Dynamic Traffic Management Measures Research Articles

Integrated Signal Control and Route Guidance based on Back-pressure Principles

Traffic signal control and route guidance are the oldest and most applied dynamic traffic management measures. Most of the time they operate in a local mode, although there is trend toward network-wide traffic management. For traffic signal control already several network systems existed, but so far the integration with route guidance is lacking. In this paper we describe a new strategy to integrate traffic signal control and route guidance, based on the principles of back-pressure control. The algorithms developed are tested in a theoretical network and it was shown that traffic signal control based on back-pressure control performs well. Using back-pressure for route guidance required some assumptions which are open for debate. The results show that the average density is not such a good measure for route pressure and that travel time as a pressure variable performs better. A combination of factors of pressure based on density and travel time seems to be the best choice. Using back-pressure for both signal control and route guidance gave promising results, although the differences with optimized local control were small. Future research is recommended on the fine-tuning of the back-pressure traffic signal model, and on further integration and coordination of the control strategies. On the part of route guidance, finding representative route pressure values and making the model applicable of larger networks requires more research.

Handling multiple objectives in optimization of externalities as objectives for dynamic traffic management

Dynamic traffic management (DTM) is acknowledged in various policy documents as an important instrument to improve network performance. This network performance is not only a matter of accessibility, since the externalities of traffic are becoming more and more important objectives as well. Optimization of network performance using DTM measures is a specific example of a network design problem (NDP) and incorporation of externality objectives results in a multi objective network design problem (MO NDP)). Solving this problem resorts in a Pareto optimal set of solutions. A framework is presented with the non-dominated sorting algorithm (NSGAII), the Streamline dynamic traffic assignment model and several externality models, that is used to solve this MO NDP. With a numerical experiment it is shown that the Pareto optimal set provides important information for the decision making process, which would not have been available if the optimization problem was simplified by incorporation of a compensation principle in advance. However, in the end a solution has to be chosen as the best compromise. Since the Pareto optimal set can be difficult to comprehend, ranking it may be necessary to assist the decision makers. Cost benefit analysis which uses the economic compensation principle is a method that is often used for ranking the alternatives. This research shows, that travel time costs are by far the most dominant objective. Therefore other ranking methods should be considered. Differences between these methods are explained and it is illustrated that the outcomes and therefore the eventual decisions taken can be different.

Effective traffic management based on bounded rationality and indifference bands

Constrained cognitive abilities cause imperfections in drivers’ choice behaviour and appear largely systematic and predictable. This study introduces the concept of ‘effective control space’ to build upon this knowledge as an opportunity to increase the effectiveness of Dynamic Traffic Management (DTM). Within the control space boundaries it is assumed that drivers do not act upon the effects of DTM measures, they behave as being indifferent to them. This study debates that: (i) drivers’ ability to detect changes in attributes of their trip or the performance of a traffic system is limited, (ii) drivers make mistakes in estimating the value of such changes and (iii) drivers apply a great diversity of choice patterns but do not necessary adapt their choice. Hence, for some DTM measures to be effective effects should not exceed the control space boundaries, whereas other DTM measures need to give drivers an incentive that exceeds these boundaries. Knowledge on the effective control space may support road authorities to operationalise their measures most effectively. With the theories of indifference bands and decision-making as starting point a theoretical and conceptual framework are provided, supported by a numerical example to demonstrate how application can steer a system towards its optimal state.

Acceleration of Solving the Dynamic Multi-Objective Network Design Problem Using Response Surface Methods

Optimization of externalities and accessibility using dynamic traffic management measures on a strategic level is a specific example of solving a multi-objective network design problem. Solving this optimization problem is time consuming, because heuristics like evolutionary multi objective algorithms are needed and solving the lower level requires solving the dynamic user equilibrium problem. Using function approximation like response surface methods (RSM) in combination with evolutionary algorithms could accelerate the determination of the Pareto optimal set. Three algorithms in which RSM are used in different ways in combination with the Strength Pareto Evolutionary Algorithm 2+ (SPEA2+) are compared with employing the SPEA2+ without the use of these methods. The results show that the algorithms using RSM methods accelerate the search considerably at the start, but tend to converge more quickly, possibly to a local optimum, and therefore loose their head start. Therefore, usage of function approximation is mainly of interest if a limited number of exact evaluations can be done or this can be used as a pre phase in a hybrid approach.

Pruning and ranking the Pareto optimal set, application for the dynamic multi‐objective network design problem

SUMMARYSolving the multi‐objective network design problem (MONDP) resorts to a Pareto optimal set. This set can provide additional information like trade‐offs between objectives for the decision making process, which is not available if the compensation principle would be chosen in advance. However, the Pareto optimal set of solutions can become large, especially if the objectives are mainly opposed. As a consequence, the Pareto optimal set may become difficult to analyze and to comprehend. In this case, pruning and ranking becomes attractive to reduce the Pareto optimal set and to rank the solutions to assist the decision maker. Because the method used, may influence the eventual decisions taken, it is important to choose a method that corresponds best with the underlying decision process and is in accordance with the qualities of the data used. We provided a review of some methods to prune and rank the Pareto optimal set to illustrate the advantages and disadvantages of these methods. The methods are applied using the outcome of solving the dynamic MONDP in which minimizing externalities of traffic are the objectives, and dynamic traffic management measures are the decision variables. For this, we solved the dynamic MONDP for a realistic network of the city Almelo in the Netherlands using the non‐dominated sorting genetic algorithm II. For ranking, we propose to use a fuzzy outranking method that can take uncertainties regarding the data quality and the perception of decision makers into account; and for pruning, a method that explicitly reckons with significant trade‐offs has been identified as the more suitable method to assist the decision making process. Copyright © 2012 John Wiley & Sons, Ltd.

Effects of Optimizing Externalities Using Cooperating Dynamic Traffic Management Measures on Network Level

Optimization of traffic network performance using dynamic traffic management (DTM) measures can be viewed as a specific example of solving a network design problem (NDP). Decision variables are the specific settings of DTM measures. DTM measures have been identified as powerful instruments not only to increase network efficiency, but also to improve externalities. As a result, in the optimization the focus is not only on efficiency, but also on climate, air quality, traffic safety, and noise. These assessment criteria are determined using the output of a dynamic traffic assignment model. This results in a dynamic multi-objective NDP, which is solved as a bilevel optimization problem; it results in a Pareto optimal set. This set provides valuable information for the decision-making process, which would not have been available if the compensation principle would have been chosen in advance. Knowledge obtained by optimization of realistic cases can be used to attain knowledge about incorporation of externalities as an objective when optimizing traffic systems using DTM measures. A case study for a realistic network of the city of Almelo shows that the objectives efficiency, climate, and air quality are mainly aligned and mainly opposed to traffic safety and noise. However, there is not a single solution that optimizes all three aligned objectives. Based on the Pareto optimal set, the trade-offs are determined and using cluster analysis the solutions and results are further analyzed for network segments.

Comparison of Multiobjective Evolutionary Algorithms for Optimization of Externalities by Using Dynamic Traffic Management Measures

The externalities of traffic are increasingly important for policy decisions related to design of a road network. Optimization of externalities with dynamic traffic management measures influencing the supply of infrastructure is a multiobjective network design problem, which in turn is a bi-level optimization problem. The presence of conflicting objectives makes the solution to the optimization problem a challenge. Evolutionary multiobjective algorithms have proved successful in solving such problems. However, like all optimization methods, these are subject to the no-free-lunch theorem. Therefore, this paper compares the nondominated sorting genetic algorithm II (NSGA-II), the strength Pareto evolutionary algorithm 2 (SPEA2), and the strength Pareto evolutionary algorithm 2+ (SPEA2+) to find a Pareto optimal solution set for this problem. Because incorporation of traffic dynamics is important, the lower level should be solved through a dynamic traffic assignment model, which increases needed CPU time. Therefore, algorithm performance is compared within a certain budget. The approaches are compared in a numerical experiment through different metrics. The externalities optimized are noise, climate, and congestion. The results show that climate and congestion are aligned and that both are opposed to noise in the case study. On average, SPEA2+ outperforms SPEA2 in this problem on all used measures. Results of NSGA-II and SPEA2+ are inconclusive. A larger population results on average in a larger space coverage, while a smaller population results in higher performance on spacing and diversity. Most performance measures are relatively insensitive for the mutation rate.

Estimating Acceleration, Fuel Consumption, and Emissions from Macroscopic Traffic Flow Data

With increasing public concern about the environment, livability and sustainability have become important issues in dynamic traffic management (DTM). Microscopic fuel consumption and emission models use vehicle speed and acceleration as inputs and are suitable for investigating the environmental effects of DTM measures at the link level. However, the lack of microscopic traffic data limits the application of these models. A method is provided for acquiring microscopic information from macroscopic traffic data. The main approach is to reconstruct the traffic state and vehicle group trajectories with an adaptive smoothing method, derive acceleration from the reconstructed vehicle trajectories, and calculate fuel consumption and emissions with filtered speed and estimated acceleration as inputs. The derived acceleration is compared with vehicle trajectories from simulation. Validation of the method shows that the estimated acceleration reflects the congestion characteristics. A case study investigating the environmental benefits of a freeway control algorithm on a Dutch freeway was conducted to illustrate the application potential of the method.

Coordination Concepts for Ramp Metering Control in a Freeway Network

The steadily increasing numbers and lengths of traffic jams on freeways have led to the application of Dynamic Traffic Management (DTM) measures all over the world. Ramp metering control has proven to be one of the most efficient means to reduce freeway congestion. Currently, it is expected that integrated and coordinated application of DTM measures will further improve its impact. This paper studies a coordinated ramp metering control algorithm called HERO/RWS. This algorithm has been developed for the current Dutch ramp metering systems and it will be applied on the Amsterdam A10 freeway network in the near future. The aim of this algorithm is to postpone congestion on freeways by effectively using ramp storage space from upstream on-ramps. VISSIM-based microscopic simulation results show that the HERO/RWS coordinated control outperforms non-coordinated ramp metering control. Parameter settings have been optimized for the specific A10-west network through a robustness study. In addition, the concept of coordination between ramp meter and upstream intersection traffic controllers is developed. The feasibility of this idea has been proven by a simulation study.

Network Impact of Dynamic Traffic Management Measures

Historically Dynamic Traffic Management (DTM) measures were applied on a local level, i.e. given a problem on a specific location, one or more DTM measures were put into action to alleviate the local problem. This contribution deals with the problem of network optimization using dynamic traffic management (DTM) measures. The problem of optimiziog network performance is formulated as a mathematical optimization problem.With a test-network and using the macroscopic simulation model METANET the network effects of locally optimized DTM measures are investigated. Furthermore it is investigated how the optimization problem may or may not be solved.