Analyzing pedestrian-car interweaving in Chinese old urban residential communities

2020 ◽  
Vol 34 (31) ◽  
pp. 2050299
Author(s):  
Yan Wang ◽  
Qun Chen
Keyword(s):  
Network Traffic ◽  
Road Network ◽  
Living Environment ◽  
Actual Situation ◽  
Community Network ◽  
Chinese Cities ◽  
Residential Community ◽  
The Road ◽  
Residential Communities ◽  
Road Segments

Pedestrian-car interweaving is a prominent problem in old residential communities in Chinese cities. To achieve a better pedestrian-car separation to create a safe and comfortable living environment in old residential communities, this paper investigated the mechanism of the flows of pedestrians and cars on a road network inside an old residential community. A method for calculating the flows of pedestrians and cars was proposed to identify the road segments or nodes where the pedestrian flows are interlaced or intersected with the vehicle flows. This method was applied to the estimation of the traffic in the Wangyuehu Community of Changsha City, China. The estimated distribution of community network traffic and pedestrian-car interweaving sites was consistent with the actual situation.

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

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.

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.

scholarly journals Continuous Anonymity with Users in the Same Direction in Road Networks

2018 ◽  
Vol 220 ◽  
pp. 10001
Author(s):  
Yu Lili ◽  
Zhang Lei ◽  
Su Xiaoguang ◽  
Li Jing ◽  
Zhang Xu ◽  
...  
Keyword(s):  
Euclidean Space ◽  
Privacy Protection ◽  
Road Network ◽  
Location Privacy ◽  
Road Networks ◽  
The Road ◽  
Shortest Distance ◽  
Location Privacy Protection ◽  
Road Segments

Compared with the Euclidean space, road network is restricted by its direction in traveling, velocity and some other attribute profiles. So the algorithms that designed for the Euclidean space are usually invalid and difficult to provide privacy protection services. In order to cope with this problem, we have proposed an algorithm to provide the service of collecting anonymous users that their directions in traveling similar with the initiator in the road networks. In this algorithm, the shortest distance between multiple road segments is calculated, and then utilizes the distance to select the user who has the same direction in traveling with the initiator. Consequently, the problem of the discrepancy of the anonymous users in the routing that invalidates the location privacy protection is solved. At last, we had compared this algorithm with other similar algorithms, and through the results of the comparison and the cause of this phenomenon, we have concluded that this algorithm is better not only in the level of privacy protection, but in the performance of execution efficiency.

scholarly journals Urban Road Network Emergency: An Integrative Vulnerability Identification Method

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Huaikun Xiang
Keyword(s):  
Network Traffic ◽  
Road Network ◽  
Road Networks ◽  
Likelihood Method ◽  
Survey Method ◽  
Urban Road Network ◽  
Urban Road ◽  
The Road ◽  
Identification Method ◽  
The Impact

The vulnerability of an urban road network is affected by many factors, such as internal road network layout, network structure strength, and external destructive events, which have great uncertainty and complexity. Thus, there is still no unified and definite vulnerability analysis scheme available to cities. This paper proposes an integrative vulnerability identification method for urban road networks, which mainly relates to the vulnerability connotation and characteristics analysis of urban road networks during emergency, and vulnerability comprehensive evaluation indices design based on urban road network connectivity, traffic efficiency and performance, and an empirical study on a vulnerability identification method of an urban road network. In the empirical case, a real road network and traffic operation data were used from Science and Technology Park of Shenzhen City, China. In the context of one certain emergency scenario, the stated preference survey method and maximum likelihood method are used to solve the road users’ random travel choice behavior parameters; subsequently, based on the traffic equilibrium distribution prediction, the traffic vulnerability identification methods of the road network in this region were verified before and after the emergency. The method presented here not only considers the impact of network topology changes on road network traffic function during emergency but also considers the impact of dynamic changes in road network traffic demand on vulnerability; therefore, it is closer to the actual distribution of urban road network traffic vulnerability.

scholarly journals Dynamic Spatiotemporal Causality Analysis for Network Traffic Flow Based on Transfer Entropy and Sliding Window Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Senyan Yang ◽  
Lianju Ning ◽  
Xilong Cai ◽  
Mingyu Liu
Keyword(s):  
Time Series ◽  
Traffic Flow ◽  
Network Traffic ◽  
Road Network ◽  
Time Series Data ◽  
Sliding Window ◽  
Transfer Entropy ◽  
Series Data ◽  
Window Approach ◽  
Road Segments

With the rapid development of sensor and communication technologies, a large amount of spatiotemporal traffic data has been accumulated, presenting the characteristics of big data. The potential information and regularity of traffic state evolution can be extracted from the huge traffic flow time series data and applied to intelligent transportation systems. This study proposes a dynamic spatiotemporal causality modeling approach to analyze traffic causal relationships for the large-scale road network. Transfer entropy algorithm is utilized to detect the spatiotemporal causality of network traffic states based on the extensive traffic time series data, which could measure the amount and direction of information transmission. A combination of Gaussian kernel density estimation and sliding window approach is proposed to calculate the transfer entropy and construct dynamic spatiotemporal causality graphs based on the causality significance test. The indexes of affected coefficient, influence coefficient, input degree, and output degree are defined to evaluate the causal interaction of traffic states among different road segments and identify the critical roads and potential bottlenecks of the existing road network. Experimental results based on real-world traffic sensor data indicate that the structures of traffic causality graphs are time-varying; the traffic cause-effect interaction among different road segments during the peak time is more significant than that during the nonpeak time; and the critical road segments can be identified, which are mainly located at the intersections of arterial roads, undertaking the convergence and dispersion of large traffic flows.

Sectional mapping of local roads in Spain

2008 ◽  
Vol 35 (11) ◽  
pp. 1200-1209
Author(s):  
Angel Ibeas ◽  
Hernan Gonzalo_Orden ◽  
Luigi Dell’Olio ◽  
Jose Luis Moura
Keyword(s):  
Decision Making ◽  
Road Network ◽  
Geographic Information ◽  
Decision Making Process ◽  
Northern Spain ◽  
The Road ◽  
Local Roads ◽  
Decision Making Processes ◽  
Low Volume ◽  
Road Segments

The management of any road network can be improved by gathering information about the different road segments that form it. Geographic information systems (GISs) can be used to map and manipulate the large amount of information collected. This helps managers in their analysis of the network and in the decision-making processes. This article explains the development and practical use of the latest mapping carried out on the local roads in the region of Cantabria in northern Spain. The aim of the current study was to perform a thorough analysis of the characteristics of each segment of the road network to update and restructure the existing mapping. A geographic information system (GIS) was used for consulting and analyzing the data obtained now and over previous years. Moreover, the ways this information could be used in the decision-making process were improved for a regional road network which has, in general, a low volume of traffic.

scholarly journals An elimination method for isolated meshes in a road network considering stroke edge feature

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0239828
Author(s):  
Chengming Li ◽  
Wei Wu ◽  
Pengda Wu ◽  
Jie Yin ◽  
Peipei Guo
Keyword(s):  
Road Network ◽  
Spatial Representation ◽  
Elimination Method ◽  
The Road ◽  
Road Map ◽  
Sample Data ◽  
Mesh Density ◽  
The Difference ◽  
Road Segments ◽  
Edge Feature

The road network is the skeletal element of topographic maps at different scales. In general, urban roads are connected by road segments, thus forming a series of road meshes. Mesh elimination is a key step in evaluating the importance of roads during the road network data management and a prerequisite to the implementation of continuous multiscale spatial representation of road networks. The existing mesh-based method is an advanced road elimination method whereby meshes with the largest density are sequentially selected and road segments with the least importance in each mesh are eliminated. However, the road connectivity and integrity may be destroyed in specific areas by this method because some eliminated road segments could be located in the middle of road strokes. Therefore, this paper proposed an elimination method for isolated meshes in a road network considering stroke edge feature. First, small meshes were identified by using mesh density thresholds, which can be obtained by the sample data statistical algorithm. Thereafter, the small meshes related to the edge segments of road strokes were taken out and defined as stroke edge meshes, and the remaining small meshes were defined as stroke non-edge meshes. Second, by computing the mesh density of all stroke edge meshes, the mesh with the largest density was selected as the starting mesh, and the least important edge segment in the mesh was eliminated. The difference between the existing mesh-based method and the proposed method is that the starting mesh is a stroke edge mesh, not any given small mesh, and the eliminated segment is just only one of edge segments of strokes not chosen from among all segments. Third, mesh elimination was implemented by iteratively processing the stroke edge meshes with the largest mesh density until all of them were eliminated and their mesh density exceeded the threshold. The stroke non-edge meshes were directly preserved. Finally, a 1:10,000 topographic road map of an area in Jiangsu Province of China was used for validation. The experimental results show that for all stroke non-edge meshes and 23% of the stroke edge meshes, compared to the mesh-based method, the road stroke connectivity and integrity of road strokes were better preserved by the proposed method, and the remaining 77% of the elimination results for the stroke edge meshes were the same under the two methods.

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