scholarly journals Road network partitioning method based on Canopy-Kmeans clustering algorithm

2020 ◽  
Vol 54 (2) ◽  
pp. 95-106 ◽  
Author(s):  
Xiaohui Lin ◽  
Jianmin Xu
Keyword(s):  
Real Time ◽  
Road Network ◽  
Clustering Algorithm ◽  
Average Density ◽  
Road Segment ◽  
Network Partitioning ◽  
Network Partition ◽  
Average Speed ◽  
The Road ◽  
Road Segments

With the increasing scope of traffic signal control, in order to improve the stability and flexibility of the traffic control system, it is necessary to rationally divide the road network according to the structure of the road network and the characteristics of traffic flow. However, road network partition can be regarded as a clustering process of the division of road segments with similar attributes, and thus, the clustering algorithm can be used to divide the sub-areas of road network, but when Kmeans clustering algorithm is used in road network partitioning, it is easy to fall into the local optimal solution. Therefore, we proposed a road network partitioning method based on the Canopy-Kmeans clustering algorithm based on the real-time data collected from the central longitude and latitude of a road segment, average speed of a road segment, and average density of a road segment. Moreover, a vehicle network simulation platform based on Vissim simulation software is constructed by taking the real-time collected data of central longitude and latitude, average speed and average density of road segments as sample data. Kmeans and Canopy-Kmeans algorithms are used to partition the platform road network. Finally, the quantitative evaluation method of road network partition based on macroscopic fundamental diagram is used to evaluate the results of road network partition, so as to determine the optimal road network partition algorithm. Results show that these two algorithms have divided the road network into four sub-areas, but the sections contained in each sub-area are slightly different. Determining the optimal algorithm on the surface is impossible. However, Canopy-Kmeans clustering algorithm is superior to Kmeans clustering algorithm based on the quantitative evaluation index (e.g. the sum of squares for error and the R-Square) of the results of the subareas. Canopy-Kmeans clustering algorithm can effectively partition the road network, thereby laying a foundation for the subsequent road network boundary control.

scholarly journals A High-Definition Road-Network Model for Self-Driving Vehicles

2018 ◽  
Vol 7 (11) ◽  
pp. 417 ◽  
Author(s):  
Ling Zheng ◽  
Bijun Li ◽  
Hongjuan Zhang ◽  
Yunxiao Shan ◽  
Jian Zhou
Keyword(s):  
Road Network ◽  
Principal Component ◽  
Automatic Generation ◽  
Road Segment ◽  
Automated Driving ◽  
High Definition ◽  
Topological Relations ◽  
Network Layer ◽  
The Road ◽  
Road Segments

High-definition (HD) maps have gained increasing attention in highly automated driving technology and show great significance for self-driving cars. An HD road network (HDRN) is one of the most important parts of an HD map. To date, there have been few studies focusing on road and road-segment extraction in the automatic generation of an HDRN. To improve the precision of an HDRN further and represent the topological relations between road segments and lanes better, in this paper, we propose an HDRN model (HDRNM) for a self-driving car. The HDRNM divides the HDRN into a road-segment network layer and a road-network layer. It includes road segments, attributes and geometric topological relations between lanes, as well as relations between road segments and lanes. We define the place in a road segment where the attribute changes as a linear event point. The road segment serves as a linear benchmark, and the linear event point from the road segment is mapped to its lanes via their relative positions to segment the lanes. Then, the HDRN is automatically generated from road centerlines collected by a mobile mapping vehicle through a multi-directional constraint principal component analysis method. Finally, an experiment proves the effectiveness of this HDRNM.

Travel Time Estimation on a Link without Real-Time Data by Correlated Links

2014 ◽  
Vol 931-932 ◽  
pp. 531-535
Author(s):  
Narong Intiruk ◽  
Sukree Sinthupinyo ◽  
Wasan Pattara-Atikom
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Nearest Neighbor ◽  
Time Estimation ◽  
Road Segment ◽  
K Nearest Neighbor ◽  
Time Data ◽  
Current Time ◽  
The Road ◽  
Road Segments

This paper presents a novel method to estimate travel time on a road segment using information from other road segments. This method is useful especially in the case that real-time traffic on such road segment is not available. The proposed method is based on the correlation between the road segment itself and the most related road segment. We measure the relation between road segments by dynamic time warping algorithm and apply the K-Nearest-Neighbor algorithm to select the best neighbor segment to estimate the travel time on the target road segment. We found that the best attributes set that can measure the correlation between road sections consists of location of the road segments, day of the week, and current time. The link correlation results can be used as reference data to determine the travel time on the roads that are related.

scholarly journals Towards a Rule-driven Approach for Updating Dynamic Road Segments

2012 ◽  
Vol 22 (6) ◽  
pp. 405-411
Author(s):  
Mohammad Reza Jelokhani-Niaraki ◽  
Ali Asghar Alesheikh ◽  
Abbas Alimohammadi ◽  
Abolghasem Sadeghi-Niaraki
Keyword(s):  
Data Model ◽  
Road Network ◽  
Road Maintenance ◽  
The Road ◽  
Dynamic Segmentation ◽  
Transportation Organization ◽  
Manual Tasks ◽  
The City ◽  
Transportation Applications ◽  
Road Segments

In recent years, the development of the GIS-T (Geographic Information System for Transportation) applications has gained much attention, providing the transportation planners and managers with in-depth knowledge to achieve better decisions. Needless to say, developing a successful GIS for transportation applications is highly dependent on the design of a well-structured data model. Dynamic segmentation (DS) data model is a popular one being used more and more for different GIS-T analyses, serving as a data model that splits linear features into new set of segments wherever its attributes change. In most cases, the sets of segments presenting a particular attribute change frequently. Transportation managers place great importance on having regular update and revision of segmented data to ensure correct and precise decisions are made. However, updating the segmented data manually is a difficult task and a time-consuming process to do, demanding an automatic approach. To alleviate this, the present study describes a rule-based method using topological concept to simply update road segments and replace the manual tasks that users are to carry out. The proposed approach was employed and implemented on real road network data of the City of Tehran provided by the Road Maintenance and Transportation Organization (RMTO) of Iran. The practical results demonstrated that the time, cost, human-type errors, and complexity involved in update tasks are all reduced. KEYWORDS: GIS-T, dynamic segmentation, segment, automatic update, change type, rule

Spatial Analysis Based Urban Road Network Partition Method for Macroscopic Evaluation

2011 ◽  
Vol 97-98 ◽  
pp. 512-517
Author(s):  
Wen Jie Zou ◽  
Jian Cheng Weng ◽  
Jian Rong ◽  
Wei Zhou
Keyword(s):  
Spatial Analysis ◽  
Road Network ◽  
Urban Road Network ◽  
Network Partition ◽  
Urban Road ◽  
The Road ◽  
Network Operation ◽  
Partition Method ◽  
Operation Status

In order to improve the reliability of urban road network operation evaluation, the road network regional Partition methods were launched in this paper. The geographic grid was introduced first, and a 4-level road network model was defined. Then, the spatial analysis based urban road network division method was proposed by analyzing the characteristics of road network operation. This method can reflect the influence between adjacent regional units, and improve the reliability of urban road network division. Finally, this research took a certain area in Beijing as a case study, and divided the road network as several regional units. Macroscopic evaluation result shows that it is effective for scientifically describing the road network operation status.

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.

Visualisation and Detection of Road Traffic Events Using Complex Event Processing

2018 ◽  
Author(s):  
Paulo Figueiras ◽  
Hugo Antunes ◽  
Guilherme Guerreiro ◽  
Ruben Costa ◽  
Ricardo Jardim-Gonçalves
Keyword(s):  
Real Time ◽  
Road Network ◽  
Road Traffic ◽  
Complex Event Processing ◽  
Advanced Technology ◽  
Transport Systems ◽  
Automatic Identification ◽  
Event Processing ◽  
Time Data ◽  
The Road

In the recent decades, we have witnessed an increase in the number of vehicles using the road infrastructure, resulting in an increased overload of the road network. To mitigate such problems, caused by the increasing number of vehicles and increasing the efficiency and safety of transport systems has been integrated applications of advanced technology, denominated Intelligent Transport Systems (ITS). However, one problem still unsolved in current road networks is the automatic identification of road events such as accidents or traffic jams, being inhibitor to efficient road management. In order to mitigate this problematic, this paper proposes the development of a technological platform able to detect anomalies (abnormal traffic events) to typical road network status and categorize such anomalies. The proposed work, adopts a complex event processing (CEP) engine able to monitor streams of events and detect specified patterns of events in real time. Data is collectively collected and analysed in real-time from loop sensors deployed in Slovenian highways and national roads, providing traffic flows. This prototype will work with a large number of data, being used to process all data, complex event processing tools. All the data used to validate the present study is based on the Slovenian road network. This work has been carried out in the context of the OPTIMUM Project, funded by the H2020 European Research Framework Program.

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.

The Mueller-Lyer Illusion Produced by Features in Maps

1996 ◽  
Vol 40 (23) ◽  
pp. 1242-1246
Author(s):  
William Schmidt ◽  
Douglas J. Gillan
Keyword(s):  
Present Experiment ◽  
Road Segment ◽  
Place Names ◽  
Line Segments ◽  
The Road ◽  
Lyer Illusion ◽  
Road Segments

Maps consist of lines converging onto line segments. These converging lines resemble elements of the Mueller-Lyer illusion (MLEs) which cause map readers to overestimate the length of a road segment (if the lines go outward from the end of the segment) or underestimate the length (if the lines go inward from the end of the segment) (Gillan, Schmidt, & Hanowski, 1996). The present experiment investigates whether a similar effect occurs when place names converge on a road segment. Subjects estimated road segments framed by outward-going MLEs made up of place names to be significantly longer than road segments framed by inward-going MLEs. The type of characters in the place names (English characters vs. symbols) and requiring subjects to locate the road segment by the names in the MLE had no effect on the degree of misestimation induced. The implications of these findings for a variety of displays are discussed.

A Safe-Region Approach to a Moving k-RNN Queries in a Directed Road Network

2017 ◽  
Vol 26 (05) ◽  
pp. 1750071 ◽  
Author(s):  
Kamil Zeberga ◽  
Rize Jin ◽  
Hyung-Ju Cho ◽  
Tae-Sun Chung
Keyword(s):  
Road Network ◽  
Nearest Neighbor ◽  
Exact Formula ◽  
Specific Situation ◽  
The Road ◽  
Query Answer ◽  
Safe Region ◽  
On The Road ◽  
Conventional Solution ◽  
Road Segments

In road networks, [Formula: see text]-range nearest neighbor ([Formula: see text]-RNN) queries locate the [Formula: see text]-closest neighbors for every point on the road segments, within a given query region defined by the user, based on the network distance. This is an important task because the user's location information may be inaccurate; furthermore, users may be unwilling to reveal their exact location for privacy reasons. Therefore, under this type of specific situation, the server returns candidate objects for every point on the road segments and the client evaluates and chooses exact [Formula: see text] nearest objects from the candidate objects. Evaluating the query results at each timestamp to keep the freshness of the query answer, while the query object is moving, will create significant computation burden for the client. We therefore propose an efficient approach called a safe-region-based approach (SRA) for computing a safe segment region and the safe exit points of a moving nearest neighbor (NN) query in a road network. SRA avoids evaluation of candidate answers returned by the location-based server since it will have high computation cost in the query side. Additionally, we applied SRA for a directed road network, where each road network has a particular orientation and the network distances are not symmetric. Our experimental results demonstrate that SRA significantly outperforms a conventional solution in terms of both computational and communication costs.

Lowering Bicycle Stress One Link at a Time: Where Should We Invest in Infrastructure?

2018 ◽  
Vol 2672 (36) ◽  
pp. 33-41 ◽  
Author(s):  
Sarah K. Moran ◽  
William Tsay ◽  
Sean Lawrence ◽  
Gregory R. Krykewycz
Keyword(s):  
Road Network ◽  
Regional Scale ◽  
Shortest Paths ◽  
Connectivity Analysis ◽  
Street Segment ◽  
The Road ◽  
Computational Optimization ◽  
Ranked List ◽  
Road Segments

This paper presents a new, regional-scale application of low-stress bicycle connectivity analysis. While prior network-based analyses have focused on the overall improvement in connectivity that could be achieved by implementing a package of projects from a comprehensive bike plan, the purpose of this project was to wholly evaluate potential improvements in connectivity through individual improvements at the street segment level. Using scripts and database tools, levels of traffic stress were assigned to the road network. Incorporating numerous computational optimization measures, shortest paths were calculated between millions of origin and destination pairs to identify the road segments that could most benefit low-stress connectivity. The resulting ranked list of links providing the greatest connectivity benefit allows planners to more efficiently prioritize locations for further investigation and analysis.

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