The abundance of vehicle trajectory data offers a new opportunity to compute driving routes between origins and destinations. Current graph-based routing pipelines, while effective, involve substantial costs in constructing, maintaining, and updating road network graphs to reflect real-time conditions. In this study, we propose a new trajectory-based routing paradigm that bypasses current workflows by directly utilizing raw trajectory data to compute efficient routes. Our method, named TrajRoute, uniquely “follows” historical trajectories from a source to a destination, constructing paths that reflect actual driver behavior and implicit preferences. To supplement areas with sparse trajectory data, the road network is also incorporated into TrajRoute's index, and tunable parameters are introduced to control the balance between road segments and trajectories, ensuring a unified and adaptable routing approach. We experimentally verify our approach by comparing it to an existing online routing service. Our results demonstrate that as the number of trajectories covering the road network increases, TrajRoute produces increasingly accurate travel time and route length estimates while gradually eliminating the need to downgrade to the road network. This highlights the potential of simpler, data-driven pipelines for routing, offering lowermaintenance alternatives to conventional systems.

Understanding human mobility patterns is crucial for urban planning, resource allocation, and personalized recommendations. However, real-world trajectory data are rarely released publicly due to privacy concerns. At the same time, metropolitan cities are becoming equipped with various roadside sensors, such as CCTV cameras and RFIDs. Unlike trajectory data, these sensors do not uniquely identify and track vehicles, making extracting mobility patterns from their detections challenging. In this paper, we propose VPE, a framework that processes roadside sensor observations to estimate the probability that a vehicle visits a road segment at a certain time. At the core of VPE, we implement LEM, a novel mathematical model that calculates location transition probabilities taking into account the sensors’ reliability. Lastly, we propose APD+, an algorithm that captures the uncertainty of movement between two endpoints. Our experiments show that the proposed methods achieve high accuracy while maintaining practical computation time.

Location measurements from people are very often sparsely sampled due to power constraints or as an attempt at location privacy. However, we would still like to reason about location changes between samples in order to infer visits or understand moving behavior. In this work, we present a method for representing this location uncertainty while constraining the moving object to the road network, which is more realistic and precise for human mobility. Unlike the most straightforward method, fastest path, our method explicitly represents the location uncertainty between location measurements with probabilities. We introduce road network-based bridgelets, which are spatiotemporal probability clouds that model the location uncertainty between two endpoints, and we propose an algorithm, APD*, to generate bridgelets efficiently. In our experimental section, we evaluate the performance of APD* and provide visual examples to compare its output with other baseline methods.

The analysis of trajectory datasets has numerous applications ranging from urban planning to human mobility understanding, but to protect the privacy of individuals trajectory datasets are rarely released to researchers. And even when they are, they are limited in size and spatio-temporal coverage. To address these issues a number of methods for generating synthetic yet realistic trajectory datasets have been proposed. These existing methods either require a lot of complex parameters to be calibrated (simulators) or rely on existing trajectory datasets (generative models). In this paper, we propose Data-Driven Trajectory Generator, dubbed DDTG, a data-driven, model-free, and parameter-less algorithm for generating realistic synthetic vehicle trajectory datasets. Unlike existing approaches, DDTG relies on aggregate origin-destination and traffic data, both of which are publicly available and free of privacy concerns. Furthermore, we show that our method is orthogonal to the existing approaches with which DDTG can be combined to generate synthetic datasets of higher quality. Our experiments with real-world trajectory and traffic data show that the datasets generated by DDTG follow distributions that are very close to the distributions of real trajectory datasets.

Effectively and accurately forecasting the congestion in indoor spaces has become particularly important during the pandemic in order to reduce the risk of exposure to airborne viruses. However, there is a lack of readily available indoor congestion data to train such models. Therefore, in this demo paper we propose EPICGen , an experimental platform for indoor congestion generation to support congestion forecasting in indoor spaces. EPICGen consists of two components: (i) Grid Overlayer , which models the floor plans of buildings; and (ii) Congestion Generator , a realistic indoor congestion generator. We demonstrate EPICGen through an intuitive map-based user interface that enables end-users to customize the parameters of the system and visualize generated datasets.

An isochrone is generally defined as a curve drawn on a map connecting points at which moving objects (e.g., cars) arrive at the same time. Their construction is an important task in many application domains. As an example, in urban planning, isochrones are essential when assessing the placement of public services like hospitals and fire departments. In this study, we formally define the isochrone and reverse isochrone problems, describe our approach to solving them and provide a fully functional system that is capable of visualizing the reachability in various ways. Unlike other studies, our approach is purely data-driven and does not depend on the underlying road network for computing the isochrone. Instead, we focus on directly processing trajectory data. Our system processes two real-world taxi datasets to visualize the reachability of the cities of Seoul and Xi'an. As our experiments show, our approach outperforms the traditional graph-theory techniques while eliminating the expensive need of preprocessing the data.

Several applications in urban planning, ride-sharing or marketing, require access to the location popularity of a geographical area (e.g., city block, city, county) in near real-time and at different resolutions. To conceptualize such an access, imagine a visualization tool to view a heatmap of location popularity of a region on-the-fly as a user interacts seamlessly by zooming in and out. The access method required to enable such a seamless visualization must support: 1) updating the heatmap cells frequently as the raw data (e.g., check-ins) arrives at a high rate in a streaming fashion, and 2) splitting and merging the adjacent cells quickly to support zooming in and out, respectively. This is challenging because the most useful metric for location popularity, location entropy, requires counting the number of unique visits per user, and hence: 1) a large data structure should be maintained and updated per cell, and 2) the adjacent cells must be aggregated/disaggregated quickly while the unique visits are not additive. Due to these challenges, the previous techniques for OLAP cubes, streaming sketches and index structures are not effective. In this paper, we propose a new index structure called MR-Cube that approximates the popularity by maintaining sketches of streamed data per cell, supports time-decay for older visits and aggregates the non-additive location popularity quickly and accurately at different resolutions. We evaluate the accuracy and efficiency of MR-Cube using real-world and synthetic datasets and show its utility for our application.

Abstract—The smartphone revolution has introduced a new era of social networks where users communicate over anonymous messaging platforms to exchange opinions, ideas and even carry out commerce. These platforms enable individuals to establish social interactions between strangers based on a common interest or attribute. In this paper we present Rayzit1, a novel anonymous crowd messaging architecture, which utilizes the location of each user to connect them instantly to their k Nearest Neighbors (kNN) as they move in space. Contrary to the very large body of location-based social networks that suffer from bootstrapping issues, our architecture enables a user to always interact with the geographically closest possible users around. We establish this communication using a fast computation of an All kNN query that generates a dynamic global social graph every few seconds. We present motivating application scenarios and the detailed back-end architecture that allows Rayzit to scale. We have collected and analyzed data from the interactions of thousands of active users and confirm our claims. Keywords—Crowd, Social, Mobile, Microblogging I.