City Streets Research Articles

Diversity, relative abundance and ecology of the mosquito (Diptera: Culicidae) fauna in district Okara, Pakistan

The aim of this study was to examine the diversity, relative abundance and ecology of mosquitoes in district Okara, Pakistan. A total of 2172 adult mosquitoes and 4317 mosquito larvae were collected, in which 13 mosquito species were identified within a single order, a single family and from three genera, Culex, Anopheles, and Aedes were identified. The identified 7 species belonged to the genus Culex, three species to the genus Anopheles and only 3 species belonged to the genus Aedes. The most abundant species was Culex quinquefasciatus with relative abundance of (17.5%), Culex theileri (15%), Culex duttoni (12.01%), Culex pipiens fatigans (10%), Anopheles subpictus (9.2%), and Anopheles annularis (8.13%). In contrast, Culex vagans, Culex sitiens, Anopheles stephensi, Culex tritaeniorhynchus, Aedes aegypti, Aedes albopictus, and Aedes vexans were identified to be the least in number with 5.61%, 5.52%, 4.41%, 4.1%, 3.8%, 3.02%, and 1.7% relative abundance. The Shannon-Weiner diversity index showed that Sardar Chok (H′=1.601) and Company Bagh (H′=1.458) had the greater diversity of mosquitoes, followed by Junior Model School, Green City, and Canal Road, i.e., 1.354, 1.345, and 0.6833. Species evenness has varied among Locations based on diversity, with values of 0.9918 (Sardar Chok), 0.9902 (Canal Road), 0.9679 (Junior Model School), 0.9592 (Green City), and 0.8596 (Company Bagh). The Simpson index (1-D) for Sardar Chok, Company Bagh, Junior Model School, Green City, and Canal Road was 0.7968, 0.7366, 0.7337, 0.7274, and 0.4902 indicating that SC and CB had the maximum Mosquitoes species. According to the results of this study, Okara district is home to a wide variety of mosquito species.

The October 7, 2023, Attack on Israel Indicates a Change in Trauma Patterns of Terror Victims, due to the Use of Thermobaric Weapons.

The terror attack on October 7, 2023, in southern Israel resulted in approximately 1,200 civilians and security personnel killed. Of the civilians murdered, approximately 200 men, women, and children were murdered inside their homes, while the remaining casualties were murdered in an open field at a music festival, in their cars, or in the streets of local cities. Currently, official data have not yet been published regarding the types of injuries suffered by Israeli civilians during the terror attack. However, the massive damage found in buildings infiltrated by the terrorists, weapons found on neutralized terrorists, and the findings of the pathologists tasked with examining and identifying the casualties, as well as preliminary clinical evidence, suggested that many died from hand-held explosive devices or rocket-propelled grenades containing a "thermobaric"-type warhead. The use of thermobaric weapons has also been reported from the Russia-Ukraine war, suggesting that these weapons are becoming more common in their use. Thermobaric injuries differ from typical battlefield injuries due to their specific blast characteristics. Considering that thermobaric weapons are relatively cheap to manufacture and highly efficient, it can be assumed that their unique injury patterns will become more common in future battlefields. This paper reports on a possible change in the nature of terror and battlefield injuries and alerts emergency organizations and military forces worldwide to prepare for the need of treating more blast injuries, burns, smoke inhalation, and asphyxiation.

Investigating factors of pedestrian safety using ordinal logistic regression in Addis Ababa City roads

Pedestrians are the most victimized group in traffic accidents, and this has raised concerns about pedestrian safety in urban Ethiopia. The aim of this study is to determine the factors that affect pedestrian safety in Addis Ababa city Roads. This study employed a concurrent mixed-methods research method and an explanatory research design. Perception-based pedestrian safety data was collected from respondents’ key informants and personal observation. Exploratory factor analysis was employed to identify significant variables. Besides, the fitness of each variable was evaluated under the assumption of ordinal logistic regression, and the effect size of significant variables was determined using the odds ratio. The result indicates pedestrians are 0.065, 0.181, 0.296, 0.324, 0.289, 0.306, 0.038, and 0.193 times less likely to be safe due to narrow sidewalk width, poor sidewalk and footpath pavement comfort, inclusivity problems of sidewalks and footpaths, illegal vehicle parking, street vending, fast vehicle speed, and absence of traffic signs and lights, respectively. Consequently, the use of ordinal logistic regression offers a practical and effective means of determining pedestrian safety factors. Better pedestrian facilities should be constructed by considering pedestrian flow and diversity. Besides, the concerned body should provide spaces for street vendors and car parking and enforce traffic rules in the appropriate system to control vehicle speed.

Quantitative analysis of the road network in the city of Constantine

The primary objective of this study is to analyze the national and provincial road network in the city of Constantine, employing a range of quantitative methods commonly used in transportation geography. The aim is to assess the current state of the city's road network, its adequacy, efficiency, and suitability in meeting the needs of the population. This evaluation will be conducted in terms of accessibility, connectivity between network components, and efficiency as measured by curvature and traffic volume. While transportation geography offers a variety of quantitative measurement techniques, this research will focus on four primary methods: (1) Network analysis based on the curvature index: This method will assess the efficiency of the road network by evaluating the degree of curvature of the road segments, (2) Network analysis based on daily traffic volume: This method will examine the capacity of the road network by measuring the volume of traffic on each road segment, (3) Network analysis based on connectivity and road dispersion: This method will assess the level of connectivity between different parts of the network and the overall distribution of roads within the study area, (4) Network analysis based on traffic volume density: This method will evaluate the concentration of traffic on the road network.

A Street of Seedsmen: Plant Retailers in Victoria Avenue, Whanganui

In the 1940s, seedsmen, florists and other plant retailers were generally located on main streets of towns and cities. This paper looks at Whanganui as a case study of the relationships between plant retailers and the landscape, people and capital. The challenges of researching businesses and quantitative history are woven throughout.

Karma economies for sustainable urban mobility – a fair approach to public good value pricing

City road infrastructure can be considered a public good, and congestion is the intersection of demand for mobility with the limited available transportation supply. Congestion pricing is effective in reducing demand to sustainable levels below network capacity limits, but also controversial, as it introduces equity issues and systematically discriminates against lower-income groups. Karma is a non-monetary, fair, and efficient resource allocation mechanism that employs an artificial currency different from money, incentivises cooperation amongst selfish individuals, and achieves a balance between giving and taking. Where money does not do its job, Karma achieves socially more desirable allocations by being aligned with consumers’ needs rather than their financial power. This work highlights the value proposition of Karma, provides guidance on Karma mechanism design, and equips the reader with a useful software framework to model Karma economies and predict consumers’ behaviour. A case study of road pricing demonstrates Karma’s potential.

Study of Distribution of Free Flow Speeds on Urban Road Sections Depending on Their Functional Purpose and One-Way Traffic—Evidence from Kharkiv (Ukraine)

Data on the distribution of the free flow speed (FFS) of cars are used to solve a wide range of tasks in the field of road transport, starting from road design and ending with the development of traffic modeling and simulation programs. The purpose of this study was to obtain the distribution of vehicle speeds on typical sections of the city road network, characterized by the presence of one-way traffic. The data were obtained by field observations using a portable radar. As a result, statistical characteristics and speed distribution laws for four sections of streets in the city of Kharkiv were analyzed. It was shown that the characteristics of FFS distributions differ depending on the functional class of the streets. Average FFS values on main street segments were on average 19 km/h higher. The one-way traffic has less impact on the FFS distribution, especially for arterial streets. The characteristics of FFS distributions differ depending on the type and functional class of streets; they can be described with sufficient accuracy by typical distribution laws, such as Normal, Log-normal, Gamma, and Chi-square. The results of this study can be useful for traffic modeling problems.

Analysis of the Influence of Open Unemployment Rate, Road Infrastructure Development, Inflation, and Economic Growth on Poverty Levels in Mataram City (2010-2023)

Although economic growth had started to improve at the time due to the World Superbike (WSBK) event being held in Mandalika, the rising poverty rate in Mataram has continued to fluctuate. For example, in 2021, the city's poverty rate increased. Economic growth, inflation, and the construction of transportation infrastructure all partially and concurrently contribute to Mataram City's poverty rate. Descriptive and associative quantitative methods are used in this study. Using multiple linear regression analysis methodologies, secondary and time series data from According to the study's findings, just one factor—inflation—had a substantial impact on the poverty rate in Mataram, whereas the open unemployment rate, road length, and economic growth had no discernible effects. The poverty rate in the city of Mataram is significantly impacted by the open unemployment rate, road length, inflation, and economic growth all at the same time. The development of MSMEs based on the creative economy, the creation of an appropriate city road network that will be more beneficial to the community's economy, targeted fiscal policies to lower the rate of inflation, and other suggestions are some ways the Mataram city government can try to alleviate poverty, and designing the right strategy to optimize foreign trade.

Natural Creation of Large Rock Cavern: Can We Construct Them? Jenolan Caves as a Case Study

The Jenolan Caves are the most spectacular limestone caves in Australia. Within this cave system, the Grand Arch, which is 24 m high, 55 m wide, and 127 m long, is the largest open cave in the country. A cave of this size could potentially accommodate small city streets, buildings, and other facilities. This paper examines a stability simulation of the Grand Arch, using numerical models to deduce foundational insights into rock openings under different geological and rock mass conditions. Following this, using numerical analysis, we investigate the creation of a man-made rock opening with the same span, height, and ground conditions of the Grand Arch but formed in two different arch shapes (i.e., with and without rock reinforcement as a stabilization measure). With all stability simulations conducted in this study, a clear explanation to describe the relationship and interaction between various parameters, such as rock mass structure and quality, rock mass strength, and in situ stress field, as well as different arch roofs shapes of the rock opening, is provided. Through its comparisons between natural rock cave and man-made rock openings, this study provides some findings and deep insight, as well as further questioning on creating a large size rock-reinforced cavern in different shapes to be opened in a range of rock conditions.

Outdoor Pollution Comparison Between Bucharest and Its Outskirts Using Mobile Laboratory

This study presents a modern mobile laboratory to monitor outdoor air quality in Bucharest, Romania, with a focus on pollutants associated with transportation. Particulate matter (PM2.5, PM10), carbon monoxide (CO), ozone (O3), sulfur dioxide (SO2), nitrogen oxides (NO, NO2), and BTEX compounds (benzene, toluene, ethylbenzene, and xylenes) were among the significant pollutants that were examined in the lab. Meteorological variables such wind direction and speed, temperature, humidity, and solar radiation were also routinely observed in order to assess their influence on pollution levels. The study looked at two locations—a bustling city road in Bucharest and a remote community 40 kmawayin Snagov—under a range of weather conditions, including sunny, rainy, warm, and chilly days. The findings showed that the primary source of pollution in the urban area, which had significantly higher pollution levels than the rural site, was transportation. Particularly in the city, alarming concentrations of harmful particulate matter and carcinogens like benzene were found, underscoring the need for continuous air quality monitoring. The weather has a major impact on the dispersal of contaminants. Because of washout effects, rainy days decreased airborne pollutants, but sunny days showed higher pollution deposition. This study highlights the importance of outdoor air quality monitoring, particularly in urban environments, where traffic and weather have a significant impact on pollution levels. These findings provide crucial data that policymakers can utilize to implement targeted pollution control measures that protect human health.

The ROADS project: Road observational assessment of driving distractions

Background: Globally, motor-vehicle collisions cause 1.35 million deaths and more than 78 million injuries every year, with distracted driving contributing to many of these tragedies. Our main objective was to covertly determine the proportion of distracted drivers in live traffic. Methods: ROADS was a covert observational study conducted from November 2020–June 2021. We observed drivers on the highways and urban streets between Hamilton and Toronto, Ontario. The research team observed drivers of moving vehicles and collected data covertly while driving beside them in live traffic. Moving passenger vehicles ahead of the research team were randomly screened for inclusion. Stopped/parked vehicles, buses, and semi-trucks were excluded. Demographic and safety variables included estimated age and sex, seatbelt usage, and two-handed driving. Driving distractions were categorized as in-vehicle, outer-vehicle, and mobile phones. Driving errors, such as lane drift, evasive maneuvers, and near-crash/crash, were recorded. We analyzed associations between demographic and situational variables (weekday/weekend, urban/highway, presence/absence of passenger) and distracted driving, as well as associations between driving errors and distracted driving. Results: Of the observed 1,105 drivers, 609 (55.1%) were distracted. In-vehicle distractions (42.3%, 467/1105) were most prevalent, while 151 (13.7%) drivers were using mobile phones. Hands-free usage was observed in 92 (8.3%) drivers, while 63 (5.7%) drivers used a handheld device, visibly manipulating (3.4%, 38/1105), or actively talking (2.3%, 25/1105). Of the 24 (2.2%) drivers observed exhibiting driving errors, 23 (95.8%) drivers were visibly distracted. Younger estimated age (under 30 years old: OR 2.0, CI 1.320–3.105; 30–50 years old: OR 1.5, CI 1.090–1.925), and driver errors were significantly associated with distracted driving (p < 0.005). Sex, urban vs highways, and weekday vs weekend did not demonstrate a statistically significant association with distracted driving. Conclusion: By covertly observing moving vehicles while actively participating in live traffic, we identified that 55.1% of drivers were distracted, and approximately one in seven drivers used their mobile phones. Of the 24 drivers who were recorded making driving errors, an astounding 95.8% (23) were distracted, with two-thirds of these drivers illegally engaging with their phones. Also, driving on city streets versus highways (>60 km/hr) did not play a role in distracted driving. All this indicates that distracted driving is not only prevalent but also pervasive. Future research should focus on targeted driver education and behavioral modification. Practical Applications: This data can be applied towards driver education programs counseling drivers on dangerous distracting behaviors, as well as influencing legislature, informing, and providing law enforcement insight into worrisome patterns of distracted driving.

Districting in last‐mile delivery with stochastic customers

AbstractDue to the peculiarities of city streets, last‐mile logistics is typically organized using territory‐based routing approaches, which divide the city into a set of districts and assign drivers to deliver in one or more of them. This allows drivers to develop a deep understanding of the characteristics of each district, and clients benefit from consistent service. However, these advantages must be carefully weighed against the flexibility of daily customer assignments, which enable planners to maximize driver utilization and minimize routing costs. In this paper, we propose a new holistic framework for defining districts, considering the impact on the quality of logistics decisions and fleet capacity usage. Specifically, we address how districting decisions affect the demand distribution within each district. Computational experiments on both simulated and realistic instances demonstrated a significant reduction in costs compared to benchmark techniques.

Analyzing Daytime/Nighttime Pedestrian Crash Patterns in Michigan Using Unsupervised Machine Learning Techniques and their Potential as a Decision-Making Tool

Background Data mining applications are becoming increasingly common across various fields. Numerous data mining methodologies have been utilized in pedestrian crash data analysis. However, association and correspondence analyses have yet to be extensively employed in pedestrian safety literature to support decision-making. Material and Methods Road lighting significantly affects pedestrian crashes, highlighting the importance of examining pedestrian crash patterns during daytime and nighttime. This study analyzed pedestrian fatal and injury crashes in Michigan from 2011 to 2021 under varying road lighting conditions. The study identified pedestrian crash patterns using unsupervised machine-learning techniques based on several multidimensional factors. The Association Rules Learning (ARL) technique was used to determine general associations of patterns leading to pedestrian crashes. At the same time, Multiple Correspondence Analysis (MCA) helped understand crash attribute patterns in two notable scenarios: elderly pedestrian crashes during daytime lighting and high-speed midblock crashes during nighttime. These methods revealed key patterns associated with contributing factors to pedestrian crashes in different lighting conditions. Results In the analysis of cloud combinations involving elderly pedestrians during daylight, it was found that: 1) elderly pedestrians were significantly involved in severe injury crashes at two-lane midblock locations with a speed limit between 45 and 65 mph; 2) improper actions by elderly drivers were significantly associated with elderly pedestrians on wide roads (&gt;5 lanes); and 3) the complex interaction between city streets with 3-5 lanes was significantly correlated with drivers 'failed to yield' actions. Additionally, in the analysis of cloud combinations involving nighttime pedestrian crashes at high-speed midblock locations, it was found that 1) a specific age group of alcohol- or drug-involved pedestrians (19-30) was significantly associated with young drivers and improper driver actions; 2) during the winter season, young pedestrians were significantly involved in moderate injury crashes at undivided midblock locations; and 3) elderly drivers and 'failed to yield' actions were highly correlated with 3-5 lane roadways. Discussion The research findings are expected to raise awareness of Michigan pedestrian crash patterns under daytime and nighttime lighting conditions. They will also recommend safety countermeasures for practitioners to enhance pedestrian safety in walkable cities. The proposed methodologies can uncover relationships that need to be well-documented in the existing literature on pedestrian safety. Additionally, these methods can identify valuable relationships without restricting variables to being either dependent or independent, and they can reveal relationships that might be challenging to detect otherwise. The results will also provide decision rules and visualizations that are easy to understand. Conclusion The results showed that the proposed techniques can analyze pedestrian crash data in a way that aligns with understanding pedestrian crash characteristics. Traffic safety administrators could benefit from this methodology as a decision-support tool.

Towards resilient communities: Evaluating the nonlinear impact of the built environment on COVID-19 transmission risk in residential areas

Reflections on urban epidemics often drive improvements in the resilience of the built environment. However, the assessment regarding the nonlinear influence of the community environment on the spread of Corona Virus Disease 2019 (COVID-19) is inadequate. This study analyzed the influential mechanism of built environment factors on the epidemic risk in residential areas, using Shanghai as a case study. During the lockdown in April 2022, Shanghai reported daily data on COVID-19 outbreaks in residential areas, amounting to a total of 90,324 entries. Based on a GIS-based grid analysis approach, we employed a Random Forest (RF) model and a Multiscale Geographically Weighted Regression (MGWR) model to investigate the marginal effects and spatial heterogeneity of environmental factors on the mean count of COVID-19 outbreak days (MC) in residential areas within each grid zone. The results show that the value of MC forms a ring-mountain distribution surrounding the city's outer ring road. The RF model (R² = 0.57) demonstrates that the house price, population density, family number, and the standard deviation of building height (BH_SD) significantly correlated with MC, with the relative importance of 25 %, 13 %, 11 %, and 6 %, respectively. The MGWR model (R² = 0.63) highlights the spatial heterogeneity of family number, house age, house price, property fee, and delivery density. We also found that property fee and green rate were negatively correlated with the MC. These findings help improve responses to public health emergencies and create more resilient communities to cope with pandemics.

Gis-Based assessment Of Traffic System In Osisioma Ngwa L.G.A,Abia State.

Geographic Information System (GIS) technology is a tool that was used to gather, store, analyze, and visualize data about the city's traffic flow, including information on roads in Osisioma Ngwa L.g.a. in Aba Abiastate.The objective is to make better decisions based on accurate and current information in order to increase the sustainability and efficiency of Osisiomatraffic flow. This could involve things like streamlining public transit lines, lowering traffic and pollution levels, and enhancing accessibility for all locals. An overview of the main GIS data models for traffic analysis is provided in this project effort. Based on that, a route database for the city of Osisioma is created in a GIS context. The GIS database displays all the elements involved in routing and analyzing traffic flow, including nearby land use in the city, various modes of transportation, and routes for these modes, the city's road network, and more. The most likely reasons of traffic congestion in Osisioma LGA were short road width, poor pavement, and indiscriminate parking of vehicles along the road corridors, mainly by commercial cab drivers, according to an analysis of the results and other performed queries. In conclusion, it was found that the Donald junction route. Mcc junction route and the Osisioma Flyover are the most crowded sections of the whole road networks evaluatedThis project shows how the application of GIS has helped in the proper assessment of the causes of traffic congestion in osisiomaNgwaL.g.a, provide Adequate policies and enforcement should be put in place so as to eradicate indiscriminate parking of vehicle on the road and reckless driving and not obeying the traffic light

Deep Sarsa Replay model for real Time Traffic Control

Traffic congestion is a major factor currently affecting the global economy thereby costing billions of dollars in terms of the time lost in traffic, productivity and fuel wastage. Although, various researchers have made concerted effort to proffer solution to the problems of traffic congestion, in addition, recent reinforcement learning research has also presented multiple possible solutions. However, based on the literature, most of the reinforcement learning traffic control agents do not possess the real-time traffic control capabilities to efficiently deal with the complex and ever-changing nature of traffic on an average urban city road. This paper presents a Real Time deep reinforcement learning model for Traffic Control for optimization of vehicle flow (minimizing waiting time and traffic congestion). The Deep SARSA (DSARSA) learning algorithm with experience replay applied in the training process. The DSARSA replay model is built to extract traffic information from all lanes by applying multilayer perceptron with two hidden layers. Then, at the output layer an approximated qvalue is produced and updated with SARSA algorithm. For training, a random batch of experiences is sampled from the replay buffer. This helps break the correlation between consecutive experiences, stabilizes training and enables the model to be to able deal appropriately with any traffic condition. Experiments on 1000 episode of low, medium and heavy traffic flow was conducted on SUMO traffic simulator. TraCL allows interface with the SUMO simulation using python showed that the proposed DSARSA replay model achieved a state-of-the-art performance compared to other baseline DRL models applied in traffic control in terms of learning stability and reward maximization.

Manhattan's Street Trees: An Unfinished Public Health Story.

Stephen Smith launched a 40-year effort to bring trees to New York City streets in 1872, the year he founded the American Public Health Association (APHA). Smith argued that street trees would mitigate the adverse health effects of Manhattan's summer heat and help purify the air. The young APHA endorsed Smith's position and gave trees a prominent role in urban sanitation, but public health turned away from trees and urban reform movements as it adopted a biomedical public health model in the late 19th century. Nevertheless, Smith wrote and campaigned for a successful 1902 law requiring the New York City Parks Department to assume management of street trees in the name of public health. He then led a 1914 campaign to force the department to uphold his law. New York's street tree program has had an erratic trajectory, but it now generally follows Smith's vision. Public health could play a bigger role in creating greener cities and mitigating climate change with more field research and the health in all policies approach that Smith used to bring trees to Manhattan's streets in 1914. (Am J Public Health. Published online ahead of print October 31, 2024:e1-e9. https://doi.org/10.2105/AJPH.2024.307856).

Understanding the Factors Affecting Traffic Danger for Children: Insights From Focus Group Discussions

Children’s safety on urban roads is a critical concern with young pedestrians and cyclists being among the most vulnerable groups to traffic-related dangers. The prioritization of motor vehicle traffic in road infrastructure poses significant risks to child pedestrians and cyclists navigating city streets. Furthermore, children’s independent mobility has been restricted due to traffic danger and their parents’ concerns about it. Given the important implications of this issue, a serious gap was identified in that no measure of traffic danger exists, with outcomes (e.g., collisions) being used as a proxy. Identifying factors contributing to traffic danger, how they interact, and how they impact traffic are imperative to identify where mitigation is needed to address these problems. This article delves into the complexities of traffic risks for children, focusing on intersections and streets. Six focus groups, including experts (&lt;em&gt;n&lt;/em&gt; = 3), parents (&lt;em&gt;n&lt;/em&gt; = 2), and children aged 8 to 12 (&lt;em&gt;n&lt;/em&gt; = 1), were conducted to gather insights on factors impacting traffic danger. Thematic analysis revealed eight key themes, highlighting the importance of addressing traffic volume, speed, vehicle size, road design, driver behavior, visibility, and land use. These findings contribute to a comprehensive framework for understanding traffic danger for children. Additionally, the article examines how stakeholders’ perspectives align with standard measures of traffic danger in the literature.

Sanitary sewer overflows, household sewage backups, and antibiotic-resistant bacteria: the new frontier of environmental health risks and disasters

Abstract Untreated sewage discharges leading to environmental contamination are increasingly common in communities across the globe. The cause of these discharges range from sewer lines in disrepair, blockages, and in the era of more extreme wet weather events, the infiltration of stormwater into the system during heavy downpours. Regardless of the driver of these events, the aftermath results in raw sewage spilling into local waterways, city streets, and commercial and residential structures. Historical research in public health has thoroughly documented the connection between exposure to untreated sewage and waterborne disease. Recent research has detected antibiotic-resistant bacteria at wastewater treatment facilities at a time when deaths by antibiotic-resistant infections are on the rise. However, no research has explored the exposure pathways of antibiotic resistant bacteria during sanitary sewer overflows and household-level sewage backups. In this commentary, we aim to introduce this new frontier of environmental health risks and disasters. To do this we describe the history of modern sanitation and sewer infrastructure with a particular focus on wastewater infrastructure in the U.S. We also explore emerging risks and current methods for identifying antibiotic-resistant bacteria in the environment. We end with future directions for interdisciplinary scholarship at the nexus of urban planning, engineering, and public health by introducing the Water Emergency Team (WET) Project. WET is a community-based multi-method effort to identify environmental risks in the aftermath of household backups through 1) residential surveys, 2) indoor visual inspections, 3) environmental sampling, and 4) laboratory processing and reporting. Our hope is that by introducing this comprehensive approach to environmental risks analysis, other scholars will join us in this effort and ultimately towards addressing this grand challenge of our time.

Identifying patterns of change in traffic flows’ parameters depending on the organization of public transport movement

This study examines the issue of traffic organization on sections of city streets, in which there are public transport stop zones of different configurations (with a drive-in pocket and along a traffic lane). Accordingly, the object of research is the parameters of traffic flows; the subject is the regularities in their change on such specific sections of the street-road network, taking into account the configuration of stops, the number of traffic lanes, and priority conditions for the movement of public transport. The task addressed in this work was the quantitative assessment of the efficiency of traffic management in public transport stop zones. It was also important to compare different configurations of street segments in terms of their impact on delays of private and public transport. As a result of the research, the speed and time characteristics of the traffic flow and their dependence on its composition and intensity for different number and specialization of traffic lanes, as well as the location of the public transport stop, were determined. The resulting quantitative indicators of delays of all types of transport for each modeling option made it possible to determine the best of them according to the criterion of minimal time loss. This applies to both private and public transport, as the values of delays may differ from each other by 10–50 %. An applied aspect related to using the scientific result is the possibility of justifying the number of traffic lanes, or their allocation for public transport, as well as determining the appropriate configuration of stops. This constitutes the prerequisites for the transfer of the scientific results to the relevant interested structures in the field of transport, which are responsible for the organization and safety of traffic under urban conditions