scholarly journals Prediction of Average Speed Based on Relationships Between Neighbouring Roads Using K-NN and Neural Network

2020 ◽  
Vol 16 (01) ◽  
pp. 18
Author(s):  
Bagus Priambodo ◽  
Azlina Ahmad ◽  
Rabiah Abdul Kadir
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Clustering Method ◽  
Traffic Condition ◽  
Average Speed ◽  
The Road ◽  
Road Congestion ◽  
Neighbouring Area ◽  
Road Segments

For decades, various algorithms to predict traffic flow have been developed to address traffic congestion. Traffic congestion or traffic jam occurs as a ripple effect from a road congestion in the neighbouring area. Previous research shows that there is a spatial correlation between traffic flow in neighbouring roads. Similar traffic pattern is observed between roads in a neighbouring area with respect to day and time. Currently, time series models and neural network models are widely applied to predict traffic flow and traffic congestion based on historical data. However, studies on relationships between road segments in a neighbouring area are still limited. It is important to investigate these relationships because they can assist drivers in avoiding roads which are impacted by road congestion. Also, the result can be used to improve the accuracy of prediction of traffic flow. Hence, this study investigates relationships of roads in a neighbouring area based on similarity of traffic condition. Traffic condition is influenced by number of vehicles and average speed of vehicles. In our study, clustering method is used to divide the speed of traffic into four (4) categories: very congested, congested, clear and very clear. We used k-means clustering method to cluster condition of traffic flow on road segments.  Then, we applied the k-Nearest Neighbour (k-NN) method to classify the traffic condition in neighbouring roads. From the classification of traffic condition in neighbouring roads, we then determine the relationship between road segments. We presented the road with highest relationship on the map and used it as input factor to predict traffic speed of the road using neural network. Results show that combination of k-means and k-NN method produced better results than using both, correlation method and using the k-means method only.

Spatio-temporal knn prediction of traffic state based on statistical features in neighbouring roads

2021 ◽  
pp. 1-15
Author(s):  
Bagus Priambodo ◽  
Azlina Ahmad ◽  
Rabiah Abdul Kadir
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Nonlinear Models ◽  
Ripple Effect ◽  
Traffic Condition ◽  
Traffic State ◽  
Road Congestion ◽  
Spatio Temporal ◽  
Occurrence Matrix

Traffic congestion on a road results in a ripple effect to other neighbouring roads. Previous research revealed existence of spatial correlation on neighbouring roads. Similar traffic patterns with regards to day and time can be seen amongst roads in a neighbouring area. Presently, nonlinear models of neural network are applied on historical data to predict traffic congestion. Even though neural network has successfully modelled complex relationships, more time is needed to train the network. A non-parametric approach, the k-nearest neighbour (K-NN) is another method for forecasting traffic condition which can capture the nonlinear characteristics of traffic flow. An earlier study has been done to predict traffic flow using K-NN based on connected roads (both downstream and upstream). However, impact of road congestion is not only to connected roads, but also to roads surrounding it. Surrounding roads that are impacted by road congestion are those having ‘high relationship’ with neighbouring roads. Thus, this study aims to predict traffic state using K-NN by determining high relationship roads within neighbouring roads. We determine the highest relationship neighbouring roads by clustering the surrounding roads by combining grey level co-occurrence matrix (GLCM) with k-means. Our experiments showed that prediction of traffic state using K-NN based on high relationship roads using both GLCM and k-means produced better accuracy than using k-means only.

scholarly journals TRAFFIC FLOW PREDICTION MODEL BASED ON NEIGHBOURING ROADS USING NEURAL NETWORK AND MULTIPLE REGRESSION

2018 ◽  
Author(s):  
Bagus Priambodo ◽  
Azlina Ahmad
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Flow Pattern ◽  
Traffic Flow ◽  
Multiple Regression ◽  
Traffic Congestion ◽  
Traffic Condition ◽  
Traffic Flow Prediction ◽  
Neighbouring Area ◽  
Flow Propagation

Monitoring and understanding traffic congestion seems difficult due to its complex nature. This is because the occurrence of traffic congestion is dynamic and interrelated and it depends on many factors. Traffic congestion can also propagate from one road to neighbouring roads. Recent research shows that there is a spatial correlation between neighbouring roads with different traffic flow pattern on weekdays and on weekends. Previously, prediction of traffic flow propagation was based on day and time during weekdays and on weekends. Results obtained from past studies show that further investigation is needed to reduce errors using a more efficient method. We observed from previous research that similarity of traffic condition on weekdays and weekends was not taken into account in predicting traffic flow propagation. Hence, our study is to create and evaluate a new prediction model for traffic flow propagation at neighbouring roads using similarity of traffic flow pattern on weekdays and weekends to achieve more accurate results. We exploit similarity of traffic flow pattern on weekdays and weekends by adding time cluster in our proposed model. Thus, our neural network model proposed high correlation road, time and day clusters as input factors in neural network model prediction. Our initial phase of the methodology involves investigation on correlation between neighbouring roads. This paper discusses the results of experiments we have conducted to determine relationship between roads in a neighbouring area and to determine input factors for our neural network traffic flow prediction model. To choose a particular road as a predicting factor, we calculated the distance between roads in neighbouring area to identify the nearest road. Then, we calculated correlation based on traffic condition (congestion) between roads in neighbouring area. The results were then used as input factors for prediction of traffic flow. We compared the results of the experiment using neural network without cluster parameters and multiple regression methods. We observed that neural network with time cluster parameter produced better results compared to neural network without parameter and multiple regression method in predicting average speed of vehicles on neighbouring roads.  

Evaluation Analysis and Optimization Design of Urban Road Traffic Condition Based on VISSIM

2011 ◽  
Vol 97-98 ◽  
pp. 531-534
Author(s):  
Xin Qiu ◽  
An Xia Zheng ◽  
Ying Zhu ◽  
Bin Xu
Keyword(s):  
Traffic Flow ◽  
Optimization Design ◽  
Road Traffic ◽  
Flow Characteristics ◽  
Simulation Software ◽  
Traffic Condition ◽  
Urban Road ◽  
The Road ◽  
Road Congestion

Based on the survey of the traffic flow characteristics in the intersection, combined with the traffic flow basic principle and the existing research results at home and abroad, the road congestion condition is evaluated by determining the microcosmic evaluation index coefficient by VISSIM microcosmic simulation software. Then, the reasonable measures are put forward to improve urban road congestion condition. The analysis shows that the residents’ trip efficiency is improved and the quality of the urban road traffic condition is ameliorated.

Research on the Influence of Last Digit Restriction to the Traffic Flow Characteristics

2012 ◽  
Vol 253-255 ◽  
pp. 1976-1981
Author(s):  
Hao Hua Du ◽  
Meng Pan
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Recovery Time ◽  
Flow Characteristics ◽  
License Plate ◽  
Traffic Condition ◽  
The Road ◽  
Vehicle Velocity ◽  
Before And After ◽  
Changing Trend

The Beijing government is taking measures to remit traffic congestions by restriction based on the last digit on a license plate, which influences the characteristic of traffic flow. This paper first builds a model about traffic flow characteristic, including vehicle velocity, roadway reliability and traffic congestion recovery time. The paper also researches different pattern of vehicle velocity before and after the rail number restriction, analyzes the changing trend of road-mobility reliability with traffic demands, and calculates the pattern of traffic congestion recovery time between the restrictions. The result indicates that when traffic flow’s density is large, the vehicle’s velocity is increased to 180% of the original after the restriction; and the recovery time from congestion to smooth is decreased to 75.8% of the original; after taken the restriction measures, the road unblocked reliability in Peak hours is increased, and the traffic condition ameliorate evidently.

Prediction of Vehicular Traffic Flow using Levenberg-Marquardt Artificial Neural Network Model: Italy Road Transportation System

2022 ◽  
Vol 24 (2) ◽  
pp. E74-E86
Author(s):  
Isaac Oyeyemi Olayode ◽  
Alessandro Severino ◽  
Tiziana Campisi ◽  
Lagouge Kwanda Tartibu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Traffic Flow ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Time Of Day ◽  
Vehicle Speed ◽  
Road Transportation ◽  
Artificial Neural

In the last decades, the Italian road transport system has been characterized by severe and consistent traffic congestion and in particular Rome is one of the Italian cities most affected by this problem. In this study, a LevenbergMarquardt (LM) artificial neural network heuristic model was used to predict the traffic flow of non-autonomous vehicles. Traffic datasets were collected using both inductive loop detectors and video cameras as acquisition systems and selecting some parameters including vehicle speed, time of day, traffic volume and number of vehicles. The model showed a training, test and regression value (R2) of 0.99892, 0.99615 and 0.99714 respectively. The results of this research add to the growing body of literature on traffic flow modelling and help urban planners and traffic managers in terms of the traffic control and the provision of convenient travel routes for pedestrians and motorists.

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.

Taxi Global Positioning System Data in Urban Road Network: A Methodology to Identify Key Road Clusters Based on Travel Speed–Traffic Volume Correlation

2021 ◽  
pp. 036119812110366
Author(s):  
Yi Li ◽  
Weifeng Li ◽  
Qing Yu ◽  
Han Yang
Keyword(s):  
Traffic Congestion ◽  
Road Network ◽  
Travel Speed ◽  
Detection Algorithm ◽  
Traffic Volume ◽  
Urban Traffic ◽  
Mitigation Strategies ◽  
Operating Characteristics ◽  
The Road ◽  
Road Segments

Urban traffic congestion is one of the urban diseases that needs to be solved urgently. Research has already found that a few road segments can significantly influence the overall operation of the road network. Traditional congestion mitigation strategies mainly focus on the topological structure and the transport performance of each single key road segment. However, the propagation characteristics of congestion indicate that the interaction between road segments and the correlation between travel speed and traffic volume should also be considered. The definition is proposed for “key road cluster” as a group of road segments with strong correlation and spatial compactness. A methodology is proposed to identify key road clusters in the network and understand the operating characteristics of key road clusters. Considering the correlation between travel speed and traffic volume, a unidirectional-weighted correlation network is constructed. The community detection algorithm is applied to partition road segments into key road clusters. Three indexes are used to evaluate and describe the characteristic of these road clusters, including sensitivity, importance, and IS. A case study is carried out using taxi GPS data of Shanghai, China, from May 1 to 17, 2019. A total of 44 key road clusters are identified in the road network. According to their spatial distribution patterns, these key road clusters can be classified into three types—along with network skeletons, around transportation hubs, and near bridges. The methodology unveils the mechanism of congestion formation and propagation, which can offer significant support for traffic management.

scholarly journals Urban Arterial Road Optimization and Design Combined with HOV Carpooling under Connected Vehicle Environment

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Lina Mao ◽  
Wenquan Li ◽  
Pengsen Hu ◽  
Guiliang Zhou ◽  
Huiting Zhang ◽  
...  
Keyword(s):  
Traffic Congestion ◽  
Negative Impact ◽  
Traffic Volume ◽  
Road Segment ◽  
Connected Vehicle ◽  
Traffic Data ◽  
Average Speed ◽  
The Road ◽  
Arterial Road ◽  
Urban Arterial

The HOV carpooling lane offers a feasible approach to alleviate traffic congestion. The connected vehicle environment is able to provide accurate traffic data, which could optimize the design of HOV carpooling schemes. In this paper, significant tidal traffic flow phenomenon with severe traffic congestion was identified on North Beijing road (bidirectional four-lane) and South Huaihai road (bidirectional six-lane) in Huai’an, Jiangsu Province. The historical traffic data of the road segments were collected through the connected vehicle environment facilities. The purpose of this study is to investigate the effect of adopting two HOV schemes (regular HOV scheme and reversible HOV carpooling scheme) on the urban arterial road under connected vehicle environment. VISSIM was used to simulate the proposed two HOV carpooling schemes at the mentioned road segment. The simulation results showed that the reversible HOV carpooling scheme could not only mitigate the traffic congestion caused by traffic tidal phenomenon but also improve the average speed and traffic volume of the urban arterial road segment, while the regular HOV scheme may exert a negative impact on the average speed and traffic volume on the urban arterial road segment.

SHORT TERM TRAFFIC FLOW PREDICTION IN HETEROGENEOUS CONDITION USING ARTIFICIAL NEURAL NETWORK

Transport ◽  
2013 ◽  
Vol 30 (4) ◽  
pp. 397-405 ◽  
Author(s):  
Kranti Kumar ◽  
Manoranjan Parida ◽  
Vinod Kumar Katiyar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Traffic Volume ◽  
Flow Data ◽  
Highway Traffic ◽  
Short Term ◽  
Artificial Neural ◽  
Input Variables

Traffic congestion is one of the main problems related to transportation in developed as well as developing countries. Traffic control systems are based on the idea to avoid traffic instabilities and to homogenize traffic flow in such a way that risk of accidents is minimized and traffic flow is maximized. There is a need to predict traffic flow data for advanced traffic management and traffic information systems, which aim to influence traveller behaviour, reducing traffic congestion and improving mobility. This study applies Artificial Neural Network for short term prediction of traffic volume using past traffic data. Besides traffic volume, speed and density, the model incorporates both time and the day of the week as input variables. Model has been validated using actual rural highway traffic flow data collected through field studies. Artificial Neural Network has produced good results in this study even though speeds of each category of vehicles were considered separately as input variables.

Discussion on Urban Road Traffic Congestion Algorithm for Automatically Determining

2014 ◽  
Vol 926-930 ◽  
pp. 3790-3793
Author(s):  
Yu Bo Dong
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Road Traffic ◽  
Traffic Signal Control ◽  
Transportation Engineering ◽  
Urban Road ◽  
Traffic Incident ◽  
Road Congestion ◽  
Road Traffic Congestion

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.

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