Tourism agglomeration and its impact on social welfare: an empirical approach to the Spanish case.

This paper measures two descriptors of tourism namely, its scale and agglomeration level and subsequently evaluates both descriptors according to their direct and joint impacts on the host communities' quality of life. The key constructs for this research are the following: (1) a tourism evaluation function that incorporates the scale and agglomeration of tourism, which is constructed for each one of the 50 Spanish provinces; and (2) a measure of the host communities' quality of life that comprises 12 objective partial indicators and an overall indicator that integrates them all. Results show the existence of carrying capacity frontiers or maximum thresholds that tourist destinations can sustain without damaging the economic, socio cultural, or environmental systems of the communities they belong.Communities; Sustainable tourism; Carrying capacity; Spain;

Hotel Location in Tourism Cities: Madrid 1936-1998.

To determine how the positioning of new hotels is affected by the distribution of similar incumbent competitors, this paper investigates geographic location, price, size, and services. With data on all 240 hotels operating in the city of Madrid between 1936 and 1998, a model of geographic and product location at the time of the hotels’ foundings is estimated based on the above mentioned variables. These are simultaneously determined and contingent upon the changing socioeconomic and urban circumstances of the city. The findings suggest that agglomeration occurs only among differentiated establishments. In the balance between agglomeration and differentiation strategies, particularly significant is the trade-off between price and geographic dimensions.Emplacement des hôtels dans les villes touristiques: Madrid 1936–1998. Pour déterminer comment le positionnement des nouveaux hôtels est affecté par la distribution des concurrents similaires et déjà établis, cet article examine situation géographique, prix, grandeur et services. Avec des données sur tous les 240 hôtels en opération à Madrid entre 1936 et 1998, on calcule un modèle de la situation géographique et des services au moment de la fondation des hôtels, en se basant sur les variables surmentionnées. Celles-ci dépendent au même temps des circonstances urbaines et socioéconomiques changeantes de la ville. Les résultats suggèrent que l’agglomération a lieu seulement parmi les établissements différenciés. Dans l’équilibre entre les stratégies d’agglomération et de différentiation, le compromis entre prix et situation est particulièrement significatif.Hotels; Location; Madrid; Hotels; Situation;

FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation

One of the most popular approaches to multi-target tracking is tracking-by-detection. Current min-cost flow algorithms which solve the data association problem optimally have three main drawbacks: they are computationally expensive, they assume that the whole video is given as a batch, and they scale badly in memory and computation with the length of the video sequence. In this paper, we address each of these issues, resulting in a computationally and memory-bounded solution. First, we introduce a dynamic version of the successive shortest-path algorithm which solves the data association problem optimally while reusing computation, resulting in significantly faster inference than standard solvers. Second, we address the optimal solution to the data association problem when dealing with an incoming stream of data (i.e., online setting). Finally, we present our main contribution which is an approximate online solution with bounded memory and computation which is capable of handling videos of arbitrarily length while performing tracking in real time. We demonstrate the effectiveness of our algorithms on the KITTI and PETS2009 benchmarks and show state-of-the-art performance, while being significantly faster than existing solvers

PIXOR: Real-time 3D Object Detection from Point Clouds

We address the problem of real-time 3D object detection from point clouds in the context of autonomous driving. Computation speed is critical as detection is a necessary component for safety. Existing approaches are, however, expensive in computation due to high dimensionality of point clouds. We utilize the 3D data more efficiently by representing the scene from the Bird's Eye View (BEV), and propose PIXOR, a proposal-free, single-stage detector that outputs oriented 3D object estimates decoded from pixel-wise neural network predictions. The input representation, network architecture, and model optimization are especially designed to balance high accuracy and real-time efficiency. We validate PIXOR on two datasets: the KITTI BEV object detection benchmark, and a large-scale 3D vehicle detection benchmark. In both datasets we show that the proposed detector surpasses other state-of-the-art methods notably in terms of Average Precision (AP), while still runs at >28 FPS.Comment: Update of CVPR2018 paper: correct timing, fix typos, add acknowledgemen

DeepSignals: Predicting Intent of Drivers Through Visual Signals

Detecting the intention of drivers is an essential task in self-driving, necessary to anticipate sudden events like lane changes and stops. Turn signals and emergency flashers communicate such intentions, providing seconds of potentially critical reaction time. In this paper, we propose to detect these signals in video sequences by using a deep neural network that reasons about both spatial and temporal information. Our experiments on more than a million frames show high per-frame accuracy in very challenging scenarios.Comment: To be presented at the IEEE International Conference on Robotics and Automation (ICRA), 201