A meso-simulation approach for the estimation of traffic flows in presence of automated vehicles

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Mesoscopic simulator data to perform dynamic origin- destination matrices estimation in urban context

The aim of this paper is to explore a new approach to obtain better traffic demand (Origin-Destination, OD matrices) for dense urban networks using traffic simulation data. From reviewing existing methods, from static to dynamic OD matrix evaluation, possible deficiencies in the approach could be identified. To improve the global process of traffic demand estimation, this paper is focusing on a new methodology to determine dynamic OD matrices for urban areas characterized by complex route choice situation and high level of traffic controls. An iterative bi- level approach will be used to perform the OD estimation. The Lower Level (traffic assignment) problem will determine, dynamically, the utilization of the network by vehicles using heuristic data from mesoscopic traffic simulator particularly adapted for urban context. The Upper Level (matrix adjustment) problem will proceed to an OD estimation using optimization least square techniques. In this way, a full dynamic and continuous estimation of the final OD matrix could be obtained. First evaluation of the proposed approach and conclusions are presented

Modeling Evacuation Risk Using a Stochastic Process Formulation of Mesoscopic Dynamic Network Loading

One of the actions usually conducted to limit exposure to a hazardous event is the evacuation of the area that is subject to the effects of the event itself. This involves modifications both to demand (a large number of users all want to move together) and to supply (the transport network may experience changes in capacity, unusable roads, etc.). In order to forecast the traffic evolution in a network during an evacuation, a natural choice is to adopt an approach based on Dynamic Traffic Assignment (DTA) models. However, such models typically give a deterministic prediction of future conditions, whereas evacuations are subject to considerable uncertainty. The aim of the present paper is to describe an evacuation approach for decision support during emergencies that directly predicts the time-evolution of the probability of evacuating users from an area, formulated within a discrete-time stochastic process modelling framework. The approach is applied to a small artificial case as well as a real-life network, where we estimate users' probabilities to reach a desired safe destination and analyze time dependent risk factors in an evacuation scenario

A Multiclass Simulation-Based Dynamic Traffic Assignment Model for Mixed Traffic Flow of Connected and Autonomous Vehicles and Human-Driven Vehicles

One of the potential capabilities of Connected and Autonomous Vehicles (CAVs) is that they can have different route choice behavior and driving behavior compared to human Driven Vehicles (HDVs). This will lead to mixed traffic flow with multiple classes of route choice behavior. Therefore, it is crucial to solve the multiclass Traffic Assignment Problem (TAP) in mixed traffic of CAVs and HDVs. Few studies have tried to solve this problem; however, most used analytical solutions, which are challenging to implement in real and large networks (especially in dynamic cases). Also, studies in implementing simulation-based methods have not considered all of CAVs' potential capabilities. On the other hand, several different (conflicting) assumptions are made about the CAV's route choice behavior in these studies. So, providing a tool that can solve the multiclass TAP of mixed traffic under different assumptions can help researchers to understand the impacts of CAVs better. To fill these gaps, this study provides an open-source solution framework of the multiclass simulation-based traffic assignment problem for mixed traffic of CAVs and HDVs. This model assumes that CAVs follow system optimal principles with rerouting capability, while HDVs follow user equilibrium principles. Moreover, this model can capture the impacts of CAVs on road capacity by considering distinct driving behavioral models in both micro and meso scales traffic simulation. This proposed model is tested in two case studies which shows that as the penetration rate of CAVs increases, the total travel time of all vehicles decreases

An Integrated Agent-Based Microsimulation Model for Hurricane Evacuation in New Orleans

Mass evacuation of urban areas due to hurricanes is a critical problem that requires extensive basic and applied research. Knowing the accurate evacuation time needed for the entire region in advance such that the evacuation order can be issued on a timely basis is crucial for the officials. Microsimulation modeling, which focuses on the characteristics of individual motorists and travel behavior, has been used widely in traffic simulation as it can lead to the most accurate result. However, because detailed driver response modeling and path processing must be incorporated, vehicle-based microscopic models have always been used only to simulate small to medium sized urban areas. Few studies have attempted to address problems associated with mass evacuations using vehicle-based microsimulation at a regional scale. This study develops an integrated two-level approach by separating the entire road network of the study area into two components, highways (i.e., interstate highways and causeways) and local roads. A vehicle-based microsimulation model was used to simulate the highway part of the road traffic, whereas the local part of the road traffic simulation utilized an agent-based model. The integrated microsimulation model was used to simulate hurricane evacuation in New Orleans. Validation results confirm that the proposed model performs well in terms of high model accuracy (i.e., close agreement between the real and simulated traffic patterns) and short model running time. Sufficient evacuation time is a premise to protect people’s life safety when an area is threatened by a deadly disaster. To decrease the network clearance time, this study also examined the effectiveness of three evacuation strategies for disaster evacuation, including a) simultaneous evacuation strategy, b) staged evacuation strategy based on spatial vulnerabilities, and c) staged evacuation strategy based on social vulnerabilities. The simulation results showed that both staged evacuation strategies can decrease the network clearance time over the simultaneous evacuation strategy. Specifically, the spatial vulnerability-based staged evacuation strategy can decrease the overall network clearance time by about four hours, while the social vulnerability-based staged evacuation strategy can decrease the network clearance time by about 2.5 hours

Nokta kuyruk modellemesi için bir dinamik düğüm noktası modeli

Point-queuing and physical queuing are the two main assumptions that have been made in problems of Dynamic Network Loading (DNL) in order to model link and network performances. The queue spillback can only be captured by physical-queue approach, which is more realistic. Accordingly, the recent trend on traffic flow modeling for Dynamic Traffic Assignment (DTA) is to propose models with physical-queue assumption. However capturing the effects of physical-queuing in DNL modelling brings difficulties in obtaining an optimal solution of a DTA problem. As an alternative, the point-queue assumption handles vehicles as points without physical lengths. The storage capacity of each link can be ignored. The queue spillback on a link can be simulated by assuming the existence of a buffer area in the initial node of the link, for the temporary storage of vehicles exceeding the maximum density. Therefore, all links can contain unconstrained number of vehicles and capacity constraint on a link can be applied without numerical and computational difficulties. Moreover, the outflow rate of a link is only affected by its own flow considering that the downstream links will always have sufficient storage capacities. In the literature, point-queue assumption has been made in a varying structure of flow models adopting both exit-flow function approach, and in travel time function approach to perform DNL. In this paper, a mesoscopic dynamic node model for network loading is proposed, based on discrete packets, to model the point-queue process on a highway node with multiple merging and diverging links. The model is run using theoretical input data to simulate point-queuing in over-saturation condition. The presented dynamic node model has two components; a mesoscopic link model set with an exit link function formulation, and an algorithm written with a set of node rules considering the constraints of conservation, capacity, flow splitting rates and non-negativities. First, the time-varying flows that enter to multiple merging links (inflows) simultaneously are input to the mesoscopic link model. The link model component is developed by both considering the over-saturation phenomenon and improving the computational efficiency on a previously proposed link model. This model, is set out with link exit function formulation, discretisation on time dimension, defining capacity constraint rules for over-saturated states and uniformly accelerated speed assumption, which allows a realistic representation of outflow dynamics. Model has an iterative structure, which enables convergence to any target performance criteria with the coded algorithm. The flows that exit from these merging links (outflows) are computed regarding the link and flow characteristics. Then outflows of the merging links are input to a node as inflows. These conflicting flows are processed within the node component with predefined splitting rates and characteristics of the diverging links, and then the nodal exiting flows are computed. The main difference of the proposed dynamic node model in comparison to other models is that it respects capacity constraints regarding to splitting rules and consequently holds first-in-first-out rule. For the link model component of integrated model structure has been set out with the point-queuing assumption, the point-queues and the delays calculated in the presence of these vertical queues are considered instead of the physical queues and the delays occurring as a result of over-saturation. The node model problem is formulated as to maximize the total flow passing through the node subject to the constraints of conservation, capacity, flow splitting rates and non-negativities. The optimization problem is solved by simulation within the modelling horizon. Simulation process of the proposed model lasted as the inflows to merging links are wholly discharged from the entire node structure. The integrated model structure provided more realistic results in representing outflow dynamics. It is seen that the outflows of the link model component existed respecting to capacity constraints and the diagrams of these outflows seemed alike the sinusoidal inflow curves under the set node configuration. Despite the flows requiring to enter the diverging links are above over-saturation rates, the capacity restraint is respected. The results show that the model appears realistic in the representation of point-queuing process and diverging link flow dynamics, and is quite easy to calculate. The future extension of this study will be on the application of the proposed model to a general network. Keywords: Traffic networks, traffic flow, node model, simulation.Bu çalışmada; karayolu ağlarında akım yayılımını modelleyen ve bir dinamik ağ yükleme sürecinde tümleşik olarak kullanılabilen analitik bir dinamik düğüm noktası modeli yardımıyla, bağ girişlerinde meydana gelen nokta kuyruklanmanın modellemesi yapılmıştır. Önerilen dinamik düğüm noktası modelinin; bağ çıkış formülasyonu temelli bir karma-boyut bağ modeli bileşeni ve akım korunumu, kapasite, akım dağılımı ve negatif olmama kısıtlarını içeren bir düğüm noktası kuralları bileşeni vardır. Oluşturulan dinamik düğüm noktası formülasyonu, belirlenen kısıtlar altında benzetim yoluyla çözülmüştür. Nokta kuyruk varsayımı ile oluşturulan bağ modeli bileşeni; aşırı-doygun trafik akım durumunu değerlendiren bir yapıdadır. Zaman boyutunda yapılan ayrıklaştırma, aşırı-doygun duruma ilişkin konulan kapasite kısıtı ve düzgün ivmelenen taşıt hareketi varsayımı ile oluşturulan bağ modeli bileşeni, gerçekçi trafik akım dinamiklerinin temsiline olanak sağlamıştır. Bağ modeli ile belirlenen akımlar, düğüm noktası bileşenine girdi olmaktadır. Akımların düğüm noktası bileşeninde, önceden tanımlı dağılım oranları ve ayrılan bağ özellikleri ile işlenmesi ile ayrılan bağ giriş akımları hesaplanır. Modellenen nokta kuyrukların; i)  kapasitenin aşıldığı herhangi durumda ve ii) modeli çözmek için zaman düzeyinde yapılan ayrıklaştırmaya bağlı olarak, bir önceki hesaplama anından arta kalan akım hacmi varolduğu durumda belirdiği varsayılmıştır. Nokta kuyruk modellemesi için önerilen yeni dinamik düğüm noktası modeli, karma-boyut yaklaşımı temeli üzerinde yapılandırılmış tek düğüm noktası modelidir. Yeni modelin aşırı-doygunlukta gerçekçi sonuçlar verdiği görülmüştür. Anahtar Kelimeler: Ulaştırma ağı, trafik akımı, düğüm noktası modeli, benzetim

Dynamic urban origin-destination matrix estimation methodology

The aim of this thesis is to develop a new methodology to determine dynamic Origin-Destination (OD) matrices for urban networks characterized by a high number of traffic hubs, complex route choice possibilities and a high level of traffic controls. By reviewing existing methods, from static to dynamic OD matrix evaluation, deficiencies in the approaches are identified: mainly, the level of detail of the traffic assignment for complex urban networks and the lack in dynamic approaches. The proposed methodology is comprised of a heuristic bi-level approach. Assignment of the initial demand is performed by mesoscopic simulation based on the Dynamic User Equilibrium to model detailed dynamic traffic patterns without numerous calibration parameters. OD flow adjustment is executed by an efficient least square solution which takes into account dynamic aspects of the flow propagation and traffic counts. For this task, a LSQR algorithm has been selected for its capacities to deal with a large matrix and its ability to constrain outputs. Parallel comparison with the most common approach for OD estimation (sequential static approach) has shown: first, the ability of the method to generate OD flows close to the actual demand, compared to the common practice; second, the utilization of the obtained demand by a dynamic traffic model has established its aptitude to reproduce realistic assignment patterns. Finally, applicability and example of utilization of the proposed method has been presented by solving realistic problems using the simulation software AIMSUN in which the proposed methodology is implemented as a plug-in. This research has shown the importance of input data for the OD estimation process and mainly the detection layout configuration used for traffic count data. Sensitivity analysis has shown that a small number of detectors is usually sufficient for efficient OD estimation in short computation time, if the traffic detectors intercept the most critical flows

Metaheuristic approaches for Complete Network Signal Setting Design (CNSSD)

2014 - 2015In order to mitigate the urban traffic congestion and increase the travelers’ surplus, several policies can be adopted which may be applied in short or long time horizon. With regards to the short term policies, one of the most straightforward is control through traffic lights at single junction or network level. The main goal of traffic control is avoiding that incompatible approaches have green at the same time. With respect to this aim existing methodologies for Signal Setting Design (NSSD) can be divided into two classes as in following described Approach-based (or Phase-based) methods address the signal setting as a periodic scheduling problem: the cycle length, and for each approach the start and the end of the green are considered as decision variables, some binary variables (or some non-linear constraints) are included to avoid incompatible approaches having green at the same time (see for instance Improta and Cantarella, 1987). If needed the stage composition and sequence may easily be obtained from decision variables. Commercial software codes following this methodology are available for single junction control only, such Oscady Pro® (TRL, UK; Burrow, 1987). Once the green timing and scheduling have been carried out for each junction, offsets can be optimized (coordination) using the stage matrices obtained from single junction optimization (possibly together with green splits again) through one of codes mentioned below. Stage-based signal setting methods dealt with that by dividing the cycle length into stages, each one being a time interval during which some mutually compatible approaches have green. Stage composition, say which approaches have green, and sequence, say their order, can be represented through the approach-stage incidence matrix, or stage matrix for short. Once the stage matrix is given for each junction, the cycle length, the green splits and the offsets can be optimised (synchronisation) through some well established commercial software codes. Two of the most commonly used codes are: TRANSYT14® (TRL, UK) (recently TRANSYT15® has been released) and TRANSYT-7F® (FHWA, USA). Both allow to compute the green splits, the offsets and the cycle length by combining a traffic flow model and a signal setting optimiser. Both may be used for coordination (optimisation of offsets only, once green splits are known) or synchronisation. TRANSYT14® generates several (but not all) significant stage sequences to be tested but the optimal solution is not endogenously generated, while TRANSYT-7F® is able to optimise the stage sequence for each single junction starting from the ring and barrier NEMA (i.e. National Electrical Manufacturers Association) phases. Still these methods do not allow for stage matrix optimisation; moreover the effects of stage composition and sequence on network performance are not well analysed in literature... [edited by Author]XIV n.s

A mesoscopic whole link continuous vehicle bunch model for multiclass traffic flow simulation on motorway networks

Modeling of heterogeneous driver behaviour is vital to understanding of dynamic traffic phenomenon taking place on motorway networks. In this research, we present a mesoscopic whole link continuous vehicle bunch model for multiclass traffic flow simulation on motorway networks. Two main attributes of traffic flow classification have been used are: (i) vehicle type, specifying in turn a vehicle length and, together with type of a preceding vehicle, time headway; and, (ii) desired speed, defining together with the speeds of the neighbouring vehicles, the vehicle acceleration/deceleration mode. It is assumed that vehicles in uncongested to moderate congested flow move in bunches dividing the drivers into the two main groups: (i) independent “free” drivers which usually manifest themselves as leaders of bunches; and, (ii) followers, or drivers which adapt their speed to the leader’s speed and follow each other at constrained headways specified by predecessor/successor pairs. The model proposes a solution to arbitrary traffic queries involving a motion in bunches having various speed and size by assuming the rate of driver arrivals follows semi-Poisson distribution and proportion of free drivers is predefined. The solution, assuming limited overtaking possibilities for all drivers, involves formation of longer queue behind bunches moving with slower speed and transformation of some of the “leaders” into “followers” because of adjustment their speed to the speed of the preceding slow-moving bunches. The present solution considers both stochastic and deterministic features of traffic flow and, therefore, may be easily extended to a specific uncertainty level

Computational framework for modeling infrastructure network performance and vulnerability

Networked infrastructures serve as essential backbones of our society. Examples of such critical infrastructures whose destruction severely impacts the defense or economic security of our society include transportation, telecommunications, power grids, and water supply networks. Among them, road transportation networks have a principal role in people's everyday lives since they facilitate physical connectivity. The performance of a road transportation network is governed by the three principal components: (a) structure, (b) dynamics, and (c) external causes. The structure defines the topology of a network including links and nodes. The dynamics (i.e., traffic flow) defines what processes are happening on the network. The external causes (e.g., disasters and driver distraction) are the phenomena that impact either structure or dynamics. These principal components do tend to influence each other. For example, the collapse of a bridge (i.e., external cause) could render certain nodes and links (i.e., structure) ineffective thereby affecting traffic flow (i.e., dynamics). A distracted driver (i.e., external cause) on a road can also cause accidents that can negatively impact traffic flow. Thus, to model the performance and vulnerability of a network, it is necessary to consider such interactions among these principal components. The main objective of this research is to formalize and develop a computational framework that can: (a) predict the macroscopic performance of a transportation network based on its multiple structural and dynamical attributes (Chapter 2), (b) analyze its vulnerability as a result of man-made/natural disruption that minimizes network connectivity (Chapter 3), and (c) evaluate network vulnerability due to driver distraction (Chapter 4). An integrated framework to address these challenges--which have largely been investigated as separate research topics, such as distracted driving, infrastructure vulnerability assessment and traffic demand modeling--needs to simultaneously consider all three principal components (i.e., structure, dynamics, and external causes) of a network. In this research, the integrated framework is built upon recent developments (theories and methods) in interdisciplinary domains, such as network science, cognitive science and transportation engineering. This is the novelty of the proposed framework compared to existing approaches. Finally, the framework were validated using real-world data, existing studies and traffic simulated results.Ph.D., Civil Engineering -- Drexel University, 201