A Personalized Trip Planner For Vulnerable Road Users

This research presents an adaptive and personalized routing model that enables individuals with disabilities to save their route preferences to a mobility assistant platform. The proactive approach based on anticipated user need accommodates vulnerable road users’ personalized optimum dynamic routing rather than a reactive approach passively awaiting input. Most of the currently available trip planners target the general public’s use of simpler route options prioritized based on static road characteristics. These static normative approaches are only satisfactory when conditions of intermediate intersections in the network are consistent, a constant rate of change occurs per each change of the segment condition, and the same fixed routes are valid every day regardless of the user preference. In this study, we model the vulnerable road user mobility problem by accommodating personalized preferences changing by time, sidewalk segment traversability, and the interaction between sidewalk factors and weather conditions for each segment contributing to a path choice. The developed reinforcement learning solution presents a lower average cost of personalized, accessible, and optimal path choices in various trip scenarios and superior to traditional shortest path algorithms (e.g., Dijkstra) with static and dynamic extensions

Smart city technologies: new barriers to investment or a method for solving the economic problems of municipalities?

The purpose of the study is to determine the degree of readiness of urban municipal entities of the Russian Federation for the implementation of Smart City technology. The author proposes a methodology for determining the level of preparedness of cities for the introduction of Smart City technologies, selecting those municipal projects (Smart-projects) most relevant to the present level of readiness and identifying the main barriers to their implementation. The study used structural and graphical analysis methods, overall assessment and ratings as well as the group method of data handling (GMDH). The study yielded the following conclusions: The majority of cities comprising administrative centres of the Subjects of the Russian Federation are not ready for the implementation of Smart City technologies. The main problems hindering the implementation of Smart Technologies are the municipalities’ low energy efficiency and high dependence on borrowed capital. The methodology proposed by the author for assessing the readiness of municipalities for the implementation of Smart City technologies will quickly and optimally identify metropolitan areas suitable for the implementation of Smart-technologies. The field of application of the obtained results is sufficiently extensive. These results will be of interest to practitioners, representatives of state and local authorities, as well as for researchers in the fields of urban economics and urban studies. The main direction for future research consists in the provision of an underlying rationale for problem solving through launching Smart-projects in depressed and economically stagnating municipalities

Optimisation using Natural Language Processing: Personalized Tour Recommendation for Museums

This paper proposes a new method to provide personalized tour recommendation for museum visits. It combines an optimization of preference criteria of visitors with an automatic extraction of artwork importance from museum information based on Natural Language Processing using textual energy. This project includes researchers from computer and social sciences. Some results are obtained with numerical experiments. They show that our model clearly improves the satisfaction of the visitor who follows the proposed tour. This work foreshadows some interesting outcomes and applications about on-demand personalized visit of museums in a very near future.Comment: 8 pages, 4 figures; Proceedings of the 2014 Federated Conference on Computer Science and Information Systems pp. 439-44

Which One is Correct, The Map or The GPS Trace

GPS data is noisy by nature. A typical location-based service would start by filtering out the noise from the raw GPS points that are generated by moving objects. Once the locations of the objects are identified, the location-based service is provided. In this paper, we decide not to throw away the noise. Instead, we consider the noise as an asset. We analyze the various noise patterns under different conditions and region characteristics. More specifically, we focus on one example where a lot of GPS noise is experienced; which is urban canyons. We believe that learning the GPS noise patterns in a supervised environment enables us to discover knowledge about new areas or areas where we have little knowledge. This paper is based on the analysis of GPS traces that are collected from the shuttle service within the Microsoft campuses around Seattle, Washington

Weak nodes detection in urban transport systems: Planning for resilience in Singapore

The availability of massive data-sets describing human mobility offers the possibility to design simulation tools to monitor and improve the resilience of transport systems in response to traumatic events such as natural and man-made disasters (e.g. floods terroristic attacks, etc...). In this perspective, we propose ACHILLES, an application to model people's movements in a given transport system mode through a multiplex network representation based on mobility data. ACHILLES is a web-based application which provides an easy-to-use interface to explore the mobility fluxes and the connectivity of every urban zone in a city, as well as to visualize changes in the transport system resulting from the addition or removal of transport modes, urban zones, and single stops. Notably, our application allows the user to assess the overall resilience of the transport network by identifying its weakest node, i.e. Urban Achilles Heel, with reference to the ancient Greek mythology. To demonstrate the impact of ACHILLES for humanitarian aid we consider its application to a real-world scenario by exploring human mobility in Singapore in response to flood prevention.Comment: 9 pages, 6 figures, IEEE Data Science and Advanced Analytic

Socially Optimal Personalized Routing With Preference Learning

Traffic congestion has become inescapable across the United States, especially in urban areas. Yet, support is lacking for taxes to fund expansion of the existing network. Thus, it is imperative to find novel ways to improve efficiency of the existing infrastructure. A major obstacle is the inability to enforce socially optimal routes among the commuters. We propose to improve routing efficiency by leveraging heterogeneity in commuter preferences. We learn individual driver preferences over the route characteristics and use these preferences to recommend socially optimal routes that they will likely follow. The combined effects of socially optimal routing and personalization help bridge the gap between utopic and user optimal solutions. We take the view of a recommendation system with a large userbase but no ability to enforce routes in a highly congested network. We (a) develop a framework for learning individual driver preferences overtime, and (b) devise a mathematical model for computing personalized socially optimal routes given (potentially partial) information on driver preferences. We evaluated our approach on data collected from Amazon Mechanical Turk and compared with Logistic Regression and our model improves prediction accuracy by over 12%

Framework for constructing multimodal transport networks and routing using a graph database: A case study in London

Most prior multimodal transport networks have been organized as relational databases with multilayer structures to support transport management and routing; however, database expandability and update efficiency in new networks and timetables are low due to the strict database schemas. This study aimed to develop multimodal transport networks using a graph database that can accommodate efficient updates and extensions, high relation-based query performance, and flexible integration in multimodal routing. As a case study, a database was constructed for London transport networks, and routing tests were performed under various conditions. The constructed multimodal graph database showed stable performance in processing iterative queries, and efficient multi-stop routing was particularly enhanced. By applying the proposed framework, databases for multimodal routing can be readily constructed for other regions, while enabling responses to diversified routings, such as personalized routing through integration with various unstructured information, due to the flexible schema of the graph database