Dynamic Tensor Modeling for Missing Data Completion in Electronic Toll Collection Gantry Systems.

The deployment of Electronic Toll Collection (ETC) gantry systems marks a transformative advancement in the journey toward an interconnected and intelligent highway traffic infrastructure. The integration of these systems signifies a leap forward in streamlining toll collection and minimizing environmental impact through decreased idle times. To solve the problems of missing sensor data in an ETC gantry system with large volumes and insufficient traffic detection among ETC gantries, this study constructs a high-order tensor model based on the analysis of the high-dimensional, sparse, large-volume, and heterogeneous characteristics of ETC gantry data. In addition, a missing data completion method for the ETC gantry data is proposed based on an improved dynamic tensor flow model. This study approximates the decomposition of neighboring tensor blocks in the high-order tensor model of the ETC gantry data based on tensor Tucker decomposition and the Laplacian matrix. This method captures the correlations among space, time, and user information in the ETC gantry data. Case studies demonstrate that our method enhances ETC gantry data quality across various rates of missing data while also reducing computational complexity. For instance, at a less than 5% missing data rate, our approach reduced the RMSE for time vehicle distance by 0.0051, for traffic volume by 0.0056, and for interval speed by 0.0049 compared to the MATRIX method. These improvements not only indicate a potential for more precise traffic data analysis but also add value to the application of ETC systems and contribute to theoretical and practical advancements in the field.

Integrated driving risk surrogate model and car-following behavior for freeway risk assessment.

Drivers' risk perception plays a crucial role in understanding vehicle interactions and car-following behavior under complex conditions and physical appearances. Therefore, it is imperative to evaluate the variability of risks involved. With advancements in communication technology and computing power, real-time risk assessment has become feasible for enhancing traffic safety. In this study, a novel approach for evaluating driving interaction risk on freeways is presented. The approach involves the integration of an interaction risk perception model with car-following behavior. The proposed model, named the driving risk surrogate (DRS), is based on the potential field theory and incorporates a virtual energy attribute that considers vehicle size and velocity. Risk factors are quantified through sub-models, including an interactive vehicle risk surrogate, a restrictions risk surrogate, and a speed risk surrogate. The DRS model is applied to assess driving risk in a typical scenario on freeways, and car-following behavior. A sensitivity analysis is conducted on the effect of different parameters in the DRS on the stability of traffic dynamics in car-following behavior. This behavior is then calibrated using a naturalistic driving dataset, and then car-following predictions are made. It was found that the DRS-simulated car-following behavior has a more accurate trajectory prediction and velocity estimation than other car-following methods. The accuracy of the DRS risk assessments was verified by comparing its performance to that of traditional risk models, including TTC, DRAC, MTTC, and DRPFM, and the results show that the DRS model can more accurately estimate risk levels in free-flow and congested traffic states. Thus the proposed risk assessment model provides a better approach for describing vehicle interactions and behavior in the digital world for both researchers and practitioners.

Spatial Distribution Characteristics of Healthcare Facilities in Nanjing: Network Point Pattern Analysis and Correlation Analysis.

The spatial distribution of urban service facilities is largely constrained by the road network. In this study, network point pattern analysis and correlation analysis were used to analyze the relationship between road network and healthcare facility distribution. The weighted network kernel density estimation method proposed in this study identifies significant differences between the outside and inside areas of the Ming city wall. The results of network K-function analysis show that private hospitals are more evenly distributed than public hospitals, and pharmacy stores tend to cluster around hospitals along the road network. After computing the correlation analysis between different categorized hospitals and street centrality, we find that the distribution of these hospitals correlates highly with the street centralities, and that the correlations are higher with private and small hospitals than with public and large hospitals. The comprehensive analysis results could help examine the reasonability of existing urban healthcare facility distribution and optimize the location of new healthcare facilities.

An Enhanced Variable Two-Step Floating Catchment Area Method for Measuring Spatial Accessibility to Residential Care Facilities in Nanjing.

Civil administration departments require reliable measures of accessibility so that residential care facility shortage areas can be accurately identified. Building on previous research, this paper proposes an enhanced variable two-step floating catchment area (EV2SFCA) method that determines facility catchment sizes by dynamically summing the population around the facility until the facility-to-population ratio (FPR) is less than the FPR threshold (FPRT). To minimize the errors from the supply and demand catchments being mismatched, this paper proposes that the facility and population catchment areas must both contain the other location in calculating accessibility. A case study evaluating spatial accessibility to residential care facilities in Nanjing demonstrates that the proposed method is effective in accurately determining catchment sizes and identifying details in the variation of spatial accessibility. The proposed method can be easily applied to assess other public healthcare facilities, and can provide guidance to government departments on issues of spatial planning and identification of shortage and excess areas.

A synthetic method for atmospheric diffusion simulation and environmental impact assessment of accidental pollution in the chemical industry in a WEBGIS context.

The chemical industry poses a potential security risk to factory personnel and neighboring residents. In order to mitigate prospective damage, a synthetic method must be developed for an emergency response. With the development of environmental numeric simulation models, model integration methods, and modern information technology, many Decision Support Systems (DSSs) have been established. However, existing systems still have limitations, in terms of synthetic simulation and network interoperation. In order to resolve these limitations, the matured simulation model for chemical accidents was integrated into the WEB Geographic Information System (WEBGIS) platform. The complete workflow of the emergency response, including raw data (meteorology information, and accident information) management, numeric simulation of different kinds of accidents, environmental impact assessments, and representation of the simulation results were achieved. This allowed comprehensive and real-time simulation of acute accidents in the chemical industry. The main contribution of this paper is that an organizational mechanism of the model set, based on the accident type and pollutant substance; a scheduling mechanism for the parallel processing of multi-accident-type, multi-accident-substance, and multi-simulation-model; and finally a presentation method for scalar and vector data on the web browser on the integration of a WEB Geographic Information System (WEBGIS) platform. The outcomes demonstrated that this method could provide effective support for deciding emergency responses of acute chemical accidents.