A Novel Spatio-Temporal Synchronization Method of Roadside Asynchronous MMW Radar-Camera for Sensor Fusion

Du, Yuchuan; Qin, Bohao; Zhao, Cong; Zhu, Yifan; Cao, Jin; Ji, Yuxiong

doi:10.1109/tits.2021.3119079

Cited by 104 publications

(58 citation statements)

References 19 publications

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“…By understanding the perception of road users concerning the facility improvements, these approaches, along with a survey analysis, can also improve work zone safety [107]. Various data collection methods can be incorporated into the proposed approach to obtain more variables [108,109]. In addition, for future studies, it is recommended to utilize other kinds of cross-validation techniques [110,111], machine learning methods [112][113][114][115][116][117][118][119], and optimization algorithms [120][121][122][123][124] to make a better decision about the interference of nonmotorized users and vehicle flow within a city and to develop models with high prediction accuracy in taking preventive measures for decreasing pedestrian accidents in urban environments.…”

Section: Discussionmentioning

confidence: 99%

Presentation of Machine Learning Approaches for Predicting the Severity of Accidents to Propose the Safety Solutions on Rural Roads

Habibzadeh

Ameri

Ziari

et al. 2022

Journal of Advanced Transportation

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The aim of the current research was to develop models to predict the severity of accidents on rural roads in Tehran province, Iran. In this regard, using accident data from 2017 to 2020, the machine learning algorithms, including multiple logistic regression, multilayer perceptron neural network (MLPNN), and radial basis function neural network (RBFNN) models, as well as statistical methods, including Kolmogorov–Smirnov test, Friedman test, and factor analysis, were implemented to determine the contributory factors in the severity of accidents. Thus, nine variables affecting the severity of accidents were considered in modeling, and then the effect of each variable was calculated. By comparing the results of artificial neural network (ANN) models and the Friedman test, it was indicated that the human factor had a remarkable effect on accident severity. In addition, both machine learning and statistical methods can be served as guidance for safety authorities to provide safety solutions, thereby leading to reducing accidents. Finally, the performances of ANN models were analyzed by other mathematical models built by MATLAB programming.

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Section: Discussionmentioning

confidence: 99%

Presentation of Machine Learning Approaches for Predicting the Severity of Accidents to Propose the Safety Solutions on Rural Roads

Habibzadeh

Ameri

Ziari

et al. 2022

Journal of Advanced Transportation

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“…The third part of the experimental results analyzes the topological values of roads by space syntax, which has been widely applied in the research fields of street planning, spatial pattern, route planning, street vitality, and so on (Hillier and Zhao, 1985;Liu et al, 2017;Chun et al, 2021;Guo and Cao, 2021;Liao et al, 2021). Among these research fields, the topological value of road mainly includes integration, total depth, connectivity, control, and choice Liu et al, 2021b;Li et al, 2021c;Du et al, 2021). Representing the accessibility of space, the topological value of roads can reflect the global depth of space and reflect the following variables, the difficulty, cost, time, and even energy of reaching a certain space (Hillier, 1996).…”

Section: Methodsmentioning

confidence: 99%

Using spatial syntax and GIS to identify spatial heterogeneity in the main urban area of Harbin, China

Sun

Meng

2022

Front. Earth Sci.

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The discourse about urban planning, urban design, urban management, and many other basic urban planning issues are related to identifying the spatial heterogeneity in the main urban area (MUA). Road networks are important infrastructure for social and economic development and also an important driving factor for urban land use. Therefore, the topological characteristics of the urban road network can be used as an important basis for the space of the division of the MUA. This study takes the MUA space in the Harbin Ring Expressway as the research object. The findings of this research establish a city model to identify the spatial heterogeneity, through a method of “Spatial essential attribute—Spatial autocorrelation analysis—Establishment of urban model.” In this model, spatial essential attributes are obtained from the analysis of space syntax. The spatial autocorrelation analysis is a method to calculate spatial clustering, and building the urban model is a kind of computer visualization method. Using this method, this research constructs a spatial heterogeneity model (SHM) of the urban built-up area in Harbin, which consists of three layers of spatial circles and two prominent regional structures. Then, it analyzes the spatial distribution of infrastructure and population agglomeration through nuclear density. We gain three conclusions: first, in addition to educational and greening facilities, the spatial distribution of infrastructure is correlated with the regional structure of SHM in Harbin’s MUA. Second, there are differences in the spatial attraction of population agglomeration between the different areas in the SHM of Harbin’s MUA. Finally, the new identification model of the SHM is scientific, but it has some advantages and demerits. Using spatial topological values to construct the SHM, it provides a new city model in urban spatial heterogeneity region recognition. Through the research results, the identification method of the SHM improves the traditional method which identifies urban heterogeneity areas according to people’s subjective wishes in urban planning. Therefore, the SHM helps relevant practitioners and scholars practice urban planning, urban analysis, urban protection, and urban management.

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“…Authors in [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] developed mathematical techniques to solve energy management problem in SG. In [7], Mixed Integer Linear Programming (MILP) is developed to solve energy management problem for PAR and cost minimization.…”

Section: Mathematical Techniquesmentioning

confidence: 99%

Optimal Demand-Side Management Using Flat Pricing Scheme in Smart Grid

Albogamy

Ashfaq

Hafeez³

et al. 2022

Processes

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This work proposes a framework to solve demand-side management (DSM) problem by systematically scheduling energy consumption using flat pricing scheme (FPS) in smart grid (SG). The framework includes microgrid with renewable energy sources (solar and wind), energy storage systems, electric vehicles (EVs), and building appliances like time flexible, power flexible, and base/critical appliances. For the proposed framework, we develop an ant colony optimization (ACO) algorithm, which efficiently schedules smart appliances, and EVs batteries charging/discharging with microgrid and without (W/O) microgrid under FPS to minimize energy cost, carbon emission, and peak to average ratio (PAR). An integrated technique of enhanced differential evolution (EDE) algorithm and artificial neural network (ANN) is devised to predict solar irradiance and wind speed for accurate microgrid energy estimation. To endorse the applicability of the proposed framework, simulations are conducted. Moreover, the proposed framework based on the ACO algorithm is compared to mixed-integer linear programming (MILP) and W/O scheduling energy management frameworks in terms of energy cost, carbon emission, and PAR. The developed ACO algorithm reduces energy cost, PAR, and carbon emission by 23.69%, 26.20%, and 15.35% in scenario I, and 25.09%, 31.45%, and 18.50% in scenario II, respectively, as compared to W/O scheduling case. The results affirm the applicability of the proposed framework in aspects of the desired objectives.

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A Novel Spatio-Temporal Synchronization Method of Roadside Asynchronous MMW Radar-Camera for Sensor Fusion

Cited by 104 publications

References 19 publications

Presentation of Machine Learning Approaches for Predicting the Severity of Accidents to Propose the Safety Solutions on Rural Roads

Presentation of Machine Learning Approaches for Predicting the Severity of Accidents to Propose the Safety Solutions on Rural Roads

Using spatial syntax and GIS to identify spatial heterogeneity in the main urban area of Harbin, China

Optimal Demand-Side Management Using Flat Pricing Scheme in Smart Grid

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