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Accurate traffic volume data are crucial for effective traffic management, infrastructure development, and demand forecasting. This study addresses the challenges associated with traffic volume data collection, including, notably, equipment malfunctions that often result in missing data and inadequate anomaly detection. We have developed a deep-learning-based model to improve the reliability of predictions for annual average daily traffic volume. Utilizing a decade of traffic survey data (2010–2020) from the Korea Institute of Civil Engineering and Building Technology, we constructed a univariate time series prediction model across three consecutive sections. This model incorporates both raw and adjusted traffic volume data from 2017 to 2019, employing long short-term memory (LSTM) techniques to manage data discontinuities. A power function was integrated to simulate various error correction scenarios, thus enhancing the model's resilience to prediction inaccuracies. The performance of the model was evaluated using certain metrics, such as the mean absolute error, the root mean squared error, and the coefficient of determination, thus validating the effectiveness of the deep learning approach in refining traffic volume estimations. [ABSTRACT FROM AUTHOR]

The devastating impact of COVID-19 on the United States has been profound since its onset in January 2020. Predicting the trajectory of epidemics accurately and devising strategies to curb their progression are currently formidable challenges. In response to this crisis, we propose COVINet, which combines the architecture of Long Short-Term Memory and Gated Recurrent Unit, incorporating actionable covariates to offer high-accuracy prediction and explainable response. First, we train COVINet models for confirmed cases and total deaths with five input features, and compare Mean Absolute Errors (MAEs) and Mean Relative Errors (MREs) of COVINet against ten competing models from the United States CDC in the last four weeks before April 26, 2021. The results show COVINet outperforms all competing models for MAEs and MREs when predicting total deaths. Then, we focus on prediction for the most severe county in each of the top 10 hot-spot states using COVINet. The MREs are small for all predictions made in the last 7 or 30 days before March 23, 2023. Beyond predictive accuracy, COVINet offers high interpretability, enhancing the understanding of pandemic dynamics. This dual capability positions COVINet as a powerful tool for informing effective strategies in pandemic prevention and governmental decision-making. [ABSTRACT FROM AUTHOR]

• Road deaths are higher on days with low traffic volume in U.S. counties. • Without speed-reducing countermeasures, lowered congestion is likely to increase deaths. Introduction: Road death risk is often characterized as deaths per volume of traffic in geographic regions, the denominator in miles or kilometers supposedly indicative of the magnitude of risk exposure. This paper reports an examination of the differences in the predictive value of factors hypothesized to influence traffic volume and road death risk. Method: The association of 11 risk factors in U.S. counties during the first 7 months of 2020 was examined for consistency of predictions of road death and traffic volume measured by cell phone and vehicle location data. The study employed least squares regression for traffic volume and Poisson regression for deaths with the population as the offset variable. Results: The directions of the regression coefficients for traffic volume and odds of road deaths per population were opposite from one another for 9 of the 11 variables in the analysis of vehicle occupant deaths. Only the coefficients for maximum daily temperature and Saturday travel were in the same direction. The confidence intervals of three risk ratios for pedestrian deaths indicated low reliability but most of the predictor variables were opposite in association with traffic volume and odds of death. Although traffic volume plunged in the first weeks of the pandemic, the results for the months before and during the COVID-19 pandemic were similar. Practical applications: Traffic volume is an inverse risk factor for road deaths at the local level, likely the result of lower speeds on congested roads. Without the application of countermeasures aimed at reducing speed and other risk factors, the reduction of road congestion is likely to increase deaths. [ABSTRACT FROM AUTHOR]

Lack of a strong reduction in ambient ozone (O3) concentrations during reduced traffic periods associated with COVID-19 calls into question the conventional wisdom that mobile sources dominate air pollution in California. Fossil-fueledmmotor vehicles emit oxides of nitrogen (NOx) and volatile organic compounds (VOCs) that are precursors to O3 formation, but the chemical reaction system that forms O3 is complex. The ratio of NOx/VOCs determines if the O3 formation regime is NOx-limited (reducing NOx reduces O3) or NOx-rich (reducing NOx increases O3). This project developed new methods to directly measure O3 chemistry in the atmosphere and applied them over long-term campaigns in multiple California cities to quantify traffic contributions to O3 formation. A seasonal-cycle was observed of NOx-rich O3 chemistry during cooler months trending toward NOx-limited chemistry in warmer months. Superimposed on this seasonal cycle was a spatial pattern of NOx-rich chemistry in dense urban cores and NOx-limited chemistry in areas downwind of urban cores. Chemistry-based models with source tagging were also developed to better understand these trends. Seasonal changes to biogenic VOC and gasoline evaporative VOC emissions likely explain the seasonal changes in O3 formation chemistry. Reduced traffic emissions in March 2020 did not reduce O3 concentrations because the chemistry was heavily NOx-rich during the spring season. Extended model predictions suggest that similar traffic reductions could have reduced ambient O3 concentrations in small and intermediate cities if they would have occurred in summer months. Traffic reductions alone would not be sufficient to reduce O3 concentrations in the urban cores of larger cities. Reduced emissions from transportation sources can improve air quality in California, but transportation sources no longer exclusively dominate O3 formation. Future emissions controls should be coordinated across multiple sectors (including transportation) to achieve their objectives.

This report describes how current methods of estimating truck traffic volumes from existing fixed roadway sensors could be improved by using tracking data collected from commercial truck fleets and other connected technology sources (e.g., onboard GPS-enabled navigation systems and smartphones supplied by third-party vendors). Using Caltrans District 1 in Northern California as an example, the study first reviews existing fixed-location data collection capabilities and highlights gaps in the ability to monitor truck movements. It then reviews emerging data sources and analyzes the analytical capabilities of StreetLight 2021, a commercial software package. The study then looks at the Sample Trip Count and uncalibrated Index values obtained from three weigh-in-motion (WIM) and twelve Traffic Census stations operated by Caltrans in District 1. The study suggests improvements to StreetLight’s “single-factor” calibration process which limits its ability to convert raw truck count data into accurate traffic volume estimates across an area, and suggests how improved truck-related calibration data can be extracted from the truck classification counts obtained from Caltrans’ WIM and Traffic Census stations. The report compares uncalibrated StreetLight Index values to observed truck counts to assess data quality and evaluates the impacts of considering alternate calibration data sets and analysis periods. Two test cases are presented to highlight issues with the single-factor calibration process. The report concludes that probe data analytical platforms such as StreetLight can be used to obtain rough estimates of truck volumes on roadway segments or to analyze routing patterns. The results further indicate that the accuracy of volume estimates depends heavily on the availability of sufficiently large samples of tracking data and stable and representative month-by-month calibration data across multiple reference locations.

The road shipping has become one of the major metal contamination sources that could impact on roadside soils. Therefore, this study was conducted to determination, possible source identification and potential ecological and human health risks assessment of trace elements (Cd, Co, Cr, Cu, Mn, Ni, Pb, and Zn) in roadside surface soil samples in Hamedan, west of Iran in 2018. In so doing, a total of 63 roadside soil specimens from three main highways, including Goltapeh (G), Razan (R), and Kermanshah (K), were collected. Then, the contents of elements in roadside soils were determined using ICP-OES. Based on the results obtained, the highways with heavy traffic have the highest accumulation of all the metals, suggesting the effect of traffic density on metals concentrations. Computed values of pollution indices with mean of 0.970 for I-geo and 2.16 for PI pointed out that the soils collected from R and K roads primarily contaminated by Zn. The results of potential ecological risk assessment (RI) indicated that the surface soNo potential conflict of interest was reported by the author(s).ils at all the sites with RI < 150 have low ecological risk. Also, based on the results of human health risk assessment there was no substantial non-carcinogenic risk found to both children and adults through exposure to studied metals in roadside soil. The carcinogenic risk of Cr for both target populations was at the tolerable or acceptable level, while the other metals have no considerable carcinogenic risk. Hazard quotient (HQ) values demonstrated that ingestion was the main path of road soil metal exposure to man beings. Multivariate statistical analyses represented that Mn in the roadside soils derived from both soil minerals erosion and exhaust sources. Other metals also derived from non-exhaust sources (e.g. wear and tear of brakes, tires, engines, and lubricating oil). Our findings could be provided a theoretic basis and data support for pollution monitoring and control, soil remediation treatment and the implementation of public prevention in roadside areas of Hamedan. [ABSTRACT FROM AUTHOR]

In early 2024, from January 31 to February 5 and from February 18 to 25,two widespread rainfall/snowfall and freezing events weather occurred in China,hereinafter referred to as the "0131" and "0218" events. The two events were characterized by complex precipitation phases,large amounts of rainfall and snowfall,a wide range of influence,and long duration. Utilizing the observation data from 2 430 national weather stations,as well as the traffic volume and highway block information,the impact characteristics of the two rainfall/snowfall and freezing weather events on highway traffic were compared and analyzed. In addition,the snowfall,freezing rain,snow depth,surface ground temperature,and surface ground ice were selected as the elemental indexes affecting highway traffic. Then an ice and snow weather index was constructed to explore the relationship between the intensity of ice and snow weather and the impact on highway traffic. The results indicate that the two weather events occurred during the peak periods of people traveling to their hometowns before the Spring Festival and returning after the festival. The "0218" event had a wider impact range,resulting in a higher overall reduction in traffic flow compared to the "0131" event. During the two events,the highway traffic in Shaanxi,Shanxi,Henan,Hubei,Hunan,and Guizhou was severely affected by snow accumulation or ice,with many road section closures. However,there were regional differences in the degree of impact. Specifically,the "0131" event had a stronger impact on Shanxi,Henan,Hubei,and Hunan. While the "0218" event had a more significant impact on Shaanxi and Guizhou. During the two events,the intensity of the ice and snow weather index was consistent with the trend of impact degree of road blockage,and inversely proportional to the rate of change in traffic volume,indicating that the ice and snow weather index has a good guiding significance for the assessment of the impact of rain and snow freezing weather on highway traffic.

With the focus of highway development transitioning from construction to maintenance, a comprehensive understanding of the characteristics and influencing factors of carbon dioxide (CO2) emissions from highway maintenance activities is crucial for formulating effective strategies to promote the low-carbon development of road infrastructure. However, the quantitative relationships between CO2 emissions from highway maintenance schemes and factors such as pavement deterioration, traffic volume, and road grade remain unclear owing to a lack of comprehensive, multi-category, and real data. Using real maintenance data from 340 arterial highway segments in China, this study conducts the life cycle assessment (LCA) to estimate CO2 emissions from maintenance activities and examines the primary emission sources among various structural layers and materials. Furthermore, multiple linear regression (MLR) analysis is conducted to investigate the impact of traffic volume, road grade, and pavement deterioration on CO2 emissions from maintenance projects, and factors influencing the early-stage degradation of pavement performance. The results demonstrate that average CO2 emissions from heavy rehabilitation projects are 6.97 times higher than those from medium rehabilitation projects. Emissions from heavy rehabilitation projects exhibit a significantly negative linear relationship with the riding quality index (RQI) before maintenance (p

The problems of traffic management in large cities are complicated as the increase in traffic volume far exceeds the road network capacity. It leads to saturation of the road network, which negatively affects its functioning. This article analyzes the state of the art of improving the quality of road traffic by predicting the size of traffic after the implementation of traffic management measures. While the issue of modeling traffic flow parameters based on technological factors has been sufficiently studied, the problems of considering the human factor need to be clarified. The object of study is traffic flow on the city's road network. The criteria used by drivers to compare the characteristics of alternative traffic routes were identified based on the field study results. Based on the data obtained, the parameters of alternative routes that drivers choose when driving on the road network are formed. According to the survey results, the most significant factor is the minimum mileage along the route. Deviations from the shortest route were determined to determine the patterns of distribution of correspondence of non-route vehicles along alternative routes. It made it possible to define the distribution of transport correspondence along alternative routes. Since the process under consideration is probabilistic, the law of distribution of a random variable was determined. After having determined the law of distribution of a random variable for the data obtained, the calculations showed that the change in the random variable is well described by the gamma distribution. It was determined that the share of transport correspondence that will be carried on them decreases as the length of the route deviates from the shortest one. The obtained results make it possible to predict road network congestion by modeling the distribution of traffic flows. In further research, it would be advisable to analyze the distribution of transport correspondence by other criteria.

Road verges can support high densities of flowers and could therefore provide new opportunities for the conservation of flower‐visiting insects. One way of optimizing road verges for vascular plant diversity is to adjust mowing regimes, but to date it is unclear how this affects flower‐visiting insects. Furthermore, for mobile organisms like wild bees and butterflies, there is a risk that the benefit of increased habitat quality in road verges is limited by the proximity to traffic, but this is poorly studied.In a crossed study design, we separated mowing time and frequency (early summer and autumn, or only late summer) from road verge habitat classification (valuable for biodiversity according to transport authority, or regular). We did so along a gradient of traffic intensity, to investigate if a mowing regime designed to enhance plant diversity can also benefit wild bees and butterflies, and if traffic limits the conservation potential of road verges.Road verges that were mown only in late summer had higher flower densities, and there was a positive relationship between flower density and wild bee abundance and species richness. Butterfly abundance and species richness only benefitted from a late summer mowing in valuable but not in regular road verges.Traffic intensity had a substantial negative impact on abundance and species richness of wild bees and butterflies. Higher traffic intensities limited the positive relationship between plant and butterfly species richness that we observed at lower traffic intensities. Increasing width of the road verges buffered negative effects of the traffic on wild bee as well as butterfly abundances, and on wild bee species richness.Synthesis and applications. Road verges can play a valuable role for the conservation of wild bees and butterflies, but there is a need to consider both traffic intensity and resource availability when implementing management strategies. To support wild bee and butterfly diversity, we recommend actions to enhance plant species richness and flower resource availability, and to focus these conservation efforts on roads with low traffic intensity, or on wide road verges. [ABSTRACT FROM AUTHOR]

In the digital age, the integrity of business operations and the smoothness of their execution heavily depend on cybersecurity and network efficiency. The need for robust solutions to prevent cyber threats and enhance network functionality has never been more critical. This research aims to utilize machine learning (ML) techniques for the meticulous analysis of network traffic, with the dual goals of detecting anomalies and categorizing network activities to bolster security and performance. Employing a detailed methodology, this study begins with data preparation and progresses through to the deployment of advanced ML models, including logistic regression, decision trees, and ensemble learning techniques. This approach ensures the accuracy of the analysis and facilitates a nuanced understanding of network dynamics. Our findings indicate a notable enhancement in identifying network inefficiencies and in the more accurate classification of network traffic. The application of ML models significantly reduces network delays and bottlenecks by providing a strong defence strategy against cyber threats and network shortcomings, thereby improving user satisfaction, and boosting the organizational reputation as a secure and effective service layer. Conclusively, the research highlights the pivotal role of machine learning in network traffic analysis, offering innovative insights and fresh perspectives on anomaly detection and the identification of malicious activities. It lays a foundation for future explorations and acts as an evaluation benchmark in the fields of cybersecurity and network management. [ABSTRACT FROM AUTHOR]

This study delves into quantifying the adverse effects of road damage on users, particularly focusing on the increased travel time and consequent financial burdens stemming from delayed repairs. Utilizing a comparative method, the research underscores notable reductions in speed and prolonged travel times due to damaged roads, leading to substantial economic losses for road users. To streamline the estimation of road user costs (RUC), the study proposes a simulation model that incorporates varying traffic volumes and repair delays. This model demonstrates a high level of accuracy in estimating RUC, revealing heightened sensitivity to fluctuations in traffic volume and repair delays compared to agency costs. Consequently, the research underscores the imperative of implementing effective repair strategies to alleviate these impacts efficiently, thereby emphasizing the significance of timely infrastructure maintenance in mitigating financial burdens on road users. [ABSTRACT FROM AUTHOR]

The gravity model is a widely recognized tool for estimating the movement of people and goods. In this study, we introduce two gravitational variables, population size and regional GDP per capita (RGDPPC), to explain the characteristics of population movement between and within cities in South Korea. A log-linearized gravity model is employed to run regression analyses at three spatial levels: the national level (encompassing the entirety of South Korea), the metropolitan level (focusing on the Seoul and Busan Metropolitan Transportation Areas) and the city level (specifically in Seoul and Busan). The study incorporates data on various modes of transportation from 246 of the 250 municipalities in South Korea. Predictive performance of the model is better when utilizing national-level data. However, as spatial area decreases and population density increases, the models explanatory power decreases significantly when relying solely on data related to either population size or RGDPPC. The findings suggest that incorporation of both population size and RGDPPC into the gravity model best captures the dynamics of traffic flow within economically integrated regions. This relationship is analogous to gravitational fields generated by two distinct types of mass. Including both population size and RGDPPC, the gravity model can be leveraged effectively to estimate traffic patterns, particularly within regions characterized by high economic integration. [ABSTRACT FROM AUTHOR]

The further promotion of cycling is a key component for each city to reach its sustainability goals. To make decisions to improve comfort or safety for cyclists, the amount of motorized traffic should be taken into account. Therefore, traffic data play a crucial role not only in the construction of roads but also in cycling planning. This data source provides insights essential for road infrastructure development and optimizing various modes of transportation, such as bike paths. However, processing municipal traffic data becomes a challenge when stationary traffic-counting stations lack geo-referencing in relational databases. In this case, the locations of traffic counters are solely displayed on a PDF-based site map without inherent geo-referencing, and the geo-coordinates are not stored in any relational database. The absence of geo-references poses a significant hurdle for traffic-planning experts in decision-making processes. Hence, this study aims to address this issue by finding a suitable approach to extract the geo-coordinates from the site maps. Several potential solutions are discussed and compared in terms of time dimension, usability, extensibility, error treatment and the accuracy of results. Leveraging the open-source tool QGIS, geo-coordinates may be successfully extracted from the PDF-based site maps, resulting in the creation of a GeoTIFF file incorporating coordinates and the rotated site map. Geo-coordinates can then be derived from the GeoTIFF files using x and y coordinates, computed through the rotation matrix formula. Over 1400 measurement locations may be extracted based on the preferred approach, facilitating more informed decision-making in traffic planning. [ABSTRACT FROM AUTHOR]

Road networks and their associated vehicular traffic disturb many terrestrial systems, but inventories of roads used to assess these effects often focus on the ‘where’ (e.g., local road type and density) and neglect the ‘when’ (e.g., temporal disturbance) or ‘how much’ (e.g., traffic volume disturbance). We developed annual estimates of the ‘when’ (road age) and ‘how much’ (vehicular traffic volume) across 148,172 km of highways, arterials, collectors, local, and gravel/graded roads within the state of Wyoming for the years 1986 to 2020 to provide a comprehensive dataset for future ecological investigations. We leveraged a suite of ancillary data on surface disturbances (e.g., oil & gas drilling operations, wind turbines, and open pit mines) with known establishment dates and combined them using graph theory and centrality metrics to estimate the age of each road. We then predicted traffic volume obtained from the Wyoming Department of Transportation for each year across Wyoming using a machine learning method, XGBoost, and a separate set of spatial covariates hypothesized to explain traffic patterns across large regions. We found that 132,476 km of these roads likely existed before 1986, but that 16,693 km (10.7 %) of roads have been built since 1986. Overall, our estimates of road age were 89 % accurate when assessed on a subset of 1,330 roads with high-resolution aerial imagery. Mean absolute error for predicting traffic volume ranged from 35.2 to 77.9 annual average daily traffic (aadt) for trucks and 269.2 to 516.7 aadt for all-vehicles across the 35 years. We found that mean traffic volume across the state increased by 23 % for both truck-only traffic and all vehicular traffic from 1986 to 2020. However, changes in traffic volume have varied substantially across the state (e.g., 100 % increases in volume in some areas, while other areas experienced declines of up to 1,786 %). We also illustrate a novel application of these data by predicting rates of reported wildlife-vehicle collisions (WVCs) along a subset of roads. We found evidence of a non-linear relationship that supported a threshold hypothesis for WVCs, wherein increases in traffic volume equate to increases in WVCs up to a threshold, above which increases in traffic volume result in declines in WVCs. The data provided here will enable better-informed studies of road ecology to address how roads may affect wildlife populations and key ecosystems across Wyoming.

The formulation of expressway traffic policy needs to accurately predict the traffic volume. Based on this, Long short-term memory (LSTM) machine learning model was selected to study it. Aiming at the problem of parameter determination in LSTM model,Particle swarm optimization (PSO) algorithm was selected to improve it. At the same time for the PSO algorithm in the particle position update problem, the meaning of each parameter in the formula for the entry point for improvement, the PSO algorithm formula for the original static inertia weights and learning weights into the iteration number and the particle position will change with the change of the dynamic value, so as to achieve the purpose of searching for the purpose of improving accuracy. Based on this, the improved PSO-LSTM model is constructed. Finally, through the calculation and analysis of an example, the working days and rest days of expressway are predicted respectively. The results show that the root mean square error of the evaluation index is increased by 12. 19% and 10. 97%, the average absolute error is increased by 17. 06% and 15. 17%, and the square absolute percentage error is increased by 24. 56, respectively. The algorithm shows that the improved PSO-LSTM model plays a significant role in traffic volume forecasting, and has strong anti-interference ability. It can provide a more reliable basis for the rational formulation of policies. [ABSTRACT FROM AUTHOR]

The geographic zone of this research is Falluja City, which is described by its overpopulation and absence of fast roadways. This zone is confronting a low level of service amid the time of overcrowding stemming from a disorganized and weak road system. This research has been conducted by suggesting the development of another street corresponding to the current one (bypass), yet with better expectations. The improvement of the road to be studied will improve the environmental aspect in addition to reducing the operating costs of vehicles. This study is planned to assess the achievability of developing a street for the bypass of the Falluja highway, environmentally and economically. This exploration depends on the speculation that the current street displays a relatively extreme situation, though the recommended roadway could be an asphaltic road pavement of top-notch condition. The traffic information was gathered utilizing a manual counting technique during seven days in the period (7:00 am-4:00 pm) to evaluate the average daily traffic (ADT) and peak hour traffic volume (PHV). Also, the number of lanes needed for LOS (C) was resolved. The development and conservation expenses of the street were likewise determined. The decrease in roadway client expenses for the current and suggested roads was assessed as a benefit. The monetary examination is conducted by equalizing the total decreased roadway costs and their benefits to the foundation year. The improvement in the roadway pavement status meant by IRI has enhanced the PCI value thusly, prompting reducing discharges within which reduces the overall discharges. The normal decrease in discharge was 1.13% because of the enhancement of pavement conditions. The accepted reduction rate for the research was assumed to be 8%. To find financial reasonability, numerous criteria were checked such as Net Present Value (NPV), Benefit-Cost Ratio (B/C), and Internal Rate of Return (IRR). For financial criteria, the results indicated that NPV was of an empowering estimation of 8761711 US$, which infers those benefits are more significant than expenses. Moreover, the B/C ratio was 1.370, contrasted with 1.0, which is encouraging. Ultimately, the determined IRR was discovered to be more prominent than the assumed value of 8% as recommended in road construction projects. The reduction in carbon monoxide (CO), carbon dioxide (CO2), nitrogen dioxides (NO2), and sulfur dioxide (SO2) are 0.367, 0.275, 0.348, and 0.339, respectively. [ABSTRACT FROM AUTHOR]

Wildlife mortality due to collisions with vehicles (roadkill) is one of the predominant negative effects exerted by roads on many wildlife species. Reducing roadkill is therefore a major component of wildlife conservation. Roadkill is affected by various factors, including road attributes and traffic volume. It is theorized that the effect of traffic volume on roadkill probability should be unimodal. However, empirical evidence for this theory is lacking. Using a large‐scale roadkill database of 18 wildlife species in Israel, encompassing 2846 km of roads over 10 years, we explored the effects of traffic volume and road attributes (e.g., road lighting, verge vegetation) on roadkill probability with a multivariate generalized linear mixed model. A unimodal effect of traffic volume was identified for the striped hyena (Hyaena hyaena), whereas 5 species demonstrated a novel quadratic U‐shaped effect (e.g., golden jackal [Canis aureus]). Four species showed a negative linear effect (e.g., wild boar [Sus scrofa]). We also identified varying effects of road attributes on roadkill. For instance, road lighting and roadside trees decreased roadkill for several species, whereas bus stops and concrete guardrails led to increased roadkill. The theorized unimodal effect of traffic volume may only apply to large, agile species, and the U‐shaped effect could be related to intraspecies variability in traffic avoidance behavior. In general, we found that both high‐traffic and low‐traffic roads can pose a high mortality risk for wildlife. It is therefore important to monitor roadkill on low‐traffic roads and adapt road attributes to mitigate roadkill. Road design for effective roadkill mitigation includes reducing the use of concrete guardrails and median barriers where possible and avoiding dense bushes in verge landscaping. These measures are complemented by employing wildlife detection systems, driver warnings, and seasonal speed reduction measures on low‐traffic roads identified as roadkill hotspots. [ABSTRACT FROM AUTHOR]

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Two road corridors in Ibadan City where the use of tricycles is prominent namely Mokola to Agodi Prison Gate Road (MAR) and Challenge to Orita Challenge Road (COR) were identified. Traffic volume data were collected from Monday to Friday during the peak periods, likewise the socio-economic characteristics of tricycle operators using questionnaires. Traffic volume counts revealed that the percentage of tricycles compared to other transportation modes ranged between 9.54% and 20.08% along MAR and between 13.27% and 19.61% along COR. The socio-economic characteristics data showed that 98.5% of the operators were male, their ages ranged between 18 and 60 years and 26.5% had a monthly income of more than N40,000. Tricycle had the second highest arrival rates along MAR and COR of 6.17 and 7.91 respectively. [ABSTRACT FROM AUTHOR]

In the context of climate change, it is desirable to increase the share of cycling. One way of doing this can be to strengthen subjective safety of cyclists. At present, many people perceive cycling as unsafe. In particular, overtaking by motor vehicles is a cause of low subjective safety and stress. In built-up areas, German road traffic regulations stipulate a minimum lateral distance of 1.50 m for motor vehicles, while overtaking cyclists. Previous research has shown that this rule is often not followed by motor vehicles. The aim of this study is to find out which factors influence the lateral distance of overtaking manoeuvres. The lateral distances of 4 081 overtaking manoeuvres were recorded using an ultrasonic sensor on 14 selected routes in the city of Stuttgart, Germany. 42% of the recorded overtaking manoeuvres were carried out with a lateral distance of less than 1.50 m. The mean value of all overtaking manoeuvres was 1.59 m. On roads with mixed traffic, higher lateral distances occurred than on roads with cycle lanes. In Germany, the motor vehicle traffic volume on a road is a key criterion for planning cycling infrastructure. However, it is not possible to confirm an influence of the motor vehicle traffic volume on the occurring lateral distances. The time of day at which overtaking manoeuvres take place also seems to have no effect on lateral distances.

In this study, the researchers examined traffic volumes and patterns in Yosemite Valley, the heart of Yosemite National Park. The purpose of this analysis was to understand which destinations attract the most visitors and to analyze how visitors move around the park on foot, by bike, and by car. Aggregated cell phone location data accessed through the StreetLight Data platform was used to produce vehicle volumes and indexes of bicycle and pedestrian volumes. This analysis reveals noteworthy patterns of travel for each mode with respect to weekdays versus weekends and times of day. An analysis of trip origins and destinations for each mode shows that drivers generally make longer trips than visitors who walk or bike, but that many driving trips are relatively short. Vehicle counts are higher in the core area of the valley than on the roads into and out of the valley, suggesting that most drivers circulate between valley destinations rather than parking and using other modes. Short distance trips by car could be a target for efforts to replace driving with walking and bicycling. This analysis can help to inform transportation planning in Yosemite Valley, particularly with respect to the bicycle network and potential changes to and expansion of the bike share service overseen by the Yosemite Conservancy. As a follow-up to this analysis, the research team plans to conduct an intercept survey of visitors to Yosemite Valley to better understand their choices about travel within the valley, especially their choices about bicycling.View the NCST Project Webpage

Significant social, economic, and environmental costs are associated with traffic congestion. The effectiveness of intersections makes a substantial contribution to the effectiveness of entire road networks. The three-leg at-grade Al-Husainea intersection, located 20 km from the center of holy Karbala city in Iraq, is subjected to serious congestion, resulting in an increase in delay time, reduction in capacity, and bad level of service (LOS). Therefore, it is essential to use advanced software tools to ensure that the current intersection can be controlled, evaluated, and improved. So, the major goal of this study is to use SIDRA, an acronym for signalized and unsignalized intersection design and research aid, software to evaluate the LOS of the Al-Husainea intersection, in which the traffic is assessed using the current LOS. LOS, delay time, and degree of saturation were the criteria utilized to evaluate the traffic flow performance. SIDRA is also used to assess benefits as a result of suggested changes in the design of particular junctions. The first stage is to gather field data regarding traffic volumes utilizing a method of traffic volume gathering. From 7 am to 6 pm, the SIDRA program gathered data for a full 7 days. The results showed that the LOS for the Al-Husainea intersection in the Al-Husainea arm is F, with an average delay of 52 s per vehicle and a saturation level of 0.86 v/c. Finally, it was determined that the Al-Husainea intersection needs additional improvements based on the study and findings from the SIDRA program, and some remedies are suggested in this study to improve the intersection traffic flow.

The current emergency vehicle priority control methods in road traffic net-works are difficult to cope with the increasing traffic demand. Therefore, a traffic control method based on multi-vehicle collaborative lane change strategy, fleet convergence and gap adjustment model is proposed and its effectiveness is verified. These experiments confirmed that the speed of vehicle Cj+1 under strategy 1 showed a positive correlation with time at 0-5s, reaching an extreme value of 16.85 m/s around 4s, and a negative correlation after 5s. Under strategy 2, its speed showed a negative correlation with time at 0-4s, and a parallel relation-ship after 4s. The multi-vehicle col-laborative lane changing strategy was validated. Except for fleet C with a density of +0.0333m-1, the actual adjustment time of vehicle C1 was gradually increasing at all other densities. The maximum time for B1 adjustment was 9.358s, and the maximum time for C1 adjustment was 10.798s. The longitudinal relative dis-placement of C1 was larger than that of B1. In addition, compared with Model T, the research method increased the average vehicle speed by 12.64% under four different flow rates. Compared with Model Y, the average flow rate of the research method under the four experimental flows was 1.45%. Overall, the research method is effective and feasible in the priority selection control of road traffic net-works. It improves the operational efficiency of emergency vehicle sections and can be effectively applied in actual traffic net-works.

Mobility problems in large cities of Ukraine and Eastern Europe are complicated by the fact that the increase of private transport volume significantly exceeds street and road network`s capacity. This is most noticeable during peak periods in terms of daylight hours and throughout the year. From the point of sustainable mobility view, this negative phenomenon significantly affects urban public transport, which does not have separate dedicated traffic lines. This article analyzes the issue regarding the deterioration of the transport situation in large cities. The reason for this is the increase in traffic on main streets during the day peaks, as well as the presence of seasonal traffic factors. If the issue of the occurrence and traffic jams duration and the increase in the correspondence time of private transport is sufficiently studied, then the problems of changing the schedules of public transport and taking into account the increase in the trip duration depending on the time of year need to be clarified. The routes of public transport, which do not have a separate infrastructure and move in the general flow together with private cars, were chosen for the study. According to the results of remote monitoring of public transport, a change in the trip duration and time lost due to the boarding and disembarking of passengers on similar trolleybus routes in different seasons was established. Based on the obtained data, a matrix of trip duration unevenness coefficients for public transport routes was formed, and a measure of the seasonality effect on these indicators was established. The obtained results make it possible to quantitatively determine the influence of the season and time of the day on the change in the trip duration, which can be applied in further studies using simulation tools and for practical use in drawing up seasonal traffic schedules. The results of the research complement the currently relevant scientific works, which concern the problems of seasonal mobility, as well as the influence of the social infrastructure objects functioning (schools, kindergartens, and other educational institutions) of cities on the peak load of the street and road network, which extends the duration of traffic not only for private but also public transport.

Accurate traffic volume data are crucial for effective traffic management, infrastructure development, and demand forecasting. This study addresses the challenges associated with traffic volume data collection, including, notably, equipment malfunctions that often result in missing data and inadequate anomaly detection. We have developed a deep-learning-based model to improve the reliability of predictions for annual average daily traffic volume. Utilizing a decade of traffic survey data (2010–2020) from the Korea Institute of Civil Engineering and Building Technology, we constructed a univariate time series prediction model across three consecutive sections. This model incorporates both raw and adjusted traffic volume data from 2017 to 2019, employing long short-term memory (LSTM) techniques to manage data discontinuities. A power function was integrated to simulate various error correction scenarios, thus enhancing the model’s resilience to prediction inaccuracies. The performance of the model was evaluated using certain metrics, such as the mean absolute error, the root mean squared error, and the coefficient of determination, thus validating the effectiveness of the deep learning approach in refining traffic volume estimations.

Motor vehicle crashes are the leading cause of accidental deaths in the US. In 2020, 38,824 people lost their lives in car-related crashes. Bicyclists and pedestrians are particularly susceptible—7,448 of these “vulnerable road users” were killed nationwide in 2020, and 29% of all reported crash-related fatalities in California were vulnerable road users. A variety of intelligent vehicle technologies hold promise for improving bicycle and pedestrian safety. Sensors in vehicles and/or used by vulnerable road users themselves could alert travelers of potential conflicts, giving them more time to react. However, these technologies all have unique technical, operational, and financial characteristics, and they might perform differently in different environmental conditions and at different levels of deployment. Little research has been done on how these technologies might affect safety. Researchers at the University of California, Davis combined aggregate historical crash data analysis and micro transportation simulation to examine the safety impacts of four different intelligent vehicle technologies: blind spot detection, a vulnerable-road-user beacon system carried by bicyclists or pedestrians, bicycle/pedestrian-to-vehicle communication, and intersection safety.View the NCST Project Webpage

This project aims to know how the Intelligent Vehicle Technologies (IVT) can improve Vulnerable Road Users’ (VRU) safety in different environments and conditions (e.g., sight distance and traffic flow) at signalized intersections. For the statistical analysis on historical aggregate crash data, the project studied risk factors on crash injury severity for VRU-related crashes at signalized intersections in California cities. The researchers summarize seven critical crash types for the micro-level traffic safety simulation. For the traffic safety simulation part, it is found that Intersection Safety (INS) is empowered to be the most efficient technology to significantly reduce average collision counts for passenger cars under all seven collision types of interest. Blind Spot Detection (BSD) has the most minimal effects on those types. The safety improvement of VRU Beacon Systems (VBS) and Bicycle/Pedestrian to Vehicle Communication (BPTV) are between INS and BSD. Results show that under a certain threshold of sight distance, IVT can significantly reduce the collision probability and IVT can still improve safety under good sight condition if collisions happen in front of vehicles. In the end, the project conducted sensitive analyses of sight distance and traffic volume. For some collision types, INS and BPTV can only reduce ~50% of collision at extremely high traffic volume conditions.View the NCST Project Webpage

Ongoing assessment of system performance monitoring is critical to successful and efficient transportation planning, ensuring that infrastructure investments provide a desired return on investment. As with any new transportation facility, it is important to understand how Express Lane facilities affect travel behavior, resulting on-road vehicle activity, and subsequent person-throughput (a function of vehicle occupancy) as part of the facility performance assessment. This report summarizes the vehicle and person throughput analysis for the I-75 Northwest Corridor (NWC) and I-85 Express Lanes in Atlanta, GA, undertaken by the Georgia Institute of Technology research team for the State Road and Tollway Authority (SRTA). The research team tracked changes in observed vehicle throughput on four managed lane corridors and collected vehicle occupancy (persons per vehicle) data to assess changes in both vehicle throughput and person throughput associated with the opening of new Express Lane facilities. The team collected traffic volumes by video observation (GDOT’s Georgia NaviGAtor machine vision system and SRTA’s vehicle activity monitoring system). The team implemented a large-scale data collection effort for vehicle occupancy across all general purpose freeway lanes and from SRTA’s Express Lanes over a two-year period (before-and-after the opening of the Express Lanes). Between the baseline year (2018) and post-opening year (2019), the team observed a decrease in average vehicle occupancy (persons/vehicle), coupled with a significant increase in traffic volumes, especially on the NWC. The combined effect of increased traffic volumes and decreased occupancy still led to an overall increase in person throughput at all sites. Vehicle throughput on the I-85 corridor increased by about 5-7% and person throughput increased by 1-2% in the morning peak, and increased by around 10% for vehicles and 5% for persons in the evening peak. Vehicle throughput increased by more than 35% on I-575 in the AM and PM peaks, and by the same on I-75 in the AM peaks (only minor increases were noted in the PM peaks), likely due to the diversion of commute traffic from arterials onto the freeway corridor once the Express Lanes opened and congestion declined. Based upon vehicle throughput and occupancy distributions, the largest share of the increase in vehicle throughput in the peak periods came from an influx of single-occupant vehicle activity onto the corridor. Even though the number of carpools traversing the I-575 corridor increased slightly during the morning peak, the overall carpool mode share (percentage of carpools) decreased after the significantly greater numbers of single-occupant vehicles began using the corridor.View the NCST Project Webpage

City events take place in the Zhangzhou Olympic Sports Center, resulting in traffic. Hence, this study analyzed the road planning and design around Zhangzhou Olympic Sports Center through traffic demand forecasting. The simulated data and road plan underwent macro prediction model technology and numerical simulation technology through Emme and TransCAD. The regional road network structure, traffic demand forecast, plane, longitudinal section, and cross-sectional program selection were investigated to determine a reasonable design program. While urban development programs were implemented, the study ensured an increase in the city's economic growth alongside the guaranteed value of citizens' welfare. [ABSTRACT FROM AUTHOR]

In order to build an appropriate prediction model of pavement friction coefficient attenuation, the effects of aggregate texture, on a basis of texture scanning and pavement testing of the dynamic friction coefficient, traffic volume, and rock characteristics on pavement slide decay resistance were studied, and the accuracy of the developed prediction model of friction coefficient was thereafter compared and analysed. First, based on the tire-pavement dynamic friction analyzer independently developed by the research group, accelerated loading tests under different traffic conditions were conducted by the orthogonal test method, and the dynamic friction coefficient of pavement during the abrasion process was recorded. At the same time, the non-contact laser profile measurement system was used to collect three-dimensional micro-texture data of coarse aggregate surface at different abrasion stages, and characteristic parameters such as height, wavelength, and shape were calculated. Second, the factors such as traffic volume and rock characteristics were incorporated, and the algorithms such as multiple linear stepwise regression, feedforward neural network, and random forest regression were adopted to develop estimation models of pavement friction. The results showed that the model constructed based on only texture feature parameters poorly represented pavement skid resistance. After introducing the two indicators of total traffic volume and rock type, the correlation coefficients of the model significantly improved, reaching 0.63, 0.58, and 0.82 respectively, indicating that the latter two factors have significant effects on the anti-skid attenuation of pavement. Moreover, the above three models can achieve satisfactory prediction results. In addition, when a large sample size can be obtained, a random forest algorithm is recommended to acquire higher prediction accuracy of pavement friction coefficient attenuation. [ABSTRACT FROM AUTHOR]

The main aim of the research was to diagnose drivers' behavior in urban road traffic. It was preceded by careful observation and testing of drivers' behavior in terms of the violations assumed to be observed. It was taken into account, inter alia, aspects such as failure to comply with road signs and signals, behavior at the rail-road crossing, or offenses occurring in the pedestrian-driver relationship. During the observations, the assumption was to capture negative behaviors of drivers, but each time attention was also paid to positive behaviors related to road culture. The analysis of the results allowed the researchers to identify the most common mistakes made by drivers. This can help not only to better enforce regulations at selected measurement sites but also lead to improved education about these specific violations.

The problem of the change in the speed of traffic flow at different traffic volumes and compositions is researched in this study. The section of the road network with different geometric parameters (descent, ascent and horizontal section) was chosen for the study. The method of investigation of traffic flow`s speed and factors which have an impact on the reduction of road network capacity are analyzed. The change in the coefficients of the unevenness of traffic flow by hours of the day in the studied area was determined and a graph of the distribution of traffic volume by hours of the day was built. A diagram of the section was built to determine the speed of the traffic flow, on which the movement along the horizontal section, uphill and downhill movement is present. It was established that at a traffic volume of 700-800 p.c.u./h, the traffic flow moves at a constant speed (up to 10-15 km/h). Cumulative curves of traffic flow speed` distribution characterizing modes of traffic flow on the road network were built. It is determined that at volume-capacity ratio 0< z ≤ 0,4 on three investigated sections traffic flow moves with the speed from 35 km/h to 59 km/h. In the specialized software product PTV VISSIM, the simulation of the traffic flow on the horizontal section, ascent and descent has been developed. Using the MATHLAB software environment, it is shown how the speed of the traffic flow changes depending on the volume-capacity ratio and the share of the heterogeneous traffic flow. It was established that the highest speed of the flow is observed during the downhill movement – 58.62 km/h at the volume-capacity ratio – 0.13 and the share of heterogeneous traffic flow – 1.0 (100% cars). At a volume-capacity ratio of 0.88 and existing road conditions, the speed of traffic flow on the horizontal section and during uphill movement is almost the same (the average deviation is 6%). It can be explained by the fact that at a volume-capacity ratio of 0.88, traffic flow is in the traffic jam, hence, the speed of movement on the three sections is the same

Particulate matter (PM) is increasingly affecting the social-economic development of countries. An increase in PM2.5 concentration increases susceptibility to cardiovascular and respiratory diseases and cancer. Tire wear particles (TWP) contribute to airborne PM. In the present work, we investigated the variation in the concentration of TWP of 3F > 2F. A good air ventilation system can be recommended to reduce TWP2.5 concentrations in indoor parking garages.

Animal behaviour is shaped by the ability to identify risks and profitably balance the levels of risks encountered with the payoffs experienced. Anthropogenic disturbances like roads generate novel risks and opportunities that wildlife must accurately perceive and respond to. Basic concepts in predator–prey ecology are often used to understand responses of animals to roads (e.g. increased vigilance, selection for cover in their vicinity). However, prey often display complex behaviours such as modulating space use given varying risks and rewards, and it is unclear if such dynamic balancing is used by animals in the context of road crossings.We tested whether animals dynamically balance risks and rewards relative to roads using extensive field‐based and GPS collar data from elk in Yoho National Park (British Columbia, Canada), where a major highway completely bisects their range during most of the year.We analysed elk behaviour by combining hidden Markov movement models with a step‐selection function framework. Rewards were indexed by a dynamic map of available forage biomass, and risks were indexed by road crossings and traffic volumes.We found that elk generally selected intermediate and high forage biomass, and avoided crossing the road. Most of the time, elk modulated their behaviour given varying risks and rewards. When crossing the highway compared with not crossing, elk selected for greater forage biomass and this selection was stronger as the number of highway crossings increased. However, with traffic volume, elk only balanced foraging rewards when they crossed a single time during a travel sequence.Using a road ecology system, we empirically tested an important component of predator–prey ecology—the ability to dynamically modulate behaviour in response to varying levels of risks and rewards. Such a test articulates how decision‐making processes that consider the spatiotemporal variation in risks and rewards allow animals to successfully and profitably navigate busy roads. Applying well‐developed concepts in predator–prey theory helps understand how animals respond to anthropogenic disturbances and anticipate the adaptive capacity for individuals and populations to adjust to rapidly changing environments. [ABSTRACT FROM AUTHOR]

The objective of this study is to evaluate the economic, social, and environmental consequences of road dualization on the 64- kilometer Owerri-Umuahia road, in Imo State, Nigerian using appropriate techniques such as baseline surveys, data collection through surveys, interviews, and questionnaires. Data obtained reveals that 30.75% of respondents agreed that, the Owerri-Umuahia road dualization had a positive impact on reducing traffic congestion. Based on the traffic criteria, the travel volume, congestion frequency, and travel time are 35.6%, 38.5%, and 20.0% enhanced with a consistency ratio of 0.00819. Statistical analyses and reliability assessment methods, such as Cronbach's alpha and Analytic Hierarchy Process AHP are used to ensure data validity and internal consistency. The study highlights the importance of inclusive infrastructure development that caters to the diverse requirements and viewpoints of the local populace. Despite concerns about insufficient engagement between government officials and local communities during the planning phases of infrastructure projects, road dualization is widely approved by residents, with the majority expressing satisfaction. Tangible improvements are observed in road quality, traffic management, safety measures, accessibility to amenities, environmental impact mitigation, and localized economic activities. [ABSTRACT FROM AUTHOR]

Traffic prediction is an important part of smart city projects. Due to the complex topology of urban road network and the dynamic change of traffic data, establishing a spatio-temporal model to accurately predict traffic volume remains a challenging task at present. Recently, Graph Convolution Networks (GCN) have been widely used to extract features from non-grid data, and time sequence models have been used to learn temporal features of traffic distributions. However, current GCN based methods only make use of the natural structure of road network, while ignoring the information of administrative units, neighborhood units and other hierarchical structures of spatial interaction. Therefore, traditional models are typically developed under one single scale, which are far from the prediction of multi-scale systems. To address this issue, we propose a novel framework, named MmgFra, to merge multi-scale information for high-precision urban traffic flow prediction. MmgFra consists of three components: a spatial feature extraction module, a feature clustering fusion module, and a temporal feature extraction module. Specifically, we use stacking GCNs to extract spatial structure information of the city road network, administrative unit road network, and neighborhood units, and employ a DIFFPOOL module to cluster and fuse the above information. Finally, we introduce GRU to capture temporal features. We evaluated its performance on a real city dataset using different time scales. The experimental results indicate that compared with state-of-the-art neural network models and GCN-based variant models, our model exhibits higher predictive accuracy. MmgFra improves by approximately 8.4%-29.5% and 7.5%-30.6% in terms of RMSE and MAPE metrics, respectively. [ABSTRACT FROM AUTHOR]

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This study explored how transportation accessibility and traffic volumes for automobiles, buses, and trucks are related. This study employed machine learning techniques, specifically the extreme gradient boosting decision tree model (XGB) and Shapley Values (SHAP), with national data sources in South Korea collected from the Korea Transport Institute, Statistics Korea, and National Spatial Data Infrastructure Portal. Several key findings of feature importance and plots in non-linear relationships are as follows: First, accessibility indicators exhibited around 5 to 10% of feature importance except for Mart (around 50%). Second, better accessibility to public transportation infrastructures, such as bus stops and transit stations, was associated with higher annual average daily traffic (AADT), particularly in metropolitan areas including Seoul and Busan. Third, access to large-scale markets may have unintended effects on traffic volumes for both vehicles and automobiles. Fourth, it was shown that lower rates of AADT were associated with higher accessibility to elementary schools for all three modes of transportation. This study contributes to (1) understanding complex relationships between the variables, (2) emphasizing the role of transportation accessibility in transportation plans and policies, and (3) offering relevant policy implications. [ABSTRACT FROM AUTHOR]

Since December 30, 2017, the Seoul Metropolitan Government, Republic of Korea, has been implementing emergency reduction measures (ERMs) restricting the operation of industrial sites, thermal power plants, and vehicles when air quality is expected to deteriorate. ERMs are implemented when the present observed concentration of particulate matter (PM) of aerodynamic diameter less than 2.5 μm (PM2.5) and/or the predicted values for the following day exceed a threshold value. In this study, the effectiveness of ERMs was evaluated for 33 days with and 6 days without ERM implementation but where the PM2.5 concentration exceeded the threshold value, until March 15, 2021. Of the 33 days of ERM implementation, on 7 days it was executed despite the thresholds not being met. The ERM on these days might have been properly executed because the pre-notice and implementation of ERM might have reduced the local emissions of air pollutants. Our major findings are that even on days of ERM implementation, there were marginal reductions in vehicle traffic, thermal power generation, and industrial emissions. Second, the concentrations of PM2.5 and related air pollutants in Seoul were almost unchanged for most ERM implementation episodes. Third, most of the 39 (= 33 + 6) days when the air quality worsened were caused by the transboundary transport of air pollutants from China. In conclusion, it was revealed that the currently executed ERM law is insufficient for effectively reducing PM2.5. To achieve the required reductions, it is necessary to undertake stricter policies in Seoul and its neighboring regions. [ABSTRACT FROM AUTHOR]