Demand pattern analysis of taxi trip data for anomalies detection and explanation

Novi Zakon o obveznim odnosima promijenio je naziv instituta bankarske garancije u bankarsko jamstvo i pojam tog instituta izložen u čl. 1039. st. 1. i 2. (tako da se sada pod nazivom bankarskog jamstva pojavljuje samostalna bankarska garancija), dok ostale odredbe ranijeg ZOO-a sadržajno nisu promijenjene. Bankovna garancija jeste samostalna obveza banke garanta koja je akcesorna obveza jamca. Banka garant ne osigurava ispunjenje obveze glavnog dužnika, naprotiv, obvezuje se korisniku garancije nadoknaditi štetu, odnosno izvršiti obvezu koju u ugovorenom roku nije izvršio glavni dužnik. U radu izlažem pitanja u svezi s oblikom i vrstama garancije, kvalifikacijom i nastankom bančine obveze prema korisniku, pretpostavkama isplate, prenosivošću i potvrdom garancije kao i njihovoj zlouporabi.The new Law of mandatory relations has changed the name of the bank warranty to bank assurance and complete connotation is represented in article 1039. in section 1 and 2. (according to which, under the name of bank warranty is independent bank assurance) while other provisions from the Law of mandatory relations have not been significantly contextually changed. Bank warranty is independent obligation of the warrant bank, which is accessory obligation of the guarantor. Warrant bank does not assure implementation of the main debtor’s obligation, but it commits to compensate potential detriment towards the warranty user, in the other words, implement the obligation which has not been realized by the main debtor in specified time period

Learning to Control Autonomous Fleets from Observation via Offline Reinforcement Learning

Autonomous Mobility-on-Demand (AMoD) systems are an evolving mode of transportation in which a centrally coordinated fleet of self-driving vehicles dynamically serves travel requests. The control of these systems is typically formulated as a large network optimization problem, and reinforcement learning (RL) has recently emerged as a promising approach to solve the open challenges in this space. Recent centralized RL approaches focus on learning from online data, ignoring the per-sample-cost of interactions within real-world transportation systems. To address these limitations, we propose to formalize the control of AMoD systems through the lens of offline reinforcement learning and learn effective control strategies using solely offline data, which is readily available to current mobility operators. We further investigate design decisions and provide empirical evidence based on data from real-world mobility systems showing how offline learning allows to recover AMoD control policies that (i) exhibit performance on par with online methods, (ii) allow for sample-efficient online fine-tuning and (iii) eliminate the need for complex simulation environments. Crucially, this paper demonstrates that offline RL is a promising paradigm for the application of RL-based solutions within economically-critical systems, such as mobility systems

Analyzing the Reporting Error of Public Transport Trips in the Danish National Travel Survey Using Smart Card Data

Household travel surveys have been used for decades to collect individuals and households' travel behavior. However, self-reported surveys are subject to recall bias, as respondents might struggle to recall and report their activities accurately. This study addresses examines the time reporting error of public transit users in a nationwide household travel survey by matching, at the individual level, five consecutive years of data from two sources, namely the Danish National Travel Survey (TU) and the Danish Smart Card system (Rejsekort). Survey respondents are matched with travel cards from the Rejsekort data solely based on the respondents' declared spatiotemporal travel behavior. Approximately, 70% of the respondents were successfully matched with Rejsekort travel cards. The findings reveal a median time reporting error of 11.34 minutes, with an Interquartile Range of 28.14 minutes. Furthermore, a statistical analysis was performed to explore the relationships between the survey respondents' reporting error and their socio-economic and demographic characteristics. The results indicate that females and respondents with a fixed schedule are in general more accurate than males and respondents with a flexible schedule in reporting their times of travel. Moreover, trips reported during weekdays or via the internet displayed higher accuracies compared to trips reported during weekends and holidays or via telephones. This disaggregated analysis provides valuable insights that could help in improving the design and analysis of travel surveys, as well accounting for reporting errors/biases in travel survey-based applications. Furthermore, it offers valuable insights underlying the psychology of travel recall by survey respondents.Comment: 22 pages, 3 figures, 7 table

A Neural-embedded Choice Model: TasteNet-MNL Modeling Taste Heterogeneity with Flexibility and Interpretability

Discrete choice models (DCMs) and neural networks (NNs) can complement each other. We propose a neural network embedded choice model - TasteNet-MNL, to improve the flexibility in modeling taste heterogeneity while keeping model interpretability. The hybrid model consists of a TasteNet module: a feed-forward neural network that learns taste parameters as flexible functions of individual characteristics; and a choice module: a multinomial logit model (MNL) with manually specified utility. TasteNet and MNL are fully integrated and jointly estimated. By embedding a neural network into a DCM, we exploit a neural network's function approximation capacity to reduce specification bias. Through special structure and parameter constraints, we incorporate expert knowledge to regularize the neural network and maintain interpretability. On synthetic data, we show that TasteNet-MNL can recover the underlying non-linear utility function, and provide predictions and interpretations as accurate as the true model; while examples of logit or random coefficient logit models with misspecified utility functions result in large parameter bias and low predictability. In the case study of Swissmetro mode choice, TasteNet-MNL outperforms benchmarking MNLs' predictability; and discovers a wider spectrum of taste variations within the population, and higher values of time on average. This study takes an initial step towards developing a framework to combine theory-based and data-driven approaches for discrete choice modeling

Semi-nonparametric Latent Class Choice Model with a Flexible Class Membership Component: A Mixture Model Approach

This study presents a semi-nonparametric Latent Class Choice Model (LCCM) with a flexible class membership component. The proposed model formulates the latent classes using mixture models as an alternative approach to the traditional random utility specification with the aim of comparing the two approaches on various measures including prediction accuracy and representation of heterogeneity in the choice process. Mixture models are parametric model-based clustering techniques that have been widely used in areas such as machine learning, data mining and patter recognition for clustering and classification problems. An Expectation-Maximization (EM) algorithm is derived for the estimation of the proposed model. Using two different case studies on travel mode choice behavior, the proposed model is compared to traditional discrete choice models on the basis of parameter estimates' signs, value of time, statistical goodness-of-fit measures, and cross-validation tests. Results show that mixture models improve the overall performance of latent class choice models by providing better out-of-sample prediction accuracy in addition to better representations of heterogeneity without weakening the behavioral and economic interpretability of the choice models