Path Planning of Follow-up Transport Robot Based on Machine Vision

With the upgrading and innovation of the logistics industry, the requirements for the level of transportation smart technologies continue to increase. The outbreak of the COVID-19 has further promoted the development of unmanned transportation machines. Aimed at the requirements of intelligent following and automatic obstacle avoidance of mobile robots in dynamic and complex environments, this paper uses machine vision to realize the visual perception function, and studies the real-time path planning of robots in complicated environment. And this paper proposes the Dijkstra-ant colony optimization (ACO) fusion algorithm, the environment model is established by the link viewable method, the Dijkstra algorithm plans the initial path. The introduction of immune operators improves the ant colony algorithm to optimize the initial path. Finally, the simulation experiment proves that the fusion algorithm has good reliability in a dynamic environment. © 2022 IEEE.

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.

Tracking the Transit Divide: A Multilevel Modelling Approach of Urban Inequalities and Train Ridership Disparities in Chicago

Using a multilevel modelling approach, this study investigates the impact of urban inequalities on changes to rail ridership across Chicago's "L” stations during the pandemic, the mass vaccination rollout, and the full reopening of the city. Initially believed to have an equal impact, COVID-19 disproportionally impacted the ability of lower socioeconomic status (SES) neighbourhoods' to adhere to non-pharmaceutical interventions: working-from-home and social distancing. We find that "L” stations in predominately Black or African American and Hispanic or Latino neighbourhoods with high industrial land-use recorded the smallest behavioural change. The maintenance of higher public transport use at these stations is likely to have exacerbated existing health inequalities, worsening disparities in users' risk of exposure, infection rates, and mortality rates. This study also finds that the vaccination rollout and city reopening did not significantly increase the number of users at stations in higher vaccinated, higher private vehicle ownership neighbourhoods, even after a year into the pandemic. A better understanding of the spatial and socioeconomic determinants of changes in ridership behaviour is crucial for policymakers in adjusting service routes and frequencies that will sustain reliant neighbourhoods' access to essential services, and to encourage trips at stations which are the most impacted to revert the trend of declining public transport use.

Short-Duration Crash Modeling to Understand the Impact of Operating Speed on Freeway Crashes During COVID-19

Gaining an understanding of speed–crash relationships is a critical issue in highway safety research. Because of the ongoing pandemic (COVID-19) there has been a reduction in traffic volume, and some early studies explain that speeding in an environment with less traffic is associated with a high number of crashes, especially fatal and serious injury crashes. This study aims to quantify the impact of operating speed on traffic crash occurrences. The study conflated several databases (speed data, roadway inventory data, and crash data) that contain data from Dallas, Texas, spanning from 2018 to 2020, to examine the speed–crash association. Using the negative binomial Lindley regression model, this study showed that the trends of crash prediction models vary over the years (2018, 2019, and 2020) by different injury severity levels (i.e., fatal crashes, fatal and incapacitating injury crashes). The 2020 models show that operating speed measures (i.e., average operating speed) have a significant impact on crash frequencies. The magnitudes of the speed measures show variations across the models at different injury severity levels.

Optimal Deployment of Cordon Sanitaire with Available Testing Capacity

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.

Brazil's Amazon Oxygen Crisis: How Lives and Health Were Sacrificed During the Peak of COVID-19 to Promote an Agenda with Long-Term Consequences for the Environment, Indigenous Peoples, and Health.

In January 2021, oxygen supplies in the Amazon region's largest city were allowed to run out at the peak of the second wave of the COVID-19 epidemic, shocking the world as hospital patients expired for lack of this basic medical resource in Manaus, which during the first COVID-19 wave had become the world's first city to bury its dead in mass graves. Brazil's authorities used this tragedy to further a political agenda that implies enormous environmental and human-rights consequences. Transport of oxygen was used to promote building a road that, together with its planned side roads, would give deforesters access to much of what remains of Brazil's Amazon Forest. Here, we demonstrate that the logistical strategy adopted by the Jair Bolsonaro administration's Ministries of Health and Infrastructure to bring oxygen to Manaus was the worst possible choice, and the foreseeable delay in the arrival of oxygen cost hundreds of lives. Rather than sending trucks to carry oxygen on the nearly impassible Highway BR-319 during the rainy season, the most appropriate transport option was barges on the Madeira River. As oxygen supplies dwindled in Manaus, the families of wealthier COVID-19 victims scrambled to buy the few remaining cylinders at prices out of reach for those in poorer (and often ethnically distinct) economic strata. Ethnic health disparities are aggravated by both the direct consequences of the oxygen crisis and, on the longer term, by the consequences of the highway project that political use of the crisis materially advanced.

Early Highway Construction Cost Estimation: Selection of Key Cost Drivers

Cost estimates in the early stages of project development are essential for making the right decisions, but they are a huge challenge and risk for owners and potential contractors due to limited information about the characteristics of a future highway project. Whereas previous studies were mainly focused on achieving the highest possible estimation accuracy, this paper aims to propose cost-estimation models that can provide satisfactory accuracy with the least possible effort and to compare the perspectives of owners and contractors as the key stakeholders on projects. To determine cost drivers (CDs) that have a high influence on highway-construction costs and require low effort for their establishment, a questionnaire survey was conducted. Based on the key stakeholders' perceptions and collected data set, cost-estimation models were developed using multiple-regression analysis, artificial neural networks, and XGBoost. The results show that reasonable cost-estimation accuracy can be achieved with relatively low effort for three CDs for the owners' perspective and five CDs for the contractors' perspective. Additional inclusion of input CDs in models does not necessarily imply an increase in accuracy. Also, the questionnaire results show that owners are more concerned about environmental issues, whereas contractors are more concerned about the possible changes in resource prices (especially after recent high increases caused by COVID-19 and the Russia-Ukraine war). These findings can help owners and potential contractors in intelligent decision-making in the early stages of future highway-construction projects.

Fusing hybrid attentive network with self-supervised dual-channel heterogeneous graph for knowledge tracing

Recently the large-scale influence of Covid-19 promoted the fast development of intelligent tutoring systems (ITS). As a major task of ITS, Knowledge Tracing (KT) aims to capture a student's dynamic knowledge state based on his historical response sequences and provide personalized learning assistance to him. However, most existing KT methods have encountered the data sparsity problem. In real scenarios, an online tutoring system usually has an extensive collection of questions while each student can only interact with a limited number of questions. As a result, the records of some questions could be extremely sparse, which degrades the performance of traditional KT models. To resolve this issue, we propose a Dual-channel Heterogeneous Graph Network (DHGN) to learn informative representations of questions from students' records by capturing both the high-order heterogeneous and local relations. As the supervised learning manner applied in previous methods is incapable of exploiting unobserved relations between questions, we innovatively integrate a self-supervised framework into the KT task and employ contrastive learning via the two channels of DHGN, supplementing as an auxiliary task to improve the KT performance. Moreover, we adopt the attention mechanism, which has achieved impressive performance in natural language processing tasks, to effectively capture students' knowledge state. But the standard attention network is inapplicable to the KT task because the current knowledge state of a student usually shows strong dependency on his recently interactive questions, unlike the situation of language processing tasks, which focus more on the long-term dependency. To avoid the inefficiency of standard attention networks in the KT task, we further devise a novel Hybrid Attentive Network (HAN), which produces both the global attention and the hierarchical local attention to model the long-term and short-term intents, respectively. Then, by the gating network, a student's long-term and short-term intents are combined for efficient prediction. We conduct extensive experiments on several real-world datasets. Experimental results demonstrate that our proposed methods achieve significant performance improvement compared to existing state-of-the-art baselines, which validates the effectiveness of the proposed dual-channel heterogeneous graph framework and hybrid attentive network. © 2023 Elsevier Ltd

On the Influence of Microscopic Mobility in Modelling Pedestrian Communication

In the recent past, wireless network simulations involving pedestrians are getting increasing attention within the research community. Examples are crowd networking, pedestrian communication via Sidelink/D2D, wireless contact tracing to fight the Covid-19 pandemic or the evaluation of Intelligent Transportation Systems (ITS) for the protection of Vulnerable Road Users (VRUs). Since in general the mobile communication depends on the position of the pedestrians, their mobility needs to be modeled. Often simplified mobility models such as the random-waypoint or cellular automata based models are used. However, for ad hoc networks and Inter-Vehicular Communication (IVC), it is well-known that a detailed model for the microscopic mobility has a strong influence – which is why state-of-the-art simulation frameworks for IVC often combine vehicular mobility and network simulators. Therefore, this paper investigates to what extent a detailed modelling of the pedestrian mobility on an operational level influences the results of Pedestrian-to-X Communication (P2X) and its applications. We model P2X scenarios within the open-source coupled simulation environment CrowNet. It enables us to simulate the identical P2X scenario while varying the pedestrian mobility simulator as well as the used model. Two communication scenarios (pedestrian to server via 5G New Radio, pedestrian to pedestrian via PC5 Sidelink) are investigated in different mobility scenarios. Initial results demonstrate that time- and location-dependent factors represented by detailed microscopic mobility models can have a significant influence on the results of wireless communication simulations, indicating a need for more detailed pedestrian mobility models in particular for scenarios with pedestrian crowds. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

FINANCIAL DISTRESS AND FINANCIAL PERFORMANCE ANALYSIS OF HIGHWAY COMPANIES BEFORE AND DURING THE COVID19 PANDEMIC: EVIDENCE FROM INDONESIA STOCK EXCHANGE

Starting in March 2020, Indonesia had the COVID-19 pandemic. Furthermore, this situation has decreased the utilization of highways due to complying with the government regulation, including work from home and large-scale social restrictions to reduce the spreading the corona virus. There are three highway companies listed on Indonesia Stock Exchange such as CMNP, META, and JSMR. On the other hand, the research about the financial performance and the financial distress prediction in Highways sector, especially in Indonesia is not available during the COVID-19 pandemic. This research is aimed to evaluate the financial distress by the Zmijewski model with two criterions: bankrupt and non-bankrupt zone and the financial performance by state-owned enterprise (SOE) rating with three criterions: healthy, less healthy, and unhealthy condition. The period of research is Q1 2019 – Q1 2020 as the period before the COVID-19 pandemic and Q2 2020 – Q2 2021 as the period during the COVID-19 pandemic. The study concludes that all highway companies was in non-bankrupt zone by the Zmijewski model for both before and during the COVID-19 pandemic. In addition, based on SOE rating on average for the period before the COVID-19 pandemic, CMNP, META, and JSMR achieved rating consecutively BBB, BBB, and BB. Meanwhile, on average, for the period during the COVID-19 pandemic, CMNP, META, and JSMR achieved ratings consecutively BB, BB, and B. © 2023 by Ari Prasetyo and Taufik Faturohman.

Statistical analysis of crash data and impact of COVID-19 on road crashes in Maharashtra, India

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).

Road Development Risks and Challenges in the Philippines

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.

Hourly forecasting of traffic flow rates using spatial temporal graph neural networks.

Traffic congestion forms a large problem in many major metropolitan regions around the world, leading to delays and societal costs. As people resume travel upon relaxation of COVID-19 restrictions and personal mobility returns to levels prior to the pandemic, policy makers need tools to understand new patterns in the daily transportation system. In this paper we use a Spatial Temporal Graph Neural Network (STGNN) to train data collected by 34 traffic sensors around Amsterdam, in order to forecast traffic flow rates on an hourly aggregation level for a quarter. Our results show that STGNN did not outperform a baseline seasonal naive model overall, however for sensors that are located closer to each other in the road network, the STGNN model did indeed perform better.

Impact of COVID-19 Prevention and Control Measures on Expressway Transportation in Guangdong-Hong Kong-Macao Greater Bay Area

Under the background of normalized COVID-19 prevention and control, regional epidemics occur frequently in China. How to quantify the impact of COVID-19 prevention and control measures on economic operation and passenger and freight transportation has become an urgent problem. To this end, we design a calculation method for expressway transportation indicators, propose the level and stage division process of COVID-19 prevention and control measures, and then establish a difference-in-difference model to further analyze their impact on expressway transportation indicators. Taking major cities in the Guangdong-Hong Kong-Macao Greater Bay Area as an example, case studies are conducted based on the expressway toll data and COVID-19 prevention and control information from May 2020 to April 2022. The results show that in the level I (strengthened) stage, the passenger vehicle flow has dropped significantly, the drop in each case is between 8% and 27%, and the freight indicators have not changed significantly. In Shenzhen and Dongguan, both passenger and freight indicators dropped sharply in the level II (strict) stage. Passenger vehicle flow in the two cities dropped by 46.3% and 33.7%, and truck flow by 42.7% and 27.6%, respectively, and cargo and turnover decreased as much as truck flow. The average inter- city distance of expressway passenger cars has a downward trend under the level I stage, but under the level II stage, the average inter-city distance of passenger cars and trucks has increased significantly. This study can provide a certain reference value for the formulation and implementation of COVID-19 prevention and control measures in cities and urban agglomerations. © 2022 Science Press. All rights reserved.

Road Development Risks and Challenges in the Philippines

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.

Influences of Use of Flipped Classroom Models on the Learning Outcomes of Students Majoring in Road and Bridge Engineering Technology

The new "Internet+” teaching mode during the COVID-19 pandemic has guaranteed the smooth learning progress of university students in China. High-efficiency reconstruction of time and space for knowledge teaching and internalization based on informationalized teaching mean is an important approach to online learning. A flipped classroom is a teaching mode that is formed through bottom-up exploration. Combined with teaching practical situations, the flipped classroom realizes the transformation from the teacher-centered mode to the student-oriented mode successfully and has important value to the teaching of professional core courses, which are difficult to be learned. In this study, 80 freshmen majoring in Road and Bridge Engineering Technology at Yellow River Conservancy Technical Institute in Henan Province of China were selected as research objects, and Road Survey Design and Lofting was chosen as the teaching course. Under these circumstances, a teaching experiment comparison was designed. The experimental group used flipped classroom technology based on Attention, Relevance, Confidence, and Satisfaction (ARCS) motivation model theory, while the control group used the traditional teaching mode. Research results demonstrate that before the experiment, the learning outcomes of two groups in Road Survey Design and Lofting were basically consistent (P=0.908>0.05) without obvious differences. After finishing the experiment, the post-test results of the control group have not improved significantly compared with the pre-test results (P=0.0938>0.05). However, the post-test results of the experimental group have improved significantly compared with the pretest results (P < 0.001). The average scores of the experimental group are far higher than that of the control group, thus indicating the evident progress of the experimental group. Noticeable differences in the post-test results between the experimental group and the control group are observed (P < 0.001). The research results are of great significance to enriching the teaching mode of core application courses for engineering majors in university, as they provide evidence that the flipped classroom increases the learning interests and motivation of students and demonstrate the teaching effect of flipped classroom technology in universities © 2023, International Journal of Emerging Technologies in Learning.All Rights Reserved.

Digitizing Material Delivery and Documentation in Highway Construction Using e-Ticketing Technology: Study Based on Qualitative Semistructured Interviews

Electronic Construction (e-Construction) is being increasingly used to minimize the amount of paperwork and simplify the daily operations inherent in the construction of highway infrastructures. Electronic ticketing (e-Ticketing) is a part of e-Construction that aids in the transfer of material tickets in a digital manner, accounting for more than half of construction costs;however, the technology has been pilot tested by several states since the beginning of 2013 and has been disbanded for various reasons. The purpose of this research is to pinpoint the challenges in the deployment of an e-Ticketing platform, as well as to determine the adoption rate of state departments of transportations (DOT) and to assess the benefits. The research method includes a combination of literature review and semistructured interviews to achieve the study's objectives. An inductive thematic analysis approach was used to analyze the interview transcripts. The study's findings are related to the reasons for delays and misconceptions about the implementation of the e-Ticketing platform. Internet connectivity at construction sites and high investment costs were found to be the major challenges that have delayed the implementation of the e-Ticketing platform. The study's results will aid decision makers in DOTs and engineers build a standard e-Ticketing platform, implement rules and guidelines, reduce investment costs, execute pilot testing, improve inspector safety, and complete projects in a timely and efficient manner. © 2023 American Society of Civil Engineers.

Predicting the Performance of Highway Project Using Gray Numbers

Earned Value Analysis is a methodology used to monitor project performance in terms of time, scope and cost and also to deal with uncertain situations that come within. Uncertainty is a part of construction project and sometimes these situations can cause a great loss in the project's success. Recently, to deal with uncertain situations a different approach has been developed to predict the project performance in a non-deterministic way, i.e., using gray interval numbers. A framework using gray interval numbers has been developed to predict the project performance and hence this study aims at using the framework to predict the performance of a real-life highway project of total duration of approximately 2 years. The analysis involves the verbal directed data from the site by the experts which were denoted as gray interval numbers. The results indicate that the project is under budget as the CPI is 1.06 and ahead of schedule as the SPI is 1.2. The results also show the worst case scenario that the project may exceed the budget as CPI is 0.83 and may run behind the schedule as SPI is 0.69. The outcomes of the study are in the form of range which provides the overall profile of the project and also helps the project team members to not always be accurate or deterministic with the outcomes. Since the construction sector was majorly hit by an uncertain event, i.e., COVID-19, this study can be very helpful in determining the performance after facing such a huge gap. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Performance of Traffic Accidents' Prediction Models

Modeling traffic-accident frequency is a critical issue to better understand the accident trends and the effectiveness of current traffic policies and practices in different countries. The main objectives of this study are to model traffic road accidents, fatalities and injuries in Jordan, using different modeling techniques, including regression, artificial neural network (ANN) and autoregressive integrated moving average (ARIMA) models and to evaluate the safety impact of travel-restriction strategies during Covid-19 pandemic on traffic-accident statistics for the year 2020. To accomplish these objectives, data of traffic accidents, registered vehicles (REGV), population (POP) and economic gross domestic product (GDP) from 1995 through 2020 were obtained from related sources in Jordan. The analysis revealed that accidents, fatalities and injuries have an increasing trend in Jordan. Root mean of square error (RMSE), mean absolute error (MAE) and coefficient of multiple determination (R2) were sued to evaluate the performance of the developed prediction models. Based on model performance, the ANN models are the best, followed by the ARIMA models and then the regression models. Finally, it was concluded that the strategies undertaken by the government of Jordan to combat Covid-19, including complete and partial banning of travel, resulted in a considerable reduction of accidents, injuries and fatalities by about 35%, 37% and 50%, respectively. © 2023, Jordan University of Science and Technology. All rights reserved.

COVID-19, traffic demand, and activity restriction in China: A national assessment.

The global COVID pandemic of 2020, affected travel patterns across the world. The level of impact was influenced not only by the virus itself, but also by the nature, extent, and duration of governmental restriction on commerce and personal activity to limit its spread. This paper focuses on the interaction between COVID-19 transmission and traffic volume and further explores the impact of traffic control policies on the interaction. Roadway traffic volume was used to quantify and assess the Chinese response to the pandemic; specifically, the relationship between government restrictions, travel activity, and COVID-19 progression across 29 provinces. Space and time distributions of traffic volume across China during the first half of 2020, were used to quantity the response and recovery of travel during the critical initial onset period of the virus. Most revealing of these trends were the impact of the Chinese restriction policies on both travel and the virus as well as the relationship of traffic trends during the closure period with the speed and extent of the recovery "bounce" across individual provinces based on location, economic activity, and restriction policy. These suggest that the most significant and rapid declines in traffic volume during the restriction period resulted in the most pronounced returns to normal (or more) demand levels. Based on these trends a Susceptible Infection Recovery model was created to simulate a range of outbreak and restriction policies to examine the relationship between COVID-19 spread and traffic volume in China.