A dynamic prizing scheme for a congestion charging zone based on a network fundamental diagram

Optimal distance- and time-dependent area-based pricing with the Network Fundamental Diagram

Network traffic instability with automated driving and cooperative merging

Agent-based Space-time Discrete Simulation of Urban Traffic Including Bicycles

Joint routing and pricing control in congested mixed autonomy networks

Urban traffic congestion is a chronic problem faced by many cities in the US and worldwide. It results in inefficient infrastructure use as well as increased vehicle fuel consumption and emission levels. Congestion is intertwined with delay, as road users waste precious hours on the road, which in turn reduces productivity. Researchers have developed, and continue to develop, tools and systems to alleviate this problem. Network perimeter control is one such tool that has been studied extensively. It attempts to control the flow of vehicles entering a protected area to ensure that the congested regime predetermined by the Network Fundamental Diagram (NFD) is not reached. In this paper, an approach derived from sliding mode control theory is presented. Its main advantages over proportional-integral controllers include (1) minimal tuning, (2) no linearization of the governing equations, (3) no assumptions with regard to the shape of the NFD, and (4) ability to handle various demand profiles without the need to retune the controller. A sliding mode controller was implemented and tested on a congested grid network. The results show that the proposed controller produces network-wide delay savings and disperses congestion effectively.

This paper presents a model specifically developed to explore the mobility impacts of connected and automated driving and shared mobility. It is an explorative iterative model that uses an elasticity model for destination choice, a multinomial logit model for mode choice and a network fundamental diagram to assess traffic impacts. To the best of the authors’ knowledge, it is the first model that combines a network fundamental diagram with choice models. A second contribution is the inclusion of automated vehicles, automated (shared) taxis, automated shared vans and new parking concepts in the model as well as the way in which they affect mobility choices and traffic conditions. The insights into the direct mobility impacts are the third contribution. The short computation time of the model enables exploration of large numbers of scenarios, sensitivity analyses and assessments of the impacts of interventions. The model was applied in a case study of the Dutch Province of North-Holland, in which the potential impacts of automated and shared vehicles and mitigating interventions were explored. In this case study, four extreme scenarios were explored, in which 100% of the vehicles have SAE-level 3/4 or 5 and people have a low or high willingness to share. The extremes were chosen to get insights into maximum effects. The results show that if automated vehicles and sharing are accepted, it is likely that there will be considerable changes in mobility patterns and traffic performance, with both positive and problematic effects.

A new gating strategy for concentric cities based on the notion of the macroscopic or network fundamental diagram and the feedback-based gating concept is introduced and successfully tested. Different regions of large-scale urban networks may experience congestion at different levels and times during the peak period. In this paper, the zone, including the initial core of congestion, is considered as the first region, which has to be protected from congestion via gating; eventually, as the congestion continues to expand, the border of an extended network part becomes the second perimeter for gating control. Remarkable extensions while distributing the ordered controller flow to the gated traffic signals in case of low demand or occurrence of spillback are also considered. A greater part of the San Francisco urban network is used as test-bed within a microscopic simulation environment. Significant improvements in terms of network-wide mean speed and average delay per kilometer are obtained compared to the single perimeter gating and non-gating simulation scenarios.

Macroscopic parking dynamics and equitable pricing: Integrating trip-based modeling with simulation-based robust optimization

SummaryTraditional macroscopic traffic flow modeling framework adopts the spatial–temporal coordinate system to analyze traffic flow dynamics. With such modeling and analysis paradigm, complications arise for traffic flow data collected from mobile sensors such as probe vehicles equipped with mobile phones, Bluetooth, and Global Positioning System devices. The vehicle‐based measurement technologies call for new modeling thoughts that address the unique features of moving measurements and explore their full potential. In this paper, we look into the concept of vehicular fundamental diagram (VFD) and discuss its engineering implications. VFD corresponds to a conventional fundamental diagram (FD) in the kinematic wave (KW) theory that adopts space–time coordinates. Similar to the regular FD in the KW theory, VFD encapsulates all traffic flow dynamics. In this paper, to demonstrate the full potential of VFD in interpreting multilane traffic flow dynamics, we generalize the classical Edie's formula and propose a direct approach of reconstructing VFD from traffic measurements in the vehicular coordinates. A smoothing algorithm is proposed to effectively reduce the nonphysical fluctuation of traffic states calculated from multilane vehicle trajectories. As an example, we apply the proposed methodology to explore the next‐generation simulation datasets and identify the existence and forms of shock waves in different coordinate systems. Our findings provide empirical justifications and further insight for the Lagrangian traffic flow theory and models when applied in practice. Copyright © 2015 John Wiley & Sons, Ltd.

Automated and partially automated vehicles will substantially change the way traffic flows, even at low market penetration rates. While the primary tool for analysis of mixed human-piloted and automated traffic has been microscopic simulation, the use of macroscopic traffic simulation allows for larger-scale simulations. With that in mind we propose a simple numerical method to estimate the composite fundamental diagram of mixed human-piloted and automated traffic flow from the fundamental diagrams of 100% human-piloted and 100% automated traffic flow and conduct calibrated microscopic traffic simulations to produce fundamental diagrams for how mixed traffic flow may behave at different market penetration rates. This method is demonstrated for both an automated vehicle and a commercially available adaptive cruise control vehicle. The proposed method is shown to be able to accurately estimate the shape of the fundamental diagram for arbitrary automated vehicle market penetration rates and the results provide insight into how the mixed traffic flow may behave at the macroscopic level.

A fundamental diagram (FD), which determine relation among flow, destiny, and speed of traffic, is one of the most important principles in traffic flow theory. Conventionally, the FD has been estimated by measurements from detectors which are fixed on roads. Therefore, it has been difficult to estimate FDs over wide spatial domain, due to high installation and maintenance cost of detectors. In this study, a method of estimating FD using measurements from GPS-equipped probe vehicles (also known as connected vehicles) is developed. Specifically, the proposed method estimate values of free-flow speed and backward wave speed of so-called triangular FD based on trajectories of multiple probe vehicles which are randomly sampled. Since probe vehicles can collect data from wide spatial domain, it can be expected that the proposed method enable to estimate FD continuously in large area. The proposed method was verified by using actual traffic data in an urban expressway.

For almost a century, several models have been developed to calibrate the pairwise relationship between traffic flow variables, that is, speed, density, and flow. Multi-regime models are well known for being superior over single-regime models in fitting the speed–density relationship. However, in modeling multi-regime models, breakpoints that separate the regimes are visually established based on the subjective judgment of data characteristics. Thus, this study proposes a data-driven approach to estimate the breakpoints of multi-regime models. It applies the Bayesian model for calibrating multi-regime models (two and three-regime models) for fitting traffic flow fundamental diagram. Furthermore, the analysis presented accounts for the random characteristics associated with the flow. To demonstrate the application of the proposed algorithm, traffic flow data from Interstate 10 (I-10) freeway in Jacksonville, Florida, were used in the analysis. The results demonstrate the potential benefit of using the proposed model in calibrating the fundamental diagram. The proposed approach can also quantify uncertainty and encode prior knowledge about the breakpoints in the model if the model developer wishes.

Stop and go traffic that leads to oscillate traffic flow frequently is observed on congestion flow. Unexpected reasons such as lane - changing maneuvers, lower speeds of leader vehicle and moving bottleneck cause stop and go traffic and amplifying delay and environment impacts. Stop and go traffic exactly can't be modeled by traffic models, and also car following models based on kinematic flow theory can't be implied correct perception of stop and go traffic. Based on asymmetric microscopic theory and trajectory data of NGSIM, traffic flow can be classified into five phases according to speed and movement of the vehicle: Free flow, acceleration and deceleration, stationary and coasting phases. Analyzing stop and go traffic based on asymmetric theory of acceleration and deceleration phase will result to classify them into three cases: generation, growth and dissipation of traffic waves. Analyzing of traffic oscillation implies that stop-and-go traffic is relatively small and can't be propagated upstream unless the following traffic is also near D-curve; while the effect on lane changes are greater, and can propagate even the following traffic is not near the D-curve. In this paper, using time window in trajectory data clarify relation between the total number of lane changes and stop-and-go waves for congestion traffic. Analyzing net lane changes inside the searching window for incoming and outgoing lane changes about growth and dissipation of traffic waves indicate how characteristics of stop-and-go waves are intimately related to driver's asymmetric behavior of acceleration and deceleration. The comparison result on the growth and dissipation indicated that under the same net lane changes, growth wave case occupy the regions in fundamental diagram, flow - density diagram, deceleration curve, and dissipation wave case occupy the regions of flow - density, acceleration curve. Language: en

On development of arterial fundamental diagrams based on surrogate density measures from adaptive traffic control systems utilizing stop-line detection

This study developed a dynamic traffic control system designated as Dynamic Intersection Signal Control Optimization (DISCO). DISCO considers the entire Fundamental Diagram of traffic flow, which is essential for controlling congested and transient traffic. As a dynamic model, DISCO works with time-variant traffic patterns and derives dynamic adaptive timing plans. In this study, we applied DISCO to a congested network in Hong Kong and compared the performance of various control strategies enabled by DISCO. The results showed that DISCO outperformed the existing plans by more than 50% in terms of overall delay reduction. Moreover, DISCO performed best under the most flexible control scheme of variable green-no cycle strategy. DISCO shows promise as a new approach for demand-responsive traffic control.