Urban Road Research Articles

3D Object Detection under Urban Road Traffic Scenarios Based on Dual-Layer Voxel Features Fusion Augmentation.

To enhance the accuracy of detecting objects in front of intelligent vehicles in urban road scenarios, this paper proposes a dual-layer voxel feature fusion augmentation network (DL-VFFA). It aims to address the issue of objects misrecognition caused by local occlusion or limited field of view for targets. The network employs a point cloud voxelization architecture, utilizing the Mahalanobis distance to associate similar point clouds within neighborhood voxel units. It integrates local and global information through weight sharing to extract boundary point information within each voxel unit. The relative position encoding of voxel features is computed using an improved attention Gaussian deviation matrix in point cloud space to focus on the relative positions of different voxel sequences within channels. During the fusion of point cloud and image features, learnable weight parameters are designed to decouple fine-grained regions, enabling two-layer feature fusion from voxel to voxel and from point cloud to image. Extensive experiments on the KITTI dataset demonstrate the significant performance of DL-VFFA. Compared to the baseline network Second, DL-VFFA performs better in medium- and high-difficulty scenarios. Furthermore, compared to the voxel fusion module in MVX-Net, the voxel feature fusion results in this paper are more accurate, effectively capturing fine-grained object features post-voxelization. Through ablative experiments, we conducted in-depth analyses of the three voxel fusion modules in DL-VFFA to enhance the performance of the baseline detector and achieved superior results.

Shallow defects identification for urban roads using interpretable dynamic broad network

Deep learning is extensively utilised in transport geotechnical engineering. However, deep architectures have large computational costs and update times, while failing to understand decisions. To this regard, we propose an interpretable dynamic broad network combined with ground-penetrating radar for internal defect identification in roadbeds. The method is more suitable for feature characterisation of two-dimensional data and satisfies incremental updates. The test results indicated that the proposed method has an average recognition accuracy of 0.9124 for the four types of internal defects in roadbeds. Compared to the other four classical machine learning methods, it balances training efficiency and recognition accuracy. Robustness analysis results demonstrated that the method is noise-resistant. However, comprehending the recognition results of intelligent algorithms is a key topic. Local interpretation approach is introduced to quantify the feature importance that affects the decision of the model. Based on the feature importance calculation, it is possible to distinguish between positive and negative regions in one sample that influence the decision of the detection model. These interpretative analyses can assist us in better understanding the reasons for decisions generated by the detection model that provide technical support for subsequent enhancements.

Analysis of Factors Influencing Driver Yielding Behavior at Midblock Crosswalks on Urban Arterial Roads in Thailand

Globally, road traffic collisions cause over a million deaths annually, with pedestrians accounting for 23%. In developing countries, most pedestrian deaths occur on urban arterial roads, particularly at midblock crossings. This study analyzes the factors influencing driver yielding behavior at midblock crosswalks on urban arterial roads in Thailand. This study analyzed the factors influencing driver yielding behavior at the midblock crosswalk before and after the upgrade from a zebra crossing (C1) to a smart pedestrian crossing (C2), which is a smart traffic signal detecting and controlling pedestrians and vehicles entering the crosswalk. Video-based observations were used to assess driver yielding behavior, with multinomial logistic regression applied to develop driver yielding behavior models. The results revealed that the chances of a driver yielding at C2 were higher than at C1, and the yielding rate increased by 74%. The models indicate that the number and width of traffic lanes, width and length of crosswalks, vulnerable group, number of pedestrians, pedestrian crossing time, number of vehicles, vehicle speed, headway, post-encroachment time between a vehicle and pedestrian, and roadside parking are the significant factors influencing yielding behavior. These findings propose measures to set proper crosswalk improvements (e.g., curb extensions), speed reduction measures, enforcement (e.g., parking restrictions), public awareness campaigns, and education initiatives.

Molecular characterization of Cryptosporidium spp., Giardia spp. and Enterocytozoon bieneusi in eleven wild rodent species in China: Common distribution, extensive genetic diversity and high zoonotic potential

Cryptosporidium spp., Giardia spp. and Enterocytozoon bieneusi are common zoonotic pathogens in humans and animals. Although rodents are important parts of the ecosystem and common hosts for these pathogens, little is known of the distribution, genetic diversity and zoonotic potential of these pathogens in wild rodents. A total of 442 fecal samples were collected from eleven wild rodent species in three provinces of China, and analyzed for these pathogens by PCR and DNA sequencing. The infection rates of Cryptosporidium spp., Giardia spp. and E. bieneusi were 19.9% (88/442), 19.8% (75/378) and 12.2% (54/442), respectively. Altogether, 23 known Cryptosporidium species/genotypes were identified and their distribution varied among different sampling locations or rodent species. Subtyping of the zoonotic Cryptosporidium species identified two novel subtype families XVe and XVf in C. viatorum, the subtype family XIIh and a novel subtype family XIIj in C. ubiquitum, and the subtype family IId in C. parvum. Three Giardia species were identified, including G. microti (n = 57), G. muris (n = 15) and G. duodenalis (n = 3), with G. duodenalis assemblages A and G identified in brown rats in urban areas of Guangdong. In addition, 13 E. bieneusi genotypes including eight known and five novel ones were identified, belonging to Groups 1, 2, 10, 14 and 15. Within nine genotypes in the zoonotic Group 1, common human-pathogenic genotypes D, Type IV, PigEbITS7 and Peru8 were detected only in brown rats and Lesser rice-field rats in urban areas of Guangdong. Apparent host adaptation and geographical differences were observed among Cryptosporidium spp., Giardia spp. and E. bieneusi genotypes in wild rodents in the present study. Furthermore, the zoonotic Cryptosporidium species and E. bieneusi genotypes commonly found here suggest a high zoonotic potential of these pathogens in wild rodents, especially in brown rats in urban areas. Hygiene and One Health measures should be implemented in urban streets and food stores to reduce the possible direct and indirect transmission of these rodent-related pathogens.

Development and Evaluation of Vibration Canceling System Utilizing Macro-Fiber Composites (MFCs) and Long Short-Term Memory (LSTM) Vibration Prediction AI Algorithms for Road Driving Vibrations.

This study developed an innovative active vibration canceling (AVC) system designed to mitigate non-periodic vibrations during road driving to enhance passenger comfort. The macro-fiber composite (MFC) used in the system is a smart material that is flexible, soft, lightweight, and applicable in many fields as a dual-purpose sensor and actuator. The target vibrations are road vibration data that were collected while driving on standard urban (Seoul) and highway roads at 40 km/s. To predict and cancel the target vibration accurately before passing it, we modeled the vibration prediction algorithm using a long short-term memory recurrent neural network (LSTM RNN). We regenerated vibrations on Seoul and highway roads at 40 km/s using MFCs and measured the displacements of the measured, predicted, and AVC vibrations of each road condition. To evaluate the vibration, we computed the root mean squared error (RMSE) and compared standard deviation (SD) values. The accuracies of LSTM RNN vibration prediction algorithms are 97.27% and 96.36% on Seoul roads and highway roads, respectively, at 40 km/s. Although the vibration ratio compared with the AVC results are different, there was no difference between the values of the AVC vibrations. According to a previous study and the principle of the AVC system, the target vibrations decrease by canceling the inverse vibration of the MFC actuator.

GreenR: An open-source framework for quantifying urban greenness

How do we quantify the levels of greenness within urban street networks? Numerous attempts to quantify this factor have been made through survey methodologies, remote sensing data, and street view imagery. The results are promising, but are often limited by scalability constraints, including limited data availability, high requirements of computational power, and validation challenges. This study introduces a comprehensive framework for urban greenness assessment, leveraging open-source data to overcome these limitations. Central to this framework is the development of the greenR R package and an accompanying Shiny app, designed to compute, visualize, and analyze a novel proximity-based green index for individual street segments. Beyond this, the framework innovatively extends its analytical capability by integrating accessibility analysis, Green View Index quantification as well as introducing the Green Space Similarity Index, a novel metric that evaluates and compares the characteristics of urban green spaces across different regions. This extension enriches the proposed framework, providing not just a measure of proximity to green spaces, but also insights into their spatial connectivity and distribution. The efficacy of greenR is demonstrated by studying urban greenness patterns across several cities, highlighting the potential impact of such an open-source framework for citizens, urban planners, and policy-makers. This study not only advances our methodological approach to quantifying urban greenness but also provides practical tools and metrics that can inform sustainable urban planning and policy decisions.

New Capacity Model for Malaysian Urban Roads Using Macroscopic Fundamental Diagram and Multiple Linear Regression Approach

The existing manuals used to estimate urban road capacity have limitations. Adapting these manuals to compute the road capacity at urban roads reduces estimation accuracy. This study attempts to develop a capacity model to characterize urban road capacity. Twenty-two sites consisting of the collector/distributor roads and local roads in the Cheras-Kajang area in Klang Valley are selected for traffic data collection during the morning peak hour from 8 a.m. to 9 a.m. Site video camera recording is carried out, and the traffic data is extracted from the video footage. The speed-density and flow-density curves of the macroscopic fundamental diagram are fitted with the traffic data extracted. The capacity value is then derived from these curves. The urban road capacity model, termed the UrbanCap model, is developed in this study using the multiple linear regression approach. The results show that the urban road capacity depends on the type of road and carriageway and speed limit. The comparison of the capacity values produced from the UrbanCap model with other manuals justifies that it has a better estimate than the existing manuals used in Malaysia. The results also signify a need to develop a manual/guideline to quantify the urban road capacity in Malaysia for road design and analysis.

A Novel Frame-Selection Metric for Video Inpainting to Enhance Urban Feature Extraction.

In our digitally driven society, advances in software and hardware to capture video data allow extensive gathering and analysis of large datasets. This has stimulated interest in extracting information from video data, such as buildings and urban streets, to enhance understanding of the environment. Urban buildings and streets, as essential parts of cities, carry valuable information relevant to daily life. Extracting features from these elements and integrating them with technologies such as VR and AR can contribute to more intelligent and personalized urban public services. Despite its potential benefits, collecting videos of urban environments introduces challenges because of the presence of dynamic objects. The varying shape of the target building in each frame necessitates careful selection to ensure the extraction of quality features. To address this problem, we propose a novel evaluation metric that considers the video-inpainting-restoration quality and the relevance of the target object, considering minimizing areas with cars, maximizing areas with the target building, and minimizing overlapping areas. This metric extends existing video-inpainting-evaluation metrics by considering the relevance of the target object and interconnectivity between objects. We conducted experiment to validate the proposed metrics using real-world datasets from Japanese cities Sapporo and Yokohama. The experiment results demonstrate feasibility of selecting video frames conducive to building feature extraction.

Road dynamic landscape design and simulation based on fuzzy clustering algorithm

Against the backdrop of rapid economic growth and continuous expansion of road networks in China, the planning, design, greening, and beautification of roads at all levels have entered a systematic implementation phase. Particularly noteworthy is the emergence of dynamic road landscape design as a major focus in the research on urban road beautification in the new era. This paper proposes a road dynamic landscape optimization design scheme based on the fuzzy clustering algorithm. It analyzes the primary elements constituting dynamic road landscapes and summarizes the fundamental principles for designing such landscapes. The fuzzy clustering method is employed to classify different road landscapes, optimizing the design system. Through simulation experiments, the effectiveness of the proposed scheme is validated, with results indicating an 8.34% higher accuracy compared to traditional methods. The study suggests that such road design not only meets basic traffic needs but also achieves greening and beautification effects while catering to users visual and psychological needs, showcasing the unique cultural, historical, and lifestyle characteristics of the city. Furthermore, this design fosters harmonious integration between roads and their ancillary structures and surrounding buildings, significantly improving the citys landscape and living environment.

Co-evolutionary traffic signal control using reinforcement learning for road networks under stochastic capacity

A co-evolutionary traffic signal control using reinforcement learning approach (CORLA) is proposed for time-varying road networks under stochastic capacity. Classic reinforcement learning based traffic signal control cannot effectively reduce traffic congestion for large-scale road networks while standard evolutionary metaheuristics often suffer from significantly high computational cost. A co-evolutionary decomposition algorithm (CODA) is proposed to improve traffic mobility for urban road networks with time-varying traffic flow. To capture time-varying spatial evolution of traffic flow inside road links, a stochastic traffic model is presented. To fully consider road users’ response, a stochastic bi-level optimization problem (SBOP) is given where road users’ route choice can be fully taken into account. To efficiently implement CORLA in a large-scale road network against high-consequence realization for stochastic capacity, a coordinated co-evolutionary two phase control (CCTPC) is proposed. Numerical experiments are performed at a real-data city road network and various sizable traffic grids. As compared to state-of-the-art traffic signal control for various traffic conditions, obtained results showed that CCTPC exhibits sufficient gain of achieving road network performance and suffers from the least computational cost in all cases.

Underground Target Extraction by Local Entropy Feature of Voxelized 3D Ground Penetrating Radar

Abstract. To solve the current problem of insufficient exploration of three-dimensional spatial information detected by 3D ground penetrating radar (3D GPR) and the data processing mainly based on the analysis and interpretation of two-dimensional slice images, a method is proposed to extract underground target based on the local entropy feature of discrete point clouds after the voxelization of 3D GPR data. First, the acquired 3D GPR data was voxelized into discrete three-dimensional point clouds. Then the local entropy feature of the voxelized point clouds over the entire region were calculated. The soil background and underground targets were distinguished by classifying them from multiple dimensions through Support Vector Machine (SVM). Finally, the urban road underground environment was taken as the research object, and this method was used for experimental analysis using measured data. The experimental results show that the accuracy of this method in extracting underground targets is as high as 90.1%, and the missing detection rate of missing underground targets is as low as 7.8%. The proposed method is accurate and effective, providing a new approach for 3D GPR to extract underground target.

Study of Urban Arterial Road Off-Ramp Configuration in Highway Municipal Reconstruction

To reduce traffic congestion and improve the overall traffic capacity of roads, the municipal reconstruction of arterial roads from high-grade highways often involves setting up auxiliary roads on both sides of the road. This paper analyzes the existing methods of determining the configuration of urban arterial road off-ramps and proposes a novel design method, includes adding a “waiting for merging” section. Based on the traffic flow characteristics of the main roads, auxiliary roads, as well as off-ramps, second-stage deceleration theory and the insertable gap theory are applied. Considering the worst-case of diverted vehicles, a calculation model for the length of the urban arterial road off-ramps is proposed and utilized to calculate the advisory values for each segment of the off-ramps. Taking the reconstruction of an expressway in Hubei Province, China, as a case study, Vissim traffic simulation software was used to compare the proposed method with the traditional design method under four different traffic volumes. The results show that the proposed design method greatly improves the overall traffic capacity of roads compared with the traditional method, with a reduction of approximately 40% and 60% in delay time and queue length for the overall traffic, respectively. Furthermore, the research results can provide direct and effective design solutions and a basis for decision-making for setting the length of main and auxiliary road off-ramps.

Estimation of the Living Vegetation Volume (LVV) for Individual Urban Street Trees Based on Vehicle-Mounted LiDAR Data

The living vegetation volume (LVV) can accurately describe the spatial structure of greening trees and quantitatively represent the relationship between this greening and its environment. Because of the mostly line shape distribution and the complex species of street trees, as well as interference from artificial objects, current LVV survey methods are normally limited in their efficiency and accuracy. In this study, we propose an improved methodology based on vehicle-mounted LiDAR data to estimate the LVV of urban street trees. First, a point-cloud-based CSP (comparative shortest-path) algorithm was used to segment the individual tree point clouds, and an artificial objects and low shrubs identification algorithm was developed to extract the street trees. Second, a DBSCAN (density-based spatial clustering of applications with noise) algorithm was utilized to remove the branch point clouds, and a bottom-up slicing method combined with the random sampling consistency iterative method algorithm (RANSAC) was employed to calculate the diameters of the tree trunks and obtain the canopy by comparing the variation in trunk diameters in the vertical direction. Finally, an envelope was fitted to the canopy point cloud using the adaptive AlphaShape algorithm to calculate the LVVs and their ecological benefits (e.g., O2 production and CO2 absorption). The results show that the CSP algorithm had a relatively high overall accuracy in segmenting individual trees (overall accuracy = 95.8%). The accuracies of the tree height and DBH extraction based on vehicle-mounted LiDAR point clouds were 1.66~3.92% (rRMSE) and 4.23~15.37% (rRMSE), respectively. For the plots on Zijin Mountain, the LVV contribution by the maple poplar was the highest (1049.667 m3), followed by the sycamore tree species (557.907 m3), and privet’s was the lowest (16.681 m3).

Streets classification models by urban features for road traffic noise estimation

Road traffic is the primary source of environmental noise pollution in cities. This problem is also spreading due to inadequate urban expansion planning. Hence, integrating road traffic noise analysis into urban planning is necessary for reducing city noise in an effective, adaptable, and sustainable way. This study aims to develop a methodology that applies to any city for the stratification of urban roads by their functionality through only their urban features. It is intended to be a tool to cluster similar streets and, consequently, traffic noise to enable urban and transportation planners to support the reduction of people's noise exposure. Three multivariate ordered logistic regression statistical models (Model 1, 2, and 3) are presented that significantly stratify urban roads into five, four, and three categories, respectively. The developed models exhibit a McFadden pseudo-R2 between 0.5 and 0.6 (equivalent to R2 >0.8). The choice between Model 1 or 2 depends on the scale of the city. Model 1 is recommended for developed cities with an extensive road network, while Model 2 is most suitable in intermediate and growing cities. On the other hand, Model 3 could be applied at any city scale but focused on local management of transit routes and for designing acoustic sensor installations, urban soundwalks, and identification of quiet areas. Urban features related to road width and length, presence of transport infrastructure, and public transport routes are associated with increased traffic noise in all three models. These models prove useful for future action plans aimed at reducing noise through strategic urban planning.

Characteristics of droplet dispersion and interpersonal exposure in indoor and outdoor dining areas of urban street: Elementary numerical investigations

Indoor and outdoor dining areas are special spaces where people generally sit close and talk face-to-face for a long time (∼hour). Semi-open dining area is of popularity among them, one of the reason is the thoughts that outdoor dining area have experienced small infectious risks, even though this opinion has not been thoroughly validated yet. As a novelty, this study conducts CFD simulations to investigate droplet dispersion and face-to-face exposure risk of customers in the indoor and outdoor dining areas of typical street canyons (H/W = 1) by coupling indoor/outdoor airflows. Different scenarios are considered including index patient locations, the setting of the desk isolation board (DIB, HDIB = 0.6 m or 1.2 m), and the outdoor semi-open street roof (SSR).Results show that indoor dining spaces with single-sided ventilation experience one-order poorer ventilation (net escape velocity NEV∼0.036 m/s) and subsequently 10–200 times higher face-to-face droplet trap fraction than those in outdoor dining areas (NEV∼0.407 m/s, TF∼3–102 ppm). Indoor desk isolation board can decrease face-to-face TF by 50 %, but in outdoors, TF without desk-isolation board is 6.5 ppm for 50 μm droplet and this value changes little or slightly increases as HDIB is 0.6 m (∼8.9 ppm) or 1.2 m (∼7.7 ppm), indicating DIB seems not helpful to reduce airborne exposure risk in outdoors. Moreover, the setting of an outdoor semi-open street roof (SSR) may reduce NEV in the indoor or outdoor dining area by half or 90 % respectively. Therefore, SSR may raise indoor and outdoor customers' face-to-face exposure risk (e.g. TF) by hundreds of times and seventy times respectively.

One-step vs horizon-step training strategies for multi-step traffic flow forecasting with direct particle swarm optimization grid search support vector regression and long short-term memory

In the increasingly complex urban transportation landscape, the necessity for accurate, multi-step forecasting has never been more apparent to help plan for long-term, strategic transportation initiatives like Intelligent Transportation Systems. This work focuses on resolving uncertainties around the proper training step size for machine learning models performing multi-step traffic flow forecasting. Two strategies are considered: one-step and horizon-step training sizes, which extend from the single-step forecasting method. This paper compares and evaluates two machine learning models: the Support Vector Regression and Long Short-Term Memory (LSTM). Data from two country road locations, including urban roads in Kuala Lumpur and the I5-North freeway in California, are employed to forecast traffic flow in 1-hour increments, projecting from 2 h up to 24 h ahead. The results reveal a significant difference in performance between the two training step sizes, with the one-step training size emerging as the more consistent and optimal strategy for both Direct and Multi-Input Multi-Output forecasting strategies. The proposed model, the Direct Particle Swarm Optimization Grid Search Support Vector Regression (Direct-PSOGS-SVR) model, exhibited comparable or slightly better performance than the LSTM model in both environments. In the I5-North freeway dataset, the Direct-PSOGS-SVR achieved similar Root Mean Squared Error values across most forecasting tasks compared to the LSTM, indicating its effectiveness in stable traffic flow patterns. Notably, in the more dynamic urban environment of Kuala Lumpur, the Direct-PSOGS-SVR model also demonstrated stability and resilience in forecasting accuracy, effectively handling the inherent noise and fluctuations in traffic patterns. These findings serve as a valuable guide for practitioners in selecting the most efficacious combination of training strategies for specific time series forecasting tasks utilizing machine learning models, particularly in traffic flow forecasting. Introducing the Direct-PSOGS-SVR model enriches the landscape of machine learning solutions for traffic forecasting, underscoring the paper’s contributions to the broader understanding of time series forecasting dynamics.

Exploration of an Open Vocabulary Model on Semantic Segmentation for Street Scene Imagery

This study investigates the efficacy of an open vocabulary, multi-modal, foundation model for the semantic segmentation of images from complex urban street scenes. Unlike traditional models reliant on predefined category sets, Grounded SAM uses arbitrary textual inputs for category definition, offering enhanced flexibility and adaptability. The model’s performance was evaluated across single and multiple category tasks using the benchmark datasets Cityscapes, BDD100K, GTA5, and KITTI. The study focused on the impact of textual input refinement and the challenges of classifying visually similar categories. Results indicate strong performance in single-category segmentation but highlighted difficulties in multi-category scenarios, particularly with categories bearing close textual or visual resemblances. Adjustments in textual prompts significantly improved detection accuracy, though challenges persisted in distinguishing between visually similar objects such as buses and trains. Comparative analysis with state-of-the-art models revealed Grounded SAM’s competitive performance, particularly notable given its direct inference capability without extensive dataset-specific training. This feature is advantageous for resource-limited applications. The study concludes that while open vocabulary models such as Grounded SAM mark a significant advancement in semantic segmentation, further improvements in integrating image and text processing are essential for better performance in complex scenarios.

An innovative simplified approach for conductive heat flux in pavement structures

Understanding the conductive heat flux at pavement surfaces and interior layers is crucial for reducing pavement distress, developing cooler pavements, and mitigating urban heat island (UHI) effects. Existing methods often rely on calculating the conductive heat flux as the residual heat flux from net radiation and heat convection, leading to significant measurement uncertainties in practice. This study overcomes this issue by introducing a novel model based on Maclaurin polynomial temperature profiles to more accurately determine the conductive heat flux across pavement surfaces and depths. Validated against designed experiments and existing literature, our model shows that both cubic and quadratic Maclaurin polynomials can approximate pavement temperature profiles properly. Our model facilitates the determination of conductive heat flux at various pavement depths, including the surface. By correlating the temperature gradient with measured conductive heat flux at specific depths, we accurately determine the thermal conductivity of the pavement. The proposed method more accurately estimates conductive heat flux compared to the traditional residual methods. The model proves useful in quantifying conductive heat flux, convection, and convection coefficients at various pavement locations, offering enhanced monitoring of conductive heat flux across urban streets.

DEDU-Net: Dual-Encoder-Decoder-U-Net for road extraction from high-resolution remote sensing images

ABSTRACT The development of the modern urban economy is closely tied to road construction, with roads being one of the most crucial components in urban development. The use of high-resolution remote sensing images to monitor the road conditions in cities and surrounding towns has become a highly emphasized research focus in recent years. However, due to the complexity of the environment, this task still faces significant challenges. For instance, urban road structures are intricate, which involve multiple lanes, intersections, building obstructions, etc. Rural roads may consist of narrow paths or irregular dirt roads. In addressing these issues, this paper proposes a road extraction algorithm based on a Dual-Encoder-Decoder U- Net (DEDU-Net). The algorithm leverages a dual encoder-decoder network to extract multi-scale information from the image. It then employs a dual decoder network to restore the feature map to the original image, achieving precise road extraction. Additionally, a new module, the Global Fusion Module (GFM), is introduced. This module achieves global context information fusion by weighting features. In the experimental section, two publicly available datasets were used for testing: the CHN6-CUG dataset and the Gansu Mountain Road dataset. For example, for the Gansu Mountain Road and CHN6-CUG mixed dataset, the model achieved an Overall Accuracy (OA) of 94.947% and a mean Intersection over Union (mIoU) of 70.971%. The results indicate that compared to traditional methods, this proposed method exhibits higher accuracy and robustness. It can adapt to both urban and rural roads, delivering outstanding performance even in complex scenarios.

Impact and flexibility support capability estimation model of urban electric vehicles based on the integration of urban traffic network and Urban Distribution Network

Electric Vehicle (EV) is regarded as the bridge between Urban Road Network (URN) and Urban Distribution Network (UDN). It can not only make negative impact on power grid, but can also provide flexibility support capability to power grid. Whether the benefit is negative or positive depends on the dynamical behavior of EVs. So with the rapid development of EVs in the urban area, accurate evaluation of both negative impact and flexibility support capability of dispersed EVs in the urban area is very important for V2G application. In order to get the accurate evaluation results, the interaction between transportation system and electric power system should be taken into consideration. In this paper, an estimation model of the impact and flexibility support capability of urban dispersed EVs considering the interaction between URN and UDN is proposed. In order to improve the accuracy of the estimation model, the interaction between URN and UDN is modeled as an integration model based on EV trip chain. It takes EVs’ temporal and spatial dynamic stochastic characteristics into consideration simultaneously and can be used to describe the energy interaction between URN and UDN. An indices set consist of multi-index is proposed to evaluate the impact and flexibility support capability of urban EVs based on the integration model. Three numerical cases are conducted to verify the validity of the proposed model. The results show that the proposed integration model of URN and UDN based on EV trip chain can not only be used to describe the energy interaction between URN and UDN, but also to estimate the impact and flexibility support capability of urban dispersed electric vehicles.