Flow In Urban Road Networks Research Articles

Predicting traffic propagation flow in urban road network with multi-graph convolutional network

AbstractTraffic volume propagating from upstream road link to downstream road link is the key parameter for designing intersection signal timing scheme. Recent works successfully used graph convolutional network (GCN) and specific time-series model to forecast traffic flow by capturing the spatial–temporal features. However, accurately predicting traffic propagation flow (tpf) is challenging, since the classical GCN model only considers the influence of adjacent road link. In complex urban road network, specific traffic propagation flow (tpf) is affected by various spatial features, such as adjacent tpf, which influences from tpf with same upstream link and tpf with same downstream link. Thus, we proposed a multi-graph learning-based model named TPP-GCN (traffic propagation prediction-graph convolutional network) in this paper to predict the traffic propagation flow in urban road network. The TPP-GCN model captures not only the temporal features but also multi-spatial features based on multi-layer convolution. We validated the model using real-world traffic flow data derived from taxi GPS data in Shenzhen, China. Finally, we compare and evaluate the proposed model with the existing models across several prediction scales.

An iterative learning identification strategy for nonlinear macroscopic traffic flow model

The parameters of traffic flow model are important to the management and control of urban road network. In order to more accurately describe the actual operation of traffic flow in urban road network, this paper proposes a nonlinear macroscopic traffic flow model containing unknown time-varying multi-parameter on the basis of the steady-state and dynamic characteristics of traffic flow, and designs a time-varying multi-parameter iterative learning identification strategy by using the inherent repetitive characteristics of traffic flow. In the finite time interval, the iterative learning identification strategy is used to transform the parameter identification problem into the optimal tracking control problem, so that the number of queuing vehicles at each entrance of intersection tends to the true value, thus improving the accuracy of the model. Finally, the convergence of the algorithm is proved by a strict theoretical derivation, and the effectiveness of the method is further verified by simulation experiments using the model-based control method.

Spatiotemporal Dynamic Forecasting and Analysis of Regional Traffic Flow in Urban Road Networks Using Deep Learning Convolutional Neural Network

The purpose is to explore the spatial-temporal dynamic prediction performance of urban road network traffic flow based on convolutional neural networks (CNN) of deep learning. A dynamic prediction model of road network traffic flow based on STGCN-BiLSTM (spatial-temporal graph convolution network Bi-directional Long Short-Term Memory) is designed in view of the complex and highly nonlinear traffic data in the actual environment. Finally, the simulation experiment is conducted on the constructed model to verify its performance which is compared with that of the LSTM (Long Short-Term Memory) model, CNN model, RNN (Recurrent Neural Network) model, AlexNet model and STGCN model. The results show that the root mean square error, mean absolute error and mean absolute percentage error of the proposed algorithm model are 4.60%, 5.46% and 7.73%, respectively. The training time is stable at about 45s, the test time is stable at about 33s, and the delay is close to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.86\times 10 ^{-4}\text{s}$ </tex-math></inline-formula> . Additionally, the traffic flow of the test section in the next 15 min, 30 min, 1 h and 2 h is further predicted. Compared with other algorithms, the predicted value of the proposed algorithm is the closest to the actual value, along with the best prediction effect. Therefore, the constructed dynamic prediction model of road network traffic flow can achieve high accuracy and better robustness under the premise of low error, which can provide experimental basis for the spatial-temporal dynamic digital development of transportation in smart cities.

Effects of static bottlenecks on traffic flow in urban road network

Existing traffic flow models do not consider the effects of road static bottlenecks on traffic flow. In this paper, a modified macroscopic continuum model for traffic flow on urban road network with static bottlenecks is presented. The model takes into account the fluctuations of traffic flow considering static bottlenecks during the morning peak period. The model results show that existence of static road bottlenecks with various configurations cause traffic flow instabilities. This phenomenon lead into stop-and-go traffic flow conditions under the moderate density and reduction of the traffic system’s efficiency. Furthermore, results show that an increase in traffic density is accompanied by a significant decrease of speed which adversely influences performance of roadway and decrease the traffic system’s efficiency and thus resulting to the occurrence of congestions. The methodological aspects of the study and results will enable traffic engineers and planners to assess and improve existing urbanroad networks.&#x0D; Keywords: Traffic flow, Bottlenecks, stability, Stop-and-go traffic, System’s efficiency, Congestion.

Modeling and simulation of network traffic flow evolution based on incomplete information feedback strategies in the ATIS environment

Considering both the high complexity of urban traffic flow systems and the bounded rationality of travelers, providing traffic information to all travelers is an effective method to induce each individual to make a more rational route-choice decision. Within Advanced Traveler Information System (ATIS) working environment, temporal and spatial evolution processes of traffic flow in urban road networks are closely related to strategies of providing traffic information and contents of information. In view of the day-to-day route-choice situations, this study constructs original updating models of the cognitive travel time of travelers under four conditions, including not providing any route travel time, only providing the most rapid route travel time, only providing the most congested route travel time, and providing all the routes travel times. The disaggregate route-choice approach is adopted for simulation to reveal the relationship between the evolution process of network traffic flow and the strategy of providing traffic information. The simulation shows that providing traffic information to all travelers cannot improve the operational efficiency of road networks. It is noteworthy that an inappropriate information feedback strategy would lead to intense variation in various routes traffic flow. Compared with incomplete information feedback strategies, it is inefficient and superfluous to provide complete traffic information to all travelers.

Simulation and analysis of road construction traffic flow in urban road networks

Road construction is an important source of occasional urban road network traffic congestion. In this article, a new cellular automaton model is proposed to simulate road construction traffic flow in urban two-way-six-lane network systems with roundabout intersections. In the proposed model, a three-lane traffic rule is adopted to represent vehicle movements on road sections, turning lane changing and overtaking lane changing are allowed, and vehicle movements in intersection areas are determined by priority which relies on vehicle position. Simulation results show that increasing the closed lane number may lead to decline of the network speed. We also found that there is a relatively fixed influence range of road construction in different cases, and a proper proportion of vehicle bypass can improve the operation efficiency of road network when semi-closed construction.

Discussion on Urban Road Traffic Congestion Algorithm for Automatically Determining

Compared with the expressway, most of the traffic flow in urban road network can be denoted as interrupted traffic flow. Based on the current employed equipment for traffic flow collection and traffic signal control in urban roads, different types of traffic flow in urban roads are analyzed with the traffic flow arrival/departure model in transportation engineering. Mathematical models complying with traffic flow changes are utilized to match the traffic flow in both entry and exit road blocks, thus, enabled the automatic detection of traffic incident. This algorithm provides a measurement for the automatic judgment of urban road congestion and the expansion utility of intelligent transportation facilities in urban areas.

Analysis of Traffic Flow in Coordinated Sections of Urban Road Networks by Application of up-to-date Low-Cost Methods

Traffic lights are used around the world to control traffic flow in urban road networks in an efficient way. Especially in higher-ranking road sections of road networks, neighbouring intersections with signal control are normally coordinated between each other in order to serve traffic volume on a high quality level. In periodical time intervals, the coordination of signalised intersections needs to be analysed. In this way, it is possible to identify defects in signal control and to adapt traffic control to changed conditions. In this paper, a method for the “Analysis of Traffic Flow in Coordinated Sections of Urban Road Networks by Application of up-to-date Low-Cost Methods” will be presented. The developed method can be used for assessment of traffic flow as well as for identification of defects in signal control in coordinated road sections. It is a stand-alone solution which can be applied independently from local technical equipment of traffic lights. It is expected that the application of the developed analysis method will contribute to a better quality of traffic flow in urban road networks in European as well as in Asian countries.