Enhancing pedestrian safety, walkability and traffic flow with fuzzy logic

2020 ◽  
Vol 701 ◽  
pp. 134454 ◽  
Author(s):  
Sharaf AlKheder ◽  
Fahad AlRukaibi
2020 ◽  
Vol 12 (8) ◽  
pp. 3206
Author(s):  
Roni Utriainen ◽  
Markus Pöllänen

Interaction between drivers and pedestrians enables pedestrians to cross the street without conflicts. When highly automated vehicles (HAVs) become prevalent, interaction will change. Although HAVs manage to identify pedestrians, they may not be able to assess pedestrians’ intentions. This study discusses two different ambitions: Prioritizing pedestrian safety and prioritizing efficient traffic flow; and how these two affect the possibilities to avoid fatal crashes between pedestrians and passenger cars. HAVs’ hypothetical possibilities to avoid different crash scenarios are evaluated based on 40 in-depth investigated fatal pedestrian crashes, which occurred with manually-driven cars in Finland in 2014–2016. When HAVs prioritize pedestrian safety, they decrease speed near pedestrians as a precaution which affects traffic flow due to frequent decelerations. When HAVs prioritize efficient traffic flow, they only decelerate, when pedestrians are in a collision course. The study shows that neither of these approaches can be applied in all traffic environments, and all of the studied crashes would not likely be avoidable with HAVs even when prioritizing pedestrian safety. The high expectations of HAVs’ safety benefits may not be realized, and in addition to safety and traffic flow, there are many other objectives in traffic which need to be considered.


2012 ◽  
Vol 546-547 ◽  
pp. 1071-1074
Author(s):  
Jian Ling Wang ◽  
Hong Bo Lai

The study object is traffic flow on main road of urban traffic networks, the traffic condition is recognized by traffic flow theory and fuzzy logic method. The average space speed is a variable of the fact flow function, the road congestion degree is described by the ratio of fact flow and traffic capacity; the ratio of congestion time length and total time length is the congestion frequency. Considering congestion degree and congestion frequency, a fuzzy logic method is used to describe the traffic state by three grades: free, congestion and serious congestion. At last, the numerical example is given to analyze traffic state.


Author(s):  
Zdenko Kljaic ◽  
Tomislav Josip Mlinaric ◽  
Danijel Pavkovic ◽  
Mihael Cipek ◽  
Mladen Niksic

2020 ◽  
Vol 69 ◽  
pp. 101285
Author(s):  
Esubalew Alemneh ◽  
Sidi-Mohammed Senouci ◽  
Mohamed-Ayoub Messous

2013 ◽  
Vol 336-338 ◽  
pp. 438-441
Author(s):  
Wei Dong Dai

In this paper, fuzzy logic control is applied to forecast the short-term traffic flow and traffic guidance. Because of the factors of time correlation and spatial correlation, we construct the short-term traffic flow forecasting model using fuzzy logic control that can handle non-linear plant behavior. In order to find a feasibility way of traffic flow prediction, we deal the combination of time correlation traffic value and space correlation traffic value as the input variables. Considering the real condition, we use triangular and trapezoid membership function to design the belongings relationship. Five fuzzy rules are applied in the control. Last, we use fuzzy logic toolbox to simulate the short term traffic flow forecasting basing on the fuzzy logic control. The system input/output curve result shows that this method can have a good performance for short-term traffic flow prediction.


2021 ◽  
Vol 72 (1) ◽  
pp. 1-8
Author(s):  
Dinh Toan Trinh

This paper presents a methodology for appraisal of congestion level for traffic control on expressways using fuzzy logic. The congestion level indicates the severity of congestion and is estimated using speed and density, being the basic traffic parameters that describe state of a traffic stream. Formulation of the fuzzy rule base is made based on knowledge on traffic flow theory and engineering judgments. Field data on a segment of the Pan-Island Expressway of Singapore were used to estimate the congestion levels for three scenarios: single input variable (speed or density) and combined input variables (speed and density), represented by congestion level on a [0 1] scale. The results showed that there were big gaps between the congestion levels evaluated based specifically on speed and density alone (single state variable), and the congestion levels estimated from both variables lie in between. Given the uncertainty in traffic data collection and dynamic nature of traffic flow, this indicates that it may be inadequate to evaluate traffic congestion level using a single variable, and the use of both speed and density represent the state of a traffic stream more properly. The study results also show that the fuzzy logic approach provides flexible combination of state variables to obtain the congestion level and to describe gradual transition of traffic state, which is particularly important under the heavy congested conditions.


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