ABSTRACT
This article presents the analysis of the demand and the characterization of mobility using public transportation in Montevideo, Uruguay, during the COVID-19 pandemic. A urban data-analysis approach is applied to extract useful insights from open data from different sources, including mobility of citizens, the public transportation system, and COVID cases. The proposed approach allowed computing significant results to determine the reduction of trips caused by each wave of the pandemic, the correlation between the number of trips and COVID cases, and the recovery of the use of the public transportation system. Overall, results provide useful insights to quantify and understand the behavior of citizens in Montevideo, regarding public transportation during the COVID-19 pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The COVID-19 pandemic has left scars on the Indian public transportation system. In order to regain its original momentum, policymakers will need to assess the barriers hindering the effectiveness of the public transportation sector. In this regard, this article analyzes the various factors affecting the public transportation sector in India and determines their interrelationships. The research is presented in three steps. First, we review the literature to identify the factors that affect the public transportation system in India. Next, we propose an integrated model of grey-DEMATEL and ANP, grey-DANP, to calculate the priority ranking and weight of the factors. The grey-DEMATEL method is used to find the interrelationships among the factors, while ANP determines the local and global weights of the factors to form a priority order. Then, we present the interrelationships in the form of influential relation maps. Furthermore, we provide a sensitivity analysis to enhance the credibility of our study. The paper reveals that governmental regulations are the most influential factors in India's public transportation system. The transportation authorities and policymakers must also focus on improving the financial stability and enhancing the customer's trust in the public transportation system. The framework provided in this paper can be applied to other countries where similar hindrances in the public transportation system have been caused by COVID-19. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
ABSTRACT
Management of crowd information in public transportation (PT) systems is crucial, both to foster sustainable mobility, by increasing the user's comfort and satisfaction during normal operation, as well as to cope with emergency situations, such as pandemic crises, as recently experienced with coronavirus disease (COVID-19) limitations. This article presents a taxonomy and review of sensing technologies based on the Internet of Things (IoT) for real-time crowd analysis, which can be adopted in the different segments of the PT system (buses/trams/trains, railway/metro stations, and bus/tram stops). To discuss such technologies in a clear systematic perspective, we introduce a reference architecture for crowd management, which employs modern information and communication technologies (ICTs) in order to: 1) monitor and predict crowding events;2) implement crowd-aware policies for real-time and adaptive operation control in intelligent transportation systems (ITSs);and 3) inform in real time the users of the crowding status of the PT system, by means of electronic displays installed inside vehicles or at bus/tram stops/stations and/or by mobile transport applications. It is envisioned that the innovative crowd management functionalities enabled by ICT/IoT sensing technologies can be incrementally implemented as an add-on to state-of-the-art ITS platforms, which are already in use by major PT companies operating in urban areas. Moreover, it is argued that, in this new framework, additional services can be delivered to the passengers, such as online ticketing, vehicle access control and reservation in severely crowded situations, and evolved crowd-aware route planning. © 2001-2012 IEEE.
ABSTRACT
With the spread of the SARS-CoV-2 virus, the return to operations for schools, workplaces, and other work-life related activities has been largely affected by social distancing and other pandemic containment policies. System operators need to consider potential virus spread to reduce infections, while ensuring operational efficiency. This is particularly important for public transit services that form social hubs and possible hotspots for the spread of virus. In this paper, we develop a simulation platform to validate the design of routes and bus schedules for a large university's transit system. We use an agent-based model to track the operations of buses, as well as the quality of service provided to each passenger and estimate their exposure to a contagious virus. Our results show that the redesign of a traditional system into a Hub-and-Spoke design can improve operational efficiency with the same number of vehicles and reduces possible infections inside buses by reducing consecutive in-vehicle travel time for all passengers. © 2022 IISE Annual Conference and Expo 2022. All rights reserved.
ABSTRACT
The COVID-19 pandemic has created significant restrictions to passenger mobility through public transportation. Several proximity rules have been applied to ensure sufficient distance between passengers and mitigate contamination. In conventional transportation, abiding by the rules can be ensured by the driver of the vehicle. However, this is not obvious in Autonomous Vehicles (AVs) public transportation systems, since there is no driver to monitor these special circumstances. Since, AVs constitute an emerging mobility infrastructure, it is obvious that creating a system that can provide a sense of safety to the passenger, when the driver is absent, is a challenging task. Several studies employ computer vision and deep learning techniques to increase safety in unsupervised environments. In this work, an image-based approach, supported by novel AI algorithms, is proposed as a service to increase the COVID-19 safety rules adherence of the passengers inside an autonomous shuttle. The proposed real-time service, can detect deviations from proximity rules and notify the authorized personnel, while it is possible to be further extended in other application domains, where automated proximity assessment is critical. © 2022, IFIP International Federation for Information Processing.
ABSTRACT
In times of ongoing pandemic outbreak, public transportation systems organisation and operation have been significantly affected. Among others, the necessity to implement in-vehicle social distancing has fostered new requirements, such as the possibility to know in advance how many people will likely be on a public bus at a given stop. This is very relevant for both potential passengers waiting at a stop, and for decision makers of a transit company, willing to adapt the operational planning. Within the domain of data-driven Intelligent Transportation Systems (ITS), some research activities are being conducted towards Bus Passenger Load (BPL) predictions, with contrasting results. In this paper we report on an academic/industrial experience we conducted to predict BPL in a major Italian city, using real-world data. In particular, we describe the difficulties and challenges we had to face in the data processing and mining steps, due to multiple data sources, with noisy data. As a consequence, in this paper we highlight to the ITS community the need of more advanced techniques and approaches suitable to support the instantiation of a data analytic pipeline for BPL prediction. © 2022, Springer Nature Switzerland AG.
ABSTRACT
The COVID-19 pandemic crisis raised public health attention closer to our global society's demands. The disease proliferation occurs typically by droplet transmission, by being close to an infected person. Social distancing, a natural solution, is not always applicable to everyday needs, such as in the public transportation system, which is a space highly susceptible to viral proliferation. A set of ways to reduce proliferation in these infrastructures is by reinforcing facial masks usage, restraining symptomatic users, and reducing physical contact with public devices. Safe Gate, an Internet of Things (IoT) solution to enforce containment measures for disease proliferation, is proposed in this paper. This IoT solution is based on a network of edge computing devices used to control access to the entrance gate of the stations. The edge devices service samples an user's temperature and facial image to verify that body temperature is within normal bounds and the user is correctly wearing a face mask. The system is contact-free and does not require an active operator, with no personal data stored, preserving privacy. Additionally, it minimizes personnel involvement with passengers, ensuring staff protection. The research question is whether the solution with two levels of facial recognition using cognitive edge computing will meet the requirements of a real system. In addition to this question, a queuing model to verify the feasibility of the solution is presented and evaluates the operational impact on a real transportation system. © 2021 ACM.