Dynamic Route Choice Prediction Model Based on Connected Vehicle Guidance Characteristics

A route choice prediction model is proposed considering the connected vehicle guidance characteristics. This model is proposed to prevent the delay in the release of guidance information and route planning due to inaccurate timing predictions of the traditional guidance systems. Based on the analysis of the impact of different connected vehicle (CV) guidance strategies on traffic flow, an indexes system for CV guidance characteristics is presented. Selecting five characteristic indexes, a route choice prediction model is designed using the logistic model. A simulation scenario is established by programming different agents for controlling the flow of vehicles and for information acquisition and transmission. The prediction model is validated using the simulation scenario, and the simulation results indicate that the characteristic indexes have a significant influence on the probability of choosing a particular route. The average root mean square error (RMSE) of the prediction model is 3.19%, which indicates that the calibration model shows a good prediction performance. In the implementation of CV guidance, the penetration rate can be considered an optional index in the adjustment of the guidance effect.

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Study On Multiscale-Multivariate Attribution Analysis and Prediction of Urban Rainstorm Flood

10.21203/rs.3.rs-929111/v1 ◽

2021 ◽

Author(s):

Jinping Zhang ◽

Yuhao Wang

Keyword(s):

Prediction Model ◽

Influencing Factors ◽

Underlying Surface ◽

Good Prediction ◽

Attribution Analysis ◽

Flood Volume ◽

Multivariate Prediction ◽

Micro Level ◽

Relative Errors ◽

The Impact

Abstract In order to explore the impact of the changing environment on urban rainstorm flood, and reveal the relationship between flood volume and its influencing factors at the micro level, the rainfall and flood volume are decomposed by the wavelet analysis method to perform the multiscale attribution analysis. Then the multiscale-multivariate prediction model of urban rainstorm flood is constructed in the Jialu River Basin in Zhengzhou city of China. The results show that the main influencing factors of flood volume are rainfall and underlying surface, where the latter causes the mutation of flood volume in 1994 and 2005. At the micro level, there is a constant linear relationship between rainfall and flood volume in d1, d2 and d3, while the impact of underlying surface on flood volume is mainly reflected in a3. The multiscale-multivariate prediction model has a good simulation effect on the flood volume of the first 45 rainstorm floods, NSE, R2 and Re are 0.966, 0.964 and 10.80%, respectively. Moreover, the model also has a good prediction effect, and the relative errors between the predicted and observed flood volume of 46th~50th rainstorm floods are all less than 20%.

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Impact of connected vehicle guidance information on network-wide average travel time

Advances in Mechanical Engineering ◽

10.1177/1687814016683356 ◽

2017 ◽

Vol 9 (1) ◽

pp. 168781401668335 ◽

Cited By ~ 1

Author(s):

Jiangfeng Wang ◽

Jiarun Lv ◽

Qian Zhang ◽

Xuedong Yan

Keyword(s):

Travel Time ◽

Data Exchange ◽

Positive Impact ◽

Penetration Rate ◽

Transportation Systems ◽

Time Variability ◽

Connected Vehicle ◽

Vehicle Guidance ◽

Average Travel Time ◽

The Impact

With the emergence of connected vehicle technologies, the potential positive impact of connected vehicle guidance on mobility has become a research hotspot by data exchange among vehicles, infrastructure, and mobile devices. This study is focused on micro-modeling and quantitatively evaluating the impact of connected vehicle guidance on network-wide travel time by introducing various affecting factors. To evaluate the benefits of connected vehicle guidance, a simulation architecture based on one engine is proposed representing the connected vehicle–enabled virtual world, and connected vehicle route guidance scenario is established through the development of communication agent and intelligent transportation systems agents using connected vehicle application programming interface considering the communication properties, such as path loss and transmission power. The impact of connected vehicle guidance on network-wide travel time is analyzed by comparing with non-connected vehicle guidance in response to different market penetration rate, following rate, and congestion level. The simulation results explore that average network-wide travel time in connected vehicle guidance shows a significant reduction versus that in non–connected vehicle guidance. Average network-wide travel time in connected vehicle guidance have an increase of 42.23% comparing to that in non-connected vehicle guidance, and average travel time variability (represented by the coefficient of variance) increases as the travel time increases. Other vital findings include that higher penetration rate and following rate generate bigger savings of average network-wide travel time. The savings of average network-wide travel time increase from 17% to 38% according to different congestion levels, and savings of average travel time in more serious congestion have a more obvious improvement for the same penetration rate or following rate.

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Tailored Prediction Model of Survival after Liver Transplantation for Hepatocellular Carcinoma

Journal of Clinical Medicine ◽

10.3390/jcm10132869 ◽

2021 ◽

Vol 10 (13) ◽

pp. 2869

Author(s):

Indah Jamtani ◽

Kwang-Woong Lee ◽

Yun-Hee Choi ◽

Young-Rok Choi ◽

Jeong-Moo Lee ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Liver Transplantation ◽

Prediction Model ◽

Risk Group ◽

Significant Risk ◽

Risk Scores ◽

Accurate Information ◽

Survival Prediction ◽

Good Prediction ◽

Recurrence Rates

This study aimed to create a tailored prediction model of hepatocellular carcinoma (HCC)-specific survival after transplantation based on pre-transplant parameters. Data collected from June 2006 to July 2018 were used as a derivation dataset and analyzed to create an HCC-specific survival prediction model by combining significant risk factors. Separate data were collected from January 2014 to June 2018 for validation. The prediction model was validated internally and externally. The data were divided into three groups based on risk scores derived from the hazard ratio. A combination of patient demographic, laboratory, radiological data, and tumor-specific characteristics that showed a good prediction of HCC-specific death at a specific time (t) were chosen. Internal and external validations with Uno’s C-index were 0.79 and 0.75 (95% confidence interval (CI) 0.65–0.86), respectively. The predicted survival after liver transplantation for HCC (SALT) at a time “t” was calculated using the formula: [1 − (HCC-specific death(t’))] × 100. The 5-year HCC-specific death and recurrence rates in the low-risk group were 2% and 5%; the intermediate-risk group was 12% and 14%, and in the high-risk group were 71% and 82%. Our HCC-specific survival predictor named “SALT calculator” could provide accurate information about expected survival tailored for patients undergoing transplantation for HCC.

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Information Acquisition and Its Incentives in an E-Commerce Supply Chain under the Offline Showroom Model

Journal of theoretical and applied electronic commerce research ◽

10.3390/jtaer16050100 ◽

2021 ◽

Vol 16 (5) ◽

pp. 1791-1804

Author(s):

Mengli Li ◽

Xumei Zhang

Keyword(s):

Supply Chain ◽

Information Acquisition ◽

Incentive Contracts ◽

Demand Forecast ◽

Competition Intensity ◽

Demand Information ◽

Optimal Information ◽

Acquisition Strategy ◽

The One ◽

The Impact

Recently, the showroom model has developed fast for allowing consumers to evaluate a product offline and then buy it online. This paper aims at exploring the optimal information acquisition strategy and its incentive contracts in an e-commerce supply chain with two competing e-tailers and an offline showroom. Based on signaling game theory, we build a mathematical model by considering the impact of experience service and competition intensity on consumers’ demand. We find that, on the one hand, information acquisition promotes supply chain members to obtain demand information directly or indirectly, which leads to forecast revenue. On the other hand, information acquisition promotes supply chain members to distort optimal decisions, which results in signal cost. The optimal information acquisition strategy depends on the joint impact of forecast revenue, signal cost and demand forecast cost. Notably, in some conditions, the offline showroom will not acquire demand information even when its cost is equal to zero. We also design two different information acquisition incentive contracts to obtain Pareto improvement for all supply chain members.

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Field Nitrogen Dioxide and Ozone Monitoring Using Electrochemical Sensors with Partial Least Squares Regression

Chemistry Proceedings ◽

10.3390/csac2021-10622 ◽

2021 ◽

Vol 5 (1) ◽

pp. 61

Author(s):

Rachid Laref ◽

Etienne Losson ◽

Alexandre Sava ◽

Maryam Siadat

Keyword(s):

Nitrogen Dioxide ◽

Electrochemical Sensors ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Calibration Model ◽

Least Squares Regression ◽

Monitoring Networks ◽

Concentration Estimation ◽

The Impact

Low-cost gas sensors detect pollutants gas at the parts-per-billion level and may be installed in small devices to densify air quality monitoring networks for the spread analysis of pollutants around an emissive source. However, these sensors suffer from several issues such as the impact of environmental factors and cross-interfering gases. For instance, the ozone (O3) electrochemical sensor senses nitrogen dioxide (NO2) and O3 simultaneously without discrimination. Alphasense proposes the use of a pair of sensors; the first one, NO2-B43F, is equipped with a filter dedicated to measure NO2. The second one, OX-B431, is sensitive to both NO2 and O3. Thus, O3 concentration can be obtained by subtracting the concentration of NO2 from the sum of the two concentrations. This technique is not practical and requires calibrating each sensor individually, leading to biased concentration estimation. In this paper, we propose Partial Least Square regression (PLS) to build a calibration model including both sensors’ responses and also temperature and humidity variations. The results obtained from data collected in the field for two months show that PLS regression provides better gas concentration estimation in terms of accuracy than calibrating each sensor individually.

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An IoT Architecture for Assessing Road Safety in Smart Cities

Wireless Communications and Mobile Computing ◽

10.1155/2018/8214989 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11 ◽

Cited By ~ 4

Author(s):

Abd-Elhamid M. Taha

Keyword(s):

Road Safety ◽

Markov Models ◽

Smart Cities ◽

Dynamic Assessment ◽

Route Planning ◽

Cost Effective ◽

World Health ◽

Road Users ◽

Health Organization ◽

The Impact

The Safe System (SS) approach to road safety emphasizes safety-by-design through ensuring safe vehicles, road networks, and road users. With a strong motivation from the World Health Organization (WHO), this approach is increasingly adopted worldwide. Considerations in SS, however, are made for the medium-to-long term. Our interest in this work is to complement the approach with a short-to-medium term dynamic assessment of road safety. Toward this end, we introduce a novel, cost-effective Internet of Things (IoT) architecture that facilitates the realization of a robust and dynamic computational core in assessing the safety of a road network and its elements. In doing so, we introduce a new, meaningful, and scalable metric for assessing road safety. We also showcase the use of machine learning in the design of the metric computation core through a novel application of Hidden Markov Models (HMMs). Finally, the impact of the proposed architecture is demonstrated through an application to safety-based route planning.

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Design and construction of the barrier system for the Halton Landfill

Canadian Geotechnical Journal ◽

10.1139/t99-131 ◽

2000 ◽

Vol 37 (3) ◽

pp. 662-675 ◽

Cited By ~ 12

Author(s):

R Kerry Rowe ◽

Chris J Caers ◽

Glenn Reynolds ◽

Cliff Chan

Keyword(s):

Hydraulic Conductivity ◽

Impact Assessment ◽

Concentration Profile ◽

Organic Contaminants ◽

Laboratory Experiments ◽

Chloride Concentration ◽

Design Concept ◽

Good Prediction ◽

Air Pocket ◽

The Impact

Considerations related to the design of the Halton Landfill as a "hydraulic trap" are summarized together with the research that was conducted to support the design concept. The interrelationship between hydrogeology and the engineered design is examined. Laboratory experiments demonstrated that there can be diffusion away from a source, even with significant inward velocity. Existing theory was found to provide a good prediction of the observed concentration profile in these experiments. It is also shown that a pressurized air pocket below the clay effectively acts as a zero-flux boundary and hence, with respect to migration of chloride, could be conservatively neglected in the impact assessment. The results of the impact calculations predict only a small increase in chloride concentration in the receptor aquifer while there is negligible predicted impact due to organic contaminants. The landfill was designed and constructed with a granular "sub-liner contingency layer" (SLCL) beneath the compacted liner. The operation of this layer is discussed. Finally, the construction of the compacted clayey liner with a hydraulic conductivity of 1 × 10-8 cm/s is documented.Key words: landfill, hydraulic containment, liner, field case, construction.

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