scholarly journals A Short-Term Traffic Flow Prediction to Control Traffics in Large Scale Transportation using Internet of Things

2019 ◽  
Vol 8 (4) ◽  
pp. 8323-8330
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Large Scale ◽  
Green Light ◽  
Traffic Density ◽  
The Road ◽  
Traffic Flow Prediction ◽  
Road Section ◽  
Time Calculation ◽  
Short Time

Traffic congestion is the key problem that occurs across urban metropolises around the world. Due to the increase in transportation vehicles the fixed light time on traffic signals not able to solve the traffic congestion problem. In this paper, First, we develop an IoT based system which is capable of streaming the traffic surveillance footages to cloud storage, then the vehicle count is recorded every 30 sec interval and updated in the traffic flow dataset. Second the traffic flow is predicted using our CNN-LSTM residual learning model. Finally, the predicted value is classified and traffic density at each road section is identified, thereby passing this density value to green light time calculation to set an optimal green time to reduce the traffic congestion. The traffic flow dataset, China is used for training and testing to forecast the short time traffic flow across the road section. Experiment results shows that our model has best accuracy by lowering the RMSE value.

scholarly journals Mixed Vehicular Traffic Flow Model on an Inclined Multilane Road

2020 ◽  
Vol 5 (7) ◽  
pp. 331-342
Author(s):  
Delina Mshai Mwalimo ◽  
Mary Wainaina ◽  
Winnie Kaluki
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Flow Model ◽  
Free Flow ◽  
Traffic Density ◽  
Fundamental Diagram ◽  
Traffic Flow Model ◽  
The Road ◽  
Slow Moving

This study outlines the Kerner’s 3 phase traffic flow theory, which states that traffic flow occurs in three phases and these are free flow, synchronized flow and wide moving jam phase. A macroscopic traffic model that is factoring road inclination is developed and its features discussed. By construction of the solution to the Rienmann problem, the model is written in conservative form and solved numerically. Using the Lax-Friedrichs method and going ahead to simulate traffic flow on an inclined multi lane road. The dynamics of traffic flow involving cars(fast moving) and trucks(slow moving) on a multi-lane inclined road is studied. Generally, trucks move slower than cars and their speed is significantly reduced when they are moving uphill on an in- clined road, which leads to emergence of a moving bottleneck. If the inclined road is multi-lane then the cars will tend to change lanes with the aim of overtaking the slow moving bottleneck to achieve free flow. The moving bottleneck and lanechange ma- noeuvres affect the dynamics of flow of traffic on the multi-lane road, leading to traffic phase transitions between free flow (F) and synchronised flow(S). Therefore, in order to adequately describe this kind of traffic flow, a model should incorporate the effect of road inclination. This study proposes to account for the road inclination through the fundamental diagram, which relates traffic flow rate to traffic density and ultimately through the anticipation term in the velocity dynamics equation of macroscopic traffic flow model. The features of this model shows how the moving bottleneck and an incline multilane road affects traffic transistions from Free flow(F) to Synchronised flow(S). For a better traffic management and control, proper understanding of traffic congestion is needed. This will help road designers and traffic engineers to verify whether traffic properties and characteristics such as speed(velocity), density and flow among others determines the effectiveness of traffic flow.

A Traffic Flow Forecasting Method Regarding Traffic Network as an Digraph

2021 ◽  
pp. 2159043
Author(s):  
Zefei Chen ◽  
Jianmin Xu ◽  
Yongjie Lin ◽  
Bin Feng ◽  
Zihao Huang
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Intelligent Transportation System ◽  
Undirected Graph ◽  
The State ◽  
Good Prediction ◽  
Traffic Network ◽  
The Road ◽  
Traffic Flow Prediction ◽  
Flow Prediction

Traffic congestion has become a major problem restricting the development of major cities. ITS (Intelligent Transportation System) can record the state of traffic and predict the future traffic state, then reasonably optimize the travel scheme, so as to achieve the purpose of alleviating traffic congestion. Meanwhile, traffic flow prediction can provide data support for ITS, so many researchers have done a lot of research on traffic flow prediction. Many researchers take the traffic network as an undirected graph, and use the GCN (Graph Convolution Network) model to study the traffic flow prediction, and have achieved good prediction results. However, the traffic network is directed, and the traffic network is regarded as an undirected graph, which loses the direction information of the road network. Therefore, this inspires us to propose a graph convolution operator DGCN (Directed GCN), which can make full use of the in degree and out degree information of each station in the traffic network. The experimental results show that the graph convolution neural network based on this operator has better prediction accuracy than the state-of-the-art models.

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 STUDI PERENCANAAN TEBAL PERKERASAN KONSTRUKSI JALAN RAYA (MENGGUNAKAN METODE BINA MARGA) PADA RUAS JALAN UMASUKAER DI KABUPATEN MALAKA

2019 ◽  
Vol 2 (1) ◽  
pp. 75
Author(s):  
Philipus Resato Nahak ◽  
Yosef Cahyo ◽  
Sigit Winarto
Keyword(s):  
Road Construction ◽  
Traffic Volume ◽  
Traffic Density ◽  
The Road ◽  
Technical Requirements ◽  
Road Section ◽  
Road Capacity ◽  
Data Survey ◽  
Pavement Thickness

The increase in traffic volume will cause a decrease in service due to decreased road capacity due to an increase in side constraints and due to the increase in traffic volume itself, which will ultimately cause the level of road saturation to increase. The situation occurred in the Umasukaer road section of the Malacca Regency. Therefore it is necessary to address improvements in the quality of the road in order to meet the feasibility of transportation facilities by taking into account the existing technical requirements. The results of planning found that through the 2015 LHR survey data with a prediction of an increase in traffic density of 6% per year, the LHR was obtained with a planned age of 7 years = 2540.7 vehicles/day/department and a 20-year plan life LHR = 5419.1 ked/day / major. The results of a gradual construction planning pavement study can be concluded that the planning model that has been designed is effective in strengthening road construction in accordance with existing technical requirements and efficient in terms of financing. The final results of gradual construction pavement thickness results are: Ashburton thickness (MS 744) = 8 cm, Ashburton (MS 744) = 13 cm, broken stone (CBR 100) = 20 cm, Sirtu (CBR 50) = 10 cm and CBR subgrade 5%. Pertambahan volume lalu lintas akan menyebabkan penurunan layanan diakibatkan menurunnya kapasitas jalan karena adanya peningkatan hambatan samping maupun karena beratambahnya volume lalu lintas itu sendiri yang pada akhirnya akan meyebabkan tingkat kejenuhan jalan meningkat. Keadaan tersebut terjadi ruas jalan Umasukaer Kabupaten Malaka, oleh karena itu perlu adanya penanganan perbaikan kualitas jalan agar memenuhi segi kelayakan sarana transportasi dengan memperhatikan syarat-syarat teknik yang ada. Hasil perencanaan didapatkan bahwa melalui data survey LHR tahun 2015 dengan prediksi peningkatan kepadatan lalu lintas sebesar 6% pertahun maka didapatkan LHR dengan umur rencana 7 tahun = 2540,7 kend/hr/jurusan dan LHR umur rencana 20 tahun = 5419,1 ked/hr/jurusan. Hasil studi perencanaan perkerasan konstruksi bertahap dapat diambil kesimpulan bahwa model perencaaan yang telah dirancang efektif dalam memperkerasa konstruksi jalan sesuai dengan syarat teknis yang ada serta efisien dalam hal pembiayaan. Hasil akhir tebal perkerasan konstruksi bertahap diperoleh hasil: Ketebalan Asbuton (MS 744) = 8 cm, Asbuton (MS 744) = 13 cm, batu pecah (CBR 100) = 20 cm, Sirtu (CBR 50) = 10 cm dan CBR tanah dasar 5%.

scholarly journals THE TRAFFIC VOLUME AND LEVEL OF SERVICE (LOS) OF UNIVERSITI PUTRA MALAYSIA (UPM) SERDANG CAMPUS MAIN ACCESS

2019 ◽  
Vol 17 ◽  
Author(s):  
Zakiah Ponrahono ◽  
Noorain Mohd Isa ◽  
Ahmad Zaharin Aris ◽  
Rosta Harun
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Noise Pollution ◽  
Traffic Volume ◽  
Level Of Service ◽  
Idle Mode ◽  
The Road ◽  
Road Capacity ◽  
Motorized Vehicles ◽  
University Setting

The inbound and outbound traffic flow characteristic of a campus is an important physical component of overall university setting. The traffic circulation generated may create indirect effects on the environment such as, disturbance to lecturetime when traffic congestion occurs during peak-hours, loss of natural environment and greenery, degradation of the visual environment by improper or illegal parking, air pollution from motorized vehicles either moving or in idle mode due to traffic congestion, noise pollution, energy consumption, land use arrangement and health effects on the community of Universiti Putra Malaysia (UPM) Serdang. A traffic volume and Level of Service (LOS) study is required to facilitate better accessibility and improves the road capacity within the campus area. The purpose of this paper is to highlight the traffic volume and Level of Service of the main access the UPM Serdang campus. A traffic survey was conducted over three (3) weekdays during an active semester to understand the traffic flow pattern. The findings on traffic flow during peak hours are highlighted. The conclusions of on-campus traffic flow patterns are also drawn.

TRAFFIC FLOWS MODELING WITH INACCESSIBLE FOR DRIVING ROAD SECTIONS

2021 ◽  
pp. 19-36
Author(s):  
Anastasiya N. Zhukova ◽  
◽  
Marina S. Shapovalova ◽  
Keyword(s):  
Network Formation ◽  
Road Transport ◽  
Traffic Density ◽  
Traffic Flows ◽  
The Road ◽  
Model Modification ◽  
Road Section ◽  
Transport Line ◽  
On The Road ◽  
Python Programming

Computerized traffic modeling makes it possible to find out the modification needs to assess the traffic flow on the roads and detect likely problem areas in order to take timely measures to eliminate them. Competent preparation of a road network formation plan based on the acquired information makes it possible to reduce the load on the road transport line, avoid traffic jams, and also reduce the average time spent by drivers on the roads. The macroscopic and microscopic models of the cars flow were analyzed by authors to implement the computer model. The article considered the model of the cellular automata by Nagel–Schreckenberg, with the author’s addition that takes into account the presence of the road sections inaccessible for driving in. The need to modify the lane change algorithm was implemented: the condition of the need to change the lane when car is meeting an inaccessible road section was added. And also the “polite” drivers algorithm for bypassing inaccessible areas with a high density of the traffic flows was proposed. Such a model is realized on Python programming language. An analysis of vehicles behavior with different traffic density and location of inaccessible road sections for two- and three-lane roads was carried out based on that model modification.

scholarly journals Improved Spatio-Temporal Residual Networks for Bus Traffic Flow Prediction

2019 ◽  
Vol 9 (4) ◽  
pp. 615 ◽  
Author(s):  
Panbiao Liu ◽  
Yong Zhang ◽  
Dehui Kong ◽  
Baocai Yin
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Road Traffic ◽  
Transportation Network ◽  
Temporal Correlation ◽  
Traffic Flow Prediction ◽  
Temporal Correlations ◽  
Building Models ◽  
Spatio Temporal ◽  
Specific Scenario

Buses, as the most commonly used public transport, play a significant role in cities. Predicting bus traffic flow cannot only build an efficient and safe transportation network but also improve the current situation of road traffic congestion, which is very important for urban development. However, bus traffic flow has complex spatial and temporal correlations, as well as specific scenario patterns compared with other modes of transportation, which is one of the biggest challenges when building models to predict bus traffic flow. In this study, we explore bus traffic flow and its specific scenario patterns, then we build improved spatio-temporal residual networks to predict bus traffic flow, which uses fully connected neural networks to capture the bus scenario patterns and improved residual networks to capture the bus traffic flow spatio-temporal correlation. Experiments on Beijing transportation smart card data demonstrate that our method achieves better results than the four baseline methods.

scholarly journals Incorporation of Duffing Oscillator and Wigner-Ville Distribution in Traffic Flow Prediction

2017 ◽  
Vol 29 (1) ◽  
pp. 13-22 ◽  
Author(s):  
Anamarija L. Mrgole ◽  
Drago Sever
Keyword(s):  
Traffic Flow ◽  
Road Traffic ◽  
Duffing Oscillator ◽  
Traffic Load ◽  
The Road ◽  
Traffic Flow Prediction ◽  
Pattern Recognition Methods ◽  
Flow Prediction ◽  
Time Period ◽  
Specific Section

The main purpose of this study was to investigate the use of various chaotic pattern recognition methods for traffic flow prediction. Traffic flow is a variable, dynamic and complex system, which is non-linear and unpredictable. The emergence of traffic flow congestion in road traffic is estimated when the traffic load on a specific section of the road in a specific time period is close to exceeding the capacity of the road infrastructure. Under certain conditions, it can be seen in concentrating chaotic traffic flow patterns. The literature review of traffic flow theory and its connection with chaotic features implies that this kind of method has great theoretical and practical value. Researched methods of identifying chaos in traffic flow have shown certain restrictions in their techniques but have suggested guidelines for improving the identification of chaotic parameters in traffic flow. The proposed new method of forecasting congestion in traffic flow uses Wigner-Ville frequency distribution. This method enables the display of a chaotic attractor without the use of reconstruction phase space.

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