Eric C. van Berkum

Modelling Externalities using Dynamic Traffic Assignment Models: A Review

Recently, there has been a growing interest in externalities in our society, mainly in the context of climate and air quality, which are of importance when policy decisions are made. For the assessment of externalities in transport, often the output of static traffic assignment models is used in combination with so-called effect models. Due to the rapidly increasing possibilities of using dynamic traffic assignment (DTA) models for large-scale transportation networks and the application of traffic measures, already several models have been developed to assess the externalities using DTA models more precisely. Different research projects have shown that there is a proven relation between the traffic dynamics and externalities, such as emissions of pollutants and traffic safety. This means that the assessment of external effects can be improved by using temporal information about flow, speed and density, which is the output of DTA models. In this paper, the modelling of traffic safety, emissions and noise in conjunction with DTA models is reviewed based on an extensive literature survey. This review shows that there are still gaps in knowledge in assessing traffic safety, much research is available concerning emissions, and although little research has been conducted concerning the assessment of noise using DTA models, the methods available can be used to assess the effects. Most research so far has focused on the use of microscopic models, while mesoscopic or macroscopic models may have a high potential for improving the assessment of these effects for larger networks.

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.

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.

From user equilibrium to system optimum: a literature review on the role of travel information, bounded rationality and non-selfish behaviour at the network and individual levels

ABSTRACT Travel information continues to receive significant attention in the field of travel behaviour research, as it is expected to help reduce congestion by directing the network state from a user equilibrium towards a more efficient system optimum. This literature review contributes to the existing literature in at least two ways. First, it considers both the individual perspective and the network perspective when assessing the potential effects of travel information, in contrast to earlier studies. Secondly, it highlights the role of bounded rationality as well as that of non-selfish behaviour in route choice and in response to information, complementing earlier reviews that mostly focused on bounded rationality only. It is concluded that information strategies should be tailor-made to an individuals level of rationality as well as level of selfishness in order to approach system-optimal conditions on the network level. Moreover, initial ideas and future research directions are provided for assessing the potential of travel information in order to improve network efficiency of existing road networks.

Drivers' perception of route alternatives as indicator for the indifference band

Although travel time is probably one of the most important attributes in route choice, the shortest time route is often not the preferred route, according to several studies in the literature. This study tries to explain this finding by testing the hypothesis that choice makers may be able to estimate travel times correctly for routes that they prefer but are biased against alternatives even if these are faster. For a few choice sets of routes in the city of Enschede, Netherlands, respondents were asked to choose a route and provide their estimated travel times for both the preferred and the alternative routes. These travel times were then compared with actual travel times from a license plate study. The comparison confirmed the hypothesis. For chosen routes, perceived travel times correspond quite well with actual travel times on average, whereas for routes not chosen, perceived travel times are overestimated by 3 to 4 min on average. These results show that drivers are not able or do not want to evaluate routes objectively. This finding implies that within an indifference band of route delay or travel time inequality of on average 3 to 4 min, drivers are probably not willing to alter their route choice, even if the traffic situation, induced, for example, by traffic management measures, changes in a negative way for their preferred route.

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 evolutionary multi objective algorithms for the dynamic network design problem

In traffic and transport a significant portion of research and application is focused on single objective optimization, although there is rarely only one objective that is of interest. The externalities of traffic are of increasing importance for policy decisions related to the design of a road network. The optimization of externalities using dynamic traffic management measures is a multi objective network design problem. The presence of multiple conflicting objectives makes the optimization problem challenging to solve. Evolutionary multi objective algorithms has been proven successful in solving multi objective optimization problems. However, like all optimization methods, these are subject to the free lunch theorem. Therefore, we compare the NSGAII, SPEA2 and SPEA2+ algorithms in order to find a Pareto optimal solution set for this optimization problem. Because of CPU time limitation as a result of solving the lower level using a dynamic traffic assignment model, the performance by the algorithms is compared within a certain budget. The externalities optimized are noise, climate and accessibility. In a numerical experiment the SPEA2+ outperforms the SPEA2 on all used measures. Comparing NSGAII and SPEA2+, there is no clear evidence of one approach outperforming the other.

Dynamic traffic management measures to optimize air quality, climate, noise, traffic safety and congestion: effects of a single objective optimization

Traditionally, traffic problems are treated in isolation in terms of location of the problem and the kind of problem. However, there is a strong correlation between these problems, so clearly solving a traffic problem at one location may result in other problems at other locations. Congestion problems on the main network can, for example, lead to “rat-running” (through-traffic using the secondary road network avoiding these congestion problems) causing liveability problems. Therefore, measures to alleviate traffic problems are nowadays increasingly focussed on the network level. In addition, solutions are sought for the optimization of traffic systems and less emphasis is placed on expanding the infrastructure of the system mainly because of financial considerations and space limitations. This optimization can be achieved using traffic management measures. Traditionally, this type of optimization is focussed on improving accessibility, given particular boundary conditions for traffic safety and liveability (set by law).

An Approach for Determining Rules used to Select Viable Junction Design Alternatives Based on Multiple Objectives

Transport planners and engineers frequently face the challenge to determine the best design for a specific junction. Many road design manuals provide guidelines for the design and evaluation of different junction alternatives, however these mostly refer to specialized software in which the performances of design alternatives can be modelled. In the first stage of the design process, such assessments of many alternatives are undesirable due to time and budget constraints. There is a need for quick design rules which need limited input data. Although some of these rules exist, their usability is limited due to inconsistencies in rules and non-transparency in combination with objectives. In this paper, we present an approach by which consistent and transparent junction design rules can be determined. The resulting rules can be used to predict a set of viable junction design alternatives for the first stage of the junction design assessment process. The predicted set is in fact the Pareto optimal set of solutions for multiple objectives, e.g. regarding operational, safety and/or environmental impact. The Pareto optimal set of solutions always contains the best solution, whatever set of weights is used for different objectives in a later stage of the assessment process, thus handling multiple objectives in a straightforward manner. The rules are derived from a dataset by using decision tree data mining techniques. For this, a large dataset is first generated, using performance models, with Pareto optimal sets of junction design alternatives for a large amount of, randomly generated, traffic volumes. The approach is applied and evaluated on cases for two different countries. Results show that for over 90% of the situations the Pareto optimal set can be predicted by the new rules, whereas existing rules hardly reach 33%. The new rules provide junction design alternatives with a better performance.

In-car Advice to Reduce Negative Effects of Phantom Jams

Congestion problems result in economic losses and also have serious implications for traffic safety. In the Netherlands, more than 20% of all congestion is recognized as shockwave jams, or so-called phantom traffic jams, and in other countries this type of jam has been recognized as a significant part of congestion. A phantom jam occurs without the existence of a physical bottleneck and is caused by the imperfect driving style of road users under metastable traffic conditions. To prevent phantom jams, the focus is on the cause of the perturbations or on the metastability of the traffic flow. Previous studies have shown that dynamic speed limits displayed by roadside equipment are successful in stabilizing traffic flow and resolving phantom jams. Several algorithms were developed to prevent or resolve phantom jams by giving in-car speed advice. For this purpose the data, typically detected by dual-loop detectors, were processed by fuzzy logic rules and clustering techniques to determine the spatiotemporal traffic conditions. The performance was tested in a microsimulation study using various compliance rates and speed advice on a two-lane facility. As expected, the occurrence of phantom jams showed a high correlation with high-intensity waves, platoons of traffic under metastable traffic conditions. The results of the algorithms showed that prevention-based algorithms could be successful in reducing phantom jam occurrence. Resolution-based algorithms were unsuccessful, with no significant improvements or deterioration.