ABSTRACT
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.
ABSTRACT
The global outbreak of coronavirus disease 2019 (COVID-19) has affected the urban mobility of nations around the world. The pandemic may even have a potentially lasting impact on travel behaviors during the post-pandemic stage. China has basically stopped the spread of COVID-19 and reopened the economy, providing an unprecedented environment for investigating post-pandemic travel behaviors. This study conducts multiple investigations to show the changes in travel behaviors in the post-pandemic stage, on the basis of empirical travel data in a variety of cities in China. Specifically, this study demonstrates the changes in road network travel speed in 57 case cities and the changes in subway ridership in 26 case cities. Comprehensive comparisons can indicate the potential modal share in the post-pandemic stage. Further, this study conducts a case analysis of Beijing, where the city has experienced two waves of COVID-19. The variations in travel speed in the road network of Beijing at different stages of the pandemic help reveal the public's responses towards the varying severity of the pandemic. Finally, a case study of the Yuhang district in Hangzhou is conducted to demonstrate the changes in traffic volume and vehicle travel distance amid the post-pandemic stage based on license plate recognition data. Results indicate a decline in subway trips in the post-pandemic stage among case cities. The vehicular traffic in cities with subways has recovered in peak hours on weekdays and has been even more congested than the pre-pandemic levels;whereas the vehicular traffic in cities without subways has not rebounded to pre-pandemic levels. This situation implies a potential modal shift from public transportation to private vehicular travel modes. Results also indicate that commuting traffic is sensitive to the severity of the pandemic. This may be because countermeasures, e.g., work-from-home and suspension of non-essential businesses, will be implemented if the pandemic restarts. The travel speed in non-peak hours and on non-workdays is higher than pre-pandemic levels, indicating that non-essential travel demand may be reduced and the public's vigilance towards the pandemic may continue to the post-pandemic stage. These findings can help improve policymaking strategies in the post-pandemic new normal. © 2023 American Society of Civil Engineers.
ABSTRACT
During the COVID-19 pandemic, authorities in many places have implemented various countermeasures, including setting up a cordon sanitaire to restrict population movement. This paper proposes a bi-level programming model to deploy a limited number of parallel checkpoints at each entry link around the cordon sanitaire to achieve a minimum total waiting time for all travelers. At the lower level, it is a transportation network equilibrium with queuing for a fixed travel demand and given road network. The feedback process between trip distribution and trip assignment results in the predicted waiting time and traffic flow for each entry link. For the lower-level model, the method of successive averages is used to achieve a network equilibrium with queuing for any given allocation decision from the upper level, and the reduced gradient algorithm is used for traffic assignment with queuing. At the upper level, it is a queuing network optimization model. The objective is the minimization of the system's total waiting time, which can be derived from the predicted traffic flow and queuing delay time at each entry link from the lower-level model. Since it is a nonlinear integer programming problem that is hard to solve, a genetic algorithm with elite strategy is designed. An experimental study using the Nguyen-Dupuis road network shows that the proposed methods effectively find a good heuristic optimal solution. Together with the findings from two additional sensitivity tests, the proposed methods are beneficial for policymakers to determine the optimal deployment of cordon sanitaire given limited resources. © National Academy of Sciences: Transportation Research Board 2021.
ABSTRACT
This study analyzes crash data from 2016 to 2020 on a National Highway in Maharashtra, India. The impact of the COVID-19 lockdown on the road crashes of the study area is presented, and recommendations to improve road safety are proposed. The crash data is collected from the "National Highways Authority of India, Kolhapur” from 2016 to 2020, and the information is classified into three scenarios: Before Lockdown, After Lockdown, and Strict Lockdown. The crash data is analyzed under three scenarios for seven different classifications followed by their sub-classifications. The time-wise analysis of crash data is performed in four-time slots, namely 00:00–05:59 AM, 06:00–11:59 AM, 12:00–17:59 PM, and 18:00–23:59 PM. The season-wise analysis of crash data is performed in three seasons: Summer, Monsoon, and Winter. The crashes that occurred on 2-lane-straight roads having T-junction are more than 90% in all three scenarios. The significant factors responsible for crashes are "Head-on collision,” "Vehicle out of control,” and "Overspeeding.” Most crashes (more than 36%) occurred between 12:00 and 17:59 PM and in the Summer season (more than 42%) in all three scenarios. The crashes in the COVID-19 "Strict Lockdown” scenario witnessed a fall of 254.55% compared to 2019 and 2018. Surprisingly, there was a rise of 137.5% and a fall of 127.27% in crashes of the COVID-19 2020 "Strict Lockdown” scenario, compared to 2017 and 2016, respectively. The crashes under the sub-classifications "Right angle collision” and "Fatal” increased in 2020 compared to the previous 4 years due to the impact of COVID-19. © 2023, The Author(s).
ABSTRACT
This study aimed to map the accessibility of the existing isolation facilities in Cabagan, Isabela, and propose probable locations suitable for establishing isolation facilities using the Geographic Information System (GIS). Digital datasets of the current isolation facilities were used in the study, along with factors such as land uses, hazards, landfills, and road networks that should be taken into consideration when choosing potential locations for isolation facilities. These factors follow the guidelines set by the Department of Health (DOH). The processing and generation of layers related to the criteria were done using GIS techniques, specifically overlay analysis tools. In order to project an appropriate map of potential isolation facilities in Cabagan, Isabela, the layers were combined and overlaid. The existing isolation facilities are accessible to Milagros Albano District Hospital (MADH) since all of them are adjacent to national or barangay roads. More than half, or 65.38%, of the isolation facilities, belong to areas with low to moderate susceptibility to flooding, and 26.92% are in areas with high susceptibility to flooding. Furthermore, all isolation facilities are open to the public, with 53.85% of existing isolation facilities in residential areas, 7.69% in commercial areas, and 38.46% in agricultural areas. The suitability map of proposed isolation facilities was successfully generated, showing that 100% of the proposed isolation facilities are accessible from any road network in the municipality with low and moderate susceptibility to flooding and low susceptibility to landslides. © 2022 IEEE.
ABSTRACT
Ridepooling service options introduced by transportation network companies (TNCs) and microtransit companies provide opportunities to increase shared-ride trips in vehicles, thereby improving congestion and environmental factors. This paper reviews the existing literature available on ridepooling and related services, specifically focusing on pooling options available from on-demand transportation companies. The paper summarizes the existing knowledge on the use of pooled-ride services, factors in travel mode service options for customers, available policy and planning strategies to incentivize sharing vehicles, and effects of the COVID-19 pandemic on shared-ride travel. Overall, research shows that ridepooling options are more likely to be considered by public transit users who have lower household incomes, while ridesourcing users of upperclass backgrounds are less likely to consider moving to a shared-ride service. Travel time and trip cost are the most important factors for travelers determining whether to use a ridesplitting or microtransit service rather than a ride-alone ridesourced trip. Existing policy and planning tools targeting pooled travel or TNCs can be expanded on and specified for on-demand ridepooling services, such as offering better incentives to use shared vehicles and increased access to curb areas or travel lanes, but the most effective strategies will include increasing the user costs for parking or riding alone. © National Academy of Sciences.
ABSTRACT
The Philippines, as a fast-growing country, has had the highest road infrastructure investment to date for the past five years compared to the previous years. The infrastructure programs of the government as a solution to decongest Metro Manila and develop the countryside for economic growth are promising yet result in various risks and challenges. This research presents the road development issues from multiple sources;primary data from interviews of stakeholders of road development, secondary data from online news articles, social network services, government issuance, policies, and related literature. The Philippines is in a dire economic situation due to the Covid-19 outbreak that resulted in the country's worst economic performance since the Asian financial crisis in 1998. The country's economic managers pinned high hopes on the government infrastructure programs as a vital strategy to help pump-prime the economy towards recovery due to its job generation and multiplier effects. Hence, it implicates enormous risks and challenges such as low tax revenues, the trade-off with more urgent Covid-19 response measures, foreign and private companies support, unsolicited project proposals, inequitable distribution of infrastructures, and delays in construction activities. Various road development stakeholders also mentioned the need for strict road regulations, urban and regional planning, aesthetic improvement, urban renewal in aid of car-centric infrastructures, and routine maintenance on-road sections. The data are structured in various categories such as public involvement, environmental preservation, public policy, project planning, road design, road safety, economic recovery, and construction time. Lastly, the implications for future research directions are discussed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The Philippines, as a fast-growing country, has had the highest road infrastructure investment to date for the past five years compared to the previous years. The infrastructure programs of the government as a solution to decongest Metro Manila and develop the countryside for economic growth are promising yet result in various risks and challenges. This research presents the road development issues from multiple sources;primary data from interviews of stakeholders of road development, secondary data from online news articles, social network services, government issuance, policies, and related literature. The Philippines is in a dire economic situation due to the Covid-19 outbreak that resulted in the country's worst economic performance since the Asian financial crisis in 1998. The country's economic managers pinned high hopes on the government infrastructure programs as a vital strategy to help pump-prime the economy towards recovery due to its job generation and multiplier effects. Hence, it implicates enormous risks and challenges such as low tax revenues, the trade-off with more urgent Covid-19 response measures, foreign and private companies support, unsolicited project proposals, inequitable distribution of infrastructures, and delays in construction activities. Various road development stakeholders also mentioned the need for strict road regulations, urban and regional planning, aesthetic improvement, urban renewal in aid of car-centric infrastructures, and routine maintenance on-road sections. The data are structured in various categories such as public involvement, environmental preservation, public policy, project planning, road design, road safety, economic recovery, and construction time. Lastly, the implications for future research directions are discussed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Background: Cycling has always been considered a sustainable and healthy mode of transport. With the increasing concerns of greenhouse gases and pollution, policy makers are intended to support cycling as commuter mode of transport. Moreover, during Covid-19 period, cycling was further appreciated by citizens as an individual opportunity of mobility. Unfortunately, bicyclist safety has become a challenge with growing number of bicyclists in the 21st century. When compared to the traditional road safety network screening, availability of suitable data for bicycle based crashes is more difficult. In such framework, new technologies based smart cities may require new opportunities of data collection and analysis. Methods: This research presents bicycle data requirements and treatment to get suitable information by using GPS device. Mainly, this paper proposed a deep learning-based approach "BeST-DAD” to detect anomalies and spot dangerous points on map for bicyclist to avoid a critical safety event (CSE). BeST-DAD follows Convolutional Neural Network and Autoencoder (AE) for anomaly detection. Proposed model optimization is carried out by testing different data features and BeST-DAD parameter settings, while another comparison performance is carried out between BeST-DAD and Principal Component Analysis (PCA). Result: BeST-DAD over perform than traditional PCA statistical approaches for anomaly detection by achieving 77% of the F-score. When the trained model is tested with data from different users, 100% recall is recorded for individual user's trained models. Conclusion: The research results support the notion that proper GPS trajectory data and deep learning classification can be applied to identify anomalies in cycling behavior. © 2023, The Author(s).
ABSTRACT
In recent years, drone delivery has become one of the most widely adopted emerging technologies. Under the current Covid-19 pandemic, drones greatly improve logistics, especially in rural areas, where inefficient road networks and long distances between customers reduce the delivery capacity of conventional ground vehicles. Considering the limited flight range of drones, charging stations play essential roles in the rural delivery system. In this study, we utilize simulation to optimize the drone delivery system design, in order to minimize the cost of serving the maximum capacity of customers. As facility siting is usually difficult to optimize, we propose a novel simulation-heuristic framework that continuously improves the objective to find near-optimal solutions. In addition, we conduct a case study using real-world data collected from Knox County, Tennessee. The results suggest that the proposed approach saves over 15% on total costs compared with the benchmark. © 2022 IEEE.
ABSTRACT
Our cars make our everyday life much easier. With their help, in a day full of programs, we can get anywhere on time without carrying our packages on our own. In our rushing world, there is the probability that even if we have enough time to get there somewhere, we will hurry, which leads to inattention or worse case an accident. Car manufacturers have a huge amount of research projects to install driver-assistance safety electronics into our cars that, even if they are not driving, instead of the driver, alert to an accident and help to avoid a stutter or a life-threatening accident. According to the Hungarian Central Statistical Office (KSH), research on car manufacturers has brought significant results, as while in 1990 there were nearly 37,000 accidents involving personal injuries on the roads, in 2020 there were only 18,000 such accidents. However, in addition to these active and passive safety features, we need to be aware of and follow the written and unwritten rules of road safety. However, the number of passenger cars has increased so significantly in the last 20–25 years—but especially in the last two years due to the coronavirus epidemic—as roads are characterized by such a high degree of congestion that pedestrians and cyclists require much more attention from participants. This way it is important children receive proper education on the rules of walking or cycling at nursery school and preschool age. The aim of the study is to enumerate and present the platforms that help children to learn, know and deepen the rules of safe traffic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The COVID-19 pandemic has led to an urgent need in emerging economies to quickly identify vulnerable populations that do not live within access of a health facility for testing and vaccination. This access information is critical to prioritize investments in mobile and temporary clinics. To meet this need, the World Bank team sought to develop an open-source methodology that could be quickly and easily implemented by government health departments, regardless of technical and data collection capacity. The team explored use of readily available open-source and licensable data, as well as non-intensive computational methodologies. By bringing together population data from Facebook's Data for Good program, travel-time calculations from Mapbox, road network and point-of-interest data from the OpenStreetMap (OSM), and the World Bank's open-source GOSTNets network routing tools, we created a computational framework that supports efficient and granular analysis of road-based access to health facilities in two pilot locations—Indonesia and the Philippines. Our findings align with observed health trends in these countries and support identification of high-density areas that lack sufficient road access to health facilities. Our framework is easy to replicate, allowing health officials and infrastructure planners to incorporate access analysis in pandemic response and future health access planning. © National Academy of Sciences: Transportation Research Board 2022.
ABSTRACT
The road transport sector has a direct effect on fossil energy sources, cost, and consumption. Indeed, it has affected the environmental situation reversely with high carbon dioxide emissions. Due to this negative impact, the transition to electric vehicle (EV) technology must be a mandatory target for governments worldwide. To achieve this objective, many countries have developed various policies to promote EV technology buying or retrofitting. Thanks to the adopted policies, the electric technology market share has been growing. Meanwhile, research studies are involved also in this project by studying the benefit of EV technology low total cost of ownership (TCO) to motivate consumers of its utilization. For that purpose, the present paper aims to review the discussed policies, and methods to boost the diffusion of electric technology as a sustainable and reliable solution to overcome the global energy situation despite the different obstacles, barriers, and the pandemic situation (COVID-19), which has affected the consumer economic and social behavior. © 2022 IEEE.
ABSTRACT
Within the context of the current day, safety and cleanliness are the most important factors to consider in order to guarantee a healthy and risk-free human existence. Research has constantly proven that basic hygiene is vital, and that wearing a mask in public areas is crucial, in light of the terrible epidemic caused by the Coronavirus. Consider mandating the use of helmets as another precautionary safety measure for users of two-wheeled vehicles. A real-time Yolov3 object detector is used to train the model to recognise face masks and helmets in video footages, live feeds, or photos in order to search for public hygiene and safety. This is done in order to ensure that people are not exposed to potentially hazardous conditions. A neural network is used for the prediction of bounding boxes and the possibilities associated with those boxes over the whole picture. The dataset was gathered via the use of the internet. The model has been taught with the use of internet photos for several classes, such as wearing a mask but not a helmet;not wearing a mask or helmet;wearing a mask but not a helmet;wearing a helmet but not a mask;and wearing both a mask and a helmet. The trained dataset is used mostly for the purposes of classifying items and locating them within the aforementioned categories. In the event that neither the helmet nor the mask is located, the SMTP library module that is supplied by Python specifies an SMTP that may deliver mail to any internet device using SMTP or ESMTP as a client session object. The suggested approach was evaluated using a dataset that included photographs of persons adhering to COVID-19 safety regulations as well as photographs of people adhering to road safety recommendations while wearing helmets. Accuracy and precision are both quite good in the outcomes that were achieved. © 2022 IEEE.
ABSTRACT
Traditionally, mobility problems and automobile traffic congestion have increased in cities around the world due to the urban development process, especially in the city of Metropolitan Lima. For this reason, the government of the Peruvian capital has established temporary detours in certain sections of the arterial roads of the network, to invite new cyclists, due to the effects of Covid-19. Today, Lima has a network of 294 km of bicycle lanes, which have been implemented without adequate planning. In view of this, we evaluated the risk of poor planning on the vulnerable user (the cyclist) at an intersection of this road network, with a high rate of motorized congestion. The main objective of this study has been to propose corrective actions to avoid the exposure to danger on the users of the bicycle lane (countermeasures);due to lack of safety at the intersection of La Marina Ave. and Universitaria Ave. In this sense, a risk matrix was developed with the most concurrent factors that occur at this intersection;to then obtain a risk level and take actions in each of them, to mitigate the impact. The result obtained in the analysis of this study for the intersection is classified as a level 2 risk: Important risk, which means that it presents several important danger factors. Finally, in addition to the analyses developed, a treatment scheme was proposed for the intersection to provide greater safety to the users of the bicycle lane, avoiding fatal and non-fatal accidents. © 2022 IEEE.
ABSTRACT
Many believe that telecommuting could be a solution for some of the significant adverse impacts of our transportation systems, e.g., traffic congestion, greenhouse gas and air pollution emissions, and energy consumption. Observations may have further strengthened this belief during the first year of the COVID-19 Pandemic, where streets were deserted and clean air and wildlife returned to urban areas. Accordingly, this study investigates the legitimacy of this belief. The NHTS 2017 data set was used to examine the travel activity of commuting workers against telecommuters. Workers were classified into one of five telecommute classes based on primary work location, telecommute engagement, logged trips, and option to telecommute: home-based workers (those who work predominantly from home), primary and ancillary telecommuters (those who telecommute), passive telecommuters (those who have the option to telecommute), and non-Telecommuters. The various forms of telecommuting were found to significantly impact average daily trip counts and average daily trip miles produced in both urban and rural contexts. Contrary to the possibly traditional belief, telecommuters made more trips per day and traveled longer distances per day compared to non-Telecommuters. Additionally, the study investigated the differences in trip rates by trip purpose for each of the five telecommuting classes. The analysis revealed that while home-based work (HBW) trips for primary telecommuters decreased significantly, all other trip purposes increased (in number and distance) and in a higher manner than the decrease of the HBW trips. These findings indicate that telecommuting is likely to increase total VMT and associated negative impacts and should inform relevant transportation policies. © ASCE. All rights reserved.
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.
ABSTRACT
The risk potential on German roads remains high: Even in 2021 with less traffic due to the Covid-pandemic, the police counted 2.3 million traffic accidents [1]. Many accidents occur due to individual mistakes of road users. Dangerous situations are often misjudged or not recognized on time, for example, due to distraction while driving [2]. Autonomous systems in vehicles show the potential to avoid driver-related accidents, but for a Dynamic Risk Management (DRM) reliable data is needed. This is exactly where the project “Early Detection of Dangerous Areas in road traffic using smart data - EDDA+” comes in. The road hazard map created by using the EDDA+ method evaluates the Germany-wide road network according to a hazard score. This digital, safety-related data includes a lot of contextual information like weather or daytime conditions and can be used as an additional basis for the DRM risk analysis. For example, an autonomous system could react more sensitively at road areas where the hazard score is high. This continuously updated hazard map is published on www.gefahrenstellen.de and also available in a more detailed way on our platform for professional users such as local authorities, police, science, engineering offices, navigation providers and car manufacturers. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Our cars make our everyday life much easier. With their help, in a day full of programs, we can get anywhere on time without carrying our packages on our own. In our rushing world, there is the probability that even if we have enough time to get there somewhere, we will hurry, which leads to inattention or worse case an accident. Car manufacturers have a huge amount of research projects to install driver-assistance safety electronics into our cars that, even if they are not driving, instead of the driver, alert to an accident and help to avoid a stutter or a life-threatening accident. According to the Hungarian Central Statistical Office (KSH), research on car manufacturers has brought significant results, as while in 1990 there were nearly 37,000 accidents involving personal injuries on the roads, in 2020 there were only 18,000 such accidents. However, in addition to these active and passive safety features, we need to be aware of and follow the written and unwritten rules of road safety. However, the number of passenger cars has increased so significantly in the last 20–25 years—but especially in the last two years due to the coronavirus epidemic—as roads are characterized by such a high degree of congestion that pedestrians and cyclists require much more attention from participants. This way it is important children receive proper education on the rules of walking or cycling at nursery school and preschool age. The aim of the study is to enumerate and present the platforms that help children to learn, know and deepen the rules of safe traffic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The outbreak of COVID-19 has greatly impacted all industries of many countries in the world. As an important part of people's daily life, transportation is one of the most severely impacted industries. Taking New York City as an example, this paper explores the decline of taxi ridership due to the COVID-19. The decreased ratio of the actual taxi ridership to the taxi ridership predicted for the no COVID-19 scenario based on historical data is calculated as the dependent variable. The fractional response model is used to study the effect of built environment factors including demographic and socioeconomic, land use, and road-related on the decline of ridership. One model is constructed for each of the four periods, to explore the influence of influencing factors on the dependent variables in different periods. The model results show that the percentage of taxi trips decline is associated with the proportion of high-income people living in the area. The reason could be that these people have more flexible working hours and working places. They can choose to telecommute or travel by private cars to avoid contacting other people during transportation. The analysis of the other factors shows that industrial jobs are related to the low percentage of decline. The model results reveal to us the problem of equity exposed in New York City during the pandemic: limited by jobs(race/income), a portion of citizens are not fully free to choose their travel mode during the pandemic. According to the findings, this paper gives traffic management some policy suggestions. As a result, this study could provide an important reference for policymakers to develop appropriate measures to control the epidemic. © 2021 IEEE.