Traffic Flows In Urban Networks Research Articles

State estimation in urban traffic networks: A two-layer approach

Modern traffic control and management systems in urban networks require real-time estimation of the traffic states. In this paper, a novel approach for modeling traffic flow in urban networks that is especially suitable for state estimation is proposed. The complexity of the urban traffic model is reduced by assuming availability of connected vehicle data. We first investigate the observability issue in urban traffic networks using a graphical approach. Then, the proposed model for the evolution of the traffic flow in urban traffic networks is developed and used in two layers, i.e., link layer and network layer, to estimate, in high-resolution (second-by-second), the traffic states in the whole network. Traffic states in the link layer include queue tail location and the number of vehicles in the queue, while in the network layer, estimation of the total number of vehicles per link and turning rates at the intersections is carried out. In a first step, it is shown that the estimation approach only requires the detectors at the borders of the network. We further demonstrate that in the proposed scheme, one may reduce or drop the need for spot detectors for the price of reduced, but still reasonable estimation accuracy. The validation of the approach has been undertaken by comparing the produced estimates with realistic micro-simulation results as ground truth, and the achieved simulation results are promising.

Application of Quantum Genetic Optimization of LVQ Neural Network in Smart City Traffic Network Prediction

Accurate prediction of traffic flow in urban networks is of great significance for smart city management. A short-term traffic flow prediction algorithm of Quantum Genetic Algorithm - Learning Vector Quantization (QGA-LVQ) neural network is proposed to forecast the changes of traffic flow. Different from BP neural network, Learning Vector Quantization (LVQ) neural network is of simple structure, easy implementation and better clustering effect. Utilizing the global optimization ability of Quantum Genetic Algorithm (QGA), it is combined with LVQ neural network to overcome some shortcomings of LVQ neural network, including sensitive to initial weights and prone to local minima. In order to test the convergence ability and the timeliness of QGA-LVQ neural network in short-term traffic flow, some contrast experiments are performed. Experimental simulation results show that, QGA-LVQ neural network obtains excellent prediction results in prediction accuracy and convergence speed. Besides, compared with GA-BP neural network and wavelet neural network, QGA-LVQ neural network performs better in short-term traffic flow prediction.

Traffic Microsimulation for Flexible Utilization of Urban Roadways

The current method of organizing traffic flows in urban networks uses directional right-of-way links to move traffic between urban intersections. Conflict resolution between vehicles is almost exclusively exercised at the intersections, which turns them into bottlenecks of our urban traffic systems. Even an attempt to model a different organization of traffic hits a major barrier, because the traditional simulation models do not offer enough flexibility to model bidirectional traffic on individual links in the network. This paper presents flexible arterial utilization simulation modeling (FAUSIM), a novel microsimulation platform designed to address this deficiency of traditional tools. The outputs from this tool are validated, successfully, in comparison with a commonly utilized Vissim model. The paper then illustrates the ability of FAUSIM to model conventional and unconventional traffic control scenarios. A combined alternate-direction lane assignment and reservation-based intersection control (CADLARIC) scenario is where directional driving paths are altered between neighboring lanes to align vehicles for decreased conflict for left and right turns at intersections where a reservation-based algorithm is utilized to process conflicts. This is compared with a conventional fixed-time (FT) control. The results of the experiments, executed on a small three-intersection corridor, show that CADLARIC significantly outperforms conventional driving with the FT control in relation to traffic efficiency (delays and stops). While the FT control generates fewer (potential) conflicting events, the CADLARIC confidently handles conflicting situations inside and outside the intersections. Future research should further validate the FAUSIM platform and investigate several other unconventional traffic scenarios with connected and automated vehicles.

Optimal Control for Traffic Flows in the Urban Road Networks and Its Solution by Variational Genetic Algorithm

A study is aimed at development and research of mathematical models and algorithms for traffic flows control in urban road networks. An optimal control problem statement for traffic flows in urban networks is given. A feature of the problem is that the model of control object is presented in finite-difference equations. A control is constrained by the durations of active phases of traffic lights at intersections. The solution is a control program that determines the moments for phase switching. A variational genetic algorithm is used to solve the problem.

A dynamic road incident information delivery strategy to reduce urban traffic congestion

Advanced information and communication technologies can be used to facilitate traffic incident management. If an incident is detected and blocks a road link, in order to reduce the incident-induced traffic congestion, a dynamic strategy to deliver incident information to selected drivers and help them make detours in urban areas is proposed by this work. Time-dependent shortest path algorithms are used to generate a subnetwork where vehicles should receive such information. A simulation approach based on an extended cell transmission model is used to describe traffic flow in urban networks where path information and traffic flow at downstream road links are well modeled. Simulation results reveal the influences of some major parameters of an incident-induced congestion dissipation process such as the ratio of route-changing vehicles to the total vehicles, operation time interval of the proposed strategy, traffic density in the traffic network, and the scope of the area where traffic incident information is delivered. The results can be used to improve the state of the art in preventing urban road traffic congestion caused by incidents.

Numerical simulation of air pollution due to traffic flow in urban networks

As it is well known, traffic flow is the main pollution source in many urban areas, where the number of vehicles ranges from many thousands to millions. Thus, estimating the pollution emission rate due to traffic flow in big cities is a very hard task. To approach this environmental issue, in this paper we propose a methodology that consists of combining the 1D Lighthill–Whitham–Richards traffic model for road networks with a classical 2D advection–diffusion–reaction pollution model for the atmosphere. Here, the pollution model uses a source term that takes into account the traffic flow contamination by means of a Radon measure supported on a road network within an urban domain. Furthermore, we establish the existence of solution of the coupled model, and detail a complete numerical algorithm to compute it (mainly, interfacing a finite volume scheme based on the supply–demand method for the traffic model, with a characteristics-Lagrange finite element method for the pollution model). Finally, several numerical experiences for a real urban domain (the Guadalajara Metropolitan Area in Mexico) are presented.

Real-time traffic jams prediction inspired by Biham, Middleton and Levine (BML) model

Urban traffic jams severely affect many cities around the world. Therefore, there is an urgent need for accurate prediction of traffic jams in real time. With many existing approaches, it is difficult to obtain favorable results for dynamic and uncertain traffic flows in urban networks. This paper proposes a real-time traffic jam prediction method based on a two-dimension cellular automaton model inspired by the well-known Biham, Middleton and Levine (BML) model. The proposed model combines the simplicity and high efficiency of the BML model with real urban traffic networks. Our work can be divided into two parts: (1) the proposal of a practical approach for mapping the urban traffic topological structure into a modified BML (M-BML) model, and (2) the development of a novel strategy to deal with different cells of the M-BML model, especially conflict points and fuzzy points, which facilitates the recognition of real traffic jams at intersections. To the best of our knowledge, this is the first time that real urban traffic road networks are directly mapped into a descriptive model for jam prediction. Extensive experiments confirm the accuracy and efficiency of the proposed M-BML model in predicting traffic jams in dynamic and uncertain real urban traffic networks.

Biham—Middleton—Levine model in consideration of cooperative willingness

In this paper, the Biham—Middleton—Levine (BML) model with consideration of cooperative willingness has been proposed to study the traffic flow in urban networks. An evolutionary game with a cooperative willingness profile is introduced to deal with conflicts between disturbing neighbors. Simulation results suggest that imitating cooperative willingness can ease the effect of premature seizure on traffic flow due to the introduction of evolutionary games. Phase diagrams with a strategy profile and cooperative willingness profile have been investigated in detail. Our findings also prove that by imitating the more successful, cooperative willingness instead of simply the more successful strategies, the evolution of cooperation is significantly promoted, hence improving the order of cooperation and relieving the pressure of traffic networks.

Model calculation of environmentally friendly traffic flows in a rapidly growing urban centre in India

The paper highlights the application of an integrated decision support system for calculation of environmental friendly traffic flows in urban networks under different management strategies. Each proposed alternative is evaluated using a GIS and GPS environment that may, on a local basis, affect the environmental burden and contribute to pollution load in the region. For each link of the network an environmental capacity is calculated, taking into consideration the hydrodynamic theory based traffic flow dependent on the length of the road network and the average traffic volume and speed. The studied management options include: modifying the existing road network; road widening activities; bus bay relocation; construction of RUB/ROBs; rescheduling the work activities; parking management. The integrated transportation decision support system offers entirely new way of using the GIS, GPS and field survey data for model calculation of pollution load from road traffic to devise environmentally friendly traffic flows.