Visually Exploring Transportation Schedules

Service sharing is an important problem in distributed systems, where it affects wait time and allows for the equal use of services by a group of nodes. Mutual exclusion provides an appropriate sharing technique of a critical section (CS) among nodes by the priority order. The primary condition of mutual exclusion is that one node can only use one CS at any given time. In real life, queues and cashier services have long wait times that do not consider the priority of each node. The work presented in this paper proposed a token-based approach, which is a type of mutual exclusion that is suitable for smart cashier systems. In addition, a Fairness algorithm for priority process (FAPP) is implemented for minimizing wait time, decreasing queue length, and promoting fair CS use. The system performance evaluation based on the experiments performed proves the superiority of the proposed algorithm over the first-in-first-out (FIFO) approach in terms of reducing the waiting time.

Traditional public transportation mostly uses fixed schedules to meet society’s needs reliably. Alternatively, one can design a dynamic policy to update the schedule according to the state of the network. Using such policy, any dynamic changes in demand, travel times, traffic, geometry, or emergency modes will be reflected in the real-time scheduling. In this study, the application of reinforcement learning for public transportation scheduling and routing is proposed considering electric vehicles. Reinforcement learning is a right choice for online decision-making in a time-variant setting. Reflecting electric vehicles’ characteristics on public transportation scheduling supports the necessity of considering a dynamic scheduling policy, such as vehicle-to-grid transaction capability or dynamic charging strategies. The proposed scheduling methodology is evaluated for a customary six-stop network. The trained agent takes actions based on the number of waiting people, the drop-off requests, battery level, time, and the current location. The assumed models for the traffic load effect, the passenger flow per stops, and the electricity price add nonlinearity to the problem in which this algorithm will behave more conveniently compared to a fixed schedule setting. Using this method, the electric bus can choose the next destination, the optimal driving speed for each interval, and the amount of electricity to charge or to sell for optimal financial gain when connected to the charging infrastructure. The proposed environment is built numerically, and a learning-based agent is trained for this purpose. Our study supports that the optimal dynamic agent outperforms a fixed schedule policy in different modes of network performance, particularly, when the network undergoes abrupt demand changes.KeywordsReinforcement LearningRoutingPublic transitOptimizationSAC (soft actor-critic)Electric vehicle

This paper compares two popular approaches to calculate access to jobs by public transport: gravity and cumulative opportunities. Using data on commute patterns and public transport schedules from Montreal, Canada, we find cumulative opportunities-based measures estimated at the mean transit commute time and gravity-based measures generated through various decay functions are highly correlated – all above 0.9. This finding holds even when replicating the analysis for low- and non-low-wage jobs available in the same metropolitan region. These findings strongly suggest that easy-to-communicate and -operationalize cumulative opportunities accessibility constructs measured at the mean commute time perform similarly to more theoretically-sound gravity-based measures.

Self-organized regularities in terms of patient arrivals and wait times have been discovered in real-world healthcare services. What remains to be a challenge is how to characterize those regularities by taking into account the underlying patients’ or hospitals’ behaviors with respect to various impact factors. This paper presents a case study to address such a challenge. Specifically, it models and simulates the cardiac surgery services in Ontario, Canada, based on the methodology of Autonomy-Oriented Computing (AOC). The developed AOC-based cardiac surgery service model (AOC-CSS model) pays a special attention to how individuals’ (e.g., patients and hospitals) behaviors and interactions with respect to some key factors (i.e., geographic accessibility to services, hospital resourcefulness, and wait times) affect the dynamics and relevant patterns of patient arrivals and wait times. By experimenting with the AOC-CSS model, we observe that certain regularities in patient arrivals and wait times emerge from the simulation, which are similar to those discovered from the real world. It reveals that patients’ hospital-selection behaviors, hospitals’ service-adjustment behaviors, and their interactions via wait times may potentially account for the self-organized regularities of wait times in cardiac surgery services.

Visual data exploration is a well-known technique in the data mining toolbox for analyzing and understanding large amounts of data. In information retrieval, visualization is still perceived as a front-end or user facing feature while, at the same time, it can be very effective in the back-end for data analysis. Currently, the research community does not sufficiently analyze the reasons underlying the assessments they use for evaluation, which may lead to bias or error when reporting system performance. In this paper we examine the integration of information visualization techniques for visually exploring and analyzing relevance assessments. We argue that visual tools should be integrated as a core component of the infrastructure for relevance assessment and evaluation.

Humans are living in the age of big data, advanced analytics, and human information interaction. The tremendous growth of ‘the data' presents enormous challenges to information-seeking. A naïve user adapts several strategies (e.g., analytical search, search by analogy, etc.) to aggregate the knowledge. Augmented visualization plays a pivotal role via visual cognitive support in user searches (i.e., simple visual search, visual exploration, etc.). Though it is hard for a user to look for specific tasks or visual metaphors, as evaluation of visual task is rarely undertaken, it is unclear what role, if any, information visualizations play in the user search. In this paper, a visual taxonomy is adopted to systematically review key visualization tools to understand the visual tasks and inherent functions and viability. The key contribution is to trace the current trends and advancements on the dimensions (i.e., visual accomplishments and visual implications) and to assert that annotations will provide a vision on the state of exploratory visual analytics, evolving frameworks, and tools.

The primary goal of visual data exploration tools is to enable the discovery of new insights. To justify and reproduce insights, the discovery process needs to be documented and communicated. A com...

The Operations-Time Chart: A graphical tool to evaluate the performance of production systems – From batch-and-queue to lean manufacturing

Schedule optimization is a proven strategy to improve service quality for public transport networks. However, current research mostly optimizes schedule design using prior knowledge of users’ routings, ignoring the optimization of public transport schedules on duplicating stretches of route vehicles. This article presents a new alignment technique of time intervals between consecutive vehicles of different routes on duplicating stretches, considering existing public transport networks in attaining optimization of public transport schedule. Scheduling technique of route vehicles on duplicating stretches includes some steps: analysis of public transport network and determining a lot of duplicating stretches, calculation of the optimal time intervals among arrivals of route vehicles and alignment of these intervals among consecutive route vehicles on duplicating stretches, realization simulation model of urban passenger transport within the simulation modeling system of GPSS World. Furthermore, testing the optimization technique of route vehicle scheduling, analysis of the quality of adjusted schedule with route vehicles of different kinds included, and determining the optimization efficiency for duplicating stretches. The adjusting technique of urban passenger transport schedule allows to increase movement steadiness of consecutive vehicles of different routes on duplicating stretches, adjust traffic intervals for each route, shorten the traffic load on stations, reducing idle time and queue lengths of route vehicles in front of transport stops and also minimize waiting time for route vehicle by those passengers who can be transported along several routes. Improving the bus schedule on duplicating stretches in Gomel is illustrated demonstrating the technique developed. This scheduling technique can be used by carriers and transport operators to improve the quality of services provided.

Algorithm for Public Electric Transport Schedule Control for Intelligent Embedded DevicesIn this paper authors present heuristics algorithm for precise schedule fulfilment in city traffic conditions taking in account traffic lights. The algorithm is proposed for programmable controller. PLC is proposed to be installed in electric vehicle to control its motion speed and signals of traffic lights. Algorithm is tested using real controller connected to virtual devices and real functional models of real tram devices. Results of experiments show high precision of public transport schedule fulfilment using proposed algorithm.

Deep-sea mining vehicles rely on forward-looking sonar systems to collect critical environmental data for mapping, navigation, and operational safety. Real-time semantic segmentation of seabed terrain from sonar images is essential for accurately interpreting the underwater environment. However, these images present several challenges, including low resolution, substantial noise, and indistinct textures and shapes. Additionally, seabed terrain features vary across multiple spatial scales, which complicates the extraction of meaningful information using conventional image processing techniques. Such limitations hinder the precision and reliability of mapping efforts necessary for effective deep-sea operations. To address these challenges, a multi-scale semantic segmentation model has been developed specifically for sonar images. The model incorporates a downsampling module based on Discrete Wavelet Transform (WT-DM), which suppresses noise while preserving critical terrain features, ensuring clearer input for subsequent analysis. Moreover, a TriScale Attention Module (TAM) is introduced to capture features at varying spatial resolutions. This module enhances segmentation performance by allowing the model to focus on relevant patterns across different scales. In the absence of publicly available datasets, experiments were conducted using a custom-built seabed terrain dataset. The proposed model achieved a precision of 82.4% and a mean intersection over union (MIoU) of 79.6%, demonstrating its effectiveness. Comparative evaluations with other mainstream models further confirmed the superior performance and competitiveness of the proposed approach.

The immense growth of the social Web, which has made a large amount of user data easily and publicly available, has opened a whole new spectrum for research in social behavioral sciences. However, as the volume of social media content increases at a very fast rate, it becomes extremely difficult to systematically obtain high-level information from this data. As a consequence, tasks related to the analysis of historical news events based on social media data have not been explored, which limits any type of comparative historical research, causality analysis, and discovery of knowledge from patterns extracted from aggregated social media event information.In this work, we target this issue by proposing a compact high-level representation of news events using social media information. This representation explicitly includes temporal information about the event and information about locations, in particular of geopolitical entities. We call this a spatio-temporal context-aware event representation. Our hypothesis is that by including social, temporal, and spatial information in the event representation, we are enabling the analysis of historical world news from a social and geopolitical perspective. This facilitates, new information retrieval tasks related to historical event information extraction and international relations analysis. We support our claims by presenting two applications of this idea: the first, a visual tool, named Galean, for retrieval and exploration of historical news events within their geopolitical and temporal context. The second, a quantitative analysis of a 2-year Twitter dataset of news events reported by U.S. and U.K. media, which we explore using data mining techniques on our event representations. We present two case studies of event exploration using Galean and user evaluation of this tool, as well as details of our data mining empirical results.

Most existing visualization tools utilize only the visual channel to represent information. Several studies have demonstrated that sonification, the use of sound as an alternative or additional channel for data representation, can help increase the dimensionality of the visual representation and aid in the interpretation of information. We introduce a new sonification module for a visual data exploration system, the Super Spider. It extends a previously developed interactive visualization tool to support data exploration using sound. We also introduce a new data sonification technique and system, 2D Sonar. We have integrated it into Super Spider. Finally, we present user studies conducted to evaluate and validate the efficacy of visual and auditory mappings devised for these tools, and their results.

Public transportation agencies call for an intuitive, interactive, and reusable visualization tool to detect patterns of crime (i.e. pickpockets and gangs) or missing commuters on public transportation systems. Few existing visualization techniques have visually explored mobility correlations of targets and their companions, who are characterized in diverse mobility types, by using discrete travel hints extracted from a massive amount of data. To fill this gap, a visual analytical system is provided to conduct a group-based and individual-based exploration of mobility correlations of passengers of interest, based on an auto integration of multiple queries. How passengers differ from or correlate with each other are further examined based on their spatiotemporal distributions in trajectories and ODs. Real-world case studies, as well as user feedback made by 30 participants, demonstrate the effectiveness of the system in detecting specific targets and their companions featured in diverse mobility types, or in characterizing their spatiotemporal aggregation patterns for a further tracking on public transportation systems.

Information visualization shows tremendous potential for helping both expert and casual users alike make sense of temporal data, but current time series visualization tools provide poor support for comparing several foci in a temporal dataset while retaining context and distance awareness. We introduce a method for supporting this kind of multi-focus interaction that we call stack zooming. The approach is based on the user interactively building hierarchies of 1D strips stacked on top of each other, where each subsequent stack represents a higher zoom level, and sibling strips represent branches in the visual exploration. Correlation graphics show the relation between stacks and strips of different levels, providing context and distance awareness among the focus points. The zoom hierarchies can also be used as graphical histories and for communicating insights to stakeholders. We also discuss how visual spaces that support stack zooming can be extended with annotation and local statistics computations that fit the hierarchical stacking metaphor.