Highway bottlenecks are responsible for the majority of traffic congestion. Although the problem of bottleneck detection is not new, contemporary methods have not solved the problem thoroughly with regards to bottleneck locations, activation time, and related congestion tracking. These elements are essential for identifying and characterizing a bottleneck. This paper proposes a comprehensive framework for detecting and extracting these features of highway bottlenecks from traffic data. We particularly focus on questions (i) whether a bottleneck is the primary source of congestion or (ii) whether it is activated due to congestion caused by another downstream bottleneck. The underlying principles of the proposed method include the detection of congestion (in spatio-temporal patterns of traffic congestion), and the detection of speed discontinuities in traffic data (since this is an important indicator of a bottleneck activation). The method is data-driven and automatic therefore can be easily applied to different highways and used to obtain meaningful statistics of existing bottlenecks. We have tested the method on simulated data and also demonstrated it on real data from a busy highway section in the Netherlands. The results suggest that the method is robust to different implementations, i.e. locations, of loop-detectors which measure traffic at discrete locations. @en

A fully separated bicycle network from vehicular traffic is not realistic even for the most bicycle-friendly cities. Thus, all around the world urban cycling entails switching between streets of different safety, convenience, and comfort levels. As a consequence, the quality of bicycle networks should be evaluated not based on one but multiple factors and by considering the different user preferences regarding these factors. More comprehensive methodologies to assess urban bicycle networks are essential to the operation and planning of modern city transportation. This work proposes a multi-objective methodology to assess—what we refer to as—bikeability between origin–destination locations and over the entire network, useful for evaluation and planning of bicycle networks. We do so by introducing the concept of bikeability curves which allows us to assess the quality of cycling in a city network with respect to the heterogeneity of user preferences. The application of the proposed methodology is demonstrated on two cities with different bike cultures: Amsterdam and Melbourne. Our results suggest the effectiveness of bikeability curves in describing the characteristic features and differences in the two networks.@en

Origin-destination travel demand matrix is the signature of travel dynamics in transportation networks. Many fundamental analyses of transportation systems rely on the origin–destination demand matrix of the network. Although extraction of origin–destination travel demand for public transportation networks from ticketing data is not a new problem, yet it entails challenges, such as ‘alighting transaction inference’ and ‘transfer identification’ which are worthy of further attention. This is mainly because the state-of-the-art solutions to these challenges, are often heavily reliant on network-specific expert knowledge and extensive parameter setting, or multiple data sources. In this paper, we propose a procedure that effectively applies statistical pattern recognition techniques to address the main challenges in extracting the origin–destination demand from passenger smartcard records. Learning from patterns in the available data allows the procedure to perform well under minimum case-specific assumptions, thus it becomes applicable to smartcard data from various public transportation systems. The performance of the proposed framework is tested on a dataset of over 100 million smartcard transaction records from Melbourne's multi-modal public transportation network. Evaluations on different aspects of the proposed procedure, suggest that the identified tasks are well addressed, and the framework is able to extract an accurate estimation of the origin–destination demand matrix for the system.

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Macroscopic pedestrian models are theoretically simpler than microscopic models, and they can potentially be solved faster while producing reasonable predictions of crowd dynamics. Therefore, they can be very useful for applications such as large-scale simulation, real-time state estimation and crowd management. However, the numerical methods presently used to solve macroscopic pedestrian models, which are mostly grid-based, have some shortcomings that limit their applicability. More specifically, they usually include complex procedures for grid generation and remeshing, and they produce simulation results that may not be sufficiently accurate (for example, because of unclear boundaries between flow states). Smoothed Particle Hydrodynamics (SPH) constitutes an alternative numerical method that could potentially overcome these limitations. SPH is a meshfree method where a crowd is represented by a set of particles that possess material properties and move according to macroscopic laws. Relevant state variables at each particle are approximated using information about the material properties of the neighboring particles and a smoothing function. This paper puts forward for the first time a generic SPH framework for solving macroscopic pedestrian models; in addition, it demonstrates that an SPH-based simulation model can produce meaningful and accurate results by means of three case studies. The first case study shows that the proposed numerical method can approximate well the analytical solution of a simple macroscopic model applied to a queue-discharge scenario. The second case study demonstrates that the proposed numerical method can potentially reproduce density dispersion (a phenomenon observed in real crowds) more accurately than grid-based methods, due to its meshfree, Lagrangian, and particle-based nature. The third case study highlights the need to reformulate the acceleration equation of the basic macroscopic model in order to reproduce lane formation in bi-directional flows (also an observed phenomenon) using the proposed SPH framework, and this paper presents a solution to do so.@en

Classification of congestion patterns is important in many areas in traffic planning and management, ranging from policy appraisal, database design, to prediction and real-time control. One of the key constraints in applying machine learning techniques for classification is the availability of sufficient data (traffic patterns) with clear and undisputed labels, e.g. traffic pattern X or Y. The challenge is that labelling traffic patterns (e.g. combinations of congested and freely flow areas over time and space) is highly subjective. In our view this means that assessment of how well algorithms label the data should also include a qualitative component that focuses on what the found patterns really mean for traffic flow operations and applications. In this study, we investigate the application of clustering analysis to obtain labels automatically from the data, where we indeed first qualitatively assess how meaningful the found labels are, and subsequently test quantitatively how well the labels separate the resulting feature space. By transforming traffic measurements (speeds) into (colored) images, two different approaches are proposed to extract the features of a large number of traffic patterns for clustering: point-based and area-based. The point-based approach is widely applied in the image processing literature, and explores local interest points in images (i.e. where large changes occur in color intensity); whereas a new area-based approach combines domain knowledge with Watershed segmentation to partition the images into different spatial-temporal segments from which domain specific features, such as wide moving jam patterns, are extracted. The results show that the Watershed segmentation separates the traffic (congestion) patterns into more meaningful and separable classes, comparable to those that have been proposed in the literature. Since there is no ground-truth set of labels, the quantitative assessment tests how well both methods are able to separate the respective feature spaces they construct for the (large) database of traffic patterns. We argue that the more crisp this separation is; the better the labelling has turned out. For this quantitative comparison we train a multinomial classifier that maps unseen patterns to the labels discovered by each of the two labeling approaches. The most important result is that the classifier using the area-based feature vector achieves the highest average levels of confidence in its decisions to classify patterns, implying a highly separable feature vector space. We argue this is good news! Not only does the combination of image processing (Watershed) and domain knowledge (traffic flow characteristics) lead to meaningful labels that can be automatically retrieved from large databases of data; this method also leads to a more efficient separation of the resulting feature space. Our next endeavor is to further refine and use this method to develop a search engine for the (rapidly growing) 200 TB historical database of traffic data hosted by the Dutch National Datawarehouse (NDW).

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In this paper, we develop a hierarchical approach to optimize the signal timings in an urban traffic network taking into account the different dynamics in all traffic regimes. The proposed hierarchical control framework consists of two layers. The first layer--the network coordination layer--uses a model predictive control strategy based on a simplified traffic flow model to provide reference outflow trajectories. These reference outflow trajectories represent average desired link outflows over time. These are then mapped to green-red switching signals which can be applied to traffic lights. To this end, the second layer--the individual intersection control layer--then selects at every intersection the signal timing stage that realizes an outflow which has the smallest error with respect to the reference outflow trajectory. The proposed framework is tested using both macroscopic and microscopic simulations. It is shown that the control framework can outperform a greedy control policy that maximizes the individual intersection outflows, and the control framework can distribute the queues over the network in a way that the network outflow is improved. Simulations using a macroscopic model allow the direct application of the reference outflows computed by the network coordination layer, and the results indicate that the mapping of the reference outflows to the detailed signal timings by the individual intersection control layer only introduces a small performance loss.

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Route choice behavior has been recognized as an important factor in the traffic management and control strategy design. State-of-the-art research in this field (e.g. anticipatory traffic control approaches) often formulates this as a bi-level optimization problem where users’ reactions to changes in signal control are taken into account in the design of the control policies. Solving such a combined optimization problem is possible but difficult and complex which often involves the determination of the dynamic user equilibrium (UE). Nevertheless, anticipatory control inherently aims to optimize the signal control that anticipates or reacts to the users’ route choice and thus is a reactive control. Furthermore, the approaches used in anticipatory control are based on the coupling between control and traffic assignment where the route choice behavior of road users is unrealistically assumed to be in equilibrium. This paper proposes a novel utility based optimization framework to design a distributed control scheme based on a so-called BackPressure principle that is scalable and proactively influences the drivers’ route choice behavior. In particular, as opposed to the well-studied anticipatory control and bi-level optimization, the proposed utility optimization formulation solves for a system optimal problem where the optimal turning fraction (as an output decision variable) is enforced as a desirable outcome. The optimal turning fractions would then be achieved in a practical deployment either by directly asking the users to follow the systems routing advice or by influencing, i.e. manipulating, the route choice decisions made by them so that the resulting turning fraction is (or is close to) the optimal value. Although drivers may not follow the computed optimal fractions as desire, there exist several ways, e.g. see Groot et al. (2015), to design appropriate incentives for drivers to follow the route guidance, and thus improve the compliance rate and overall journey experience for everyone inside the network. To this end, we demonstrate by numerical results that better network performance is achieved using the proposed framework even with a limited compliance from the users. Furthermore, we have formally proved that our proposed utility optimization based BackPressure scheme stabilizes the network for any feasible traffic demands thus maximizing network throughput. In addition, we show through simulation and numerical results that the new policy outperforms the original BackPressure-based distributed control scheme both in terms of network throughput and other congestion measures such as travel time.

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Identifying and classifying traffic and congestion patterns are essential parts of modern traffic management underpinned by the emerging intelligent transport systems. This paper explores the potential of using a combination of image processing methods to identify and classify regions of congestion within spatiotemporal traffic (speed, flow) contour maps. The underlying idea is to use these regions as (archetype) shapes that in many combinations can make up a wide variety of larger-scale traffic patterns. In this paper, use of a so-called statistical shape model is proposed as a low-dimensional representation of the archetype shape, and an active shape model algorithm coupled with linear classification is developed to classify the patterns of interest. Application of the proposed method is demonstrated with a preliminary set of speed contour maps reconstructed from loop detector data in the Netherlands. The results show that the extended active shape model can be used as a multiclass classifier. In particular, 70% of the traffic patterns in the test data were correctly classified with use of only two archetype shapes and simple logistic classifiers. The results point to the importance of use of expert knowledge by means of (a priori) manual classification of the training examples. This work opens many research directions, including semiautomated searches through traffic databases, automatic detection, and classification of new traffic patterns.@en

Searching through and selecting data sets from large traffic databases with sensor information is often a cumbersome manual process. In this paper we present an idea that may dramatically fasten and streamline this process. The idea is to build a fast search index (COSI: COngestion Search engIne) based on meta data in combination with features from the traffic patterns along routes. Instead of ploughing through the raw detector data, COSI makes it possible to search through higher level traffic (congestion) patterns. This paper explores a first step in developing COSI: A method to classify traffic congestion patterns reconstructed with the well known adaptive smoothing method. We demonstrate the method using a preliminary set of loop detector data in the Netherlands. We extract and store individual traffic congestion patterns of these regions as images. We then use SURF (Speeded Up Robust Features) to identify the descriptors vector of each image, and apply the bag-of-features technique to generate low-dimensional representation vectors for them. Finally, we use a multiclass extended SVM algorithm to classify these patterns. The results of the method are presented and a synthesis of the findings is given. We close with some preliminary conclusions and an outlook to the further development of COSI.

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