Morning Rush Research Articles

First top-down diurnal adjustment to NOx emissions inventory in Asia informed by the Geostationary Environment Monitoring Spectrometer (GEMS) tropospheric NO2 columns

Pioneering the use of the Geostationary Environment Monitoring Spectrometer’s (GEMS) observation data in air quality modeling, we adjusted Asia’s NOx emissions inventory by leveraging the instrument’s unprecedented sampling frequency. GEMS tropospheric NO2 columns served as top-down constraints, guiding our Bayesian inversion to constrain NOx emissions in Asia during spring 2022. This enabled the model to better capture the diurnal variation in NOx emissions, such as its morning rush hour peak, particularly when more retrievals were available each day, improving the simulation accuracy to a certain extent. The GEMS-informed adjustment reduced the extent of model underestimation of surface NO2 concentrations from 17.38 to 5.58% in Korea and from 13.05 to 4.54% in China, showing about 9.40% and 5.77% greater improvements, respectively, compared to the adjustment based on the sun-synchronous low earth orbit observation proxy. Our findings highlight the potential of geostationary observation data in refining the diurnal cycle of inventoried NOx emissions, thereby more effectively improving the accuracy of air quality simulations.

An Application of Mist Generator as a Way to Reduce Particulate Matter during High Concentration Episodes in Urban Forests

Previous conventional mist devices can induce a detrimental effect of leaf burn by intense, focused sunlight in summer. A mist generator is designed to prevent particulate matter (PM) damage to trees by combining mist with PM during high PM episodes. We measured changes in microclimate conditions and the concentration of PM before, during, and after mist spraying in urban parks (Yangjae Citizen Forest, YCF; Cheongdam Road Park, CRP) from May 6 to 8, 2020. PM changes in YCF and CRP were observed immediately after mist spraying and were found to return to the previous concentrations. Mist spraying had no significant effects on the meteorological traits of air temperature, humidity, and wind speed but had significant effects on the concentration of PMx and the ratio of PM during a short time. Also, the ratio of PMx was partially affected by mist spraying. During the morning rush hour and lunch, mist, high wind speed, and low relative humidity conditions were related to the increase in mist movement, resulting in increasing PM (2.5–10 μm) and the deposition of these PM. During the evening rush hour, high relative humidity and low wind speed affected PM concentrations more than mist. This prototype of mist spraying could effectively condense and deposit the PM during high PM episodes.

Aerosol composition, air quality, and boundary layer dynamics in the urban background of Stuttgart in winter

Abstract. Aerosol distributions are of great relevance for air quality, especially for cities like Stuttgart, which has limited air exchange due to its location in a basin. We collected a comprehensive set of data from remote sensing and in situ methods including radiosondes for the urban background of downtown Stuttgart to determine the impact of boundary layer mixing processes on local air quality and to evaluate the simulation results of the high-resolution large eddy simulation (LES) model PALM-4U at 10 m grid spacing. Stagnant meteorological conditions caused accumulation of aerosols, and chemical composition analysis shows that ammonium nitrate (37 ± 9 %) and organic aerosol (OA; 34 ± 9 %) dominated during this winter study. Case studies show that clouds during previous nights can weaken temperature inversion and accelerate boundary layer mixing after sunrise by up to 3 h. This is important for ground-level aerosol dilution during the morning rush hour. Furthermore, our observations validate results of the LES model PALM-4U in terms of boundary layer heights and aerosol mixing for 48 h. The simulated aerosol concentrations follow the trend of our observations but are still underestimated by a factor of 4.5 ± 2.1 due to missing secondary aerosol formation processes and uncertainties of emissions and boundary conditions in the model. This paper firstly evaluates the PALM-4U model performance in simulating aerosol spatio-temporal distributions, which can help to improve the LES model and to better understand sources and sinks for air pollution as well as the role of horizontal and vertical transport.

In-Vehicle Air Pollutant Exposures from Daily Commute in the San Francisco Bay Area, California

With urbanization and increased vehicle usage, understanding the exposure to air pollutants inside the vehicles is vital for developing strategies to mitigate associated health risks. In-vehicle air quality influences the comfort of the driver during long commutes and has gained significant interest. This study focuses on studying in-vehicle air quality in the San Francisco Bay Area in California, an urban setting with significant traffic congestion and varied emission sources and road conditions. Each trip is about 80.5 km (50 miles) in length, with commute times of approximately one hour. Two low-cost portable sensors were employed to simultaneously measure in-vehicle pollutants (PM2.5, PM10, and CO2) during morning and evening rush hours from May 2023 to December 2023. Seasonally averaged PM2.5 varied from 5.07 µg/m3 to 6.55 µg/m3 during morning rush hours and from 4.38 µg/m3 to 4.47 µg/m3 during evening rush hours. In addition, the impacts of local PM2.5, vehicle ventilation settings, and speed of the vehicle on in-vehicle PM concentrations were also analyzed. CO2 buildup in vehicles was studied for two scenarios: one with inside recirculation enabled (RC on) and the other with circulation from outside (RC off). With RC off, CO2 concentrations are largely within the 1100 ppm range recommended by many organizations, while the average CO2 concentrations can be three times high under recirculation mode. This research suggests that low-cost sensors can provide valuable insights into the dynamics of air pollution in the in-vehicle microenvironment, which can better help commuters reduce health risks.

Strong upwards transport of HONO in daytime over urban area of Beijing, China

Strong upwards transport of Nitrous acid (HONO) in daytime over urban area of Beijing was observed based on combined observations of HONO, NOx (NO and NO2), nitrate, and PM2.5 at two heights (90 m and 528 m) on the highest building of Beijing (528 m above ground). The mean HONO at the 528 m (0.26 ppb) was lower than that at the 90 m (0.54 ppb), and a clear difference in diurnal variation of HONO between the two heights was observed. HONO at the 90 m showed two peaks in the morning rush hour and mid-night, but decreased sharply in daytime (e.g., from 0.62 ppb at 08:00 to 0.34 at 14:00); while the decreasing trend of HONO in daytime significantly weakened at the 528 m (e.g., from 0.26 ppb at 08:00 to 0.27 at 14:00).With PBL development in the morning, HONO in low layer was upwards transported to the 528 m, which compensated partly HONO loss via photolysis and resulted in a relatively stable concentration at the 528 m in daytime. A positive relationship of the bulk Richardson number (Ri) in 0–500 m with the difference of HONO between the two heights during daytime (08:00–18:00) confirmed the above analyses. HONO budget analysis indicated that a strong unknown HONO source existed at the 528 m in daytime, which was negative correlated to the Ri. These results further confirmed that vertical transport of HONO from low layer was a potential HONO source at the 528 m. Moreover, the contribution of photolysis of particulate nitrate significantly increased at the 528 m. Its contribution in total HONO sources increased from 11.9 % at the 90 m to 16.0 % at the 528 m.

Evaluating the impact of unmanned aerial vehicles (UAVs) on air quality management in smart cities: A comprehensive analysis of transportation-related pollution

Urban environments face significant air pollution challenges affecting health and sustainability. Using unmanned aerial vehicles for air quality monitoring offers a promising solution. This research aims to improve these vehicles' ability to identify pollution sources and develop strategies, with a focus on transportation-related pollution. Detailed air pollution tests revealed that pollutant concentrations surged during morning rush hours, especially in high-traffic coastal areas, while areas away from traffic had much lower levels. Evening tests showed that pollutants from daytime traffic had dispersed throughout the urban area. The consistent northward shift in pollutant concentrations underscored the link between traffic patterns and pollutant distribution. Quantitative analyses and the resulting air pollutant maps showed average increases in pollutant concentrations between morning and evening hours in high-traffic areas: 12.1% for NO2, 5.3% for CO, 9.8% for PM10, and 11.4% for PM2.5. In contrast, the increases in pollutant concentrations in less trafficked urban regions were 45.9%, 48.2%, 32.7%, and 29.5%, respectively. This demonstrates how air pollution originating from areas with heavy traffic impacts other regions through environmental and geographical factors. The findings underscore the need for comprehensive air quality management strategies at the city level, targeting emissions in high-traffic areas and adapting to temporal fluctuations in pollutant levels.

A bathtub model with nonlinear velocity–density relation

The bathtub model is an applicable and effective tool for simulating the characteristics of traffic dynamics in downtown areas during morning rush hour, especially the characteristics of hypercongested traffic. In order to reduce the complexity of the research, previous studies have adopted a linear velocity–density relationship. This has limitations in analyzing the cost and duration of hypercongestion in traffic dynamics. In this paper, we develop a bathtub model with one kind of nonlinear velocity–density Relation, i.e., Pipes–Munjal Relation, based on continuous scheduling preferences and analyze its solutions in both no-toll user equilibrium and social optimum states. Due to the intractability in solving this bathtub model, there exists local analytical solutions, but for a complete solution of the model, only numerical solutions are available. The results indicate that as the value of n increases, the model can simulate traffic dynamics with higher cost and longer duration of hypercongestion under equilibrium, and can simulate traffic states with lower system cost under social optimum. We also explore the results of implementing optimal time-varying toll to realize social optimum. This study expands the relevant theories of bathtub models and provides a new perspective for traffic congestion.

Estimating near-roadway air pollution from multi-frequency noise measurements

Air pollution is a major environmental problem and its monitoring is essential for regulatory purposes, policy making, and protecting public health. However, dense networks of air quality monitoring equipment are prohibitively expensive due to equipment costs, labor requirements, and infrastructure needs. As a result, alternative lower-cost methods that reliably determine air quality levels near potent pollution sources such as freeways are desirable. We present an approach that couples noise frequency measurements with machine learning to estimate near-roadway particulate matter (PM2.5), nitrogen dioxide (NO2), and black carbon (BC) at 1-min temporal resolution. The models were based on data collected by co-located noise and air quality instruments near a busy freeway in Long Beach, California. Model performance was excellent for all three pollutants, e.g., NO2 predictions yielded Pearson's R = 0.87 with a root mean square error of 7.2 ppb; this error represents about 10 % of total morning rush hour concentrations. Among the best air pollutant predictors were noise frequencies at 40 Hz, 500 Hz, and 800 Hz, and meteorology, particularly wind direction. Overall, our method potentially provides a cost-effective and efficient approach to estimating and/or supplementing near-road air pollutant concentrations in urban areas at high temporal resolution.

Urban public transport system accessibility for different groups of residents: Case of Bratislava city

The main goal of the article is to characterize the functioning of the Urban Public Transport System (UPTS) in the capital city of the Slovak Republic (Bratislava) during different time periods (morning rush, evening, night, average working day, and weekend − Sunday) and express its accessibility for various groups of city residents (low-income households, unemployed individuals, seniors (age 60+), children (age 0–14), women (for comparison, men as well). To achieve this goal, we combined datasets of UPTS in Bratislava with the pedestrian communication network and identified an effective service area for which we subsequently determined the sizes of the respective population groups of residents. Finally, we verified the functionality of the entire UPTS model by measuring the accessibility of the selected destination, Ružinov Hospital. Our results could be summarized as follows: The functioning of UPTS is very similar during the morning rush, evening, average working day (Monday to Friday), and weekend − Sunday. During these time periods, approximately the same main lines operate, and there is a higher utilization of the same transfer points. The difference lies mainly in the frequency of departures of specific lines. During the night, the system operates based on different lines compared to the daytime, with transfers possible only at one central point/corridor. This fact also affects the usability and accessibility of UPTS, reflected in the accessibility of Ružinov Hospital, which is very limited during the night-time period. However, the accessibility of the hospital by UPTS is higher for the unemployed residents, low-income individuals, and children than the overall residents average, which is also reflected in the distribution of women. A negative aspect is that UPTS is less accessible for seniors. It must be said that this fact varies significantly in the heterogeneous spatial layout of the city. This was confirmed in our measurement of the accessibility to Ružinov Hospital, where seniors have roughly the same level of accessibility as the overall residents average.

Explainable geospatial-artificial intelligence models for the estimation of PM2.5 concentration variation during commuting rush hours in Taiwan

PM2.5 concentrations are higher during rush hours at background stations compared to the average concentration across these stations. Few studies have investigated PM2.5 concentration and its spatial distribution during rush hours using machine learning models. This study employs a geospatial-artificial intelligence (Geo-AI) prediction model to estimate the spatial and temporal variations of PM2.5 concentrations during morning and dusk rush hours in Taiwan. Mean hourly PM2.5 measurements were collected from 2006 to 2020, and aggregated into morning (7 a.m.–9 a.m.) and dusk (4 p.m.–6 p.m.) rush-hour mean concentrations. The Geo-AI prediction model was generated by integrating kriging interpolation, land-use regression, machine learning, and a stacking ensemble approach. A forward stepwise variable selection method based on the SHapley Additive exPlanations (SHAP) index was used to identify the most influential variables. The performance of the Geo-AI models for morning and dusk rush hours had accuracy scores of 0.95 and 0.93, respectively and these results were validated, indicating robust model performance. Spatially, PM2.5 concentrations were higher in southwestern Taiwan for morning rush hours, and suburban areas for dusk rush hours. Key predictors included kriged PM2.5 values, SO2 concentrations, forest density, and the distance to incinerators for both morning and dusk rush hours. These PM2.5 estimates for morning and dusk rush hours can support the development of alternative commuting routes with lower concentrations.

Impact of traffic policy on travel population exposure during heavy pollution episodes: A Chengdu case study

Previous studies on exposure have often omitted the impact of travelers' response to policies during air pollution, particularly for multi-modal traffic system. To address this gap, we investigate the population exposure of three modes (cars, buses, and metro) during morning rush hour in Chengdu, China, amidst the implementation of contingent traffic-related policies during heavy pollution episodes with elevated background pollutant concentrations of PM2.5. We introduce a trip-based average population exposure model to evaluate travel population exposure. This model allows to calculate the exposure for all modes, and assess its changes owing to modal shifts. Additionally, we propose a traffic-modal exposure coefficient to quantify PM2.5 exposure disparities among traffic modals. The results indicate that: the population exposure of total travelers experiences a growth effect, influenced not only by increased pollutant concentration but also by modal shift. Lastly, the inside-to-outside ratio, bus waiting time, and attractiveness of active traveler are discussed for more policy implications. This study not only offers a quantitative model to uncover potential overestimation of the effect of policies on reducing exposure if failure to consider modal shift, but also provides valuable insights for managing exposure growth to safeguard health of urban residents and promote sustainable development of cities.

Estimation of stops of last-mile delivery vehicles: An application in the food industry in the city of Santiago de Chile

The study of vehicle stops in last-mile delivery has gained ground in the specialized logistics literature. An efficient last-mile delivery reduces distribution costs and mitigates negative externalities such as pollution and congestion. This paper estimates the stops of last-mile trucks that deliver food products in Santiago, Chile. The aim is to study last-mile delivery operations using a non-intrusive, low-cost method. Particularly, we devise a novel methodology that employs multiple data sources to detect the primary stops of cargo vehicles. The proposed methodology involves the following two steps. First, we use GPS data to identify all the candidates for stops, that is, clusters of points close to each other in terms of distance and time, and then, these stops are classified as primary according to the proximity to planned visits or check-out markings. Finally, we conduct a case study involving food distribution, deriving managerial and public policy insights. We find that the most adequate time threshold to detect stops in our context is 4 min, which is considerably lower than previous studies. This last may be explained by the last-mile nature of our study. Our results show that primary (i.e., delivery) stops are concentrated mainly in the center of Santiago, with a duration that decreases as the hours go by. This last means that some of the externalities caused by truck stops (e.g., road capacity reduction) are exacerbated during the morning rush hour. We also find that the average duration of the primary stops is 12.5 min, while the mean distance traveled between two consecutive stops is 4.68 km.

Assessing the collision risk of mixed lane-changing traffic in the urban inter-tunnel weaving section using extreme value theory

Urban inter-tunnel weaving (UIW) sections are characterized by short lengths and frequent lane-changing behaviors in the area, commonly used for fast through traffic. These features increase the likelihood of collisions, however, collision risk assessment in this area has been inadequate. The aim of this study was to evaluate the potential collision risk of urban inter-tunnel weaving (UIW) sections in mixed lane-changing traffic conditions in morning rush hours, utilizing surrogate safety measures. The investigation involved the collection of trajectory data via an unmanned aerial vehicle (UAV). Time to collision (TTC) and extended time to collision (ETTC) were chosen as surrogate safety indicators. The estimation of collision risk was conducted using Extreme Value Theory (EVT) by means ofsurrogate safety indicators. It was found that the threshold of TTC and ETTC in this area was 1.25 s. Furthermore, a comprehensive evaluation of collision risks associated with various vehicle types was performed, revealing an inverse relationship between thecollisions riskof vehicles in mixed traffic and their size. It was worth noting that while heavy vehicles exhibit a lower collision risk, they resulted in the highest energy loss and inflicted greater harm in the event of a collision. By an examination of the distribution features pertaining to conflict types during the operation of heavy vehicles, it showed that the highest likelihood of conflict with heavy vehicles occurred when adjacent lanes are involved. Consequently, the implementation of assisted driving technology for heavy vehicles was imperative in order to mitigate the risk associated with side collisions.

Expanding bottlenecks reveals hidden bottlenecks and leads to more congested city centers

Urban traffic congestion is a perennial and critical issue. During the morning and evening rush hours, a large number of people flood into the urban road network within a short period of time, resulting in severe traffic congestion and significant inconvenience to daily life. Bottlenecks are weak links of the road network and congestion frequently occurs at these points. Many researchers postulate that unblocking bottlenecks can enhance the traffic environment. However, this perspective overlooks the propagation characteristics of congestion. To mitigate traffic congestion, our study focuses on the traffic flow propagation process post-expansion of bottlenecks in urban road networks. Our findings indicate that merely unblocking bottlenecks may lead to an increased influx of suburban residents into city centers for work within a short period of time. It leads to a surge in vehicle density in the central area, which can cause severe traffic congestion problems and make significant increase in the length of commuting time. Additionally, we identify numerous hidden bottlenecks within the road network that become active and transform into new bottlenecks once existing bottlenecks are unblocked. We simulate the commuting behavior of residents during morning rush hours on a simplified road network to quantify the adverse effects of bottleneck expansion. A large-scale simulation of the Beijing road network supports our finding that simply unblocking bottlenecks just only greatly increases residents’ commuting time. This research can provide an in-depth understanding of traffic congestion problems’ mechanisms. It is very beneficial for optimizing the construction of cities.

Dynamic planning of office building elevator scheduling

Every morning rush hour is the most crowded time for elevators in office buildings, and many office workers will be late because they can't wait for the elevator. How to reasonably call the existing elevator resources has become a problem we need to pay attention to. In this paper, we will discuss the modeling of elevator scheduling optimization problem for office buildings. In this paper, the background and significance of the research is firstly explained, and then the elevator group linkage control system is briefly understood and analyzed, and the evaluation indexes are determined according to its multi-objective and diversified characteristics, and then the group control method focusing on making the elevator run time the shortest is adopted, and the principle of the ratio of elevator running period/total running time equal to the number of passengers transported/total passengers transported by the elevator in one cycle is followed to compare several common elevator running methods, and the results are summarized in the following table. Common elevator operation methods are compared, the most effective way to arrive at, and finally use dynamic programming to solve the partition point, to establish the optimal scheme of elevator scheduling for the office building division. Since it is not possible to expand the elevator in the office building, reducing the number of stops of each elevator to shorten the time required for the elevator to carry passengers becomes the most feasible method. After comparing the common elevator operation methods, it can be found that zoning can shorten the final average arrival time of passengers more than no zoning. That is, the elevators in an office building are divided into different zones, and each zone has a designated elevator serving a designated floor segment. In order to solve the floor segment allocation problem, the shortest path algorithm is used in this paper. The paper concludes with a summary and future outlook of the resulting optimization model.

Assessing on-road emission inventory and compatible control measures for morning rush hour in Rasht, Iran: 2030 perspective

Assessing on-road emission inventory and compatible control measures for morning rush hour in Rasht, Iran: 2030 perspective

Respiratory infection transmission risk and indoor air quality at outpatient departments and emergency treatment units of Sri Lankan teaching hospitals.

Indoor carbon dioxide (CO2) concentration has been used as a proxy of the degree of ventilation and, by extension, as an indicator of the risk of contracting respiratory infections. No publications exist regarding indoor air quality (IAQ) parameters of Sri Lankan hospitals.We measured the levels of CO2 and seven other IAQ parameters during morning rush hours for three days, in outpatient departments (OPDs) and emergency treatment units (ETUs) of all 21 teaching hospitals of Sri Lanka. We measured the same parameters of outdoor air also. We calculated the mean values of those parameters. We looked for correlations between outdoors and OPD and ETU levels of selected air quality parameters.The average CO2 levels of outdoors, OPDs and ETUs respectively were 514ppm (ppm = parts per million), 749ppm and 795ppm. The average levels of PM2.5 (particulate matter with diameters <2.5μm) outdoors, OPDs and ETUs respectively, were 28.7μg/m3,32μg/m3 and 25.6 μg/m3. The average levels of PM10 (particulate matter with diameters <10μm) outdoors, OPDs and ETUs respectively, were 49.4μg/m3, 55.5μg/m3 and 47.9 μg/m3. The median levels of formaldehyde outdoors, OPDs and ETUs respectively, were 0.03mg/m3, 0.04mg/m3 and 0.08mg/m3. The median levels of total volatile organic compounds (VOC) outdoors, OPDs and ETUs respectively were 0.12mg/m3, 0.19mg/m3 and 0.38mg/m3.CO2 levels of air in OPDs and ETUs generally were below the national ceilings but above the ceilings used by some developed countries. Outdoors, OPDs and ETUs air contain PM10, PM2.5 levels higher than WHO ceilings, although below the national ceilings. VOC and formaldehyde levels are generally below the national ceilings. Air in OPDs and ETUs is hotter and humid than national ceilings. Outdoor PM10, PM2.5 levels influence OPDs and ETUs levels. We propose methods to reduce the risk of nosocomial respiratory infections and to improve IAQ of Sri Lankan OPDs and ETUs.

Optimal lane allocation strategy for shared autonomous vehicles mixed with regular vehicles

Autonomous driving technology has the potential to alter the way we travel and is rapidly evolving. Sharing rides in autonomous vehicles may become a popular mode of public transportation in the future. One strategy to enhance operational efficiency is to deploy designated autonomous vehicle (AV) lanes for shared autonomous vehicles (SAVs). This study investigated optimal allocation for dedicated AV lanes considering mixed SAVs and regular vehicles (RVs) flows. First, the standard bottleneck model for morning rush hour was extended to account for both ridesharing and automation, and user equilibrium (UE) solutions were obtained by assuming dedicated AV lanes co-exist with general-purpose (GP) lanes. The equilibrium capacity allocation for dedicated AV lanes was determined for multi-modal traffic scenarios. The study found that commuters may prefer to use SAVs because SAVs were more cost-effective and time-saving. Furthermore, analysis of the fixed payment for ridesharing in SAVs revealed that the system performance could be negatively impacted by high fixed payments. The study proposed an enumeration algorithm for developing lane strategies for discrete lane settings. It was found that the deployment of dedicated AV lanes should be matched to the penetration rate of SAVs and different management objectives, following a gradual rather than a radical process. Finally, a sensitivity analysis was performed considering the value of travel time for SAVs and the bottleneck capacity of a dedicated AV lane. This work provides a new solution to AV lane capacity allocation and offers theoretical support for managers to deploy dedicated AV lane allocation.

Dynamic load prediction of charging piles for energy storage electric vehicles based on Space-time constraints in the internet of things environment

Abstract This paper puts forward the dynamic load prediction of charging piles of energy storage electric vehicles based on time and space constraints in the Internet of Things environment, which can improve the load prediction effect of charging piles of electric vehicles and solve the problems of difficult power grid control and low power quality caused by the randomness of charging loads in time and space. After constructing a traffic road network model based on the Internet of Things, a travel chain model with different complexity and an electric vehicle charging model, the travel chain is randomly extracted. With the shortest travel time as a constraint, combined with the traffic road network model based on the Internet of Things, the travel route and travel time are determined. According to the State of Charge (SOC) and the travel destination, the location and charging time of the energy storage electric vehicle charging pile are determined. After obtaining the time-space distribution information of the energy storage electric vehicle charging pile at different times and in different regions, it is used as the input of the deep multi-step time-space dynamic neural network, and the network output is the dynamic electric vehicle charging pile. The experimental results show that this method can realize the dynamic load prediction of electric vehicle charging piles. When the number of stacking units is 11, the indexes of Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) are the lowest and the index of R 2 is the largest. The load of charging piles in residential areas and work areas exists in the morning and evening peak hours, while the load fluctuation of charging piles in other areas presents a decentralized change law; The higher the complexity of regional traffic network, the greater the load of electric vehicle charging piles in the morning rush hour.

Reducing the environmental impact of transport by reducing the time it takes to find parking for a car in a metropolis

The study analyzed aspects of reducing the environmental impact of transport based on reducing the time to find parking for a car. The time spent by drivers searching for parking in the central part of the metropolis was analyzed. Data on the amount of time spent was obtained by interviewing drivers. The survey was conducted after completing a task to find parking in the central part of the metropolis at a certain time of the day (morning rush hour, daytime, evening rush hour). The data obtained reflect the time spent by drivers searching for parking in the central part of megacities; the data allows us to estimate the amount of environmental load arising from the “useless” movement of vehicles in the center of the metropolis, arising from the lack of free parking spaces. These studies can be used in the development of parking space planning programs and environmental programs aimed at improving the environmental situation in cities.