scholarly journals Enabling and Emerging Sensing Technologies for Crowd Management in Public Transportation Systems: A Review

2021 ◽  
Author(s):  
Donatella Darsena ◽  
Giacinto Gelli ◽  
Ivan Iudice ◽  
Francesco Verde
Keyword(s):  
Real Time ◽  
Information And Communication Technologies ◽  
Public Transportation ◽  
Urban Areas ◽  
Transportation Systems ◽  
Normal Operation ◽  
Reference Architecture ◽  
Sustainable Mobility ◽  
Crowd Management ◽  
Bus Stops

Management of crowd information in public transportation (PT) systems is crucial 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 COVID-19 limitations. This paper presents a taxonomy and review of sensing technologies based on Internet of Things (IoT) for real-time crowd analysis, which can be adopted in various segments of the PT system (buses/trams/trains, railway/subway stations, and bus 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 (ICT) in order to: (i) monitor and predict crowding events; (ii) adapt in real-time PT system operations, by modifying service frequency, timetables, routes, and so on; (iii) 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 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 traditional intelligent transportation system (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, such as, e.g., on-line ticketing, vehicle access control and reservation in severely crowded situations, and evolved crowd-aware route planning.

scholarly journals Enabling and Emerging Sensing Technologies for Crowd Avoidance in Public Transportation: A Review

2021 ◽  
Author(s):  
Donatella Darsena ◽  
Giacinto Gelli ◽  
Ivan Iudice ◽  
Francesco Verde
Keyword(s):  
Information And Communication Technologies ◽  
Public Transportation ◽  
Urban Areas ◽  
Intelligent Transportation System ◽  
Route Planning ◽  
Transportation System ◽  
Normal Operation ◽  
Sustainable Mobility ◽  
Detection Techniques ◽  
Bus Stops

Avoidance of crowding situations in public transportation (PT) systems is crucial to foster sustainable mobility, by increasing the user’s comfort and satisfaction during normal operation, as well as to manage emergency situations, such as pandemic crises as recently experienced with COVID-19 limitations. This paper presents a comprehensive review of several crowd detection techniques based on Internet of Things (IoT) technologies, which can be adopted to avoid crowding in various segments of the PT system (buses/trams/trains, railway/subway stations, and bus stops). To discuss such techniques in a clear systematic perspective, we introduce a reference framework called SALUTARY (Safe and Reliable Public Transportation System), which in our vision employs modern information and communication technologies (ICT) in order to: (i) monitor and predict crowding events; (ii) adapt in real-time PT system operations, i.e., by modifying service frequency, timetables, routes, and so on; (iii) inform the users of crowding events by electronic displays installed in correspondence of the bus stops/stations and/or by mobile transport applications. It is envisioned that the new anti-crowding functionalities can be incrementally implemented as an addon to the intelligent transportation system (ITS) platform, which is 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, such as, e.g., online ticketing, vehicle access control and reservation in severely crowded situations, and evolved crowd-aware route planning.

scholarly journals Enabling and Emerging Sensing Technologies for Crowd Avoidance in Public Transportation: A Review

2021 ◽  
Author(s):  
Donatella Darsena ◽  
Giacinto Gelli ◽  
Ivan Iudice ◽  
Francesco Verde
Keyword(s):  
Information And Communication Technologies ◽  
Public Transportation ◽  
Urban Areas ◽  
Intelligent Transportation System ◽  
Route Planning ◽  
Transportation System ◽  
Normal Operation ◽  
Sustainable Mobility ◽  
Detection Techniques ◽  
Bus Stops

Avoidance of crowding situations in public transportation (PT) systems is crucial to foster sustainable mobility, by increasing the user’s comfort and satisfaction during normal operation, as well as to manage emergency situations, such as pandemic crises as recently experienced with COVID-19 limitations. This paper presents a comprehensive review of several crowd detection techniques based on Internet of Things (IoT) technologies, which can be adopted to avoid crowding in various segments of the PT system (buses/trams/trains, railway/subway stations, and bus stops). To discuss such techniques in a clear systematic perspective, we introduce a reference framework called SALUTARY (Safe and Reliable Public Transportation System), which in our vision employs modern information and communication technologies (ICT) in order to: (i) monitor and predict crowding events; (ii) adapt in real-time PT system operations, i.e., by modifying service frequency, timetables, routes, and so on; (iii) inform the users of crowding events by electronic displays installed in correspondence of the bus stops/stations and/or by mobile transport applications. It is envisioned that the new anti-crowding functionalities can be incrementally implemented as an addon to the intelligent transportation system (ITS) platform, which is 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, such as, e.g., online ticketing, vehicle access control and reservation in severely crowded situations, and evolved crowd-aware route planning.

Mobile Enabled RFID-GPS Based Bus Tracking System

2014 ◽  
Vol 6 (4) ◽  
pp. 10-34 ◽  
Author(s):  
Suresh Sankaranarayanan ◽  
Paul Hamilton
Keyword(s):  
Public Transportation ◽  
Tracking System ◽  
Research Work ◽  
Transportation Systems ◽  
Urban Transport ◽  
Time Of Arrival ◽  
Rfid Technology ◽  
Arrival Times ◽  
Traffic Lights ◽  
Bus Stops

Public transportation in many countries is being used as a means of transport for travelling and accordingly people would prefer these public transportation to be scheduled properly, on time and the frequency be adequately fixed for commuters to make good use of it. It has been found that quite an amount of research work has been carried out, by way of using RFID technology in the public transportation systems towards the tracking of passengers when they board and exit buses. In addition research has also been carried out in using GPS towards the tracking of buses along with RFID technology at traffic lights, bus stops, intersections etc and also displaying expected arrival times on LCD screen at bus stops along with their current positions. Taking these aspects into consideration, an intelligent mobile bus tracking system for the Jamaican Urban Transport Corporation has been proposed and validated as a case study. The proposed system also enables commuters towards tracking the bus of their choice and also knowing their expected arrival times. So taking the above aspects into consideration, in this research the authors have proposed and validated on how control center of a bus company could track the location of a bus based on information received from RFID reader and GPS Transmitter positioned at various Bus stops and in the Bus and accordingly the expected time of arrival calculated for displaying the information on commuter's handset via Gmap. The implementation of the bus tracking scheme has been carried out using Adobe Flash player and Java.

Distributed Data Management of Daily Car Pooling Problems

2011 ◽  
pp. 408-412
Author(s):  
Roberto Wolfler Calvo ◽  
Fabio de Luigi ◽  
Palle Haastrup ◽  
Vittorio Maniezzo
Keyword(s):  
Public Transportation ◽  
Urban Areas ◽  
Transportation Systems ◽  
Transfer Velocity ◽  
Public And Private ◽  
Transportation Demand ◽  
Freight Transportation Demand ◽  
Set Up ◽  
Public Transportation Systems

The increased human mobility, combined with high use of private cars, increases the load on the environment and raises issues about the quality of life. The use of private cars lends to high levels of air pollution in cities, parking problems, noise pollution, congestion, and the resulting low transfer velocity (and, thus, inefficiency in the use of public resources). Public transportation service is often incapable of effectively servicing non-urban areas, where cost-effective transportation systems cannot be set up. Based on investigations during the last years, problems related to traffic have been among those most commonly mentioned as distressing, while public transportation systems inherently are incapable of facing the different transportation needs arising in modern societies. A solution to the problem of the increased passenger and freight transportation demand could be obtained by increasing both the efficiency and the quality of public transportation systems, and by the development of systems that could provide alternative solutions in terms of flexibility and costs between the public and private ones. This is the rationale behind so-called Innovative Transport Systems (ITS) (Colorni et al., 1999), like car pooling, car sharing, dial-a-ride, park-and-ride, card car, park pricing, and road pricing, which are characterized by the exploitation of innovative organizational elements and by a large flexibility in their management (e.g., traffic restrictions and fares can vary according with the time of day).

scholarly journals A Comprehensive Comparative Analysis of the Basic Theory of the Short Term Bus Passenger Flow Prediction

Symmetry ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 369 ◽  
Author(s):  
Huawei Zhai ◽  
Licheng Cui ◽  
Yu Nie ◽  
Xiaowei Xu ◽  
Weishi Zhang
Keyword(s):  
Real Time ◽  
Public Transportation ◽  
Transportation Systems ◽  
Training Data ◽  
Time Data ◽  
Short Term ◽  
Passenger Flow ◽  
Modeling Process ◽  
Automatic Passenger Counters ◽  
Public Transportation Systems

In order to meet the real-time public travel demands, the bus operators need to adjust the timetables in time. Therefore, it is necessary to predict the variations of the short-term passenger flow. Under the help of the advanced public transportation systems, a large amount of real-time data about passenger flow is collected from the automatic passenger counters, automatic fare collection systems, etc. Using these data, different kinds of methods are proposed to predict future variations of the short-term bus passenger flow. Based on the properties and background knowledge, these methods are classified into three categories: linear, nonlinear and combined methods. Their performances are evaluated in detail in the major aspects of the prediction accuracy, the complexity of training data structure and modeling process. For comparison, some long-term prediction methods are also analyzed simply. At last, it points that, with the help of automatic technology, a large amount of data about passenger flow will be collected, and using the big data technology to speed up the data preprocessing and modeling process may be one of the directions worthy of study in the future.

scholarly journals Enhancing evacuation response to extreme weather disasters using public transportation systems: a novel simheuristic approach

2020 ◽  
Vol 7 (2) ◽  
pp. 195-210 ◽  
Author(s):  
Maziar Yazdani ◽  
Mohammad Mojtahedi ◽  
Martin Loosemore
Keyword(s):  
Public Transportation ◽  
Urban Areas ◽  
Extreme Weather ◽  
Transportation Systems ◽  
Decision Makers ◽  
Extreme Weather Events ◽  
Optimization Approach ◽  
Opposition Based Learning ◽  
Populations At Risk ◽  
Emergency Decision

Abstract In recent years, there have been an increasing number of extreme weather events that have had major impacts on the built environment and particularly on people living in urban areas. As the frequency and intensity of such events are predicted to increase in the future, innovative response strategies to cope with potential emergency conditions, particularly evacuation planning and management, are becoming more important. Although mass transit evacuation of populations at risk is recognized to play a potentially important role in reducing injury and mortality rates, there is relatively little research in this area. In answering the need for more research in this increasingly important and relatively new field of research, this study proposes a hybrid simulation–optimization approach to maximize the number of evacuees moved from disaster-affected zones to safe locations. In order to improve the efficiency of the proposed optimization approach, a novel multipopulation differential evolution approach based on an opposition-based learning concept is developed. The results indicate that even for large populations the proposed approach can produce high-quality options for decision makers in reasonable computational times. The proposed approach enables emergency decision makers to apply the procedure in practice to find the best strategies for evacuation, even when the time for decision making is severely limited.

Transit Travel Demand Analysis Method Based on APTS Multi-Source Data

2013 ◽  
Vol 361-363 ◽  
pp. 2122-2126
Author(s):  
Jun Chen ◽  
Xiao Hua Li ◽  
Lan Ma
Keyword(s):  
Public Transportation ◽  
Large Scale ◽  
Travel Demand ◽  
Low Cost ◽  
Transportation Systems ◽  
Demand Analysis ◽  
Sample Survey ◽  
Analysis Method ◽  
Source Data ◽  
Bus Stops

Traditional transit travel information is acquired by Trip Sample Survey which has some disadvantages including high cost and short data lifecycle. This paper researched transit travel demand analysis method using Advanced Public Transportation Systems (APTS) data. The study collected APTS data of Nanning City in China and established APTS multi-source data analysis platform applying data warehouse technology. Based on key problems research, the paper presented the analysis procedure and content. Then, this study proposed the core algorithms of the method which are determinations of boarding bus stops, alighting bus stops and transfer bus stops of smart card passengers. Finally, these algorithms programs are experimented using large scale practical APTS data. The results show that this analysis method is low cost, operability and high accuracy.

scholarly journals COVID-19 and Public Transportation: Current Assessment, Prospects, and Research Needs

2020 ◽  
Vol 22 (1) ◽  
Author(s):  
Alejandro Tirachini ◽  
Oded Cats
Keyword(s):  
Public Transportation ◽  
Transportation Systems ◽  
The State ◽  
Research Needs ◽  
Sustainable Mobility ◽  
The Public ◽  
Critical Issues ◽  
Contagion Risk ◽  
State Of Affairs ◽  
Public Transportation Systems

The COVID-19 pandemic poses a great challenge for contemporary public transportation worldwide, resulting from an unprecedented decline in demand and revenue. In this paper, we synthesize the state-of-the-art, up to early June 2020, on key developments regarding public transportation and the COVID-19 pandemic, including the different responses adopted by governments and public transportation agencies around the world, and the research needs pertaining to critical issues that minimize contagion risk in public transportation in the so-called post-lockdown phase. While attempts at adherence to physical distancing (which challenges the very concept of mass public transportation) are looming in several countries, the latest research shows that for closed environments such as public transportation vehicles, the proper use of face masks has significantly reduced the probability of contagion. The economic and social effects of the COVID-19 outbreak in public transportation extend beyond service performance and health risks to financial viability, social equity, and sustainable mobility. There is a risk that if the public transportation sector is perceived as poorly transitioning to post-pandemic conditions, that viewing public transportation as unhealthy will gain ground and might be sustained. To this end, this paper identifies the research needs and outlines a research agenda for the public health implications of alternative strategies and scenarios, specifically measures to reduce crowding in public transportation. The paper provides an overview and an outlook for transit policy makers, planners, and researchers to map the state-of-affairs and research needs related to the impacts of the pandemic crisis on public transportation. Some research needs require urgent attention given what is ultimately at stake in several countries: restoring the ability of public transportation systems to fulfill their societal role.

scholarly journals Passive Wi-Fi monitoring in the wild: a long-term study across multiple location typologies

2020 ◽  
Author(s):  
Miguel Ribeiro ◽  
Nuno Nunes ◽  
Valentina Nisi ◽  
Johannes Schöning
Keyword(s):  
Public Transportation ◽  
Rural Areas ◽  
Urban Areas ◽  
Large Scale ◽  
Tracking System ◽  
Human Mobility ◽  
Transportation Systems ◽  
Mobility Patterns ◽  
Systematic Analysis ◽  
Long Term Study

Abstract In this paper, we present a systematic analysis of large-scale human mobility patterns obtained from a passive Wi-Fi tracking system, deployed across different location typologies. We have deployed a system to cover urban areas served by public transportation systems as well as very isolated and rural areas. Over 4 years, we collected 572 million data points from a total of 82 routers covering an area of 2.8 km2. In this paper we provide a systematic analysis of the data and discuss how our low-cost approach can be used to help communities and policymakers to make decisions to improve people’s mobility at high temporal and spatial resolution by inferring presence characteristics against several sources of ground truth. Also, we present an automatic classification technique that can identify location types based on collected data.

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