Traffic prediction method by fuzzy logic

Iokibe, Tadashi; Naomi, Mochizuki; Kimura, Takashi

doi:10.1109/fuzzy.1993.327408

Cited by 14 publications

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“…is the value of the th input node and is the activation function defined by the following sigmoid function: [12] is a process of mapping an input space onto an output space using membership functions and linguistically specified rules. Figure 1 shows the process of the traffic prediction based on the FLS.…”

Section: Backpropagation Neural Network (Bpnns)mentioning

confidence: 99%

“…According to the basic concept of this prediction method, the pattern traffic which is regarded as reference traffic is based on the traffic variation pattern, the actual traffic at preceding control period is compared with the pattern traffic at every prediction and the pattern traffic at the current control is corrected to obtain a predicted traffic. The traffic measured for previous preceding hour Z k 1 is gained from the previous traffic record and the pattern traffic for previous preceding hour P k 1 from traffic variation pattern, the variation coefficient J can be obtained by the formula [12] :…”

Section: Backpropagation Neural Network (Bpnns)mentioning

confidence: 99%

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Short-term traffic flow prediction using different techniques

Anavatti

Ray

2011

IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society

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Due to the complexity of traffic flow characteristics and the drawbacks of the traditional methods, the short-term predictions using the existing individual methods generally lack accuracy and robustness during all the time periods of the day. In order to overcome the drawbacks of traditional methods, the present paper proposes a fuzzy rule-based system (FRBS), which is used to combine the traffic flow forecasts resulting from the Exponential Smoothing Method (ESM), Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Networks (ANN) and the Fuzzy Logic System (FLS). A comparative study shows that the proposed FRBS can represent the traffic flow more accurately and the results gained from the FRBS are found to outperform those of the traditional methods, when modelling in the real urban traffic.

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Section: Backpropagation Neural Network (Bpnns)mentioning

confidence: 99%

Section: Backpropagation Neural Network (Bpnns)mentioning

confidence: 99%

Short-term traffic flow prediction using different techniques

Anavatti

Ray

2011

IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society

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“…The FUZZY model FLS [8] is a process of mapping an input space onto an output space using membership functions and linguistically specified rules. The process of fuzzy inference involves Membership Functions, Logical Operations, and If-Then Rules.…”

Section: Bmentioning

confidence: 99%

Adaptive route guidance system with real-time traffic information

Anavatti

Ray

2012

2012 15th International IEEE Conference on Intelligent Transportation Systems

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Ahstract-This paper presents a systematic approach to adaptively realize the vehicle routing with the real-time traffic information during the whole time period of a day. It focuses on the route planning procedures for determining the optimal route based on analytical hierarchy process (AHP) and fuzzy logic theory. This approach can adaptively switch from pre-routing to real-time routing based on real-time traffic information to find an optimal route for each O-D (Origin-Destination) pairs. The AHP-FUZZY approach is a multi-criteria combination system, which can greatly simplify the definition of decision strategy and represent the multiple criteria explicitly. A simulation is implemented to compare with the traditional route guidance system and the results are validated.

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“…RELATED WORK Several works on traffic prediction or estimation involves fixed probes that have high installation cost and maintenance fee such as loop detector, traffic counter, close circuit camera, etc [1], [3], [4], [5]. Fix probes are highly reliable but can cover a small area.…”

Section: IImentioning

confidence: 99%

Cell-based traffic estimation from multiple GPS-equipped cars

Sananmongkhonchai

Tangamchit

Pongpaibool

2009

TENCON 2009 - 2009 IEEE Region 10 Conference

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we propose a cell-based algorithm used to estimate road traffic condition from multiple Global Positioning System (GPS)-equipped taxis. The GPS data is received intermittently at a central server from approximately 4,000 taxis running within Bangkok metropolitan area. The algorithm divides the Bangkok area into 6,400 square cells. Traffic condition considered here is travel time between two cells. The A* algorithm is used to predict the travel path between any pair of origin-destination. The incremental weighted update algorithm is used to update and estimate travel time between cells. The algorithm can estimate traffic condition of all areas anytime, even if there is no GPS data sent from that area. The result shows that the cell-based algorithm can achieve a good traffic estimation covers all areas and times, compared to the traditional average space-speed method, which can only give traffic condition when there are at least two data points in that area.

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Traffic prediction method by fuzzy logic

Cited by 14 publications

References 0 publications

Short-term traffic flow prediction using different techniques

Short-term traffic flow prediction using different techniques

Adaptive route guidance system with real-time traffic information

Cell-based traffic estimation from multiple GPS-equipped cars

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