Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 47
Filter
1.
Sustainability ; 14(19):12122, 2022.
Article in English | ProQuest Central | ID: covidwho-2066383

ABSTRACT

This study aimed to evaluate the spatial accessibility of tourism attractions in the urban destination city. An analytical framework for assessing urban tourism accessibility at different spatial scales was proposed to provide references on the interaction of urban transport and tourism systems. In addition to the travel time-based measure, a modified gravity model integrating the tourism destination attractiveness, urban transport system characteristics, and tourist demand distribution was developed to evaluate tourism accessibility in this study. Real-time travel data obtained from the Web Maps service were used to take the actual road network operation conditions into consideration and improve the accuracy of estimation results. Taking Nanjing as an example, the analysis results revealed the spatial heterogeneity of tourism accessibility and inequality in tourism resource availability at different levels. Road transport service improvement plays a dominant role in increasing tourism accessibility in areas with insufficient tourism resources, such as the outskirts of the destination city. As for areas with abundant attractions, authorities could pay attention to destination attractiveness construction and demand management in addition to the organization and management of road network operations around attractions during holidays. The results of this study provide a potentially valuable source of information for urban tourism destination management and transport management departments.

2.
International Conference on Transportation and Development 2022, ICTD 2022 ; 4:239-250, 2022.
Article in English | Scopus | ID: covidwho-2062380

ABSTRACT

In 2017, the town of Innisfil, Ontario, launched Innisfil Transit in partnership with Uber, a transportation network company, to provide a subsidized on-demand public mobility service as an alternative to investing in a new fixed-route bus service. The performance of Innisfil Transit is documented in a 2021 Ryerson University report by Sweet, Mitra, and Benaroya, which shows greater cost effectiveness of the mobility provided over the proposed bus alternative. This paper expands on those findings by assessing Innisfil Transit with respect to sustainability, scalability, and resiliency. First, we quantify the energy and emissions of this program relative to traditional transit and driving alone across varying powertrains. We then characterize a conservative first-order estimate of the percentage of US communities that fall within a similar spatial-demographic tier as Innisfil. Replicability also hinges on service cost and performance in comparison to average values for low-density transit in the US. Lastly, most transit agencies experienced a significant drop in demand (as much as 90%) with slowly rebounding ridership since the onset of the COVID-19 pandemic. The resiliency of the Innisfil program to the pressures induced by the pandemic is examined in comparison to other transit operations. The lessons learned across these three dimensions complement prior work to better understand the efficiency and sustainability of on-demand public mobility service for low-density communities like Innisfil. © ASCE. All rights reserved.

3.
22nd COTA International Conference of Transportation Professionals, CICTP 2022 ; : 952-962, 2022.
Article in English | Scopus | ID: covidwho-2062371

ABSTRACT

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.

4.
22nd COTA International Conference of Transportation Professionals, CICTP 2022 ; : 919-927, 2022.
Article in English | Scopus | ID: covidwho-2062369

ABSTRACT

With the effective control of Novel coronavirus pneumonia, the priority problem which all cities have to face is how to provide convenient transportation services for the resumption of production. Take Qingdao for example, It is proposed that the transformation of public transportation users to private transportation is the key to effectively control the spread of the epidemic before the NCP is completely resolved at the urban traffic level. On this basis, we put forward some suggestions on how to provide urban transportation services for commuting during the special period of the epidemic. Such as, we need to focus on the low-income groups who commute over long distances, the traffic environment of slow traffic commuter groups and traffic accessibility within the influential scope of core area. © ASCE.

5.
Energies ; 15(17):6166, 2022.
Article in English | ProQuest Central | ID: covidwho-2023314

ABSTRACT

Short-term car rental services, i.e., carsharing, is a solution that has been developing better and better in urban transport systems in recent years. Along with intensive expansion, service providers have to face an increasing number of challenges to compete with each other. One of them is meeting the expectations of customers about the fleet of vehicles offered in the system. While this aspect is noticed in the literature review mainly in terms of fleet optimization and management, there is a research gap regarding the appropriate selection of vehicle models. In response, the article was dedicated to identifying the vehicles that were best suited to carsharing systems from the point of view of frequent customers. The selection of appropriate vehicles was treated as a multi-criteria decision issue, therefore the study used one of the multi-criteria decision support methods—ELECTRE III. The work focuses on researching the opinions of users (experts) who often use carsharing services in Poland. The study included a list of the most popular vehicles in Europe in 2021, including classic, electric, and hybrid cars, and a list of 11 evaluation criteria. The research results indicate for frequent users the advantage of conventional drive vehicles over electric and hydrogen vehicles. Moreover, they indicate that the best vehicles are relatively large cars (European car segments C and D) with the greatest possible length, boot capacity, engine power, number of safety systems, and quality. On the other hand, the least important issues are the number of seats in the vehicle and the number of doors. Interestingly, the vehicles selected by frequent users questioned the concept of small city cars, which occupied a small public space on which carsharing was supposed to focus. The results obtained support the operators of carsharing services in making fleet decisions.

6.
2022 International Conference on Cloud Computing, Internet of Things, and Computer Applications, CICA 2022 ; 12303, 2022.
Article in English | Scopus | ID: covidwho-2019669

ABSTRACT

As one of the main means of transportation for citizens in Wuhan, urban rail transit has assumed the dual responsibility of ensuring the travel needs of citizens and blocking the spread of the epidemic in the context of COVID-19. Taking the security check space of Wuhan subway Street entrance station as an example, the paper aims at putting forward the optimization strategy of security space design to solve the obstruction problem caused by the excessive flow of subway stations at present. The paper takes the COVID-19 prevention and control requirements in Wuhan into consideration, uses intelligent technology, combines the construction of social force model to conduct pedestrian simulation, and applies simulation variable analysis. The findings indicate that the optimization strategy of security space design effectively shortens the arrival time and effectively controls the flow of people. It is expected to provide some reference and research basis for the design and optimization of security inspection space of subway transportation system in the future. © 2022 SPIE.

7.
8th IEEE International Conference on Smart Computing, SMARTCOMP 2022 ; : 56-61, 2022.
Article in English | Scopus | ID: covidwho-2018981

ABSTRACT

Accurately predicting the ridership of public-transit routes provides substantial benefits to both transit agencies, who can dispatch additional vehicles proactively before the vehicles that serve a route become crowded, and to passengers, who can avoid crowded vehicles based on publicly available predictions. The spread of the coronavirus disease has further elevated the importance of ridership prediction as crowded vehicles now present not only an inconvenience but also a public-health risk. At the same time, accurately predicting ridership has become more challenging due to evolving ridership patterns, which may make all data except for the most recent records stale. One promising approach for improving prediction accuracy is to fine-tune the hyper-parameters of machine-learning models for each transit route based on the characteristics of the particular route, such as the number of records. However, manually designing a machine-learning model for each route is a labor-intensive process, which may require experts to spend a significant amount of their valuable time. To help experts with designing machine-learning models, we propose a neural-architecture and feature search approach, which optimizes the architecture and features of a deep neural network for predicting the ridership of a public-transit route. Our approach is based on a randomized local hyper-parameter search, which minimizes both prediction error as well as the complexity of the model. We evaluate our approach on real-world ridership data provided by the public transit agency of Chattanooga, TN, and we demonstrate that training neural networks whose architectures and features are optimized for each route provides significantly better performance than training neural networks whose architectures and features are generic. © 2022 IEEE.

8.
22nd International Conference on Man-Machine-Environment System Engineering, MMESE 2022 ; 941 LNEE:309-316, 2023.
Article in English | Scopus | ID: covidwho-2014061

ABSTRACT

Entering the post-epidemic era, the travel demand for shared cars is increasing day by day. In the normalized epidemic prevention and control, epidemic prevention in shared cars needs to be designed systematically. This paper analyzes the existing risk of COVID-19 propagation based on two perspectives: scenario and data, and discusses the existing means of protection. Then based on the existing measures, the design suggestions are given from two aspects: scenario-based and data-based. Based on the scenario, the layout design and disinfection is implemented in regard to various ways that COVID-19 is transmitted;based on data, travel data integration should be promoted to achieve macro-structural dynamic adjustment and integrated governance from the overall transportation system. In the context of the industries, the shared car industry should response to new trend immediately and implement innovative ideas to obtain a service that is better suited for individuals in the post-epidemic era. In the end, several major functions of design in terms of developing the urban transportation system are discussed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

9.
22nd International Conference on Computational Science and Its Applications , ICCSA 2022 ; 13382 LNCS:439-456, 2022.
Article in English | Scopus | ID: covidwho-2013921

ABSTRACT

The combination of concerns about the Covid-19 pandemic and structural problems relating to social injustice, climate change, and public health requires a radical reorganisation of transport structures, urban services, and the built fabric of metropolitan regions. This need is central to the pandemic era’s public debate: more significantly, it is reflected in the stimuli to metropolitan and urban policies aimed at adapting regional and mobility plans in order to realise a model of a smart, inclusive, sustainable, competitive and resilient city. The paper proposes a comparative content analysis to investigate the SUMPs adopted by the Italian metropolitan cities of Milan and Bologna, as well as their modification via the adoption of emergency plans and adaptation strategies for the post-pandemic scenario. The study’s purpose is to deduce a set of transferable guidelines. Based on earlier research, this study selects the Metropolitan City of Cagliari as a case study for implementing the set of guidelines derived from the comparative content analysis. The study significantly contributes to urban studies by investigating the transformation of concepts and criteria that underpin transport and mobility policies in the Italian context. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

10.
22nd International Conference on Computational Science and Its Applications , ICCSA 2022 ; 13380 LNCS:496-508, 2022.
Article in English | Scopus | ID: covidwho-2013912

ABSTRACT

Italy was one of the first country in Europe which was severely affected by COVID-19 pandemic. Several critical issues emerged during the different pandemic phases, especially in the health and mobility sector. Restrictions on public transport reduced the supply of transport, highlighting the need to rethink complementary transport systems. Since May 2020, in the post-lockdown phase, the provision of local public transport has been based on ordinary services, such as bus services, which are mainly intended to meet the needs of systematic travel between the places of residence and work on main development routes of the territory. These services have undergone reductions both in the on-board capacity and in some cases the complete elimination of transit routes. The rebalancing in favour of sustainable modes of transport and the reduction of the share of road mobility is pursued through the encouragement of ad-hoc measures aimed at balancing-off the supply-demand mechanism and improving the quality of services. The application of an on-demand responsive transit system has the ability to improve the transit needs in order to reach the places where personal or family services are provided or to enjoy the resources distributed within desired territory. In Italy since March 2020, new areas of weak demand for transport have been created, i.e. areas with a certain number of users that need to be transferred to and from places that have generally never had access to public transport or have had it restricted. The Demand Responsive Transport (DRT) system is, therefore, used in both urban and suburban areas, allowing even those who do not have their own means of transport (for example, disadvantaged social categories or users with a short stay in the area) or who are suitably equipped (people with reduced or no motor skills), to move around in areas easily. The present work focuses on an analysis of the current state of affairs, starting from the literature and regulations concerning the diffusion of the DRT systems in Italy, and offers some ideas for the optimisation of an integrated public transport service. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

11.
22nd International Conference on Computational Science and Its Applications , ICCSA 2022 ; 13380 LNCS:453-468, 2022.
Article in English | Scopus | ID: covidwho-2013910

ABSTRACT

The COVID-19 has significantly led to changes in the mobility needs and in user travel behavior, due to the measures adopted to reduce the spread of the virus. While on the one hand this has resulted in a reduction in the number of trips, on the other this has entailed an increase in the use of the private car, considered as the safest form of transportation in urban contexts. Thus, administrations and policy makers have to promote actions and strategies to encourage soft mobility (i.e. walking and cycling), viewed as solutions to reduce transport emissions and ensure social distancing. This often implies the need for a redesign of urban spaces as pedestrians experience uncomfortable or unsafe situations about the surrounding environment. Within this framework, the paper proposes a methodological framework to evaluate the interactions between pedestrians and vehicular traffic using a microsimulation approach. The analyzed case study concerns a road intersection within the S. Benedetto neighbourhood in Cagliari (Italy). A scenario assessment has been performed through the computation of several performance indicators related both to private transport (i.e. level of service and emissions) and pedestrian users (i.e. density;speed and crossing time). The comparative analysis of results demonstrates that this research approach could represent a flexible and effective tool in guiding administrations through the decision-making process during the planning and development of projects for redevelopment of urban spaces and the promotion of soft mobility. Further research will focus on an extended study area, by modelling the behaviour of different categories of pedestrians and introducing in-field data. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

12.
Sustainability ; 14(15):9588, 2022.
Article in English | ProQuest Central | ID: covidwho-1994189

ABSTRACT

Urban passenger transport is one of the most significant sources of fossil energy consumption and greenhouse gas emission, especially in developing countries. The rapid growth of urban transport makes it a critical target for carbon reduction. This paper establishes a method for calculating carbon emission from urban passenger transport including ground buses, private cars, cruising taxis, online-hailing taxis, and rail transit. The scope of the study is determined according to the transportation mode and energy type, and the carbon emission factor of each energy source is also determined according to the local energy structure, etc. Taking into consideration the development trend of new energy vehicles, a combination of “top-down” and “bottom-up” approaches is used to estimate the carbon dioxide emission of each transportation mode. The results reveal that carbon emission from Qingdao’s passenger transport in 2020 was 8.15 million tons, of which 84.31% came from private cars, while the share of private cars of total travel was only 45.66%. Ground buses are the most efficient mode of transport. Fossil fuels emit more greenhouse gases than other clean energy sources. The emission intensity of hydrogen fuel cell buses is better than that of other fuel type vehicles. Battery electric buses have the largest sensitivity coefficient, therefore the carbon emission reduction potentially achieved by developing battery electric buses is most significant.

13.
IEEE Access ; : 1-1, 2022.
Article in English | Scopus | ID: covidwho-1992569

ABSTRACT

One of the major challenges imposed by the SARS-CoV-2 pandemic is the lack of pattern in which the virus spreads, making it difficult to create effective policies to prevent and tackle the pandemic. Several approaches have been proposed to understand the virus behavior and anticipate its infection and death curves at country ans state levels, thus supporting containment measures. Those initiatives generalize well for general extents and decisions, but they do not predict so well the trajectory of the virus through specific regions, such as municipalities, considering their distinct interconnection profiles. Specially in countries with continental dimensions, like Brazil, too general decisions imply that containment measures are applied either too soon or too late. This study presents a novel scalable alternative to forecast the numbers of case and death by SARS-CoV-2, according to the influence that certain regions exert on others. By exploiting a single-model architecture of graph convolutional networks with recurrent networks, our approach maps the main access routes to municipalities in Brazil using the modals of transport, and processes this information via neural network algorithms to forecast at the municipal level ans for the whole country. We compared the performance in forecasting the pandemic daily numbers with three baseline models using Mean Absolute Error (MAE), Symmetric Mean Absolute Percentage Error (sMAPE) and Normalized Root Mean Square Error (NRMSE) metrics, with the forecasting horizon varying from 1 to 25 days. Results show that the proposed model overcomes the baselines when considering the MAE and NRMSE (p ˂0.01), being specially suitable for forecasts from 14 to 24 days ahead. Author

14.
IEEE Transactions on Signal and Information Processing over Networks ; : 1-14, 2022.
Article in English | Scopus | ID: covidwho-1985507

ABSTRACT

The rapid spread of COVID-19 disease has had a significant impact on the world. In this paper, we study COVID-19 data interpretation and visualization using open-data sources for 351 cities and towns in Massachusetts from December 6, 2020 to September 25, 2021. Because cities are embedded in rather complex transportation networks, we construct the spatio-temporal dynamic graph model, in which the graph attention neural network is utilized as a deep learning method to learn the pandemic transition probability among major cities in Massachusetts. Using the spectral graph wavelet transform (SGWT), we process the COVID-19 data on the dynamic graph, which enables us to design effective tools to analyze and detect spatio-temporal patterns in the pandemic spreading. We design a new node classification method, which effectively identifies the anomaly cities based on spectral graph wavelet coefficients. It can assist administrations or public health organizations in monitoring the spread of the pandemic and developing preventive measures. Unlike most work focusing on the evolution of confirmed cases over time, we focus on the spatio-temporal patterns of pandemic evolution among cities. Through the data analysis and visualization, a better understanding of the epidemiological development at the city level is obtained and can be helpful with city-specific surveillance. IEEE

15.
5th International Conference on Traffic Engineering and Transportation System, ICTETS 2021 ; 12058, 2021.
Article in English | Scopus | ID: covidwho-1962044

ABSTRACT

Aiming at the role of urban transportation systems in the prevention and control of the new crown pneumonia epidemic and emergency support. Based on epidemic prevention and control, this paper introduced the concept of resilience. The change process of system performance was divided into the prevention stage, maintenance stage, and recovery stage. Analyzed the factors affecting urban transportation systems resilience at various stages and the causal relationship between the factors. Assessment indicators of the transportation system resilience was established. Bayesian network (BN) was used to build resilience assessment model of urban transportation systems. And BN was used to evaluate and reason the model. Taking Xi'an as an example, the paper assessed the resilience of Xi'an transportation system. GeNIe was used for causal inference and sensitivity analysis of the network. Identify factors with high sensitivity and propose improvement measures. The results show that the model can quantify the resilience of urban transportation systems during the COVID-19 Pandemic, evaluate the current situation of the systems, and analyze the effects of factors on the resilience, to provide decision support for improving the resilience of urban transportation systems and dealing with epidemic risk. © 2021 SPIE

16.
5th International Conference on Traffic Engineering and Transportation System, ICTETS 2021 ; 12058, 2021.
Article in English | Scopus | ID: covidwho-1962043

ABSTRACT

The prediction of bus passenger volume is the fundamental research content of bus transfer optimization. In order to get more accurate passenger volume data and improve the utilization efficiency of urban traffic resources, according to randomness, time-varying and uncertainty of public transport passenger volume in Beijing, combined with the current new coronavirus pneumonia epidemic, this paper collected the relevant data of Beijing in the past 40 years, and predicted and analyzed them from four dimensions of public transport, urban scale and residents' economic level, taxi and sudden health events by BP neural network and regression analysis. The results show that BP neural network has good prediction results, and BP neural network is suitable for large sample size, which needs to fit or predict complex nonlinear relationships. © 2021 SPIE

17.
8th IEEE International Conference on Big Data Security on Cloud, 8th IEEE International Conference on High Performance and Smart Computing, and 8th IEEE International Conference on Intelligent Data and Security, BigDataSecurity/HPSC/IDS 2022 ; : 92-94, 2022.
Article in English | Scopus | ID: covidwho-1961365

ABSTRACT

Public transit demand is an import indicator of economic and social activity level. To accurately predict the public transport demand change during the COVID pandemic, in this paper, we investigate various factors affecting such demand change and collect related data from multiple sources. Different prediction models including linear regression and deep neural networks are explored. Experiments were conducted and the results show that though COVID-19 pandemic greatly affect the public transport, our proposed approach can accurately predict the next day public transit volume. © 2022 IEEE.

18.
Journal of Transport and Supply Chain Management ; 16, 2022.
Article in English | ProQuest Central | ID: covidwho-1954242

ABSTRACT

Background: After coronavirus disease 2019 (COVID-19) was declared a pandemic, movement restrictions were implemented across sub-Saharan Africa. There has been much speculation on what the long-term impacts on urban transport might be. Objectives: The aim of this paper is to identify the revealed and future travel impacts of the pandemic. Method: To pursue this aim, evidence was compiled from two sources: secondary big data;and a ( n = 15) two-wave Delphi panel survey of experts in the region. Results: It is predicted that longer-term impacts will take the form of: reduced travel by, and accessibility for, low-income households residing in peripheral locations because of decreased welfare;reduced transport service availability;operator reduction (particularly amongst unsubsidised formal operators);increased remote activity participation for a minority of better resourced households with white-collar workers;and disrupted trip distributions as the mix of city-centre land use changes in response to business attrition in economic recession rather than to disrupted bid rents. Conclusion: The major impact of the pandemic is likely to be on welfare, rather than on trip substitution. There is a need, therefore, to focus policy on the mitigation of these impacts and, more particularly, on ways of measuring changes in transport disadvantage and exclusion so that reliable data are available to inform mitigation strategies. The mitigation strategies considered should include investment in affordable ‘digital connectivity’ as a means of complementing accessibility from physical proximity and mobility. The pandemic also highlights the need to develop more robust transport planning practices to deal with uncertainty.

19.
6th International Conference on Transportation Information and Safety, ICTIS 2021 ; : 240-244, 2021.
Article in English | Scopus | ID: covidwho-1948788

ABSTRACT

The major ports along the coast of China that undertake container transportation are all facing problems in collection and dispatching to a certain extent. In particular, due to the recent impact of the COVID-19 epidemic, truck drivers have difficulty moving across regions, and there was once a phenomenon of no containers being transported by vehicles. This paper sorted out the basic situation of container port collection and dispatching methods all over the world. Taking Shenzhen Port as an example, this paper focused on the analysis of the structural characteristics of container transportation and the impact on the rear urban traffic and atmospheric environment. Then it proposed a intermodal transportation network and established the 'Port Shuttle Hub System' model, which would closely link the port with the railway and inland port, and integrate the transportation organization mode, which greatly improves the efficiency of port containers' transportation. © 2021 IEEE.

20.
2021 IEEE International Conference on Space-Air-Ground Computing, SAGC 2021 ; : 165-166, 2021.
Article in English | Scopus | ID: covidwho-1922767

ABSTRACT

This paper proposes the Susceptible-exposed of Small-world Network Model (SSEM) by combining the Susceptible-Exposed-Infectious-Recovered (SEIR) model with the Small-world Network (SN) model. The scenarios of COVID-19 propagation in urban public transport network was set by the specific model parameters. A study area, Huicheng District, Huizhou City, Guangdong Province, China, was selected to estimate the temporal and spatial distribution characteristics of COVID-19 within 12 hours based on SSEM. The results show that, without taking protective measures, if the two infectious source were in a bus, after 12 hours, 324 bus stations, 762 infected people and 68.85km2 were covered in the study area. The results of this study will provide a reference for the future study of COVID-19 virus transmission mechanism in the small enclosed environment. © 2021 IEEE.

SELECTION OF CITATIONS
SEARCH DETAIL