A tutorial on recursive models for analyzing and predicting path choice behavior

The problem at the heart of this tutorial consists in modeling the path choice behavior of network users. This problem has been extensively studied in transportation science, where it is known as the route choice problem. In this literature, individuals' choice of paths are typically predicted using discrete choice models. This article is a tutorial on a specific category of discrete choice models called recursive, and it makes three main contributions: First, for the purpose of assisting future research on route choice, we provide a comprehensive background on the problem, linking it to different fields including inverse optimization and inverse reinforcement learning. Second, we formally introduce the problem and the recursive modeling idea along with an overview of existing models, their properties and applications. Third, we extensively analyze illustrative examples from different angles so that a novice reader can gain intuition on the problem and the advantages provided by recursive models in comparison to path-based ones

A model-free approach for solving choice-based competitive facility location problems using simulation and submodularity

This paper considers facility location problems in which a firm entering a market seeks to open a set of available locations so as to maximize its expected market share, assuming that customers choose the alternative that maximizes a random utility function. We introduce a novel deterministic equivalent reformulation of this probabilistic model and, extending the results of previous studies, show that its objective function is submodular under any random utility maximization model. This reformulation characterizes the demand based on a finite set of preference profiles. Estimating their prevalence through simulation generalizes a sample average approximation method from the literature and results in a maximum covering problem for which we develop a new branch-and-cut algorithm. The proposed method takes advantage of the submodularity of the objective value to replace the least influential preference profiles by an auxiliary variable that is bounded by submodular cuts. This set of profiles is selected by a knee detection method. We provide a theoretical analysis of our approach and show that its computational performance, the solution quality it provides, and the efficiency of the knee detection method it exploits are directly connected to the entropy of the preference profiles in the population. Computational experiments on existing and new benchmark sets indicate that our approach dominates the classical sample average approximation method on large instances, can outperform the best heuristic method from the literature under the multinomial logit model, and achieves state-of-the-art results under the mixed multinomial logit model.Comment: 36 pages, 6 figures, 6 table

Enforcement of IFRS in Sweden - Achievements for building trust to the financial information

The study was conducted through an abductive approach and a qualitative research strategy using a hermeneutic epistemological stance. The data was collected using interviews and respondents selected with a purposive sample technique. With interviews as a data collection technique, we have gathered data from five respondents representing larger listed companies, auditors from the four largest auditing firms, and from the enforcement bodies FI and OMX This thesis has found evidence suggesting that trust towards the financial statements has changed, unfortunately to the worse. Although there is a perception today among preparers (the companies) and attesters (the auditors) that the work contributes to a creation of trust, the magnitude and actual impact are hard to specify. Due to the transparency, competence and impact of the surveillance made by the former enforcement body Övervakningspanelen, there is not an increased trust towards the financial statements in comparison to the situation today

Enforcement of IFRS in Sweden

Purpose: Has the trust changed for the Swedish financial reports based on IFRS standards with the enforcement work done by FI and OMX, and if so, why? This thesis has found evidence suggesting that trust towards the financial statements has changed, unfortunately to the worse. Although there is a perception today among preparers (the companies) and attesters (the auditors) that the work contributes to a creation of trust, the magnitude and actual impact are hard to specify. Due to the transparency, competence and impact of the surveillance made by the former enforcement body Övervakningspanelen, there is not an increased trust towards the financial statements in comparison to the situation today. With interviews as a data collection technique, we have gathered data from five respondents representing larger listed companies, auditors from the four largest auditing firms, and from the enforcement bodies FI and OMX. We have used theoretical perspectives related to the concept of trust in business and enforcement of IFRS in order to grasp their relations. Trust is fundamental when working with surveillance and financial statements today

A decomposition method for estimating recursive logit based route choice models

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On the similarities between random regret minimization and mother logit: The case of recursive route choice models

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A nested recursive logit model for route choice analysis

We propose a route choice model that relaxes the independence from irrelevant alternatives property of the logit model by allowing scale parameters to be link specific. Similar to the the recursive logit (RL) model proposed by Fosgerau et al. (2013), the choice of path is modelled as a sequence of link choices and the model does not require any sampling of choice sets. Furthermore, the model can be consistently estimated and efficiently used for prediction. A key challenge lies in the computation of the value functions, i.e. the expected maximum utility from any position in the network to a destination. The value functions are the solution to a system of non-linear equations. We propose an iterative method with dynamic accuracy that allows to efficiently solve these systems. We report estimation results and a cross-validation study for a real network. The results show that the NRL model yields sensible parameter estimates and the fit is significantly better than the RL model. Moreover, the NRL model outperforms the RL model in terms of prediction

Route choice analysis:data, models, algorithms and applications

This thesis focuses on the route choice behavior of car drivers (uni-modal networks). More precisely, we are interested in identifying which route a given traveler would take to go from one location to another. For the analysis of this problem we use discrete choice models and disaggregate revealed preferences data. Route choice models play an important role in many transport applications, for example, intelligent transport systems, GPS navigation and transportation planning. The route choice problem is particularly difficult to analyze because it involves the modeling of choice behavior in large transportation networks. Several issues need to be addressed in order to obtain an operational model. First, trip observations in their original format rarely correspond to link-by-link descriptions of chosen paths and they therefore need to be matched to the network representation used by the modeler. This involves data processing that can introduce bias and errors. Second, the actual alternatives considered by the travelers are unknown to the analyst. Since there is a large, possibly infinite, number of feasible paths in the network, individual specific choice sets of paths need to be defined. Third, alternatives are often highly correlated due to physical overlap between the paths (shared links). Models with flexible correlation structure are complex to specify and to estimate. Simple models are therefore often used in practice even tough the associated assumptions about correlation are violated. Fourth, most route choice models assume that the decision is performed pre-trip. Their application in a context where drivers receive real-time information about traffic conditions is questionable. In this thesis we address each of the aforementioned issues. First, we propose a general modeling scheme that reconciles network-free data with a network based model so that the data processing related to map-matching is not anymore necessary. The framework allows the estimation of any existing route choice model based on original trip observations that are described as sequences of locations. We illustrate the approach with a real dataset of reported long distance trips in Switzerland. Second, a new paradigm for choice set generation in particular and route choice modeling in general is presented. Instead of focusing on finding alternatives actually considered by travelers, we propose an approach where we focus on obtaining unbiased parameter estimates. We present a stochastic path generation algorithm based on an importance sampling approach and derive the corresponding sampling correction to be added to the path utilities in the route choice model. This new paradigm also has implications on the way to describe correlation among alternatives. We argue that the correlation should be based not only on the sampled alternatives but also on the general network topology. Estimation results based on synthetic data are presented which clearly show the strength of the approach. Third, we propose an approach to capture correlation that allows the modeler to control the trade-off between the simplicity of the model and the level of realism. The key concept capturing correlation is called a subnetwork. The importance and originality of this approach lie in the possibility to capture the most important correlation without considerably increasing the model complexity. This makes it suitable for a wide spectrum of applications, namely involving large-scale networks. We illustrate the model with a GPS dataset collected in the Swedish city of Borlänge. The final contribution of this thesis concerns adaptive route choice modeling in stochastic and time-dependent networks, as opposed to the static network setting assumed in existing models. Optimal adaptive routing problems have been studied in the literature but the estimation of such choice models based on disaggregate revealed preference data is a new area. We propose an estimator for a routing policy choice model and use synthetic data for illustration. Given the uncertainty related to travel times and traffic conditions in transportation networks, we believe that adaptive route choice modeling is an important direction for future research. To summarize, this thesis addresses issues related to data processing (network-free data approach), algorithms for choice set generation (sampling of alternatives) and models (subnetwork approach and adaptive route choice model). Moreover, we use real applications (Borlänge GPS dataset and reported trips in Switzerland) to illustrate the models and algorithms

Bike route choice modeling using GPS data without choice sets of paths

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