Real-time Public Transport Research Articles

YOLOv8-Based XR Smart Glasses Mobility Assistive System for Aiding Outdoor Walking of Visually Impaired Individuals in South Korea

This study proposes an eXtended Reality (XR) glasses-based walking assistance system to support independent and safe outdoor walking for visually impaired people. The system leverages the YOLOv8n deep learning model to recognize walkable areas, public transport facilities, and obstacles in real time and provide appropriate guidance to the user. The core components of the system are Xreal Light Smart Glasses and an Android-based smartphone, which are operated through a mobile application developed using the Unity game engine. The system divides the user’s field of vision into nine zones, assesses the level of danger in each zone, and guides the user along a safe walking path. The YOLOv8n model was trained to recognize sidewalks, pedestrian crossings, bus stops, subway exits, and various obstacles on a smartphone connected to XR glasses and demonstrated an average processing time of 583 ms and an average memory usage of 80 MB, making it suitable for real-time use. The experiments were conducted on a 3.3 km route around Bokjeong Station in South Korea and confirmed that the system works effectively in a variety of walking environments, but recognized the need to improve performance in low-light environments and further testing with visually impaired people. By proposing an innovative walking assistance system that combines XR technology and artificial intelligence, this study is expected to contribute to improving the independent mobility of visually impaired people. Future research will further validate the effectiveness of the system by integrating it with real-time public transport information and conducting extensive experiments with users with varying degrees of visual impairment.

Smarter but more unequal transport? How socioeconomic and digital inequalities hinder adoption of mobility apps in the Global South

All over the world, cities are becoming “smarter.” The improved use of data generated by the transport system (e.g. smart cards data), combined with GPS trackers and citizens’ mobile data enabled the development of a new generation of smarter mobile apps (e.g., Waze) and several local real-time public transport services and trip planners. These technologies have the potential to reduce transport inequalities via two mechanisms. First, governments can develop more efficient and evidence-based public transport plans. Second, citizens have access to better, real-time information about public transport for trip planning and, thus, can make better—i.e., more efficient or cheaper—decisions regarding their everyday mobility. To assess whether citizens’ appropriation of smart mobility—as with their appropriation of any other digital service—depends strongly on having a certain set of socioeconomic and digital attributes, we analyzed both a mobility survey and a digital inequalities survey based on representative samples of adult Uruguayans living in the capital Montevideo. We find that while the use of mobility apps increased in the second half of the 2010s, the adoption of these tools was stratified by traditional digital inequalities, such as type of internet access and digital literacy, and by variables that predict inequalities in both digital and transport fields, such as age, gender and education. As a result, mobility continues to be stratified in favor of those who are more knowledgeable (i.e., tech-savvy) and have greater access to digital technology. Thus, “smart mobility” has failed to ameliorate—and may even exacerbate—transport inequality. Policy implications of these findings are discussed.

Smart Public Transportation Sensing: Enhancing Perception and Data Management for Efficient and Safety Operations.

The use of cloud computing, big data, IoT, and mobile applications in the public transportation industry has resulted in the generation of vast and complex data, of which the large data volume and data variety have posed several obstacles to effective data sensing and processing with high efficiency in a real-time data-driven public transportation management system. To overcome the above-mentioned challenges and to guarantee optimal data availability for data sensing and processing in public transportation perception, a public transportation sensing platform is proposed to collect, integrate, and organize diverse data from different data sources. The proposed data perception platform connects multiple data systems and some edge intelligent perception devices to enable the collection of various types of data, including traveling information of passengers and transaction data of smart cards. To enable the efficient extraction of precise and detailed traveling behavior, an efficient field-level data lineage exploration method is proposed during logical plan generation and is integrated into the FlinkSQL system seamlessly. Furthermore, a row-level fine-grained permission control mechanism is adopted to support flexible data management. With these two techniques, the proposed data management system can support efficient data processing on large amounts of data and conducts comprehensive analysis and application of business data from numerous different sources to realize the value of the data with high data safety. Through operational testing in real environments, the proposed platform has proven highly efficient and effective in managing organizational operations, data assets, data life cycle, offline development, and backend administration over a large amount of various types of public transportation traffic data.

Passenger Alert Application

Abstract: This paper describes the creation of an intelligent real-time interactive public transport application. The application focuses on enhancing the user experience and improving communication among passengers. The main features of the application include a chat module that enables users to have conversations, facilitating the exchange of information and providing a platform for passengers to inform others about any issues or disruptions in the public transportation system. Additionally, the application incorporates an alarm module designed to alert or awaken passengers from their sleep or quick naps when the bus enters a buffer zone, indicating that their destination is approaching.By leveraging real-time data and intelligent algorithms, the application enhances the overall quality of public transportation services. The chat module promotes community engagement and empowers passengers to actively contribute to the improvement of the system's reliability and efficiency. Furthermore, the alarm module serves as a valuable tool for passengers, ensuring they are informed and prepared as their destination approaches, thus minimizing the risk of missing their stop.The development of this intelligent application addresses the growing need for efficient and user-centric public transportation solutions. It harnesses the power of real-time data and interactive communication to create a seamless and reliable travel experience for passengers. The findings and insights from this project contribute to the advancement of intelligent transportation systems, offering practical solutions to the challenges faced by public transportation networks worldwide.

En-Route Planning of Multi-Destination Public-Transport Trips Using Smartphones

Public transport travelers require well-integrated, real-time information systems to use a network. The objective of this study was twofold: (1) to develop a model to determine the effect of personalized information provision through smartphones on user ability to plan multi-destination trips, and (2) to understand user perception of riding public transport aided by real-time, multi-destination trip-planning smartphone applications. Auckland Pioneer was developed for multi-destination trip planning and integrates real-time public transport services with search functions for places of interest. A test trial was conducted in Auckland, New Zealand, and the findings show the effects of the application on multi-destination trip planning in comparison to traditional information sources such as Google Transit. This research contributes to existing literature by demonstrating the importance of personalized information en-route for user multi-destination trip planning. With information to assist trip planning and provide step-by-step guidance en-route, users tend to feel more confident to ride public transport.

Designing an Intelligent Real-Time Public Transportation Monitoring System Based on IoT

In recent years, smart public transportation has become one of the most important things for developing cities and improving people’s quality of life. Public transportation users face many problems, the most important of which is the long wait at the bus station. The proposed system in this paper helps users of public transportation to find public transportation, arrival times, and other information from any place and at any time using a mobile application. The main objective of the prototype is to reduce the wait time at the bus station by knowing the nearest buses to a user, the real-time location of buses on the Google map to help passengers track buses in real-time, the arrival time of buses, and speed. The system was implemented based on Internet of Things (IoT) technology, by using the Global Positioning System (GPS), a microcontroller with a built-in Wi-Fi module (ESP32), and a mobile user interface by the Blynk IoT platform. The proposed system has been implemented and tested in real-time, where all data obtained is displayed by the GPS sensors for bus locations (longitude and latitude) and speed on the smartphone application. The distance between the bus location and the passenger that will appear in the mobile app was calculated by using the Haversine formula. It measured the accuracy of the distance obtained based on a study of several days at different times on multiple roads in Mosul city and compared it with the actual distance. The average difference between the calculated values computed by the Haversine equation compared to the data obtained from the actual distance is (177 meters) with a minimum error of 8 meters. The arrival time of each bus was calculated based on the distance and average speed of the bus registered along the road.

Increasing Efficient Usage of Real-Time Public Transportation Using IOT, Cloud and Customized Mobile App

Nowadays, reliability in public transportation is very important. People who use public buses wastes quality amount of time in waiting for buses in bus stops by without knowing the current status of bus. In day-to-day life, movement of bus is affected by many of the factors such as traffic or irregular dispatching time of buses. If users get the bus information such as bus location and time of the bus arrival based on normal traffic conditions, the passengers count would increase the trustworthiness for people in the public transport system. Our paper proposes an android application for tracking public bus using global positioning system, informs the passenger count in bus using IR (Infrared) sensors and also the estimated time arrival to the user. The location of bus can be tracked by users using Android application. Our app includes the details of bus like bus number, bus routes, bus stops, bus arrival timings, dispatching timings, etc.

AI Chatbot Providing Real-Time Public Transportation and Route Information

AI Chatbot Providing Real-Time Public Transportation and Route Information

Autonomous shuttle bus service timetabling and vehicle scheduling using skip-stop tactic

The development of new technologies that allow us to know more precisely real-time public transport (PT) passenger demand enables the introduction of new methodologies to improve autonomous shuttle bus service (ASBS) from the perspective of both the user and the operator. This work develops a new methodology to determine the optimized PT timetable integrated with vehicle scheduling, including the possibility of using the skip-stop tactic based on real-time passenger demand. The consideration of a skip-stop operation allows for a reduction in the passengers’ total travel time and in the number of autonomous shuttle vehicles in use. The analysis is based on the deficit function (DF) graphical concept and considers the constraints of the vehicle with regard to capacity. The methodology is developed for a single-depot bidirectional circle ASBS route using the DF as a multi-decision choice model. Because of the complexity involved in large-size genetic algorithm problems, a binary-variable iteration method is applied to attain efficient solutions. A case study in Auckland, New Zealand, is employed to assess the new model. The case study shows a reduction of 1.83% in total passenger travel time and 8.11% in the number of vehicles as compared to solutions that do not consider the skip-stop tactic. Moreover, it is shown that the advanced DF-based methodology could be used to develop fully autonomous PT in a network in the future.

Design and Implementation of a Realtime Public Transport Route Guidance System using Big Data Analysis

Design and Implementation of a Realtime Public Transport Route Guidance System using Big Data Analysis

Build an app and they will come? Lessons learnt from trialling the GetThereBus app in rural communities

Real-time passenger information (RTPI) systems have been identified as having benefits in terms of passenger willingness to travel by public transport and their satisfaction levels with services provided. The lack of this amenity in rural areas, however, may hamper public transport use, thus reinforcing patterns of over-reliance on personal vehicles. To explore the potential impacts of providing RTPI in rural areas, a smartphone application (GetThereBus) was developed to allow rural bus passengers to share real-time public transport data, and access real-time and timetable information. Through user testing of GetThereBus, this work aimed to address questions related to the impact of the limited availability of rural digital infrastructure on the provision of RTPI; the potential for crowdsourced information to supplement published timetable information given digital limitations; and the potential impacts of such a system on the traveller experience. This study describes the GetThereBus development and evaluation phases. The authors found it was possible to design and develop a system that overcame many of the technological limitations experienced in rural areas, and users reported a positive response to the system. However, despite a campaign of user engagement, it proved difficult to recruit and motivate sufficient users to provide the data needed to achieve area-wide coverage.

Environmental impacts of public transport systems using real-time control method

Public Transport (PT) systems rely more and more on online information extracted from both operator’s intelligent equipment and user’s smartphone applications. This allows for a better fit between supply and demand of the multimodal PT system, especially through the use of PT real-time control actions/tactics. In doing so there is also an opportunity to consider environmental-related issues to approach energy saving and reduced pollution. This study investigates and analyses the benefits of using real-time PT operational tactics in reducing the undesirable environmental impacts. A tactic-based control (TBC) optimization model is used to minimize total passenger travel time and maximize direct transfers (without waiting). The model consists of a control policy built upon a combination of three tactics: holding, skip-stops, and boarding limit. The environmental-related measure is the global warming potential (GWP) using the life cycle assessment technique. The methodology developed is applied to a real life case study in Auckland, New Zealand. Results show that TBC could reduce the GWP by means of reduction of total passenger travel times and vehicle travel cycle time. That is, the TBC model results in a 5.6% reduction in total GWP per day compared with an existing no-tactic scenario. This study supports the use of real-time control actions to maintain a reliable PT service, reducing greenhouse gas emissions and subsequently moving towards greener PT systems.

Adaptive control algorithm to provide bus priority with a pre-signal

In urban areas, where road space is limited, it is important to provide efficient public and private transportation systems to maximize person throughput, for example from a signalized intersection. To this end, this research looks at providing bus priority using a dedicated bus lane which is terminated upstream of the intersection, and placing an additional signal at this location, called a pre-signal. Although pre-signals are already implemented in some countries (e.g. UK, Denmark, and Switzerland), an adaptive control algorithm which responds to varying traffic demands has not yet been proposed and analyzed in the literature. This research aims to fill that gap by developing an adaptive control algorithm for pre-signals tailored to real-time private and public transportation demands. The necessary infrastructure to operate an adaptive pre-signal is established, and guidelines for implementation are provided. The relevant parameters regarding the boundary conditions for the adaptive algorithm are first determined, and then quantified for a typical case using a micro-simulation model. It is demonstrated with case studies that, under all considered scenarios, implementing a pre-signal with the proposed adaptive control algorithm will result in the least average person delay at the intersection. The algorithm is expected to function well with a wide range of car demands, bus frequencies, and bus passenger occupancies. Moreover, the algorithm is robust to errors in these input values, so exact information is not required.

Crowdsending based public transport information service in smart cities

Thanks to the development of technology and the emergence of intelligent services smart cities promise to their inhabitants enhanced perception of city life. For example, a live timetable service of public transportation can increase the efficiency of travel planning substantially. However, its implementation in a traditional way requires the deployment of some costly sensing and tracking infrastructure. Mobile crowdsensing is an alternative, when the crowd of passengers and their mobile devices are used to gather data for almost free of charge. In this paper, we put the emphasis on the introduction of our crowdsensing based public transport information service, what we have been developing as a prototype smart city application. The front-end interface of this service is called TrafficInfo. It is a simple and easy-to-use Android application which visualizes real-time public transport information of the given city on Google Maps. The lively updates of transport schedule information relies on the automatic stop event detection of public transport vehicles. TrafficInfo is built upon our Extensible Messaging and Presence Protocol (XMPP) based communication framework what we designed to facilitate the development of crowd assisted smart city applications. The paper introduces shortly this framework, than describes TrafficInfo in detail together with the developed stop event detector.

Collaborative prediction for bus arrival time based on CPS

To improve the accuracy of real-time public transport information release system, a collaborative prediction model was proposed based on cyber-physical systems architecture. In the model, the total bus travel time was divided into three parts: running time, dwell time and intersection delay time, and the data were divided into three categories of historical data, static data and real-time data. The bus arrival time was obtained by fusion computing the real-time data in perception layer together with historical data and static data in collaborative layer. The validity of the collaborative model was verified by the data of a typical urban bus line in Shanghai, and 1538 sets of data were collected and analyzed from three different perspectives. By comparing the experimental results with the actual results, it is shown that the experimental results are with higher prediction accuracy, and the collaborative prediction model adopted is able to meet the demand for bus arrival prediction.