Traffic Volume Reduction Research Articles

The Relationships between Adverse Weather, Traffic Mobility, and Driver Behavior

Adverse weather conditions impact mobility, safety, and the behavior of drivers on roads. In an average year, approximately 21% of U.S. highway crashes are weather-related. Collectively, these crashes result in over 5300 fatalities each year. As a proof-of-concept, analyzing weather information in the context of traffic mobility data can provide unique insights into driver behavior and actions transportation agencies can pursue to promote safety and efficiency. Using 2019 weather and traffic data along Colorado Highway 119 between Boulder and Longmont, this research analyzed the relationship between adverse weather and traffic conditions. The data were classified into distinct weather types, day of the week, and the direction of travel to capture commuter traffic flows. Novel traffic information crowdsourced from smartphones provided metrics such as volume, speed, trip length, trip duration, and the purpose of travel. The data showed that snow days had a smaller traffic volume than clear and rainy days, with an All Times volume of approximately 18,000 vehicles for each direction of travel, as opposed to 21,000 vehicles for both clear and wet conditions. From a trip purpose perspective, the data showed that the percentage of travel between home and work locations was 21.4% during a snow day compared to 20.6% for rain and 19.6% for clear days. The overall traffic volume reduction during snow days is likely due to drivers deciding to avoid commuting; however, the relative increase in the home–work travel percentage is likely attributable to less discretionary travel in lieu of essential work travel. In comparison, the increase in traffic volume during rainy days may be due to commuters being less likely to walk, bike, or take public transit during inclement weather. This study demonstrates the insight into human behavior by analyzing impact on traffic parameters during adverse weather travel.

Examining Seasonal Changes in Light-Vehicle Traffic Volume on Freeways Under Extreme Weather Conditions: A Combination of Temporal Statistical and Data Mining Non-Parametric Techniques

Today, with the increasing changes in weather patterns and the huge amount of data related to weather and traffic in different parts of the freeway network, the use of data mining methods to quantify the impact of weather on traffic flow is inevitable. The main objective of this study is to present a geostatistical method for computing and analyzing the effects of base and extreme cases of weather variables on light-vehicle traffic volumes on freeways, with an emphasis on temporal changes on different days of the week and between daytime and nighttime. In the proposed method for statistical analysis, the parametric test of two-way analysis of variance was used. In the following, with the development of a nonparametric method based on the classification and regression tree (CART) decision tree algorithm, the weather-related parameters with the greatest effect on traffic volumes were investigated separately for different seasons. For this purpose, nine years of statistics covering traffic and weather data for the studied freeway were analyzed. The computational results show that a fall in temperature of more than 35% on weekdays during the cold season and the parameters of horizontal visibility and rainfall during the day and temperature during the night in the spring cause a reduction of traffic volume of more than 45%. This study has shown that the combination of data-driven parametric and nonparametric data mining techniques is effective for traffic managers in planning and traffic control under extreme weather conditions. In this regard, an adverse weather conditions dynamic message sign (AWCDMS) framework was proposed as a practical way to warn drivers of adverse weather.

Air Quality Improvement in Urban Street Canyons: An Assessment of the Effects of Selected Traffic Management Strategies Using OSPM Model

Constantly changing vehicle stock, modification of road infrastructure, and other conditions result in a need to update the knowledge on the effectiveness of individual traffic management strategies, which could form the basis for actions taken by local authorities to improve air quality in crowded city centers, especially in street canyons. The article presents research results that evaluate the theoretical effects of introducing select traffic reorganization scenarios in the example of four street canyons located in Krakow (Poland) that are different in terms of vehicle traffic volume and canyon geometry. These scenarios were based on a reduction in the average traffic speed, road capacity or the admission of cars meeting certain exhaust emission standards. The authors estimated changes in emissions of nitrogen oxides (NO, NO2 and total NOx) and particulate matter (PM10 and PM2.5) as well as investigated the effect of these changes on air quality in the canyons using the Operational Street Pollution Model (OSPM). Significant effects in terms of improving air quality were identified only in scenarios based on a significant reduction in traffic volume and the elimination of passenger cars and light commercial vehicles with internal combustion engines that did not meet the requirements of the Euro 4, Euro 5 or Euro 6 emission standards. For these scenarios, depending on the variant and canyon analyzed, the emission reduction was achieved at a level of approximately 36–66% for NO, 28–77% for NO2, 35–67% for NOx and 44–78% for both PM10 and PM2.5. The expected effect of improving air quality in individual street canyons for these substances was 15–44%, 5–14%, 11–36% and 3–14%, respectively. The differences obtained in the percentage reduction of emissions and pollutant concentrations in the air were the result of a relatively high background of pollutants that suppress the achieved effect of improving air quality to a large extent.

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.

Modeling Traffic Volume Reduction as a Function of Winter Weather Factors for a Cold Region Highway

This research aims to quantify the impact of winter weather conditions (cold temperatures and snowfall) on traffic volume. Traffic data and winter weather data (temperature and snowfall intensities from November to March) were collected for 5 years from a cold region highway in Alberta, Canada. The study results suggest that, overall, traffic volume substantially reduces under extremely cold temperatures and heavy snowfall conditions. Yet, truck traffic was estimated to increase in the combined presence of some weather conditions, which could be due to more trucks being used and/or trucks potentially being rerouted from other routes. Highway agencies may use the proposed methodology to determine (1) the optimal snowplowing schedules, and (2) when to close (or reopen) highways in winter.

Spatiotemporal Variation of Nitrogen Dioxide and Nighttime Light Dataset of Iranian Metropolises in the COVID-19 Outbreak

COVID-19 lockdown has caused a reduction in traffic volume and industrial activities which are the main sources of air pollution in whole of the world. As tropospheric NO2 pollutant and nighttime light (NTL) are the representative of human activities, this study focused to quantify the annual and monthly change of NO2 concentration and NTL in 14 metropolises of Iran before, during and after the lockdown months such as March, April, October and November. TROPOMI images of Sentinel-5p were used for investigation of NO2 column density in 2019, 2020 and 2021, and the variation of NTL was monitored by VIIRS images. The findings showed the majority of metropolises have an increase of NO2 concentration in March and October and a decrease in April and November in 2020 but a significant increase in 2021. The similar pattern of NTL change as NO2 was observed in the most metropolises. The correlation coefficient between NO2 concentration and NTL was calculated from 0.66 to 0.75. So, in majority of metropolises, the reduction of NO2 was observed with reduction of NTL. According to the results, reducing traffic volume as mobile source does not has an effective contribution in NO2 emission in some metropolises of Iran which the stationary sources are dominant such as Isfahan. Tehran as the capital of Iran showed the highest annual mean NO2 reduction in lockdown, this finding showed the important role of traffic volume on air quality of Tehran compared to industrial activities. The integrated application of TROPOMI and NTL data will help to better decision making for controlling and managing of air quality in country’s urban area.

Investigating the impact of a 20 miles per hour speed limit intervention on road traffic collisions, casualties, speed and volume in Belfast, UK: 3 year follow-up outcomes of a natural experiment

BackgroundEvidence regarding the effectiveness of 20 miles per hour (mph) speed limit interventions is limited, and rarely have long-term outcomes been assessed. We investigate the effect of a 20 mph...

Developing a Risk Assessment Model for a Highway Site during the Winter Season and Quantifying the Functional Loss in Terms of Traffic Reduction Caused by Winter Hazards Conditions

This paper introduces a methodology to quantify traffic change triggered by the combined effect of weather hazards based on winter weather hazards models. The winter weather hazards models for three vehicle types were developed with weigh-in-motion (WIM) data collected in the commuter highway in the cold Canadian region for 5 years. The developed model was utilized to simulate the variations of the percentage reduction for each vehicle type based on the 239 pairs of weather combinations composed of six cold categories and various amounts of snowfall. The first phase involved measuring the marginal effect of weather factors such as cold category (or temperature) on the percentage reduction in traffic volume. The second phase involved utilizing the same winter weather traffic model to quantify the effect of combined weather factors on the percentage reduction. The percentage reduction of the total traffic and passenger cars increased as temperature deteriorated and snowfall increased. Truck traffic decreased as snowfall increased, but interestingly, as temperature deteriorated, it was estimated that the truck traffic volume increased. This phenomenon assumed that truck traffic moves from low-maintenance to high-maintenance highways as the weather deteriorates. The methodology to quantify traffic volume changes can be adopted by highway agencies to determine the timing of the snowplow operation based on the risk assessed in terms of traffic volume reduction. It can also be used to predict the percentage reduction of traffic and then determine whether to open or close a highway.

Traffic restrictions during the 2008 Olympic Games reduced urban heat intensity and extent in Beijing

Satellite thermal remote sensing has been utilized to examine the urban heat dynamics in relation to the urban traffic restriction policy. During the 2008 Olympic Games in Beijing, the traffic volume was approximately cut off by half through the road space rationing. Based on daily MODIS satellite thermal observations on the surface temperature, statistical models were developed to analyze the contribution of traffic volume reduction to the urban heat intensity and spatial extent. Our analyses show that cutting off half of the traffic volume has led to a marked decrease in the mean surface temperature by 1.5–2.4 °C and shrinkage of the heat extent by 820 km2 in Beijing. This research suggests that the impact of urban traffic on heat intensity is considerably larger than previously thought, and the management of urban traffic and vehicle fossil fuel use should be included in the future urban heat mitigation plan.

Transport impacts in Germany and State of Qatar: An assessment during the first wave of COVID-19

Response measures to contain COVID-19 spread varied from country to country, some imposed a complete lockdown while some imposed partial restrictions. This paper compares the transport impacts of the COVID-19 pandemic for two countries having dissimilar characteristics, Germany and State of Qatar, based on the rates of infection and response measures. Secondary data, obtained from Google mobility reports, and primary data, collected from local agencies, were used for comparison purposes. The secondary data comparison from February 2020 to July 2020 indicated an overall decline in mobility for all commercial activities and an increase was noted for parks and residential locations for Saxony, Germany. For State of Qatar, the mobility was decreased to all places except residential locations. Further, the comparison for traffic volumes and the number of crashes during the first wave of the pandemic indicated that the reduction in traffic volumes, major, and minor crashes was coupled with restrictive measures rather than COVID-19 incidences for both countries. Further, the traffic volumes showed a statistically significant inverse linear relationship with the stringency index for both countries during weekdays as well as weekends. These results suggest that the policy measures are key in governing movement restrictions and containing the spread of pandemic rather than the number of COVID-19 incidences. Further, the authorities should monitor the traffic trends during the pandemic and enforce the traffic rules and regulations as soon as the movement restrictions are lifted.

Towards a sustainable road transportation system in urban areas: A case study of Owerri, Nigeria

Urban Areas in almost all cases develop outwards from the centre. Public facilities (schools, offices, markets) are usually located at about the centre while residential abodes radiate outwards. Such development pattern necessitates road transportation movement to the centre. This paper examines strategies that can be adopted to achieve the necessary movement in a sustainable manner for these urban areas, with Owerri Nigeria as a case study. A field study was conducted to ascertain the total passenger requirement, number and mix of passenger vehicles as well as measure three main road transport-induced air pollutants. The result of the field work showed existing commuter vehicles mix of 56.2:63.7: 19.6:1.6:1 for salons, wagons, mini-buses, coaster buses and big buses respectively, of a total of 85,950 vehicles. The survey also revealed ambient air pollutants level higher than the recommended standards. A new model was developed to achieve a vehicles remix of 10:33:53: 14:1 of same vehicle types and reduction in traffic volume and target air pollutants. The analyses showed that mini-buses and coaster buses have advantage over salon cars, wagon vehicles and big buses in terms of traffic congestions and pollutants release into the environment. The two bus types have least pollutants release per passenger carried. An optimal vehicle remix, which gives higher priority to these buses has been shown to reduce congestion by 40%, Carbon monoxide by 40%, Nitrogen Dioxide by 50% and Methane by 50%. Based on the findings, it is recommended that vehicular remix of 10:33:53:14:1, for salon: wagon: mini-buses: coaster buses: big buses be adopted for Owerri commuters’ transportation need. Some policy measures were put forward to help achieve this. The measures include an outright ban on use of low-passenger-carrying vehicles for commercial purposes.

Management of road transport-induced air pollution in urban areas: A case study of Owerri, Nigeria

Environmental concerns have continued to be on the increase as human activities soar. One such environmental concern is air pollution. A major contributor to air pollution is the road transportation activities. This paper sets out to provide a management strategy for air pollution owing to road transportation in urban areas, with Owerri Nigeria as a case study. A field study was conducted in Owerri to ascertain the total passenger requirement, number and mix of passenger vehicles as well as measure three main road transport-induced air pollutants at five locations in the city. The result of the field work showed existing commuter vehicles mix of 56.2:63.7: 19.6:1.6:1 of salons, wagons, mini-buses, coaster buses and big buses respectively, of a total of 85,950 vehicles and air ambient pollutants level higher than the recommended standards. A new model was developed to achieve a remix of 10:33:53: 14:1 of same vehicle types and reduction in traffic volume and target air pollutants. The analyses show that mini-buses and coaster buses have advantage over salon cars, wagon vehicles and big buses in terms of traffic congestions and pollutants release into the environment. The two bus types could be said to have least pollutants release per passenger carried. An optimal vehicle remix, which gives higher priority to these buses have been shown to reduce congestion by 40%, Carbon monoxide by 40%, Nitrogen Dioxide by 50% and Methane by 50%. It therefore recommends that vehicular remix of 10:33:53: 14:1, for salon: wagon: mini-buses: coaster buses: big buses be adopted for Owerri commuters’ transportation need. It concludes that governments should adopt economic instruments embedded in a “push and pull” strategy, leveraging on disincentive and incentive measures to skew road transportation to the use of mini and coaster buses as a deliberate means of reducing air pollution in cities.

Has COVID-19 Lockdown Affected on Air Quality?—Different Time Scale Case Study in Wrocław, Poland

Due to the COVID-19 pandemic, there are series of negative economic consequences, however, in limiting mobility and reducing the number of vehicles, positive effects can also be observed, i.e., improvement of air quality. The paper presents an analysis of air quality measured by concentrations of NO2, NOx and PM2.5 during the most restrictive lockdown from 10 March to 31 May 2020 on the case of Wrocław. The results were compared with the reference period—2016–2019. A significant reduction in traffic volume was identified, on average by 26.3%. The greatest reduction in the concentration of NO2 and NOx was recorded at the station farthest from the city center, characterized by the lowest concentrations: 20.1% and 22.4%. Lower reduction in the average concentrations of NO2 and NOx was recorded at the municipal station (7.9% and 7.7%) and the communication station (6.7% and 10.2%). Concentrations of PMs in 2020 were on average 15% and 13.4% lower than in the reference period for the traffic station and the background station. The long-term impact of the lockdown on air quality was also examined. The analysis of the concentrations of the pollutants throughout 2020, and in the analyzed period of 2021, indicated that the reduction of concentrations and the improvement in air quality caused by the restrictions should be considered as a temporary anomaly, without affecting long-term changes and trends.

How did the COVID-19 pandemic affect road crashes and crash outcomes in Alabama?

With the rising number of cases and deaths from the COVID-19 pandemic, nations and local governments, including many across the U.S., imposed travel restrictions on their citizens. This travel restriction order led to a significant reduction in traffic volumes and a generally lower exposure to crashes. However, recent preliminary statistics in the US suggest an increase in fatal crashes over the period of lockdown in comparison to the same period in previous years. This study sought to investigate how the pandemic affected road crashes and crash outcomes in Alabama. Daily vehicle miles traveled and crashes were obtained and explored. To understand the factors associated with crash outcomes, four crash-severity models were developed: (1) Single-vehicle (SV) crashes prior to lockdown order (Normal times SV); (2) multi-vehicle (MV) crashes prior to lockdown order (Normal times MV); (3) Single-vehicle crashes after lockdown order (COVID times SV); and (4) Multi-vehicle crashes after lockdown order (COVID times MV). The models were developed using the first 28 weeks of crashes recorded in 2020. The findings of the study reveal that although traffic volumes and vehicle miles traveled had significantly dropped during the lockdown, there was an increase in the total number of crashes and major injury crashes compared to the period prior to the lockdown order, with speeding, DUI, and weekends accounting for a significant proportion of these crashes. These observations provide useful lessons for road safety improvements during extreme events that may require statewide lockdown, as has been done with the COVID-19 pandemic. Traffic management around shopping areas and other areas that may experience increased traffic volumes provide opportunities for road safety stakeholders to reduce the occurrence of crashes in the weeks leading to an announcement of any future statewide or local lockdowns. Additionally, increased law enforcement efforts can help to reduce risky driving activities as traffic volumes decrease.

Impact of COVID-19 on Traffic Signal Systems: Survey of Agency Interventions and Observed Changes in Pedestrian Activity.

The COVID-19 pandemic, the most significant public health crisis since the 1918-1919 influenza epidemic, is the first such event to occur since the development of modern transportation systems in the twentieth century. Many states across the U.S. imposed lockdowns in early spring 2020, which reduced demand for trips of various types and affected transportation systems. In urban areas, the shift resulted in a reduction in traffic volumes and an increase in bicycling and walking in certain land use contexts. This paper seeks to understand the changes occurring at signalized intersections as a result of the lockdown and the ongoing pandemic, as well as the actions taken in response to these impacts. The results of a survey of agency reactions to COVID-19 with respect to traffic signal operations and changes in pedestrian activity during the spring 2020 lockdown using two case study examples in Utah are presented. First, the effects of placing intersections on pedestrian recall (with signage) to stop pedestrians from pushing the pedestrian button are examined. Next, the changes in pedestrian activity at Utah signalized intersections between the first 6 months of both 2019 and 2020 are analyzed and the impact of land use characteristics is explored. Survey results reveal the importance of using technologies such as adaptive systems and automated traffic signal performance measures to drive decisions. While pedestrian pushbutton actuations decreased in response to the implementation of pedestrian recalls, many pedestrians continued to use the pushbutton. Pedestrian activity changes were also largely driven by surrounding land uses.

Spatial and temporal variations of the PM2.5 concentrations in Hanoi metropolitan area, Vietnam, during the COVID-19 lockdown

ABSTRACT The Vietnamese government has issued several directives of lockdown in Hanoi, the capital city, for a month (1–30 April 2020) to prevent the human-to-human transmission of COVID-19. This action has affected air pollution due to a decline in transportation. Therefore, this study investigates spatial and temporal change in the PM2.5 concentrations during the first 4 months of 2020 in Hanoi metropolitan area. Spatial distribution maps of the PM2.5 concentration in Hanoi were provided for the first time. The average PM2.5 concentrations at the 22 air monitoring stations were strongly correlated with the population. April had a significantly lower level of PM2.5 than the other 3 months. In particular, the concentrations of PM2.5 and NO2 decreased by 12 and 54%, respectively, between March and April, especially at areas for commercial activities. In April, a higher level of PM2.5 was recorded between Tuesday–Thursday, which is a reverse trend with that in March. Furthermore, Monday and Friday did not show rush hour peaks for the PM2.5 levels in April. A decrease in the PM2.5 concentrations was partly influenced by the long-range transport from the outside to Hanoi. This study implies that a reduction in traffic volumes and public activities may possibly improve the air quality.

Evaluation and Prediction of PM10 and PM2.5 from Road Source Emissions in Kuala Lumpur City Centre

Particulate matter (PM) is one of the major pollutants emitted by vehicles that adversely affect human health and the environment. This study evaluates and predicts concentrations and dispersion patterns of PM10 and PM2.5 in Kuala Lumpur city centre. The OML-Highway model calculates hourly time series of PM10 and PM2.5 concentrations and distribution caused by traffic emissions under different scenarios; business as usual (BAU) and 30% traffic reduction to see the impact of traffic reduction for sustainable traffic management. Continuous PM10 and PM2.5 data from a nearby monitoring station were analysed for the year 2019 and compared with modelled concentrations. Annual average concentration at various locations of interest for PM10 and PM2.5 during BAU runs were in the ranges 41.4–65.9 µg/m3 and 30.4–43.7 µg/m3 respectively, compared to during the 30% traffic reduction run ranging at 40.5–59.5 µg/m3 and 29.9–40.3 µg/m3 respectively. The average concentration of PM10 and PM2.5 at the Continuous Air Quality Monitoring Station (CAQMS) was 36.4 µg/m3 and 28.2 µg/m3 respectively. Strong correlations were observed between the predicted and observed data for PM10 and PM2.5 in both scenarios (p < 0.05). This research demonstrated that the reduction of traffic volume in the city contributes to reducing the concentration of particulate matter pollution.

Quantifying the Traffic Impacts of the COVID-19 Shutdown

The coronavirus disease (COVID-19) pandemic has significantly disrupted transportation and travel patterns across the US and around the world. A significant driving factor in the significant reduction in travel in the US was the declaration of varying state-, county-, and city-level stay-at-home orders with varying degrees of reduction. However, it is still not clear how significantly any one of those orders contributed to the reduction in travel. This article looks at continuous count data from the Minneapolis–St. Paul, Minnesota, area to quantify the disruption in terms of reductions in traffic volume as well as the abnormality of the disruption to travel patterns. A nearly 50% reduction in total traffic volume is found, and regional trends both in reductions and the gradual recovery toward normal travel patterns are identified. Furthermore, key dates are identified that led to significant reductions in travel, and this disruptive event is compared with other significantly disruptive events in Minnesota for context. It is found that although the stay-at-home order was a significant milestone in the fight against COVID-19, traffic volumes had already reduced significantly before the order went into effect, and traffic volumes had recovered significantly before the order expired. These findings will be helpful in understand the impact of stay-at-home orders on future outbreaks of COVID-19 or other pandemics.

Disruption and Recovery: Initial Assessment of COVID-19 Traffic Impacts in North Carolina and Virginia

The coronavirus disease of 2019 (COVID-19) pandemic has affected every aspect of peoples’ lives, including their mobility. In this study, the impact of closures related to the pandemic on traffic patterns is assessed for the state of North Carolina and the Commonwealth of Virginia. The results of the initial assessments indicated that the average reduction in traffic volume was 27% across North Carolina and Virginia. Findings suggest that a slight increase in traffic at the beginning of the stay-at-home orders was followed by at most a 40% reduction after closures. The changes in traffic volumes in terms of road and vehicle classification are most substantial in Interstate routes and for passenger cars and buses. The average recovery rate of traffic volume after the occurrence of the maximum reductions was between 2.3% and 3.4% per week.

Health Risk of Increased O3 Concentration Based on Regional Emission Characteristics under the Unusual State of the COVID-19 Pandemic

Photochemical oxidant concentration increases with the decrease in nitrogen oxide (NOx) concentration in volatile organic compound (VOC)-sensitive areas with several automobiles and factories. We aimed to quantify the changes in health risks from ozone (O3) and nitrogen dioxide (NO2) using disability-adjusted life years (DALY) in Osaka City, which is one of the major cities in Japan. ADMER-PRO version 1.0, an atmospheric model for secondary products, was used to estimate the concentration distribution of NO2, VOC, and O3 using the year-on-year change of traffic during the declaration of the state of emergency in response to the coronavirus disease 2019 (7 April to 21 May 2020). NO2 concentration decreased by an average of 0.962 ppb in 88.9% of the grids in Osaka City, whereas O3 concentration increased by an average of 1.00 ppb in all the grids with a 26–28% reduction of traffic volume due to the pandemic. We also found three intensities for the VOC-sensitive condition depending on the different regional emission characteristics, with the DALYs of health risks from the decrease in NO2 exceeding those from the increase in O3, reaching 811.4 and 55.90 total DALYs in the city, respectively.