Fuzzy Inference System Framework to Prioritize the Deployment of Resources in Low Visibility Traffic Conditions

Ortega, Luz C.; Otero, Luis Daniel; Otero, Carlos E.

doi:10.1109/access.2019.2956918

Cited by 8 publications

(9 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Therefore, users can use the event so that they may guess the value of the event in the future in our system [21][22][23][24][25]. If they put it in our system, they can see the included event of traffic volume they choose.…”

Section: Proposed System Architecturementioning

confidence: 99%

Traffic Inference System Using Correlation Analysis with Various Predicted Big Data

Kim

Chung

2021

Electronics

View full text Add to dashboard Cite

Currently, most of the transportation systems require changes to intelligent transportation systems, but most of them focus on efficient transportation rather than on improvement in human life. Sometimes, traffic systems are designed for economic value, and safety-related issues are neglected. A traffic information system that reflects various kinds of environmental information related to people’s safety must be able to reflect not only the existing economic goals but also a safe traffic environment. The traffic environment can be thought of as safety and direct information such as rainfall, including information on specific days when many people are scheduled to be gathered for certain events nearby. Intelligent transportation systems using this information can provide safety-related information for traveling to a specific area or for business trips. In addition, traffic congestion is a social problem and is directly related to a comfort life for individuals. Therefore, addressing various social and environmental factors could make human life more stable and reduce stress as a result. To do that, we need to estimate the impact on traffic based on environmental Big Data. The data can generally be divided into structured data and unstructured data. In inference, structured data analysis is relatively easy due to the precise meaning of the data. Nonetheless, it can be very difficult to predict environmentally sensitive data, such as traffic volume in intelligent transportation systems. To cope with this problem, there are a few systems for handling unstructured data to find out specific events that affect the traffic volume and improve its reliability. This paper shows that it is possible to estimate the exact volume of traffic using correlation analysis with various predicted data. Thus, we may apply this technique to the existing intelligent transportation system to predict the exact volume of traffic with environmentally sensitive data including various unstructured data.

show abstract

Section: Proposed System Architecturementioning

confidence: 99%

Traffic Inference System Using Correlation Analysis with Various Predicted Big Data

Kim

Chung

2021

Electronics

View full text Add to dashboard Cite

show abstract

“…This rule base is used to provide output in the inference system. The order of the rules does not affect the output value because it is evaluated in parallel using fuzzy reasoning [23]. Fuzzy rules are established using common knowledge to select locations based on traffic conditions.…”

Section: Fuzzy Rulesmentioning

confidence: 99%

An improved fuzzy ant colony system for route selection based on real time traffic condition

Lisangan

Sumarta

2021

IJ-AI

View full text Add to dashboard Cite

<span id="docs-internal-guid-43a74def-7fff-f5f6-9f3c-4828e591ebd1"><span>With city conditions that often have congestion, a driver need to find a route of the many possible routes that may occur from origin to destination by considering several factors. The weaknesses of the ant algorithm is the dependency of required parameter values and must be set manually. This paper will make improvements to the fuzzy ant colony system (FACS) with minimize parameter dependency by using fuzzy logic to determine the probability of the next node visited by ants. There are four criteria or input variables for fuzzy inference system, that is “Pheromone Intensity”, “Distance”, “Vehicle Intensity”, and “Average Speed”. The routes generated by improved FACS (I-FACS) are more varied than the FACS algorithm. The best condition obtained by I-FACS was at point O/D= 112/34 where at 05:00, I-FACS was able to obtain the route with the best length compared to ACS and FACS. The result of the comparison of the route distance obtained shows that I-FACS is able to produce a better route by 4.16% than FACS by looking at the average distance difference on ACS. This method is expected to be a reference for the development of smart transportation in support of smart mobility, which is one of the components in the smart city concept.</span></span>

show abstract

“…The model had 75 rules developed based on the data and expert opinions. Moreover, the Mamdani inference system was used by [76] to develop a framework to help the transportation managers deploy available resources considering three FISs for the occurrence of fog, risk conditions of the road, and priority of deployment.…”

Section: Fuzzy Inference System (Fis) and Public Transportationmentioning

confidence: 99%

Influence of Socio-Cultural Attributes on Stigmatizing Public Transport in Saudi Arabia

et al. 2021

View full text Add to dashboard Cite

Several factors over the years have contributed to stigma in public transport. Many studies have highlighted the need to make the transport system more equitable both from economic and gender perspectives. This study attempts to demonstrate how the perceptions of public transport users and non-users are stigmatized from social and cultural standpoints. Thus, it identifies the social and cultural stigma-induced barriers embedded with the use and people’s general perception about the public bus service, taking SAPTCO (Saudi Public Transport Company) as a case study. The study results suggest that privacy concern is the primary cause of stigma. Most of the users are unwilling to ride with their families as SAPTCO does not account for gender needs (e.g., privacy, travel convenience, safety, comfort, etc.). Moreover, people from the high-income classes are more stigmatized against this ridership. A fuzzy inference system (FIS) model is used to analyze the survey questionnaire responses and understand what stigma means for the public bus service. Expert opinions are employed to generate “if–then” rules of the FIS models. Sensitivity of the defined fuzzy model is conducted to different aspects of the ridership. The study results further suggest that “inconvenience” poses the highest impact while “feeling safe”, “privacy”, “fare”, “timing”, and “comfort” are found to be the medium impact-making variables for stigma. The stigma-defining variables would be critical for the public bus service to improve its service quality and help (re-)design the policies that would attract a high amount of ridership. Some solutions are suggested in the end that would complement, strengthen, and promote the current SAPTCO service. The demonstrated methodology of this study would be relevant and adaptive to any relevant context to improve public transportation service and pertaining policies.

show abstract

Fuzzy Inference System Framework to Prioritize the Deployment of Resources in Low Visibility Traffic Conditions

Cited by 8 publications

References 42 publications

Traffic Inference System Using Correlation Analysis with Various Predicted Big Data

Traffic Inference System Using Correlation Analysis with Various Predicted Big Data

An improved fuzzy ant colony system for route selection based on real time traffic condition

Influence of Socio-Cultural Attributes on Stigmatizing Public Transport in Saudi Arabia

Contact Info

Product

Resources

About