Transit Shift Response Analysis Through Fuzzy Rule Based-Choice Model: A Case Study of Indian Metropolitan City

Kedia, Ashu; Sowjanya, D.; Salini, P.; Jabeena, M; Katti, Bhimaji K.

doi:10.1007/s40890-017-0038-9

Cited by 10 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Kumar et al [ 14 ] applied the fuzzy logic technique in which the mode-choice model provides the estimated modal split at the specified work centres in Delhi and concluded that the fuzzy logic-based mode-choice model offers significant flexibility in evaluating any kind of sensitive policy for public transport system. Kedia et al [ 13 ] attempted to analyse the transit choice behaviour of urbanites, with the help of fuzzy rule-based mode-choice model, considering Surat, an Indian metropolitan city in the state of Gujarat as the study area, and concluded that fuzzy based models overcome the shortcomings of Logit models as travellers’ perceptions of accessibility attributes can be better expressed in linguistic form. Pulugurta et al [ 19 ] applied the concept of fuzzy logic for modelling mode choice in Port Blair city, India, and compared the results with traditional MNL model, concluding that the fuzzy logic model gave better prediction accuracy in comparison to the traditional MNL model as it was better able to capture and incorporate the human knowledge and reasoning into mode-choice behaviour, and further, selected transport policies were evaluated using the model.…”

Section: Literature Reviewmentioning

confidence: 99%

Public Transport Passenger Attraction Using Policy Interventions for a Post-COVID Scenario

Kamar

Shaheem

Vinayaka

et al. 2022

Transp. in Dev. Econ.

View full text Add to dashboard Cite

Public transport (PT) ridership has seen a continual decline over the past years as more people are opting to use private vehicles, contributing to increased traffic congestions, road accidents, pollution, etc. The COVID-19 pandemic has exacerbated this problem, with official guidelines discouraging the use of PT systems to prevent contagion. Passenger attraction policies for a post-pandemic phase should be formulated by examining the changes in daily trips brought about by the COVID-induced lockdowns. With data collected from employees working in Thiruvananthapuram city, the present study develops a post-lockdown mode-choice model using fuzzy logic programming to evaluate different policies to increase PT ridership. The policies such as introducing parking prohibition on major streets, reducing return-trip fares, improving PT coverage and supply, and early-bird pre-peak hour discounts were tested using sensitivity analysis and the choice model estimated a (private to PT) modal shift of 5.8%, 6.9%, 6.2% and 6.2%, respectively. It is concluded that passenger attraction policies should concentrate more on improving PT services than discouraging private modes to improve ridership.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Public Transport Passenger Attraction Using Policy Interventions for a Post-COVID Scenario

Kamar

Shaheem

Vinayaka

et al. 2022

Transp. in Dev. Econ.

View full text Add to dashboard Cite

show abstract

“…However, triangular MFs have been used in several studies (e.g. Dhulipala et al, 2017;Kedia et al, 2015Kedia et al, , 2017 1994) and were found to be promising. Thus, triangular MFs are adopted to fuzzify the crisp inputs with five fuzzy sets for both input and output variables.…”

Section: Observations From the Surveymentioning

confidence: 99%

Multi-route choice modelling in a metropolitan context: A comparative analysis using Multinomial Logit and Fuzzy Logic based approaches

Dhulipala¹

2020

European Transport/Trasporti Europei

View full text Add to dashboard Cite

Route choice plays a vital role in the traffic assignment and network building, as it involves decision making on part of riders. The vagueness in travellers’ perceptions of attributes of the available routes between any two locations adds to the complexities in modelling the route choice behaviour. Conventional Logit models fail to address the uncertainty in travellers’ perceptions of route characteristics (especially qualitative attributes, such as environmental effects), which can be better addressed through the theory of fuzzy sets and linguistic variables. This study thus attempts to model travellers’ route choice behaviour, using a fuzzy logic approach that is based on simple and logical ‘if-then’ linguistic rules. This approach takes into consideration the uncertainty in travellers’ perceptions of route characteristics, resembling humans’ decision-making process. Three attributes – travel time, traffic congestion, and road-side environment are adopted as factors driving people’s choice of routes, and three alternative routes between two typical locations in an Indian metropolitan city, Surat, are considered in the study. The approach to deal with multiple routes is shown by analyzing two-wheeler riders’ (e.g. motorcyclists’ and scooter drivers’) route choice behaviour during the peak-traffic time. Further, a Multinomial Logit (MNL) model is estimated, to enable a comparison of the two modelling approaches. The estimated Fuzzy Rule-Based Route Choice Model outperformed the conventional MNL model, accounting for the uncertain behaviour of travellers.

show abstract

“…;Jung and Sohn 2017;Kedia et al 2017;Sun and Yang 2018;Tang et al 2020;Xue et al 2014;Yu et al 2015;Zhang et al 2019;Shalit et al 2020). • Understanding/predicting the travellers' mode choice(Chapleau et al 2019;Ferrara et al 2019;Hagenauer and Helbich 2017;Lazar et al 2019;Liang et al 2019;Niklas et al 2020;Tu et al 2016;Victoriano et al 2020;Zhou et al 2019).…”

unclassified

Artificial intelligence for improving public transport: a mapping study

Jevinger,

Zhao,

Persson

et al. 2023

Public Transp

View full text Add to dashboard Cite

The objective of this study is to provide a better understanding of the potential of using Artificial Intelligence (AI) to improve Public Transport (PT), by reviewing research literature. The selection process resulted in 87 scientific publications constituting a sample of how AI has been applied to improve PT. The review shows that the primary aims of using AI are to improve the service quality or to better understand traveller behaviour. Train and bus are the dominant modes of transport investigated. Furthermore, AI is mainly used for three tasks; the most frequent one is prediction, followed by an estimation of the current state, and resource allocation, including planning and scheduling. Only two studies concern automation; all the others provide different kinds of decision support for travellers, PT operators, PT planners, or municipalities. Most of the reviewed AI solutions require significant amounts of data related to the travellers and the PT system. Machine learning is the most frequently used AI technology, with some studies applying reasoning or heuristic search techniques. We conclude that there still remains a great potential of using AI to improve PT waiting to be explored, but that there are also some challenges that need to be considered. They are often related to data, e.g., that large datasets of high quality are needed, that substantial resources and time are needed to pre-process the data, or that the data compromise personal privacy. Further research is needed about how to handle these issues efficiently.

show abstract

Transit Shift Response Analysis Through Fuzzy Rule Based-Choice Model: A Case Study of Indian Metropolitan City

Cited by 10 publications

References 18 publications

Public Transport Passenger Attraction Using Policy Interventions for a Post-COVID Scenario

Public Transport Passenger Attraction Using Policy Interventions for a Post-COVID Scenario

Multi-route choice modelling in a metropolitan context: A comparative analysis using Multinomial Logit and Fuzzy Logic based approaches

Artificial intelligence for improving public transport: a mapping study

Contact Info

Product

Resources

About