system can easily be considered, and different variants of an application, e. g., due to various implementations by different car-manufacturers, can work concurrently.

Furthermore, different levels of a particular application are considerable. Examples are the RACC and the greedy-cruise-control: while a subset of cars might be able to receive messages from an infrastructural device only and react to it, another

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Fuelconsumptioninmilliliters

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Figure 6.19: The average fuel consumption of cars for real-world traffic and the different methods on the single-intersection network with the shorter cycle-time if present. The different colors depict different equipment rates.

subset of cars might be capable of transmitting messages to the device as well and negotiate a time for a transit. Even cars which do not run any of these systems might be included in this scenario. We proposed an extension of the RACC, which could also be considered for an application based on the greedy-algorithm, where not only the actual movement of other cars is detected and reacted to. In fact, cars might exchange information about intended maneuvers among each other and incorporate these information. Some of the dead times that occur when an acceleration controller is applied could be avoided, probably leading to an even bigger improvement in traffic flow.

In contrast, a traffic-scenario which is optimized from a global point of view, as it is the case in the global-MILP, requires that all participants run the particular system – although different parametrizations could be allowed. Furthermore, some efforts have to be made in order to derive algorithms and methods which can provide solutions for a globally optimal traffic flow in reasonable time for considerable instances. Certainly, there is still potential beyond the considera-tions stated in this thesis. Nonetheless, the introduced global-MILP constitutes a benchmark for other algorithms or applications. Besides the discussed pros and cons of the respective systems and optimization approaches, the main advantage of suboptimal individual systems – especially the RACC – is that they can be implemented with contemporary technology and have been successfully tested.

6 Numerical Results 113 Future research might focus on further solving strategies. It seems promising to combine the presented branch-and-bound algorithm and the iterative solving algorithm, as both methods lead to noticeable reductions in solving times during the respective experiments. One possibility is to incorporate the branch-and-bound algorithm in the solving procedure of the MILPs occurring in the iterative solving algorithm. Moreover, there certainly is still potential in improving the respective solving methods themselves, e. g., by calculating better bounds and introducing other kinds of cuts during the iterative conflict resolution.

In fact, for developing (non-optimal) driver-assistance systems or other appli-cations, it is an important tool to value the resulting behaviors of the cars. Thus, extending the global-MILP, such that additional maneuvers, e. g., lane changes and turning maneuvers, can be modeled seems quite reasonable. Of course, also the possibility to investigate more complex networks and traffic situations, which is mainly achieved by improving the solving process, would be desirable.

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