Estimating inter-regional mobility during disruption: Comparing and combining different data sources

A quantitative understanding of people's mobility patterns is crucial for many applications. However, it is difficult to accurately estimate mobility, in particular during disruption such as the onset of the COVID-19 pandemic. Here, we investigate the use of multiple sources of data from mobile phones, road traffic sensors, and companies such as Google and Facebook in modelling mobility patterns, with the aim of estimating mobility flows in Finland in early 2020, before and during the disruption induced by the pandemic. We find that the highest accuracy is provided by a model that combines a past baseline from mobile phone data with up-to-date road traffic data, followed by the radiation and gravity models similarly augmented with traffic data. Our results highlight the usefulness of publicly available road traffic data in mobility modelling and, in general, pave the way for a data fusion approach to estimating mobility flows. © 2022 The Author(s)

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.

Transportation System Operating Characteristics Analysis of Beijing during COVID-19

Traffic operation has shown abnormal characteristics during COVID-19. This paper obtains traffic data from multiple fields in Beijing for the whole year of 2020, combines traffic operation data with the number of confirmed cases, and deeply explores the operating characteristics of road networks, public transportation, and intercity transportation at various stages during the major epidemic. The results showed that travel demand decreased significantly during the epidemic period. From the perspective of urban road network traffic pressure, the demand for rigid travel in peak hours during the epidemic recovery period is relatively large. Based on this research, it can provide decision support for the government to formulate relevant prevention and control measures and policies, thereby improving the ability of urban traffic to respond to public health emergencies. © ASCE.

Forecast daily tourist volumes during the epidemic period using COVID-19 data, search engine data and weather data.

The COVID-19 epidemic has brought a devastating blow to the tourism industry. Affected by the epidemic situation, the change of tourism volume of scenic spots is very unstable. Therefore, forecasting tourist volume in the context of COVID-19 epidemic is a new and challenging problem. In response, a novel multivariate time series forecasting framework based on variational mode decomposition (VMD) and gated recurrent unit network (GRU), i.e., VMD-GRU, is proposed to forecast daily tourist volumes during the epidemic. It takes the lead in using COVID-19 data, search traffic data and weather data. Through sufficient experiments and comparisons, the superiority of the approach is illustrated, and the predictive power of the above three types of data, especially the COVID-19 data, is revealed. Accurate forecast results from the method can help relevant government officials and tourism practitioners to better adjust tourism resources, cooperate with anti-epidemic work and reduce operational risks.

Dynamic Linear Model for Urban Essential Traffic Congestion and Emissions

Congestion is a phenomenon that impacts most cities in the world. Due to car emissions, it is a significant source of pollution. Even though mobility restrictions can reduce congestion and emissions, essential activities still need cars. With lockdown measures during the global pandemic of Covid-19, measuring essential traffic data has been made possible. This paper concerns analysis and modelling of such essential traffic. It appears that congestion dynamics of essential traffic exhibits dynamics than can be represented with a linear model. This paper introduces such a model and provide a method to jointly estimate the parameters and the model input. The model is validated with data collected in Johannesburg, South Africa. Copyright © 2021 The Authors.

Impacts of COVID-19 on Means of Transport Choice: Cars and Cyclists Monitoring

The Covid-19 pandemic caused vast changes in all areas of peoples' lives, especially in the transportation field. This study is mainly focused on Třebíč town and also the Czech Republic as a whole, with a goal to compare data and find correlation and causation. Data used in this comparison includes the number of active cases of COVID-19, number of cars and cyclists and also weather conditions. The focal point of the study is the contrast and comparison in the usage of certain types of transport based on varying aspects, mainly the impact of COVID-19. As the pandemic grew stronger or weaker, so did the volumes of cyclists, pedestrians and cars. Data was collected by automatic counters in Třebíč town and also some data was collected by national agencies, such as Czech Hydrometeorological Institute and the Ministry of Health of the Czech Republic. All of the data was gathered from the pre-covid time to the present with some minor gaps. The study shows distinctive rise of cycling in year 2020, as people were afraid of catching the disease. In the year 2021 people became less afraid as the vaccination rates increased and the numbers of people were quite similar to pre- times. We can see that the pandemic had an immediate impact on traffic distribution and human behavior. After the end of the pandemic, it can be assumed that human behavior will never return to its original state. © 2022 IEEE.

The Analysis for the Traffic Risks in Barcelona before and after the COVID-19 Based on Deep Learning

Big Traffic data [1] is cross-border multi-source data for multiple industries, but traffic roads have brought significant economic and social benefits, the number of traffic accidents and casualties is on the rise. Among them, traffic accidents are related to many factors, such as weather and population density. The data set used in this article is open source in Barcelona. The Random Forest algorithm is used to screen essential risk factors, establish a traffic risk prediction model, and compare traffic risks before and after COVID-19. It is concluded that the outbreak of the new crown virus -19-19 has a great impact on people's travel and transportation. Finally, the R square of the model established by Random Forest is 0.9. The K-means clustering algorithm is used to determine the location of the accident handling centre. Moreover, the scope of each accident risk management centre can cover more than 85 percent of traffic accident sites from 2016 to 2020. © 2021 IEEE.

Improving Traffic Load Prediction with Multi-modality: A Case Study of Brisbane

Fast and accurate traffic load prediction is a pivotal component of the Intelligent Transport System. It will reduce time spent by commuters and save our environment from vehicle emissions. During the COVID-19 pandemic, people prefer to use private transportation;thus predicting the traffic load becomes more critical. In these years, researchers have developed some traffic load prediction models and have applied these models successfully on data from the US, China or Europe. However, none of these models has been applied to traffic data in Australia. Considering that Australia bears different political, geographical, and climate conditions from other countries, these models may not be suitable to predict the traffic load in Australia. In this paper, we investigate this problem and proposes a multi-modal method that is capable of using Australia-specific data to assist traffic load prediction. Specifically, we use daily social media data together with traffic data to predict the traffic load. We illustrate a protocol to pre-process raw traffic and social media data and then propose a multi-modal model, namely DM2T, which accurately make time-series prediction by using both time-series data and other media data. We validate the effectiveness of our proposed method by a case study on Brisbane city. The result shows that with the help of Australia-specific social media data, our proposed method can make more accurate traffic load prediction for Brisbane than conventional methods. © 2022, Springer Nature Switzerland AG.

A space-time analysis of Puerto Madryn arrivals during COVID-19

The aim of this work is to characterize and analyze the flow of arrivals to the city of Puerto Madryn (Patagonia - Argentina), during the first six month of social isolation while the pandemic for Covid-19 was occurring. Results remarked how simple traffic data collection based on provenance, local destination, age and gender is more important to understand the spread of the virus and for the design of health policies than reinforcing traffic restrictions. The intention is to expose the potentialities of a more comprehensive analogous analysis of this kind of mobility for local public health policy and urban planning in a post-pandemic context. ©2021 IEEE

Traffic Analysis on Intersection Using PTV Vissim

As India is in its developing stage and the traffic on the other side in India is very heterogeneous or mixed in its nature and the average growth rate of vehicles in India is about 8%. With the increase rate of urbanization in India it will lead to the considerable traffic and travel growth on the roads which will result in vehicular delays, long queues and traffic congestion. So, in this paper with the help of traffic simulation software, i.e. VISSIM, three simulation of an unsignalized intersection {Dadour and Una-Jahu, Nerchowk Rd. (NH-21),H.P} will be analyzed and will compare them on the basis of vehicular delays and long queues. These three simulation will be analyzed on the basis of real world traffic data which is less from the expectations due to the pandemic covid-19, theoretical traffic data (increase in real data by 30%) and theoretical traffic data {with traffic signals as theoretical data follows warrant 1 (Min. Vehicular Volume) shown in IRC:93:1985}. Result showed that with increase in vehicular data there was not so much variation in vehicular delays, whereas there was an increase in long queues or queue stops and whilst third simulation (with traffic lights) is done it shows that it overcomes the queue stops of the intersection. © 2022, Springer Nature Singapore Pte Ltd.

The association of opening K-12 schools with the spread of COVID-19 in the United States: County-level panel data analysis.

This paper empirically examines how the opening of K-12 schools is associated with the spread of COVID-19 using county-level panel data in the United States. As preliminary evidence, our event-study analysis indicates that cases and deaths in counties with in-person or hybrid opening relative to those with remote opening substantially increased after the school opening date, especially for counties without any mask mandate for staff. Our main analysis uses a dynamic panel data model for case and death growth rates, where we control for dynamically evolving mitigation policies, past infection levels, and additive county-level and state-week "fixed" effects. This analysis shows that an increase in visits to both K-12 schools and colleges is associated with a subsequent increase in case and death growth rates. The estimates indicate that fully opening K-12 schools with in-person learning is associated with a 5 (SE = 2) percentage points increase in the growth rate of cases. We also find that the association of K-12 school visits or in-person school openings with case growth is stronger for counties that do not require staff to wear masks at schools. These findings support policies that promote masking and other precautionary measures at schools and giving vaccine priority to education workers.

Analysis and monitoring of post-COVID mobility demand in Rome resulting from the adoption of sustainable mobility measures.

The paper describes research activities of monitoring, modeling, and planning of people mobility in Rome during the Covid-19 epidemic period from March to June 2020. The results of data collection for different transport modes (walking, bicycle, car, and transit) are presented and analyzed. A specific focus is provided for the subway mass transit, where 1 m interpersonal distancing is required to prevent the risks for Covid-19 contagion together with the use of masks and gloves. A transport system model has been calibrated on the data collected during the lockdown period -when people's behavior significantly changed because of smart-working adoption and contagion fear- and was applied to predict future mobility scenarios under different assumptions on economic activities restarting. Based on the estimations of passenger loading, a timing policy that differentiates the opening hours of the shops depending on their commercial category was implemented, and an additional bus transit service was introduced to avoid incompatible loads of the subway lines with the required interpersonal distancing.