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 META-ANALYSIS OF CHOICE SET GENERATION EFFECTS ON ROUTE CHOICE MODEL ESTIMATES AND PREDICTIONS

Transport ◽  
2012 ◽  
Vol 27 (3) ◽  
pp. 286-298 ◽  
Author(s):  
Carlo Giacomo Prato
Keyword(s):  
Large Scale ◽  
Choice Model ◽  
Meta Analysis ◽  
Route Choice ◽  
Behavior Modeling ◽  
Path Generation ◽  
Choice Sets ◽  
True Model ◽  
Choice Set Generation ◽  
Choice Set

Large scale applications of behaviorally realistic transport models pose several challenges to transport modelers on both the demand and the supply sides. On the supply side, path-based solutions to the user assignment equilibrium problem help modelers in enhancing the route choice behavior modeling, but require them to generate choice sets by selecting a path generation technique and its parameters according to personal judgments. This paper proposes a methodology and an experimental setting to provide general indications about objective judgments for an effective route choice set generation. Initially, path generation techniques are implemented within a synthetic network to generate possible subjective choice sets considered by travelers. Next, ‘true model estimates’ and ‘postulated predicted routes’ are assumed from the simulation of a route choice model. Then, objective choice sets are applied for model estimation and results are compared to the ‘true model estimates’. Last, predictions from the simulation of models estimated with objective choice sets are compared to the ‘postulated predicted routes’. A meta-analytical approach allows synthesizing the effect of judgments for the implementation of path generation techniques, since a large number of models generate a large amount of results that are otherwise difficult to summarize and to process. Meta-analysis estimates suggest that transport modelers should implement stochastic path generation techniques with average variance of its distribution parameters and correction for unequal sampling probabilities of the alternative routes in order to obtain satisfactory results in terms of coverage of ‘postulated chosen routes’, reproduction of ‘true model estimates’ and prediction of ‘postulated predicted routes’.

scholarly journals A link based network route choice model with unrestricted choice set

2013 ◽  
Vol 56 ◽  
pp. 70-80 ◽  
Author(s):  
Mogens Fosgerau ◽  
Emma Frejinger ◽  
Anders Karlstrom
Keyword(s):  
Choice Model ◽  
Route Choice ◽  
Choice Set

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.

STOCHASTIC ROUTE CHOICE SET GENERATION: BEHAVIORAL AND PROBABILISTIC FOUNDATIONS

2007 ◽  
Vol 3 (3) ◽  
pp. 173-189 ◽  
Author(s):  
Piet H.L. Bovy ◽  
Stella Fiorenzo-Catalano
Keyword(s):  
Route Choice ◽  
Choice Set Generation ◽  
Choice Set

The Normalization of Consumer Valuations: Context-Dependent Preferences From Neurobiological Constraints

2020 ◽  
Author(s):  
Ryan Webb ◽  
Paul W. Glimcher ◽  
Kenway Louie
Keyword(s):  
Decision Making ◽  
Discrete Choice ◽  
Choice Model ◽  
Judgment And Decision Making ◽  
Choice Behaviour ◽  
Divisive Normalization ◽  
Choice Sets ◽  
Observed Behaviour ◽  
The Brain ◽  
Choice Set

Consumer valuations are shaped by choice sets, exemplified by patterns of substitution between alternatives as choice sets are varied. Building on recent neuroeconomic evidence that valuations are transformed during the choice process, we incorporate the canonical divisive normalization computation into a discrete choice model and characterize how choice behaviour depends on both size and composition of the choice set. We then examine evidence for such behaviour from two choice experiments that vary the size and composition of the choice set. We find that divisive normalization more accurately captures observed behaviour than alternative models, including an example range normalization model. These results are robust across experimental paradigms. Finally, we demonstrate that Divisive Normalization implements an efficient means for the brain to represent valuations given neurobiological constraints, yielding the fewest choice errors possible given those constraints. This paper was accepted by Elke Weber, judgment and decision making.

Understanding Issues in Airport Ground Access Modeling: Lessons from a Revealed Preference Study of New York Metropolitan Area Airports

2017 ◽  
Vol 2626 (1) ◽  
pp. 85-98
Author(s):  
Duncan Kisia
Keyword(s):  
New York ◽  
Choice Model ◽  
Mode Choice ◽  
Revealed Preference ◽  
Choice Models ◽  
Model Parameters ◽  
Preference Data ◽  
Access Mode ◽  
Analytical Strategies ◽  
Airport Access

Airport ground access mode choice models can provide a great deal of utility for airport facility managers tasked with landside access planning. However, the absence of definitive standards to guide the development of these airport planning tools often results in wide variations in methodological approaches that in turn generate counterintuitive mode choice model parameters and that often leads to improper understanding of the air passenger ground access trip. A new regional airport ground access model was developed in support of the New York City Department of Transportation’s LaGuardia Airport Access Alternatives Analysis Study. The air passenger model developed for the study included a set of market-segmented ground access mode choice models, developed by using revealed preference data from a 2005 survey commissioned by FAA. The model estimation process tested a number of analytical strategies to address some of the challenges typically encountered with revealed preference data and, in the process, uncovered some findings that should both aid future airport ground access mode choice modeling efforts and further illuminate the modeling community’s understanding of the value of time, particularly as it interacts with household income levels and various dimensions of business travel.

scholarly journals Research on metro passenger flow route selection model based on passengers' familiarity with road network

2021 ◽  
Vol 283 ◽  
pp. 02028
Author(s):  
Yiting Liu ◽  
Shunping Jia
Keyword(s):  
Road Network ◽  
Choice Model ◽  
Route Choice ◽  
Model Parameters ◽  
Route Selection ◽  
Passenger Flow ◽  
Selection Behavior ◽  
Before And After ◽  
Generalized Cost ◽  
Decision Making Rule

Road network familiarity is a key attribute that affects passengers’ travel route choice. This paper constructs a differentiated travel generalized cost function based on the passenger’s road network familiarity and the influencing factors of route choice, and uses the Regret Theory to construct a route choice model. By setting passenger decision-making rule weights increase the flexibility of the model. The paper uses the method of combining RP survey and SP survey to conduct route selection behavior survey and calibrate model parameters. Finally, the prediction results before and after the passenger classification are compared with the survey data. The prediction error value is 5.98%, and the prediction accuracy after passenger classification is improved by 6.03%. The effectiveness of the prediction model is verified and the necessity of passenger classification is verified.

Sign in / Sign up

Export Citation Format

Share Document