Fuzzy route choice model for traffic assignment☆
References (40)
- et al.
Rating and ranking of multiple-aspects alternatives using fuzzy sets
Automatica
(1977) - et al.
A review of some methods for ranking fuzzy numbers
Fuzzy Sets and Systems
(1985) - et al.
Ranking fuzzy numbers in the setting of possibility theory
Inform. Sci.
(1983) Aggregation and heterogeneity of choice sets in discrete choice models
Transportation Res. B
(1994)- et al.
A driving simulator and its application for modeling route choice in the presence of information
Transportation Res. C
(1994) Fuzzy sets
Inform. Control
(1965)The -equilibrium in transportation networks
Fuzzy Sets and Systems
(1994)- et al.
Data Structures and Algorithms
(1982) - T. Akiyama, T. Kawahara, Traffic assignment model with fuzzy travel time information, in 9th Mini EURO Conf. Fuzzy Sets...
- T. Akiyama, K. Nakamura, T. Sasaki, Traffic diversion model on urban expressway by fuzzy reasoning, in WTCR’92, Proc....
Comparison of fuzzy sets on the same decision space
Fuzzy Sets and Systems
Dynamic processes and equilibrium in transportation networkstowards a unifying theory
Transportation Sci.
A probabilistic multipath traffic assignment model which obviates path enumeration
Transportation Res.
A note on two problems in connection with graphs
Numer. Math.
Algorithmes de plus court chemin pour traiter des donnés floues
RAIRO Rech. Opér.
Operations on fuzzy numbers
Internat. J. Systems Sci.
Théorie des possibilités, application à la représentation des connaissances
Cited by (89)
Route optimization issues and initiatives in Bangladesh: The context of regional significance
2021, Transportation EngineeringA simulation-based approach to investigate the driver route choice behavior under the connected vehicle environment
2019, Transportation Research Part F: Traffic Psychology and BehaviourCitation Excerpt :Besides, transportation planning, route planning and guidance for driving also have been going through great changes due to the application of the big data mining technology to traffic information (Liao & Hu, 2008; Nha, Djahel, & Murphy, 2012; Sujit, Lucani, & Sousa, 2012). The connected vehicles make it possible to realize the balanced distribution for time-variant traffic demand in route networks by sending information about current network performance to travelers at the stage of en-route and pre-trip (Henn, 2000; Londono & Lozano, 2014; Peeta & Mahmassani, 1995). However, the road traffic can be defined as the set of complex phenomena resulting from the behaviors of road users (Doniec, Mandiau, Piechowiak, & Espié, 2008), these methods whether can play a actual role or not lies in the route choice behavior of drivers under the real-time traffic information.
Modeling the dynamic effect of information on drivers’ choice behavior in the context of an Advanced Traveler Information System
2017, Transportation Research Part C: Emerging TechnologiesCitation Excerpt :Several authors in the literature have used other frameworks than random utility for modeling route choice behavior. For example, Lotan and Koutsopoulos (1993), Lotan (1997), Henn (2000) and Rilett and Park (2001) propose different models based on fuzzy logic. Dougherty (1995) gives a review of work using artificial neural networks and Yamamoto et al. (2002) use decision trees for modeling the route choice between two alternatives.
A multi-class model-based control scheme for reducing congestion and emissions in freeway networks by combining ramp metering and route guidance
2017, Transportation Research Part C: Emerging TechnologiesBuilding A Neuro-Fuzzy Based Route Choice Model in Metropolitan Context: Surat City in India
2017, Transportation Research ProcediaIncorporating Driver Behaviors in Network Design Problems: Challenges and Opportunities
2016, Transport Reviews
- ☆
Expanded version of a talk presented at the 9th mini Euro conference Fuzzy Sets in Traffic and Transport Systems held in Budva (Yugoslavia), September 15–19, 1997.