scholarly journals Development of approach to improvement of urban traffic flow diagram

2018 ◽  
Vol 216 ◽  
pp. 02026
Author(s):  
Andrey Burlutsky ◽  
Galina Pushkareva ◽  
Elena Kirgisarova
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Flow Diagram ◽  
Transport Systems ◽  
Street Network ◽  
Optimization Criteria ◽  
Big City ◽  
Account Interaction

The result of generalized analysis of Russian and foreign studies focused on urban transportation systems demonstrate that the existing methods of forming schemes of passenger transport routes only partially account interaction of transport flows and urban highways. Usually, insufficient attention is paid to optimization criteria that allow performing comprehensive analysis of rationality of transport route schemes. It is defined that speed is one of the key optimization criteria for transport systems that accounts specific feature of traffic flow organization on a street network of a big city and its state with regard to the use of traffic. An approach to reasonable scheduling of route scheme reorganization was developed basing on the routing experience, it allows accounting the factors that defines technical state of a road network and characteristics of transport flows.

Make More Connections: Urban Traffic Flow Forecasting with Spatiotemporal Adaptive Gated Graph Convolution Network

2022 ◽  
Vol 13 (2) ◽  
pp. 1-25
Author(s):  
Bin Lu ◽  
Xiaoying Gan ◽  
Haiming Jin ◽  
Luoyi Fu ◽  
Xinbing Wang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Large Scale ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Propagation Mechanism ◽  
Urban Road ◽  
The Road ◽  
Traffic Flow Forecasting ◽  
Gating Mechanism

Urban traffic flow forecasting is a critical issue in intelligent transportation systems. Due to the complexity and uncertainty of urban road conditions, how to capture the dynamic spatiotemporal correlation and make accurate predictions is very challenging. In most of existing works, urban road network is often modeled as a fixed graph based on local proximity. However, such modeling is not sufficient to describe the dynamics of the road network and capture the global contextual information. In this paper, we consider constructing the road network as a dynamic weighted graph through attention mechanism. Furthermore, we propose to seek both spatial neighbors and semantic neighbors to make more connections between road nodes. We propose a novel Spatiotemporal Adaptive Gated Graph Convolution Network ( STAG-GCN ) to predict traffic conditions for several time steps ahead. STAG-GCN mainly consists of two major components: (1) multivariate self-attention Temporal Convolution Network ( TCN ) is utilized to capture local and long-range temporal dependencies across recent, daily-periodic and weekly-periodic observations; (2) mix-hop AG-GCN extracts selective spatial and semantic dependencies within multi-layer stacking through adaptive graph gating mechanism and mix-hop propagation mechanism. The output of different components are weighted fused to generate the final prediction results. Extensive experiments on two real-world large scale urban traffic dataset have verified the effectiveness, and the multi-step forecasting performance of our proposed models outperforms the state-of-the-art baselines.

scholarly journals Overview of emerging road traffic data collection methods

2021 ◽  
Author(s):  
Sandra Mihalinac ◽  
Maja Ahac ◽  
Saša Ahac ◽  
Miroslav Šimun
Keyword(s):  
Data Collection ◽  
Traffic Flow ◽  
Road Network ◽  
Road Traffic ◽  
Traffic Volume ◽  
Transport Systems ◽  
Traffic Data ◽  
Data Collection Methods ◽  
Traffic Data Collection ◽  
Collection Methods

It is a well-known fact that the data on road traffic flow characteristics is essential for sustainable road network management. First road traffic volume counts date back to the 1950s when short-term periodic road traffic counts were carried out in cities worldwide. Manual traffic counting is one of the oldest and most technologically simple methods to obtain data on road traffic volume and its composition. Today, because of the ever-growing road transport demand, it has become clear that the development of Intelligent Transport Systems (ITS) is vital to increase safety and tackle increasing emission and congestion problems. The introduction of ITS highly depends on the quality and quantity of traffic data. Under the growing requirement of long-term traffic flow information, various traffic data collection methods have evolved. They allow systematic recording of the traffic flow volume and composition but also vehicle speed, total gross weight, number of axles, axle load and travel destination. This data, which is collected continuously over longer periods, enables a detailed analysis of traffic flows, and represents the basis for decision making in planning, designing, construction and maintenance of road infrastructure. This paper gives an overview of traditional and emerging traffic data collection methods - both fixed and mobile - and the analysis of the current road traffic data collection methods used on the Croatian road network, in terms of their potential and limitations.

Analysis of Dynamic Impedance Functions in Urban Traffic Flow Guidance System

2012 ◽  
Vol 238 ◽  
pp. 503-506 ◽  
Author(s):  
Zhi Cheng Li
Keyword(s):  
Traffic Flow ◽  
Network Model ◽  
Large Scale ◽  
Choice Model ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Guidance System ◽  
Traffic Network ◽  
Dynamic Traffic ◽  
Run Time

The successful application of Intelligent Transportation Systems (ITS) depends on the traffic flow at any time with high-precision and large-scale assessments, it is necessary to create a dynamic traffic network model to evaluate and forecast traffic. Dynamic route choice model sections of the run-time function are very important to the dynamic traffic network model. To simplify the dynamic traffic modeling, improve the calculation accuracy and save computation time, the flow on the section of the interrelationship between the exit flow and number of vehicles are analyzed, a run-time functions into the flow using only sections of the said sections are established.

scholarly journals FUNCTIONING OF THE CAR PARKING PLACES NEAR HOUSES: FORMULATION OF THE PROBLEM OF THE ROAD CITY NETWORK

2019 ◽  
Vol 16 (6) ◽  
pp. 670-679 ◽  
Author(s):  
I. E. Agureev ◽  
D. A. Yurchenko
Keyword(s):  
Computer Aided Design ◽  
Road Network ◽  
Transport Model ◽  
Urban Traffic ◽  
Transport Systems ◽  
Traffic Flows ◽  
Residential Areas ◽  
The Road ◽  
Network Loading ◽  
On The Road

Introduction. The load models of the road network make it possible to understand a lot of the transport, social, environmental, and other city problems. Creating transport models requires knowledge of the traffic flows’ formation and functioning. The paper formulates a goal and poses tasks for the research conducting of the adjoining territories of residential areas in Tula as one of the urban traffic flows’ sources and of the identifying patterns of the parking places near houses’ influence on the road network loading.Materials and methods. The basis of the research was the development in the field of predictive simulation of automobile transport systems. The authors used complex of computer-aided design “TransNet”, which allowed adjusting the initial data in the base model by the results of the parking places’ functioning.Discussion and conclusions. As a result, the improved transport model of Tula allows making the forecast for determining the main parameters of the transport system taking into account the dynamics of vehicles’ local area departure at different time intervals. Moreover, the proposed methodological tools and algorithm for solving the problem of the road network loading in a quasi-dynamic setting helps to solve existing transport problems and to improve the traffic organization.The authors have read and approved the final manuscript. Financial transparency: the authors have no financial interest in the presented materials or methods. There is no conflict of interest.

scholarly journals Rule Optimization of Self-organization Control of Traffic Signals in Urban Net Based on Hydrodynamic

2018 ◽  
Vol 237 ◽  
pp. 03004
Author(s):  
Fusheng Zhong ◽  
Anlin Wang
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Dynamic Change ◽  
Traffic Signals ◽  
Urban Traffic ◽  
Self Organization ◽  
Hydrodynamic Equations ◽  
The Road ◽  
Control Rules ◽  
Rule Optimization

Prior researchers indicate that hydrodynamics models of traffic-flow is lack of description of changing mechanism under urban traffic, and self-organization control system can not explain the dynamic characteristics of urban traffic flow clearly. The aim of the paper is to puts forward an optimized method on control rules that make the united application of hydrodynamics and self-organization system in signal control. The parameter sets of control rules are built from parameter sets of road network which are evolution under hydrodynamic equations such as the length of each lane, phase, queue length and so on .With the aim of the maximum traffic volume at each intersection in the road network, the control rules optimize its parameter sets to adapt to the dynamic change. By means of the computer simulation, the application of signal self-organizing control under hydrodynamic is proved effective in urban traffic.a

scholarly journals Building the Traffic Flow Network with Taxi GPS Trajectories and Its Application to Identify Urban Congestion Areas for Traffic Planning

2020 ◽  
Vol 13 (1) ◽  
pp. 266
Author(s):  
Jiayu Qin ◽  
Gang Mei ◽  
Lei Xiao
Keyword(s):  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Traffic Planning ◽  
Flow Network ◽  
Gps Trajectories ◽  
Congestion Problems

Traffic congestion is becoming a critical problem in urban traffic planning. Intelligent transportation systems can help expand the capacity of urban roads to alleviate traffic congestion. As a key concept in intelligent transportation systems, urban traffic networks, especially dynamic traffic networks, can serve as potential solutions for traffic congestion, based on the complex network theory. In this paper, we build a traffic flow network model to investigate traffic congestion problems through taxi GPS trajectories. Moreover, to verify the effectiveness of the traffic flow network, an actual case of identifying the congestion areas is considered. The results indicate that the traffic flow network is reliable. Finally, several key problems related to traffic flow networks are discussed. The proposed traffic flow network can provide a methodological reference for traffic planning, especially to solve traffic congestion problems.

scholarly journals GIS-based analysis of urban traffic model: Case study in Beijing

2021 ◽  
Vol 268 ◽  
pp. 01056
Author(s):  
Yongkai Liang ◽  
Jingyuan Li ◽  
Hai Liu
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Road Traffic ◽  
Flow Characteristics ◽  
Low Frequency ◽  
Flow Speed ◽  
Urban Traffic ◽  
Model Case ◽  
Sudden Rise ◽  
Cubic Polynomial

Taking the traffic flow characteristics of Beijing's entire road network as the object, and using the low-frequency traffic big data of GIS (Geographic Information System), the roads of the whole road network are divided into four road grades, and the traffic flow-speed models are constructed respectively. In view of the deviation of the model calculation caused by the sudden rise and fall of the traveling vehicle at night, the flow of the traffic flow model is corrected by cubic polynomial fitting, and the mathematical model is compared, calibrated and verified. Focus on analyzing the influence of roads of different grades and seasons on the characteristics of road traffic flow, and provide data support for further research on intelligent transportation.

scholarly journals Urban Traffic Flow Forecast Based on FastGCRNN

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Ya Zhang ◽  
Mingming Lu ◽  
Haifeng Li
Keyword(s):  
Computational Complexity ◽  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Large Scale ◽  
Geometric Graph ◽  
Transportation Systems ◽  
Urban Traffic ◽  
The Road ◽  
Traffic Forecasting ◽  
Traffic Flow Forecast

Traffic forecasting is an important prerequisite for the application of intelligent transportation systems in urban traffic networks. The existing works adopted RNN and CNN/GCN, among which GCRN is the state-of-the-art work, to characterize the temporal and spatial correlation of traffic flows. However, it is hard to apply GCRN to the large-scale road networks due to high computational complexity. To address this problem, we propose abstracting the road network into a geometric graph and building a Fast Graph Convolution Recurrent Neural Network (FastGCRNN) to model the spatial-temporal dependencies of traffic flow. Specifically, we use FastGCN unit to efficiently capture the topological relationship between the roads and the surrounding roads in the graph with reducing the computational complexity through importance sampling, combine GRU unit to capture the temporal dependency of traffic flow, and embed the spatiotemporal features into Seq2Seq based on the Encoder-Decoder framework. Experiments on large-scale traffic data sets illustrate that the proposed method can greatly reduce computational complexity and memory consumption while maintaining relatively high accuracy.

scholarly journals The Impact of Automated Vehicles on Traffic Flow and Road Capacity on Urban Road Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ji Eun Park ◽  
Wanhee Byun ◽  
Youngchan Kim ◽  
Hyeonjun Ahn ◽  
Doh Kyoum Shin
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Traffic Volume ◽  
Transport Systems ◽  
Automated Vehicles ◽  
Average Delay ◽  
The Road ◽  
Capacity Increase ◽  
Road Capacity ◽  
The Impact

Automated vehicles (AVs) are believed to have great potential to improve the traffic capacity and efficiency of the current transport systems. Despite positive findings of the impact of AVs on traffic flow and potential road capacity increase for highways, few studies have been performed regarding the impact of AVs on urban roads. Moreover, studies considering traffic volume increase with a mixture of AVs and human-driven vehicles (HDVs) have rarely been conducted. Therefore, this study investigated the impact of gradual increments of AV penetration and traffic volume on urban roads. The study adopted a microsimulation approach using VISSIM with a Wiedmann 74 model for car-following behavior. Parameters for AVs were set at the SAE level 4 of automation. A real road network was chosen for the simulation having 13 intersections in a total distance of 4.5 km. The road network had various numbers of lanes from a single lane to five lanes in one direction. The network consists of a main arterial road and a parallel road serving nearby commercial and residential blocks. In total, 36 scenarios were investigated by a combination of AV penetrations and an increase in traffic volumes. The study found that, as AV penetration increased, traffic flow also improved, with a reduction of the average delay time of up to 31%. Also, as expected, links with three or four lanes had a more significant impact on the delay. In terms of road capacity increase, when the penetration of AVs was saturated at 100%, the road network could accommodate 40% more traffic.

Sign in / Sign up

Export Citation Format

Share Document