Traffic congestion leads to delays and increased carbon dioxide (CO2) emissions. Traffic management measures such as providing information on environmental route costs have been proposed to mitigate congestion. Multi-criteria routing dynamic traffic assignment (MCR-DTA) models are needed to evaluate the effectiveness of such measures. This paper presents a simulation-based bi-level optimization method to solve the MCR-DTA problem, which works as follows. Route costs include travel times and emissions, but those are updated inside two different loops. In the inner loop, emission costs are considered fixed; the assignment is performed by updating route travel times, using a traditional DTA tool. Then, in the outer loop, emissions are calculated based on link loads and fed back to the DTA tool, which performs a new assignment. The MCR user equilibrium is found when emissions or predefined generalized costs converge to an equilibrium. The bi-level method is first tested on a small network, showing that the proposed method is able to effectively solve the MCR-DTA problem. Next, the method is applied to a medium-size urban network. The results show that if drivers choose routes based on emissions besides travel time, the average travel time and emissions per vehicle decrease. This occurs because congested links have a higher impact on route costs; hence the equilibrium is pushed away from the single-criteria routing (SCR) user optimum towards the SCR system optimum. Results support the conclusion that informing drivers about CO2 emissions per route can potentially lead to decreased delay and emissions in real networks.@en

Sags are roadway sections along which the gradient increases gradually in the direction of traffic. Sags are generally bottlenecks in freeway networks. Previous research suggests that traffic management measures using advanced Adaptive Cruise Control (ACC) systems could reduce congestion on freeways, but little is known about their potential effectiveness at sags. This article evaluates the effectiveness of a basic ACC system (B-ACC) and two advanced ACC systems – Traffic State-Adaptive ACC (TSA-ACC) and Cooperative ACC (C-ACC) – in mitigating congestion at sags. TSA-ACC adapts the ACC parameters to the macroscopic traffic state estimated by the vehicle itself. C-ACC uses information of other vehicles in the surroundings to adjust its accelerations. Results are obtained using microscopic traffic simulations with different penetration rates. They show that, under high-demand conditions, congestion decreases with increasing percentage of vehicles equipped with B-ACC. With high penetration rates (75% and above), traffic no longer becomes congested at the sag. Moreover, the results show that TSA-ACC and C-ACC reduce congestion more than B-ACC, mainly because they increase the queue discharge capacity of the sag. The two advanced ACC systems prevent the formation of congestion at the sag at lower penetration rates than B-ACC. TSA-ACC is the most effective system. C-ACC is only more effective than B-ACC in scenarios with 20% penetration rate or higher; below that, connectivity between equipped vehicles is too low. Our findings show the potential of using advanced ACC systems to mitigate congestion at sags and indicate some challenges of this traffic management approach.@en

Sags are bottlenecks in freeway networks. According to previous research, the main cause is that most drivers do not accelerate enough at sags. Consequently, they keep longer headways than expected given their speed, which leads to congestion in high demand conditions. Nowadays, there is growing interest in the development of traffic control measures for sags based on the use of in-car systems. This paper aims to determine the optimal acceleration behavior of vehicles equipped with in-car systems at sags and the related effects on traffic flow, thereby laying the theoretical foundation for developing effective traffic management applications. We formulate an optimal control problem in which a centralized controller regulates the acceleration of some vehicles of a traffic stream moving along a single-lane freeway stretch with a sag. The control objective is to minimize total travel time. The problem is solved for scenarios with different numbers of controlled vehicles and positions in the stream, assuming low penetration rates. The results indicate that the optimal behavior involves performing a deceleration-acceleration-deceleration-acceleration (DADA) maneuver in the sag area. This maneuver induces the first vehicles located behind the controlled vehicle to accelerate fast along the vertical curve. As a result, traffic speed and flow at the end of the sag (bottleneck) increase for a time. The maneuver also triggers a stop-and-go wave that temporarily limits the inflow into the sag, slowing down the formation of congestion at the bottleneck. Moreover, in some cases controlled vehicles perform one or more deceleration-acceleration maneuvers upstream of the sag. This additional strategy is used to manage congestion so that inflow is regulated more effectively. Although we cannot guarantee global optimality, our findings reveal a potentially highly effective and innovative way to reduce congestion at sags, which could possibly be implemented using cooperative adaptive cruise control systems.@en

Sags are freeway sections along which the gradient changes significantly from downward to upward. The capacity of sags is considerably lower than the capacity of normal sections. Consequently, sags are often freeway bottlenecks. Recently, several control measures have been proposed to improve traffic flow efficiency at sags. Those measures generally aim to increase the capacity of the bottleneck, to prevent traffic flow perturbations in nearly saturated conditions, or both. This paper presents an alternative type of measure based on the concept of mainstream traffic flow control. The proposed control measure regulates traffic density at the bottleneck area to keep it below the critical density and hence prevent traffic from breaking down while maximizing outflow. Density is regulated by means of a variable speed limit section that regulates the inflow to the bottleneck. Speed limits are selected on the basis of a feedback control law. The authors evaluate the effectiveness of the proposed control strategy by means of a simple case study by using microscopic traffic simulation. The results show a significant increase in bottleneck outflow, particularly during periods of high demand, which leads to a considerable decrease in total delay. This finding suggests that mainstream traffic flow control strategies that use variable speed limits have the potential to improve substantially the performance of freeway networks containing sags.@en

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

An increasing number of people use the bicycle for urban trips resulting in local congestion at intersections, especially during peak hours. Understanding the queue dynamics is key to find the correct measures that can reduce the delays for cyclists without affecting other traffic modes. To this end, the discharge process of bicycle queues is studied, focusing on the impact of jam density on the queue discharge rate and how this process is affected by cyclists that merge into the queue during the discharge phase. The impact of merging cyclists is captured by a newly introduced bicycle equivalent (BE) value. This direction-specific BE value is used to convert a merging cyclist into a cyclist that is waiting in the original queue. Results show that the queue discharge rate increases with increasing density of the queue. Furthermore, cyclists that merge by overtaking contribute to the queue discharge rate, while cyclists who merge from a perpendicular direction hinder the discharge process, thereby decreasing the bicycle flow at the intersection. The insights can be used to develop measures which minimise delay at intersections and to design efficient infrastructure for bicyclists.@en

Sags are bottlenecks in freeway networks. Nowadays, there is a growing interest in the development of traffic management measures for sags based on the use of in-car systems. This contribution determines the movements that individual (equipped) vehicles should make in order to minimise congestion. Specifically, we optimise the accelerations of some selected vehicles as they move along a one-lane freeway stretch with a sag, setting as objective the minimisation of total travel time. The optimisation results highlight the relevance of two traffic management strategies: a) motivating drivers to accelerate fast along sags; and b) limiting the inflow to sags. Also, they suggest ways to apply these strategies in practice by regulating the acceleration of vehicles equipped with in-car systems. These results prove the usefulness of the proposed method as a tool for control measure development.@en

Signalized intersections are one of the most common types of bottleneck in urban cycling networks. Gaining knowledge on the macroscopic characteristics of bicycle flow during the queue discharge process is crucial for developing ways to reduce the delay experienced by cyclists at intersections. This paper aims to determine these characteristics (including jam density, shockwave speed, and discharge flow), and to unveil possible relationships between them, particularly whether and to what extent discharge flow is correlated with jam density and shockwave speed (which is of high relevance from a traffic management viewpoint). To this end, the study analyzes high-resolution bicycle trajectories derived from video footage on a one-direction cycle path leading to an intersection in Amsterdam (the Netherlands). Linear regression analysis is used to investigate the relationships between macroscopic variables. The results indicate that jam density, shockwave speed, and discharge flow vary considerably across traffic-signal cycles, which highlights the stochastic nature of bicycle flow. Furthermore, the results show that discharge flow is strongly positively correlated with jam density and shockwave speed. It is hypothesized that there is a causal relationship between these variables, which would imply that traffic engineers can increase discharge flows (thus reducing delay) at signalized intersections if they find effective ways to increase jam densities and shockwave speeds.@en

Predicting the bicycle flow capacity at signalized intersections of various characteristics is crucial for urban infrastructure design and traffic management. However, it is also a difficult task because of the large heterogeneity in cycling behavior and several limitations of traditional capacity estimation methods. This paper proposes several methodological improvements, illustrates them using high-resolution trajectory data collected at a busy signalized intersection in the Netherlands, and investigates the influence of key variables of capacity estimation. More specifically, it shows that the (virtual) sublane width has a significant effect on the shape of the headway distribution at the stop line. Furthermore, a new method is proposed to calculate the saturation headway (a key variable determining capacity), which excludes the cyclists initially located close to the stop line using a distance-based rule instead of a fixed number (as is usually done in practice). It is also shown that the saturation headway is quite sensitive to the sublane width. Moreover, a new, empirically based method is proposed to identify the number of sublanes that can be accommodated in a given cycle path, which is another key influencing variable. This method yields considerably lower estimates of the number of sublanes than traditional methods, which rely solely on the (available) cycle path width. Finally, the authors show that methodological choices such as the sublane width and the method used to estimate the number of sublanes have a considerable effect on capacity estimates. Therefore, this paper highlights the need to define a sound methodology to estimate bicycle flow capacity at signalized intersections and proposes some steps to move toward that direction.@en

Nowadays, there is a need for tools to support city planners in assessing the performance of cycling infrastructure and managing bicycles and mixed flows. Microscopic and macroscopic bicycle traffic models can be used to fulfill this need. However, fundamental knowledge on individual cyclist interaction behavior (which should underpin these models) is hardly available in literature. Detailed bicycle traffic data are necessary if we want to gain insight into cyclist interaction behavior and develop sound behavioral theories and models. Laboratory experiments have been proven to be one of the most effective ways to collect detailed traffic data. For this reason, a controlled experiment aimed to investigate cyclist interaction behavior has been carried out at Delft University of Technology. This paper describes the experimental design, the resulting microscopic bicycle trajectories, and some preliminary results regarding one of the most common interaction situations: the bidirectional interaction. The preliminary results reveal how and to what extent cyclists interact in bidirectional cycling. It is found that cyclists perform a clearly-visible evading (collision avoidance) maneuver when they have face-to-face encounters. During these maneuvers, changes in speed and displacements in the lateral direction are observed. Cyclists start to deviate from their original path when they are around 30 m from each other, and they strongly prefer passing on the right-hand side. Moreover, the expectation of gender differences in cycling behavior reported in the literature is confirmed: our results show that women generally cycle more slowly than men and deviate more from their intended paths in face-to-face encounters. More observations will be available in the next stage of data analysis. These findings can be used to formulate improved microscopic bicycle traffic models for infrastructure design and policy development.@en

In many countries, an increasing number of people are using the bicycle for urban trips. The increased bicycle flow sometimes creates local congestion at intersections and demands better bicycle traffic management. To provide policy makers with models and advice on how to prevent congestion, an increased understanding of queue dynamics is required. This study analyzed the queue discharge process of cyclists at a controlled intersection, focusing on how queue density and merging cyclists influence the discharge rate. A bicycle equivalent (BE) value was introduced to correct for the impact of merging cyclists from different directions, with respect to the impact of cyclists in the original queue. For an intersection in Delft, the Netherlands, the discharge rate was found to increase for increasing queue density. Furthermore, cyclists who merged by overtaking were found to contribute more to the discharge rate compared to cyclists that were standing in the original queue. Cyclists that merged from a direction perpendicular to the queuing direction were found to hinder the discharge process, decreasing the observed outflow rate. These insights can be used as input for bicycle flow models to assess new plans for bicycle infrastructure and to develop measures to minimize delay at intersections.@en

Signalized intersections are one of the most common sources of inconvenience for cyclists. The aim of this paper is to develop an approach that helps cyclists to meet their cycling preferences (regarding, e.g., the energy they use and their preference to avoid unnecessary stopping) while crossing intersections. Uncertainty in traffic light timing is considered explicitly in the approach, which makes it applicable for intersections with traffic-responsive signals. The suggested approach provides cyclists with optimal and personalized speed advice. The advice is communicated to the cyclists through a roadside sign located upstream of the intersection. It is assumed that the roadside sign can measure the speed of the approaching bike and can also communicate with the traffic light to ascertain the traffic light’s state and give advice accordingly. To consider the behavior of cyclists when they disregard the advice as they get close to the intersection, the problem is separated in two parts and formulated as a Markov reward process combined with a Markov decision process and stochastic dynamic programming is used to solve the corresponding optimization problem. The approach is generic in relation to the underlying process model and the objective function. The results of an illustrative case study show how much improvement, in relation to the cyclist’s average number of stops, and average energy consumption, could be achieved by use of the suggested approach in a simulated intersection. We also investigate how the location of the sign may affect the performance of the approach.@en