Cyber-Physical Modeling of Intelligent Traffic Controllers Using Simulator for Urban Mobility (SUMO)

Transportation problems have always been a global concern. The challenges in traffic congestion were easily observed during pre-pandemic times. However, traffic congestion still persists even during the COVID-19 pandemic (2020 and present) where there has been less number of vehicles because of travel restrictions. The emergence of wireless communication technologies and intelligent transportation systems (ITS) pave the way for solving some of the problems found in the transportation industry. Subsequently, traffic control systems are used at various intersections to manage the flow of traffic and reduce car collisions. However, some intersections are better off without these traffic control systems. The proposed study will analyze a T-junction road in five different setups using different types of traffic controllers. The simulation tool used is SUMO. The study found that an adaptive or vehicle-actuated traffic controller is the ideal method for regulating traffic flow in a T-junction with a one-way or two-way main road. It was observed in the simulation that it reduced the potential car collisions in the non-TL junction. However, the average speed and completion time of the road network was affected by the method. © 2022 IEEE.

Sensing Technologies for Crowd Management, Adaptation, and Information Dissemination in Public Transportation Systems: A Review

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.

A Deep Spatiotemporal Fuzzy Neural Network for Subway Passenger Flow Prediction With COVID-19 Search Engine Data

Passenger flow prediction is of great significance in the operation and management of subways, especially in reducing energy consumption and improving service quality. Due to the impact of COVID-19, subway passenger flow fluctuates a lot, which makes passenger flow estimation or forecasting a very challenging task. This article mainly carries out two aspects of work to solve the task of subway passenger flow prediction under pandemic. First, this article introduces search engine data as a new data source and provides a systematic method to extract valid quires and search volumes that are closely associated with subway passenger flow under pandemic. Second, this article combines the fuzzy theory and neural network to propose a deep learning architecture called "deep spatiotemporal fuzzy neural network” to deal with the complex spatiotemporal features and uncertain external data of subway passenger flow prediction. Experiments on the actual dataset of the Beijing subway prove the superiority of the model and the effectiveness of search engine data in subway passenger flow forecasting.

AI-based traffic counting: A Case Study in Vietnam

Ho Chi Minh City, particularly Vietnamese cities in general, is so busy and crowded since tremendous numbers of motorbikes move on roads. Ho Chi Minh City leaders have encountered several challenges in fully understanding and effectively dealing with problems of urban traffic for the past few decades. Software-based solutions are proper and dramatically necessary, currently. This paper presents the deployment of an AI-based application at the Ho Chi Minh City Department of Transportation. The paper mainly concentrates on traffic counting problems during the outbreak of the Covid-19 pandemic from June 2021. The performance of the AI-based application was compared with medical declaration data and achieved an accuracy of 93.80%. © 2022 IEEE.

Mobility Pattern Analysis during 2020 Chinese National Day under the COVID-19 pandemic

With the fast development of new technologies, such as Internet of Things, big data and Internet plus, Intelligent Transportation Systems (ITS) have made remarkable achievements and the intelligence in ITS has also been continuously increased, which a new field, i.e., Social Transportation, is emerging. In social transportation systems, physical and cyber elements are tightly conjoined, coordinated, and integrated with human and social characteristics. In this paper, we collect and analyze traffic data from physical world and social media data from cyberspace to sense the human mobility patterns during holidays under the COVID-19 pandemic. © 2022 IEEE.

Guest Editorial Introduction to the Special Issue on Intelligent Transportation Systems in Epidemic Areas

The COVID-19 pandemic has posed significant challenges to transportation systems in various aspects, such as transferring patients and medical resources, enforcing physical distancing in public transportation, and controlling virus transmission through transportation networks. To address these challenges, a variety of artificial intelligence technologies, such as autonomous driving, big data analytics, intelligent vehicle routing and scheduling, and intelligent traffic control, have been employed in the design of intelligent transportation systems. This Special Issue provides a forum for researchers and practitioners to present the most recent advances in presenting and applying intelligent technologies to promote transportation systems in large-scale epidemics.

Using Data Mining to Estimate Patterns of Contagion-Risk Interactions in an Intercity Public Road Transport System

The COVID-19 pandemic has had very negative effects on public transport systems. These effects have compromised the role they should play as enablers of social equity and environmentally sustainable mobility and have caused serious economic losses for public transport operators. For this reason, in the context of pandemics, meaningful epidemiological information gathered in the specific framework of these systems is of great interest. This article presents the findings of an investigation into the risk of transmission of a respiratory infectious disease in an intercity road transport system that carries millions of passengers annually. To achieve this objective, a data mining methodology was used to generate the data required to ascertain the level of risk. Using this methodology, the occupancy of vehicle seats by passengers was simulated using two different strategies. The first is an empirical approach to the behaviour of passengers when occupying a free seat and the second attempts to minimise the risk of contagion. For each of these strategies, the interactions with risk of infection between passengers were estimated, the patterns of these interactions on the different routes of the transport system were obtained using k-means clustering technique, and the impact of the strategies was analysed.

Artificial Intelligence-Powered Contactless Face Recognition Technique for Internet of Things Access for Smart Mobility

A contactless system became necessary for smart mobility during the COVID-19 pandemic. There are many touchpoints in private and public areas where contact is essential, such as intelligent transportation systems for vaccine carriers, patient ambulances, elevators, metros, buses, hospitals, and banks. A secured contactless device reduces the chances of COVID-19 infection spread. Several devices use smart cards, fingerprint identification, or code-based access. Most of these devices require some form of touch. The cost of such devices varies, depending on their capability and intended use. Sensors developed by using artificial intelligence (AI) to provide secured access are an emerging area. This paper presents an AI-powered contactless face recognition system. The solution has the Internet of Things (IoT) enabled access system. To identify a person, it uses AI assistance for face recognition with the help of Python Dlib’s facial recognition network. Dlib offers a wide range of functionality across several machine learning sectors and is open-source. The Arduino Uno (ATmega328P) and STK500 protocol has been used for communication to testify and validate the performance of the proposed technique. The objective is to detect and recognize faces by the proposed contactless approach. The obtained result shows 92% accuracy, 94% sensitivity, 96% precision and FRR 6% for face detection. There is a significant improvement in FRR in our work compared to the published 27.27%. The implemented solution in this paper provides accurate and secure contactless access to conventional, readily available techniques in public health safety.

A Multi-Mode Public Transportation System Using Vehicular to Network Architecture

The number of accidents in the campus of Suranaree University of Technology (SUT) has increased due to increasing number of personal vehicles. In this paper, we focus on the development of public transportation system using Intelligent Transportation System (ITS) along with the limitation of personal vehicles using sharing economy model. The SUT Smart Transit is utilized as a major public transportation system, while MoreSai@SUT (electric motorcycle services) is a minor public transportation system in this work. They are called Multi-Mode Transportation system as a combination. Moreover, a Vehicle to Network (V2N) is used for developing the Multi-Mode Transportation system in the campus. Due to equipping vehicles with On Board Unit (OBU) and 4G LTE modules, the real time speed and locations are transmitted to the cloud. The data is then applied in the proposed mathematical model for the estimation of Estimated Time of Arrival (ETA). In terms of vehicle classifications and counts, we deployed CCTV cameras, and the recorded videos are analyzed by using You Only Look Once (YOLO) algorithm. The simulation and measurement results of SUT Smart Transit and MoreSai@SUT before the covid-19 pandemic are discussed. Contrary to the existing researches, the proposed system is implemented in the real environment. The final results unveil the attractiveness and satisfaction of users. Also, due to the proposed system, the CO2 gas gets reduced when Multi-Mode Transportation is implemented practically in the campus.

A Deep Spatio-Temporal Fuzzy Neural Network for Subway Passenger Flow Prediction with COVID-19 Search Engine Data

Passenger flow prediction is of great significance in the operation and management of subways, especially in reducing energy consumption and improving service quality. Due to the impact of COVID-19, subway passenger flow fluctuates a lot, which makes passenger flow estimation or forecasting a very challenging task. This paper mainly carries out two aspects of work to solve the task of subway passenger flow prediction under pandemic. First, this paper introduces search engine data as a new data source and provides a systematic method to extract valid quires and search volumes that are closely associated with subway passenger flow under pandemic. Second, this paper combines the fuzzy theory and neural network to propose a deep learning architecture called ‘Deep Spatio-Temporal Fuzzy Neural Network (DST-FNN)’to deal with the complex Spatio-temporal features and uncertain external data of subway passenger flow prediction. Experiments on the actual data set of the Beijing subway prove the superiority of the model and the effectiveness of search engine data in subway passenger flow forecasting. IEEE

Bus Passenger Load Prediction: Challenges from an Industrial Experience

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.

C-ITS Relevant Critical Vehicle-to-Vehicle Accident Scenarios for Accident Analysis

The relevance of scientific investigations, whether simulative or empirical, is strongly related to the environment used and the scenarios associated with it. Within the field of cooperative intelligent transport systems, use-cases are defined to describe the benefits of applications. This has already been conducted in the available safety-relevant Day 1 applications longitudinal and intersection collision risk warning through the respective technical specifications. However, the relevance of traffic scenarios is always a function of accident severity and frequency of a retrospective consideration of accident databases. In this study, vehicle-to-vehicle scenarios with high frequency and/or severe personal injuries are therefore determined with the help of the CISS database and linked to the use-cases of the safety-relevant Day 1 applications. The relevance of the scenarios thus results on the one hand from the classical parameters of retrospective accident analysis and on the other hand from the coverage by the named vehicle-to-x applications. As a result, accident scenarios with oncoming vehicles are the most relevant scenarios for investigations with cooperative intelligent transport systems. In addition, high coverage of the most critical scenarios within the use-cases of longitudinal and intersection collision risk warning is already apparent.

Could Technology and Intelligent Transport Systems Help Improve Mobility in an Emerging Country? Challenges, Opportunities, Gaps and Other Evidence from the Caribbean

Apart from constituting a topic of high relevance for transport planners and policymakers, support technologies for traffic have the potential to bring significant benefits to mobility. In addition, there are groups of “high potential” users, such as young adults, who constitute an essential part of the current market. Notwithstanding, and especially in low and middle-income countries (LMICs), their knowledge and acceptance remain understudied. This study aimed to assess the appraisal of intelligent transport systems (ITS) and other technological developments applicable to mobility among Dominican young adults. Methods: In this study, we used the data gathered from 1414 Dominicans aged between 18 and 40, responding to the National Survey on Mobility in 2018 and 2019. Results: Overall, and although there is a relatively high acceptance, attributed value, and attitudinal predisposition towards both intelligent transportation systems and various support technologies applicable to mobility, the actual usage rates remain considerably low, and this is probably exacerbated by the low and middle-income status of the country. Conclusions: The findings of this study suggest the need to strengthen information and communication flows over emerging mobility-related technologies and develop further awareness of the potential benefits of technological developments for everyday transport dynamics.

Intelligent Decision Support System for Modeling Transport and Passenger Flows in Human-Centric Urban Transport Systems

Engineering human-centric urban transport systems should be carried out using information technology in forecasting traffic and passenger flows. One of the most important objects of urban transport systems’ progress is modeling patterns of transport flows and their distribution on the road network. These patterns are determined by the subjective choice of city residents of traffic routes using public and private transport. This study aimed to form a sequence of stages of modeling transport and passenger flows in human-centric urban transport systems and passenger flows in the human-centric urban intelligent transport systems and to determine the patterns of change to the gravity function of employees of municipal services. It was revealed that the trip distribution function of workers of urban service enterprises can be described by the attributes of the structure of the city, socio-economic data, and attributes characterizing the zones and its residents.

The Effect of COVID-19 on the Transit System in Two Regions: Japan and USA

The communication revolution that happened in the last ten years has increased the use of technology in the transportation world. Intelligent Transportation Systems wish to predict how many buses are needed in a transit system. With the pandemic effect that the world has faced since early 2020, it is essential to study the impact of the pandemic on the transit system. This paper proposes the leverage of Internet of Things (IoT) devices to predict the number of bus ridership before and during the pandemic. We compare the collected data from Kobe city, Hyogo, Japan, with data gathered from a college city in Virginia, USA. Our goal is to show the effect of the pandemic on ridership through the year 2020 in two different countries. The ultimate goal is to help transit system managers predict how many buses are needed if another pandemic hits. © 2021 IEEE.

Key Aspects for IT-Services Integration in Urban Transit Service of Medium-Sized Cities: A Qualitative Exploratory Study in Colombia

In the last ten years, approximately, urban transit systems of Latin American capital cities have evolved significantly. Colombia, specifically, has concentrated this development in its capital cities, consolidated through digital transformation programs in the transportation sector. However, the same phenomenon does not occur in medium-sized cities for different reasons that are important to analyze. This paper presents an exploratory qualitative study involving eight medium-sized cities in the implementation phase of their strategic urban transit systems. Three main aspects that drive this study were identified: technologies and their cost, functional requirements to implement information technology services in transit systems, and economy and administration associated with this type of implementation. Based on this, a semi-structured interview data collection instrument was designed, with the participation of 15 officials distributed in the eight target cities, and one expert from an intelligent transportation system in a capital city. With the information collected, an exploratory analysis was made contrasting the responses given by each interviewee. The most relevant results show that the interviewees prioritize technologies based on open standards to provide information to users;that the northern medium-sized cities of the country do not have strategies that regularize and motivate the use of public transportation;instead, the southern medium-sized cities of the country consider the use of transportation to be necessary. Finally, it was concluded that the information technology services to be included in the provision of transit services should promote these cities’ cultural and economic growth.

Video Analytics based Intelligent Transport System for passenger flow forecast and Social Distancing Indication

Crowd density management in the transport sector is still one of the ongoing research problems. Intelligent Transportation System (ITS) is one of the branches of smart cities that aim to achieve better traffic efficiency. The intelligent transport system optimizes the traffic congestion control by acquiring real time data. Optimized traffic congestion control demands a robust system that could count the number of people inside a carrier for taking optimized decisions. In this paper we proposed an intelligent algorithm named Modified Intelligent Centroid Tracker and Counter (MICTC) that could detect, count, and measure the distance between humans in a closed and controlled environment. The proposed algorithm is vision based and the scope of the work is to optimize the congestion control inside the passenger carrier and supports countless use cases like smart transport, buildings, and other demography where social distancing is enforced. MICTC algorithm not only offers visual indication with a bounding box but also generates metadata which gives a clear picture to the concerned operational or administrative head regarding the current passenger count status. The work deployed in the public transport sector as a candid spot to operate. The algorithm delivers an adequate transport facility to the public, as it gathers information on crowd density in a public transport medium to the commuters of every region. The work gathers crowd density information and provides commuters a suggestion on availability of seats in the carrier, which then saves time, avoids catching the crowded carriers, ensures social distancing, and standardizes the public transportation system which has practical significance. On experimental analysis we could infer that the proposed approach works with accuracy of 0.81,0.83, 0.85, 0.88, 0.82, 0.89 on VISOR, Kaggle, CALTECH, Penn-Fudan, Daimler Mono and INRIA respectively.

Thank You for Your Support [President’s Message]

By the time this issue of IEEE Intelligent Transportation Systems Magazine is published, I will have completed my term as president of the IEEE Intelligent Transportation Systems (ITS) Society. My two-year service coincided with an unprecedented time—the COVID-19 crisis. During the past two years, the pandemic has dramatically changed the lives of everyone on Earth and, most certainly, greatly impacted how the ITS Society operates. Fortunately, our colleagues have made substantial efforts to adapt to the new reality of the pandemic and created opportunities and environments for the Society to innovate and grow.

Nonnegative Matrix Factorization to Understand Spatio-Temporal Traffic Pattern Variations During COVID-19: A Case Study

Due to the rapid developments in Intelligent Transportation System (ITS) and increasing trend in the number of vehicles on road, abundant of road traffic data is generated and available. Understanding spatio-temporal traffic patterns from this data is crucial and has been effectively helping in traffic plannings, road constructions, etc. However, understanding traffic patterns during COVID-19 pandemic is quite challenging and important as there is a huge difference in-terms of people’s and vehicle’s travel behavioural patterns. In this paper, a case study is conducted to understand the variations in spatio-temporal traffic patterns during COVID-19. We apply nonnegative matrix factorization (NMF) to elicit patterns. The NMF model outputs are analysed based on the spatio-temporal pattern behaviours observed during the year 2019 and 2020, which is before pandemic and during pandemic situations respectively, in Great Britain. The outputs of the analysed spatio-temporal traffic pattern variation behaviours will be useful in the fields of traffic management in Intelligent Transportation System and management in various stages of pandemic or unavoidable scenarios in-relation to road traffic. © 2021, Springer Nature Singapore Pte Ltd.

Modifying the SERVPERF to assess paratransit minibus taxis trotro in Ghana and the relevance of mobility-as-a-service features to the service.

In Ghana, minibus taxis (trotros) are an important mode of transport that commute about 60% of the traveling public. In spite of their popularity, minibuses are generally inefficient, disorganized and have low service quality. In an attempt to assess service quality of the service, a modified SERVPERF tool was developed. Differences in perceptions of service quality between male and female respondents were also assessed, and the attractiveness of certain technological features as possible remedies to service quality issues were determined. Using an online Google forms version of the modified SERVPERF, responses from nearly one thousand commuters were collected. The link to the questionnaire was dispersed via social media (Whatsapp and Telegram) since the data was collected during the outbreak of COVID-19 in Ghana. Following a factor reduction, the most important service quality factors determined to affect trotro users were (i) Reliability of the service, (ii) Variability in cost and (iii) Responsiveness. Respondents also identified technologies that could help them (a) book, (b) report driver misbehavior, (c) make safe e-payments and (d) track the location of trotros, as most likely to improve their trotro service quality. The findings suggest that some mobility as a service features could have possible benefit for the trotro. The study is however limited in its ability to determine the exact impact of these technologies since it uses a stated preference approach. Future research could explore the willingness of other stakeholder groups such as operators in adopting these technologies since their participation would be key to the success of any such scheme.