Link Counts Research Articles

Traffic count estimation using crowd-sourced trajectory data in the absence of dedicated infrastructure

Traffic count (or link count) data represents the cumulative traffic in the lanes between two consecutive signalised intersections. Typically, dedicated infrastructure-based sensors are required for link count data collection. The lack of adequate data collection infrastructure leads to lack of link count data for numerous cities, particularly those in low- and middle-income countries. Here, we address the research problem of link count estimation using crowd-sourced trajectory data to reduce the reliance on any dedicated infrastructure. A stochastic queue discharge model is developed to estimate link counts at signalised intersections taking into account the sparsity and low penetration rate (i.e., the percentage of vehicles with known trajectory) brought on by crowdsourcing. The issue of poor penetration rate is tackled by constructing synthetic trajectories entirely from known trajectories. The proposed model further provides a methodology for estimating the delay resulting from the start-up loss time of the vehicles in the queue under unknown traffic conditions. The proposed model is implemented and validated with real-world data at a signalised intersection in Kolkata, India. Validation results demonstrate that the model can estimate link count with an average accuracy score of 82% with a very low penetration rate (not in the city, but at the intersection) of 5.09% in unknown traffic conditions, which is yet to be accomplished in the current state-of-the-art.

Machine Learning based Data Sensing Device Network with Optimized Group Formation and Group Head Selection

Sensor devices for data sensing (referred to as DSDs) are used in use cases such as border control and vehicle tracking. The architecture of the Data Sensing Device Network (DSDN) is established by integrating numerous DSDs across a given region, forming multiple groups. Within each group, a specific DSD is designated to facilitate communication between independent groups. The multi attribute values are captured and these attribute values effect the selection of head DSDs. For each of DSD this value ranges between 0.1 to 1. The DSD which has the highest value of range will be treated as Group Head in LEACH. The attributes are namely distance, battery level for each DSD. From the source DSD to destination DSD the link formation will happen end to end by making use of DSDs and base station, generally the end-to-end link communication has larger hops. This will have a ripple effect on battery level for DSDs and can cause reduced lifetime. The Energy based LEACH is modified on top of LEACH by computing the battery level for DSDs and picking DSD with highest battery level. The Energy based LEACH will have two DSDs in each group acting like head DSDs. Machine Learning Data Sensing Device Network (ML-DSDN) is proposed which will first create group DSDNs based on k means machine learning algorithm. ML-DSDN will find the head group DSD based on combination of random forest and SVM algorithm with set theory. The comparison is done of ML-DSDN with respect to ELEACH and LEACH method and it is proved that ML-DSDN performs better with respect to delay, link count, energy consumption, alive DSD count, dead DSD count, lifetime ratio, routing overhead.

A Novel Incentive Routing Protocol with Virtual Projection for Mobile Packet Forwarding Nodes in Wireless Sensor Networks

The size of the Packet Forwarding Nodes (PFNs) is becoming very small as the technology advances in Wireless Sensor Networks (WSNs). The node has an additional parameter with low energy levels. The PFNs are distributed in a square cross-sectional area with each node acting as a Sensing Point (SP) that can be used for various kinds of applications like temperature, atmospheric humidity, acoustic, and pressure measurements. The packet is divided into several fragments where each fragment is considered as fixed or variable length. Each of these packets is sent over multiple PFNs toward the data center using PFNs. The selection of PFNs in the path is picked based on the trust level. In the network even special PFNs are placed which are responsible to deliver the packets toward the data center without losing the data during the transmission. The selection of special PFNs is done by computing the meeting probability, remaining energy computation, computation of data weight, and security value computation. The proposed Incentive Routing Protocol with Virtual Projection (IRPVP) method is compared with the conventional approaches concerning the parameters like delay, link count, resource energy, healthy PFNs, non-healthy PFNs, health ratio computation, remaining energy, control to data ratio, and balancing factor. The simulation outcomes show that the performance of the proposed IRPVP algorithm is better than the other conventional algorithms.

A Novel Modified Energy and Throughput Efficient LOYAL UN-Supervised LEACH Method for Wireless Sensor Networks

Data Sensing Devices (DSD’s) have gained lot of traction for various use cases like border control, vehicle tracking. Data Sensing device network (DSDN) is shaped with the aid of combining lot of DSD’s across a random area. Like this multiple groups are formed. In each of the group the specific DSD is elected which is responsible for communication between two independent groups. Each of the group head has multiple attributes with first attribute based on distance, the second attribute based on remaining energy. These attributes will be input for the group head selection based on machine learning, The entire DSD’s inside a group are classified into HIGH, MEDIUM and LOW. The first priority will be given to HIGH followed by others for the primary group head selection. LEACH is a classical method used for transmission of chunks to the control center in a DSDN network. The selection of head DSD by LEACH will happen by making use of the random selection of DSD in each group using random probability selection mechanism. During the data chunk deliver the scanning process will happen from the initiator DSD to head DSD and from there the link is established with the base station (BS), the BS will then scan each group until the destination DSD is reached. The selection of head DSD by LEACH causes more holes in the DSDN because there are chances that the non-performer DSD can become a head DSD. Secondly for the transmission of chunks there is lot of back-and-forth propagation between the BS and the normal DSDs which reduces the battery level of the DSD by a large amount. The Energy based LEACH is modified on top of LEACH by measuring the energy of the DSDs and then selecting the group heads but suffers from multiple group head maintenance as well as more number of links. The proposed method will improve this by reducing the links used for end-to-end communication. In the proposed system the communication will happen based on initiator DSD, primary DSDs in different groups and then destination DSD which will avoid overhead compared to existing methods namely E-LEACH and LEACH. The proposed method is compared with LEACH and E-LEACH with respect to time taken, link count, energy consumption, residual energy measure, lifetime and overhead.

Traffic Count Estimation at Basis Links Without Path Flow and Historic Data

Traffic counts (or link counts) are defined as cumulative traffic in the lanes between two consecutive intersections on a road network. Established methods of link count estimation assume the availability of count data at a set of basis links: a minimum subset of all the links of a network that still allow complete network traffic count estimation. If traffic count data are missing even at some basis links, current research must introduce additional assumptions, on path flow or historical data, to compensate. In this research, we present an approach to estimate the missing basis link count without the need for historical data or path-flow information, thereby overcoming the limitations of state-of-the-art estimation approaches. We develop a stochastic method using a canonical correlation analysis-based constrained minimization problem for estimation purposes. The proposed method has been validated with real-world link count data collected in Melbourne, Australia, between <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2016$</tex-math> </inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2019$</tex-math> </inline-formula> . The validation results indicate that we can achieve an accuracy of up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$90\%$</tex-math> </inline-formula> in the real world, despite the unknown traffic patterns of the estimation period. Depending on the time of day, the modelling strategy selected, and the consistency of input data available at the road intersection, the estimation accuracy varies. The proposed methodology is useful when there is a general shortage of data since there is inadequate infrastructure for data collection in many major cities around the world.

Integrating link count data for enhanced estimation of deterrence functions: A case study of short-term bicycle network interventions

Owing to the lack of demand-related data from interim timespans between consecutive household surveys, it is difficult to assess short-term interventions in the bicycling network and their impact on potential accessibility. In this study, we propose a novel method that incorporates easier and cheaper demand-related data for estimating the parameters of deterrence functions of gravity-based distribution models. In particular, an origin-destination (OD) matrix-updating procedure that uses bicycle count data was incorporated into a two-step line-search algorithm for estimating a two-parameter deterrence function. The method was applied to a case study in the city of São Paulo, where potential accessibility was calculated during the years 2014–2017, which was a period interim to the two most recent household surveys. Three different models were used to estimate the generalized costs of the network links. This method proved useful for capturing accessibility variations over short timespans. Results show how demand is affected by the bicycling network and provide insights into how short-term assessments can benefit decision-making such as providing well-connected and continuous bicycling infrastructure positively influences accessibility. However, they fail as a unique solution apart from land-use policies. Finally, our proposed method can become a valuable tool for decision-making from an accessibility perspective in the short term.

A hybrid modelling framework for the estimation of dynamic origin–destination flows

The dynamic origin–destination flow estimation (DODE) problem requires scalable methods for large scale traffic networks and consistent techniques for capturing both uncongested and congested traffic conditions. Despite numerous efforts on incorporating multifold data sources and developing manifold mathematical models, the DODE problem remains a challenging problem in terms of both scalability and consistency. To fill this gap, we propose a novel hybrid DODE framework that integrates region-level (macro) and centroid-level (micro) traffic dynamics. The region-level traffic flows are described by the macroscopic fundamental diagram, while the centroid-level traffic flows are represented by the linear mapping of origin–destination flows onto link counts. This hybrid approach enables us to (i) incorporate region-level traffic measures into the problem, addressing scalability issues arising in large scale traffic networks, and (ii) capture non-linear behaviour of traffic in the regional context, enhancing consistency of the estimation results with respect to traffic conditions. The proposed methodology is experimented in a large-scale traffic network, which is benchmarked for DODE problems. The results indicate an outstanding performance of the hybrid DODE particularly in congested traffic conditions and highlight the effectiveness of aggregated (regional) traffic models in enhancing DODE methods with minimal computational burden.

Iterative Backpropagation Method for Efficient Gradient Estimation in Bilevel Network Equilibrium Optimization Problems

Network optimization or network design with an embedded traffic assignment (TA) to model user equilibrium principle, sometimes expressed as bilevel problems or mathematical programs with equilibrium constraints (MPEC), is at the heart of transportation planning and operations. For applications to large-scale multimodal networks with high dimensional decision variables, the problem is nontrivial, to say the least. General-purpose algorithms and problem-specific bilevel formulations have been proposed in the past to solve small problems for demonstration purposes. Research gap, however, exists in developing efficient solution methods for large-scale problems in both static and dynamic contexts. This paper proposes an efficient gradient estimation method called Iterative Backpropagation (IB) for network optimization problems with an embedded static TA model. IB exploits the iterative structure of the TA solution procedure and simultaneously calculates the gradients while the TA process converges. IB does not require any additional function evaluation and consequently scales very well with higher dimensions. We apply the proposed approach to origin-destination (OD) estimation, an MPEC problem, of the Hong Kong multimodal network with 49,806 decision variables, 8,797 nodes, 18,207 links, 2,684 transit routes, and 165,509 OD pairs. The calibrated model performs well in matching the link counts. Specifically, the IB-gradient based optimization technique reduces the link volume squared error by 98%, mean absolute percentage error (MAPE) from 95.29% to 21.23%, and the average GEH statistics from 24.18 to 6.09 compared with the noncalibrated case. The framework, even though applied to OD estimation in this paper, is applicable to a wide variety of optimization problems with an embedded TA model, opening up an efficient way to solve large-scale MPEC or bilevel problems. Funding: The study is supported by IVADO Postdoctoral Fellowship scheme 2021, HSBC 150th Anniversary Charity Programme HKBF17RG01, National Science Foundation of China (71890970, 71890974), General Research Fund (16212819, 16207920) of the HKSAR Government, and the Hong Kong PhD Fellowship.

Exploring the Potential of Microgrids in the Effective Utilisation of Renewable Energy: A Comprehensive Analysis of Evolving Themes and Future Priorities Using Main Path Analysis

Microgrids are energy systems that can operate independently or in conjunction with the main electricity grid. Their purpose is to link different energy sources, enhance customer participation in energy markets, and improve energy system efficiency and flexibility. However, regulatory, technical, and financial obstacles hinder their deployment. To comprehend the current state of the field, this study utilized citation network analysis (CNA) methodology to examine over 1500 scholarly publications on microgrid research and development (R&amp;D). The study employed modularity-based clustering analysis, which identified seven distinct research clusters, each related to a specific area of study. Cluster 1, focused on control strategies for microgrids, had the highest proportion of publications (23%) and the maximum citation link count (151), while Cluster 4, which examined microgrid stability, had the lowest proportion of papers (10%). On average, each publication within each cluster had four citation links. The citation network of microgrid research was partitioned using cluster analysis, which aided in identifying the main evolutionary paths of each subfield. This allowed for the precise tracing of their evolution, ultimately pinpointing emerging fronts and challenges. The identification of key pathways led to the discovery of significant studies and emerging patterns, highlighting research priorities in the field of microgrids. The study also revealed several research gaps and concerns, such as the need for further investigation into technical and economic feasibility, legislation, and standardization of microgrid technology. Overall, this study provides a comprehensive understanding of the evolution of microgrid research and identifies potential directions for future research.

A New Framework for Regional Traffic Volumes Estimation with Large-Scale Connected Vehicle Data and Deep Learning Method

Connected vehicle (CV) data in this paper refer to the in-vehicle telematic data, including trajectories and driving events (e.g., hard braking) collected by vehicle manufacturers when vehicles are moving. Recently manufactured vehicles are equipped with cellular modems and Internet of Things (IoT) devices to collect vehicle data. Such data, after removing personal information, are being redistributed to third-party organizations. Compared to other probe vehicle data, the CV data has a higher penetration rate, ubiquitous coverage, and almost lane-level positioning accuracy. These features pave the road for novel transportation applications in transportation planning and traffic operations. In this paper, we represent a novel framework to estimate the regional link volumes based on the CV data and a deep neural network (DNN) model. The training data are generated according to the link volumes (targeted model output) and the corresponding CV counts (input features) at the same locations. The DNN model’s performance was compared with other estimation methods like linear regression and random forest and showed superior performance. The trained DNN model takes ubiquitous CV counts from other locations to estimate the corresponding link volumes. As a case study, the proposed DNN model was trained with a large training data set derived from CV data and time-dependent link counts collected at over 1,200 locations on freeways in the Dallas Fort Worth, Texas, area. The results reveal good accuracy and robustness.

Are link-based and citation-based journal metrics correlated? An Open Access megapublisher case study

Abstract The current value of link counts as supplementary measures of the formal quality and impact of journals is analyzed, considering an open access megapublisher (MDPI) as a case study. We analyzed 352 journals through 21 citation-based and link-based journal-level indicators, using Scopus (523,935 publications) and Majestic (567,900 links) as data sources. Given the statistically significant strong positive Spearman correlations achieved, it is concluded that link-based indicators mainly reflect the quality (indexed in Scopus), size (publication output), and impact (citations received) of MDPI’s journals. In addition, link data are significantly greater for those MDPI journals covering many subjects (generalist journals). However, nonstatistically significant differences are found between subject categories, which can be partially attributed to the “series title profile” effect of MDPI. Further research is necessary to test whether link-based indicators can be used as informative measures of journals’ current research impact beyond the specific characteristics of MDPI.

On Heterogenous Sampling Rates in Origin–Destination Matrix Estimation Based on Trajectory Data and Link Counts

Being a fundamental metric of the transportation network, the origin–destination (O-D) flow matrix is a critical input for various transportation models and studies. This paper deals with the estimation of an O-D matrix of trip flows based on two kinds of data: probe trajectory data and local traffic counts. A Bayesian assignment framework is developed for demonstrating the relationship between the link probe sampling rates and the fractional contributions from the sampling rates on different O-D pairs. The unknown O-D matrix is estimated by applying cross entropy minimization using a prior matrix from the probe trajectories, along with the Bayesian assignment rules on link sample rates as the constraints. The methodology was applied using floating car data and camera link flow counts for a numerical experiment. The results show that the method can achieve a robust estimation of O-D matrices, even using different prior matrices. The issue of the heterogeneous sampling rates can be well addressed with link count constraints, effectively correcting the unknown bias in the probe sampling. The case study using real data also proves the feasibility of mining observed trajectory data to obtain the assignment fractions and estimate the O-D matrix inversely, avoiding the conventional sophisticated process of traffic assignment modeling.

High Analytics Information Technological Routes: a patent network analysis

Knowledge-intensive significantly contribute to technological development. This paper aims to explore technological routes (TR) on high analytics information (HAI) technologies for sake of technological forecasting through social network analysis (SNA) in a patent database from 2001 to 2020. Applying search path link count (SPLC) algorithm, this study provides five different TR in various business sectors. This study support decision makers to find additional core of technologies for their innovation strategies and help researchers in identifying HAI technologies that may still emerge in different industries support strategic R&amp;D decisions about prioritizing investments, identifying partnerships to innovate, and collaborating in public policies based on promoting new HAI technologies development.

Open access digital repositories of agricultural sciences in Africa: a webometric study

The study examines different characteristics of link analysis and visibility of websites of agricultural digital repositories in Africa. The different link structures and the presence of web through different indicators like Internet access, web pages and link count are also highlighted. This study has used popular search engines Google) to analyse and measure the web presence of African agricultural repositories. An attempt has also been made to find out the correlation among the WIF and WISER indicators of selected agricultural repositories in Africa. The result shows that KARI e-repository-(Kenya Agricultural Research Institute) occupies the first place with 102467.5325 SWIF among 37 agricultural repositories in Africa. Again, it ranks 23rd position with 77 Web Pages and 02 In-link Web Pages and 0.025974026 RWIF. The findings of this study may guide webmasters and library professionals to identify the web presence of repositories and also help them to compare the repository websites of agricultural sciences in Africa by their WIF and WISER Rank.

Mapping technological innovation dynamics in artificial intelligence domains: Evidence from a global patent analysis.

Artificial intelligence (AI) is emerging as a technology at the center of many political, economic, and societal debates. This paper formulates a new AI patent search strategy and applies this to provide a landscape analysis of AI innovation dynamics and technology evolution. The paper uses patent analyses, network analyses, and source path link count algorithms to examine AI spatial and temporal trends, cooperation features, cross-organization knowledge flow and technological routes. Results indicate a growing yet concentrated, non-collaborative and multi-path development and protection profile for AI patenting, with cross-organization knowledge flows based mainly on interorganizational knowledge citation links.

Influence difference main path analysis: Evidence from DNA and blockchain domain citation networks

• A main path model considering influence difference is proposed based on PageRank. • Several traversal weights are designed in this paper named SPX-PR. • Both cases are adopted to verify the effectiveness of the proposed algorithms. • These algorithms can reveal the evolutionary path appropriately. • They are more powerful in distinguish the citations in the network. Main path analysis is a useful tool to form the backbone of a citation network by linking important connections, which has been widely used to track the knowledge diffusion paths in a specific domain. Contrary to the traditional assumption that all citations in the citation network are treated equally, this paper proposes the influence difference main path analysis model by distinguishing citations based on the prestige of citing papers. Three algorithms named search path count with PageRank (SPC-PR), search path link count with PageRank (SPLC-PR) and search path node pair with PageRank (SPNP-PR) are devised to weight the citation network in this paper. Finally, two cases, the DNA citation network and a large-scale citation network related to the blockchain domain, are investigated to examine the effectiveness of the proposed algorithms. The results show that the proposed model can not only uncover the evolutionary process appropriately, but also can effectively distinguish the citations in the network by taking the influence difference into account. This study enriches the methodology research of main path analysis and provides the scholars with practical reference for the further development of main path analysis.

A Bayesian Method for Dynamic Origin-Destination Demand Estimation Synthesizing Multiple Sources of Data.

In this paper a Bayesian method is proposed to estimate dynamic origin–destination (O–D) demand. The proposed method can synthesize multiple sources of data collected by various sensors, including link counts, turning movements at intersections, flows, and travel times on partial paths. Time-dependent demand for each O–D pair at each departure time is assumed to satisfy the normal distribution. The connections among multiple sources of field data and O–D demands for all departure times are established by their variance-covariance matrices. Given the prior distribution of dynamic O–D demands, the posterior distribution is developed by updating the traffic count information. Then, based on the posterior distribution, both point estimation and the corresponding confidence intervals of O–D demand variables are estimated. Further, a stepwise algorithm that can avoid matrix inversion, in which traffic counts are updated one by one, is proposed. Finally, a numerical example is conducted on Nguyen–Dupuis network to demonstrate the effectiveness of the proposed Bayesian method and solution algorithm. Results show that the total O–D variance is decreasing with each added traffic count, implying that updating traffic counts reduces O–D demand uncertainty. Using the proposed method, both total error and source-specific errors between estimated and observed traffic counts decrease by iteration. Specifically, using 52 multiple sources of traffic counts, the relative errors of almost 50% traffic counts are less than 5%, the relative errors of 85% traffic counts are less than 10%, the total error between the estimated and “true” O–D demands is relatively small, and the O–D demand estimation accuracy can be improved by using more traffic counts. It concludes that the proposed Bayesian method can effectively synthesize multiple sources of data and estimate dynamic O–D demands with fine accuracy.

Stochastic programming approach for static origin–destination matrix reconstruction problem

We propose a stochastic programming approach for a static origin–destination (OD) reconstruction problem. We focus on the reconstruction of route flows such that the likelihood function of route flows is maximized. The route volumes are assumed to follow exponential families that are known or estimated in advance. The consideration of the joint distribution function of route flows eliminates the route selection from the model, as the route choice patterns are embedded in the distribution of the route flows. We assume that additional information regarding the traffic counts (i.e., node counts, link counts, and turn counts) is available.Finally, solution methodologies for different stochastic programmings are proposed: barrier method and Primal-dual interior point method for Quadratic Programming and Convex Programming respectively. We compared the proposed stochastic models with the entropy approach. Experimental results indicate that the inclusion of traffic-count information in the stochastic model significantly improves the accuracy of OD reconstruction if we can predict the correct distribution of route flows. Meanwhile the entropy approach requires the inclusion of the additional information on the true volumes of route flows to achieve a similar level of performance. We apply the proposed algorithm to the bus transit system of Seoul, Korea using bus-card data. Compared with the real OD volumes, the reconstruction is fairly accurate.

A Novel Routing and Hybrid Based Clustering Scheme in vehicular adhoc networks

The intention of this research is to design a new Novel Routing and Hybrid based Clustering Scheme (NRHCS) for VANETs. The vehicle link counts, and vehicle displacement is employed under various concepts like cluster formation, adding and deleting cluster, cluster head election and re-clustering are carried out. During cluster arrangement, the vehicles with comparatively improved extent of linkage preliminarily arrange as support which is chosen as the head. The management then takes part in cluster head selection and effective re-cluster with the aid of combined comparative displacement of vehicles using cluster head. The proposed technique processes the information dissemination in the VANET necessitate smart and vibrant decisions centered on the contexts to operate on dense and sparse instances to ensure that information transfer is possible for motor flexibility.

Evaluation of Smart Library Portal Website Based on Link Analysis

[Purpose/Significance]Library Portal, as the core of smart library construction, is the key window of providing online services. While how to construct a high-quality and high-effective library portal is the question that current smart library needs to solve urgently. [Methods/Proceeding] With the support of Link Analysis, this paper analyzes each three smart library portal models of two major Chinese third-party companies on Smart Library Portal Research and Development, through selecting the following seven website influencing factors: total number of the web pages, internal web link counts, external web link counts, External WIF, Baidu weight, Sogou weight and PR Value. Then the values of these seven influencing factors are synthetically sorted and sequenced based on Grey Relation Analysis. Thereafter, construction models of the two companies’ smart library portal are analyzed and evaluated. [Consequence/Conclusion] The smart library portal models in the third-party companies have higher comprehensive quality and clear advantages. Then when smart library portal website is constructed, model to cooperate with the third-party company to develop solutions is encouraged. At the same time, to strengthen the input of human resources and the funding to construct a high-quality and most-effective library portal.