scholarly journals Route Choice Behaviour Modeling using IoT Integrated Artificial Intelligence

2021 ◽  
Vol 2 (4) ◽  
pp. 232-237
Author(s):  
Joy Iong-Zong Chen ◽  
Jen-Ting Chang
Keyword(s):  
Likelihood Function ◽  
Choice Model ◽  
Mixed Logit ◽  
Route Choice ◽  
Likelihood Estimation ◽  
Accurate Information ◽  
Choice Behaviour ◽  
Vehicle Identification ◽  
High Penetration ◽  
Automatic Vehicle Identification

Automatic Vehicle Identification (AVI) data is used to identify the location of a particular vehicle in and can also be used for route choice behaviour modelling. But the use of AVI doesn’t provide accurate information on OD pair and the particular route that is chosen. This problem is addressed in this paper using a semi-supervised learning method which can be used to identify the route on prior training. As the first step, the AVI trace is segregated into observation pairs using the Maximum Likelihood Estimation and then it is further joined with GPS co-ordinates to tackle the sparse issues. As the next step, the heterogeneity and correlation between the various pairs are determined using Mixed Logit model. As the final step, a relationship between the likelihood function and route choice model is established using Maximum to log-likelihood function. Based on the observations, the results are recorded and the proposed work shows significant improvement in the accuracy in route determination. The evaluation scenario shows that the proposed work could be expanded to a larger area. Moreover, the robustness of the system is illustrated using sensitivity analysis. This work uses AVI data with respect to its behaviour in routes through high penetration.

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

2020 ◽  
Vol 79 (ET.2020) ◽  
pp. 1-17
Author(s):  
Sowjanya Dhulipala
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Choice Model ◽  
Route Choice ◽  
Fuzzy Rule ◽  
Multinomial Logit ◽  
Choice Modelling ◽  
Choice Behaviour ◽  
Linguistic Rules ◽  
Mnl Model

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.

scholarly journals Exploring Route Choice Behaviours Accommodating Stochastic Choice Set Generations

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Shin-Hyung Cho ◽  
Seung-Young Kho
Keyword(s):  
Radio Frequency Identification ◽  
Choice Model ◽  
Route Choice ◽  
Revealed Preference ◽  
Guidance System ◽  
Model Parameters ◽  
Generation Model ◽  
Choice Behaviour ◽  
Choice Set Generation ◽  
Choice Set

Modelling route choice behaviours are essential in traffic operation and transportation planning. Many studies have focused on route choice behaviour using the stochastic model, and they have tried to construct the heterogeneous route choice model with various types of data. This study aims to develop the route choice model incorporating travellers’ heterogeneity according to the stochastic route choice set. The model is evaluated from the empirical travel data based on a radio frequency identification device (RFID) called dedicated short-range communication (DSRC). The reliability level is defined to explore the travellers’ heterogeneity in the choice set generation model. The heterogeneous K-reliable shortest path- (HK α RSP-) based route choice model is established to incorporate travellers’ heterogeneity in route choice behaviour. The model parameters are estimated for the mixed path-size correction logit (MPSCL) model, considering the overlapping paths and the heterogeneous behaviour in the route choice model. The different behaviours concerning the chosen routes are analysed to interpret the route choice behaviour from revealed preference data by comparing the different coefficients’ magnitude. There are model validation processes to confirm the prediction accuracy according to travel distance. This study discusses the policy implication to introduce the traveller specified route travel guidance system.

scholarly journals Did Attitudes Interpret and Predict “Better” Choice Behaviour towards Innovative and Greener Automotive Technologies? A Hybrid Choice Modelling Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-22
Author(s):  
Stefano de Luca ◽  
Roberta Di Pace
Keyword(s):  
Latent Variables ◽  
Psychological Factors ◽  
Choice Model ◽  
Combustion Engine ◽  
Innovative Technology ◽  
Choice Modelling ◽  
Choice Behaviour ◽  
Model Framework ◽  
Main Research ◽  
Automotive Technology

It is common opinion that traditional approaches used to interpret and model users’ choice behaviour in innovative contexts may lead to neglecting numerous nonquantitative factors that may affect users’ perceptions and behaviours. Indeed, psychological factors, such as attitudes, concerns, and perceptions may play a significant role which should be explicitly modelled. By contrast, collecting psychological factors could be a time and cost consuming activity, and furthermore, real-world applications must rely on theoretical paradigms which are able to easily predict choice/market fractions. The present paper aims to investigate the above-mentioned issues with respect to an innovative automotive technology based on the after-market hybridization of internal combustion engine vehicles. In particular, three main research questions are addressed: (i) whether and how users’ characteristics and attitudes may affect users’ behaviour with respect to new technological (automotive) scenarios (e.g., after-market hybridization kit); (ii) how to better “grasp” users’ attitudes/concerns/perceptions and, in particular, which is the most effective surveying approach to observe users’ attitudes; (iii) to what extent the probability of choosing a new automotive technology is sensitive to attitudes/concerns changes. The choice to install/not install the innovative technology was modelled through a hybrid choice model with latent variables (HCMs), starting from a stated preferences survey in which attitudes were investigated using different types of questioning approaches: direct questioning, indirect questioning, or both approaches. Finally, a comparison with a traditional binomial logit model and a sensitivity analysis was carried out with respect to the instrumental attributes and the attitudes. Obtained results indicate that attitudes are significant in interpreting and predicting users’ behaviour towards the investigated technology and the HCM makes it possible to easily embed psychological factors into a random utility model/framework. Moreover, the explicit simulation of the attitudes allows for a better prediction of users’ choice with respect to the Logit formulation and points out that users’ behaviour may be significantly affected by acting on users’ attitudes.

scholarly journals Evaluating the Observed Log-Likelihood Function in Two-Level Structural Equation Modeling with Missing Data: From Formulas to R Code

Psych ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 197-232
Author(s):  
Yves Rosseel
Keyword(s):  
Structural Equation ◽  
Structural Equation Models ◽  
Likelihood Function ◽  
Likelihood Estimation ◽  
Missing At Random ◽  
Equation Modeling ◽  
Computationally Efficient ◽  
Log Likelihood ◽  
Efficient Expression ◽  
Special Case

This paper discusses maximum likelihood estimation for two-level structural equation models when data are missing at random at both levels. Building on existing literature, a computationally efficient expression is derived to evaluate the observed log-likelihood. Unlike previous work, the expression is valid for the special case where the model implied variance–covariance matrix at the between level is singular. Next, the log-likelihood function is translated to R code. A sequence of R scripts is presented, starting from a naive implementation and ending at the final implementation as found in the lavaan package. Along the way, various computational tips and tricks are given.

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