Introduction

The prevalence of traffic congestion on highways in cities globally represents a significant issue. The expansion of traffic jams in metropolitan areas emphasizes the incapacity of transportation infrastructure to align with population demand. Subsequently, traffic congestion impedes mobility and affects economic activity negatively within regions. The challenge of traffic congestion plaguing densely populated urban areas worldwide underscores the need for innovative transportation solutions, enhanced infrastructure, and strategic urban planning to mitigate its adverse effects on urban communities.

The deleterious effects of traffic congestion on highways are well-documented, including but not limited to increased fuel consumption, air pollution, and accelerated wear and tear of vehicle parts. The transportation system, a key source of carbon footprint, is a major contributor to the carbon dioxide released into the atmosphere. In the United States, one-third of total carbon dioxide emissions can be attributed to the transportation sector, with nearly 80% of these emissions stemming from automobiles and trucks (IPCC Fourth Assessment Report, 2007).

The problems of traffic jams have a negative effect on the public transport system in cities. In particular, congestion in the city of Kumasi, Ghana, have caused minibus drivers operating in the private sector to lose up to 22% of their daily earnings, while taxi drivers have lost up to 14.3% of their daily earnings (Amoh-Gyimah R., 2013). Lagos, a city of more than 8 million people who use public transport every day, spends an average of 7 days a year due to that issue (Economic Intelligence Unit, 2013). Inrix publishes the ranking of time lost due to traffic in cities. According to Inrix data, a driver spends an average of 156 hours a year in London, 155 hours in Chicago, and 138 hours in Paris due to traffic congestion.

Like the urban centers of the world, the significance of congestion is increasing in Tashkent city. According to data from the Statistics Agency, the number of cars owned by citizens in Tashkent city was recorded as an estimated 562.1 thousand in 2022, which equates to approximately 178 cars per 1,000 permanent residents.

The present data suggests that the capital city is significantly ahead compared to other regions, having a clear advantage in the given indicator. Following the capital are the Tashkent (106), Khorezm (104), and Bukhara (100) regions with respect to the number of cars per 1,000 residents. As the center of administration, trade, and education in the country, the number of cars in Tashkent is likely to keep growing, posing challenges due to the constant use and presence of vehicles. Importantly, the traffic congestion in Tashkent can cause fatal accidents, with around 1,200 incidents resulting in 136 deaths and 1,463 injuries recorded in 2022.

The primary research indicates that traffic congestion happens in two forms: periodic and non-periodic. Periodic traffic jams take place regularly at set times when demand surpasses supply, while non-periodic congestion results from events such as weather, accidents, roadworks, or construction that interferes with traffic flow (Stopher, Peter R., 2004).

Figure 1. Traffic heatmap of Tashkent city

Note: green dots indicate deployment of higher educational institutions

Multiple studies have demonstrated that the inappropriate positioning of higher educational institutions (HEIs) can notably impact a city's transport infrastructure. Drawing from global experience, in 2012, the Ministry of Science and Higher Education of the Russian Federation established a plan to reduce road congestion in Moscow by relocating the campuses of five major capital universities outside of the Moscow Automobile Ring Road until the year 2020. Significant optimization and restructuring of the locations of prestigious HEIs occurred as a result of this plan. Notably, 16 out of the 25 most prestigious HEIs listed in the QS World University Ranking are situated outside of the country's capitals. These include the Massachusetts Institute of Technology, Stanford University, Harvard University, and the California Institute of Technology in the United States, as well as the University of Edinburgh in Great Britain and ETH Zurich in Switzerland.

Presently, Uzbekistan counts a total of 199 higher educational institutions (HEIs), comprising 150 state-run and 49 privately-funded establishments. Notably, Tashkent city has the highest concentration of HEIs in Uzbekistan, housing 43.2% of all HEIs in the country, including many renowned universities and foreign university branches. Across the republic, the HEIs collectively cater to over 1 million students, with approximately 34% (around 354,000) of these students pursuing higher education in the capital city.

Literature review

Traffic congestion is a growing concern in many urban areas around the world. The deployment of higher educational institutions can have a significant impact on traffic congestion due to increased demand for transportation services. According to Cairns et al. (2004), “rising traffic congestion, environmental concerns and social issues are making it increasingly important to encourage walking, cycling and public transport use, and to reduce car dependence”. The establishment of new colleges and universities can lead to further development in the surrounding area, attracting more people and increasing traffic congestion.

To address the issue of traffic congestion, various strategies have been proposed to mitigate the effects of higher educational institutions. One approach is to encourage the use of public transportation. Transportation Research Board (1999) notes that “transit-oriented development (TOD) can be an effective way to reduce automobile use and traffic congestion”. This approach can include implementing discounted fares for students and faculty, improving accessibility to public transport, and enhancing infrastructure to support the use of public transportation.

Another tactic to reduce traffic congestion is the promotion of alternative modes of transportation. Martens et al. (2012) suggest that “policy makers should focus on promoting change from car travel to cycling, walking or public transport for journeys to and from higher education institutions”. This can be achieved by constructing bike lanes, pedestrian walkways, alternative parking arrangements, and implementing policies to incentivize the use of alternative modes of travel.

Finally, some researchers suggest that the establishment of new higher educational institutions can have a positive impact on traffic congestion by promoting more sustainable and efficient transportation systems. Cervero (2013) highlights that “linking land use and transportation planning in UCs (urban centers) can provide a foundation for smart growth, transit-oriented development, and sustainable transportation”. By investing in new transportation infrastructure, such as improved public transportation services or new bicycle and pedestrian pathways, higher education institutions can promote more sustainable modes of travel.

According to Litman (2005), “some colleges are major trip generators, particularly during peak periods, causing parking problems and traffic congestion”. This highlights the need for effective transportation management strategies to mitigate the impact of higher education institutions on traffic congestion.

In a study by Sall et al. (2019), it was found that “the implementation of an on-campus bike-sharing system could reduce the number of motor vehicle trips to and from higher education institutions by up to 8.7%”. This suggests that incentivizing and promoting the use of alternative modes of transportation, such as bike-sharing programs, can have a significant impact on reducing traffic congestion.

Methodology

The main approach to gauging traffic congestion entails calculating delays incurred. Delay, which denotes the additional time consumed by drivers to cover a given distance at a free-flow or permissible speed, has been the basis for assessing traffic congestion since the Texas Transportation Institute introduced it in 1982 (Texas Transportation Institute, 2004). Measured in terms of total delay, this indicator offers a comprehensive tool for assessing the overall duration of traffic in urban settings and ascertaining the chain reactions that result from modifications in one part of the roadway. Assessing total delay also facilitates performance of cost-benefit analyses and the consideration of cost-effectiveness when making decisions. Another technique to quantify congestion involves measuring the length of the affected roadways. This method enjoys public acceptance owing to its accessibility.

To evaluate traffic levels on the roads of Tashkent city, this study analyzed both the average speeds and lengths of routes with traffic. Specifically, we calculated the time required to reach a destination by entering various routes into Yandex Map and tracking progress in real time. The average speed in each direction was determined from the travel time displayed on the Yandex Map.

The QGis software with QuickMapServices data was utilized to measure the length of congested roadways. In accordance with the established method, a real-time traffic map was obtained via the QuickMapServices plugin and confined to the Tashkent city area. The traffic map distinguishes roads in varying colors, including green, yellow, red, and dark red to represent the levels of traffic congestion. For instance, if vehicles move on highways at speeds below 10 km/h, the highest level of traffic congestion (indicated by dark red) is represented; speeds between 10 and 25 km/h represent second-level traffic congestion (red); speeds between 25 and 45 km/h represent the third level of traffic congestion (yellow); and speeds above 45 km/h denote uncongested roads (green).

To identify congested roads, the study determined the RGB parameter concentration corresponding to red and dark red colors on the traffic map, which is represented in 255 different colors. The colors that corresponded to this concentration were mapped as 1 pixel, while others were labeled as 0 pixels.

As the traffic map is in raster format, the length of the roads cannot be computed automatically, and manual measurements are required because the computer cannot analyze the lines from images in this format. Measuring an extensive number of lines manually is impractical and time-consuming. For simplification of calculations, images in raster format must be converted to vector format. When road images are converted to a vector, the program identifies them not as lines but as polygons, which must be avoided. Thus, the extracted map lines must undergo smoothing and are subsequently converted to vector form. The length of the vector-shaped lines is calculated based on table data related to the map using the field calculator tool in the QGIS program.

A public survey was carried out as a component of the research process to examine the perspectives of the inhabitants of Tashkent city on traffic congestion and to gather their recommendations for reducing it. The survey was conducted online via the Google platform, involving a 13-question questionnaire. Among the 13 items, four catered to the participants' personal information, whereas the remaining nine encompassed seven necessary queries and two optional items, providing citizens with an outlet to express their viewpoints.

Analysis

Average speed. As part of the research, an investigation into the average speed of automobiles in congested traffic was carried out on streets with high levels of economic and social activity. In this case, the calculations were carried out on the streets leading to the city center of the capital, connecting the districts, and the Small Ring Road and the streets adjacent to it.

The results indicate that during the morning traffic congestion period of 08:00 - 09:30, automobiles on streets leading to the city center moved at a speed of 22 km/h, whereas they moved at a speed of 28 km/h on the Small Ring Road and adjacent streets, and 24 km/h on streets connecting districts. Similarly, during the evening traffic congestion period of 17:30 - 19:00, automobiles moved at a low speed of 22 km/h, 26 km/h, and 21 km/h on streets leading to the city center, Small Ring Road and adjacent streets, and streets connecting districts, respectively.

Further analysis revealed that during the morning traffic, automobiles encountered a bottleneck on one section of the road, particularly on the streets leading to the central part of the city while in the evening traffic, a decrease in speed was observed on both sections of the traffic roads. This finding suggests that evening traffic congestion is more severe than morning traffic.

Additionally, the average speed of automobiles during traffic congestion, which was calculated by the time spent waiting at traffic lights, was observed to be the highest on Bunyodkor street (36 km/h on average) and between the South Railway station - Pushkin subway station section of the Small Ring Road (31 km/h). On the other hand, the lowest speeds were recorded on Farobi Street (19 km/h) and Mukimi street (18 km/h) during the evening traffic congestion period.

Length of traffic congestion. When measuring the length of congested roads in Tashkent during peak hours from January 12 to 31, 2023, the collected observations revealed that the peak traffic hours occurred at 8:30 - 9:30 am, 12:30 - 2:30 pm, and 5:00 - 7:30 pm in the morning, afternoon, and evening, respectively.

According to the data analyzed, it can be observed that the most elevated traffic levels in the capital were experienced during the evenings, as demonstrated by Table 1. Additionally, the levels of traffic remained relatively stable during the afternoon and evening periods, with negligible fluctuations. However, a significant increase in the levels of morning traffic has been noted since January 23rd. For instance, on January 25th, congested roads extending over 400 meters were reported in five locations across the city (totaling 2,633 meters); whereas, on January 30th, this figure had increased to twelve locations of the city (totaling 6,250 meters).

Table 1. The highest level of traffic congestion in Tashkent from January 12 to January 31

Based on the calculations derived from the collected data, it has been determined that the total length of roads experiencing high traffic congestion, designated by the red and dark red colorations on the map, exceeded 120 kilometers at present. Moreover, this length surpasses the distance between Tashkent and Gulistan, the administrative center of the Syrdarya region, which accounts for 119 kilometers. Notably, the data collected on January 25th indicated that the length of dark red roads measured 8,520 meters, subsequently increasing to 11,330 meters on January 30th, as demonstrated in Figures 2 and 3.

Figure 2. Traffic map of Tashkent city

Figure 3. Results of extraction of traffic roads of Tashkent city

It is worth noting that the study period in higher educational institutions commenced on January 23rd, and its influence was largely reflected in the accelerated level of traffic during the morning and afternoon hours. In light of this, the effects of the heightened activity due to school days on traffic were assessed through the implementation of the difference-in-differences method, as presented in Table 2.

Table 2. Impact of increased activity on school days on traffic, score

Survey among the population. As a part of the research, a survey was conducted in February 2023 that garnered participation from 891 individuals. The survey participants were found to be predominantly male, with 85.7% of participants (764 individuals) identifying as men. Additionally, the results indicated that the vast majority of respondents (60.8% of total participants) were car drivers, of which 10.3% (56 individuals) were women.

Further analysis of the survey revealed that 41.8% (146 individuals) of respondents who reported not driving a car used a bus as their primary mode of transportation, 27.5% (96 individuals) opted for the subway, 24.1% (84 individuals) used taxis, 1.7% (6 individuals) utilized minibus services, and 4.9% (17 individuals) stated a preference for walking.

According to the survey conducted, it was found that 74.2% of the participants regarded traffic congestion in Tashkent as a significant problem, with 21.2% indicating it as a moderately severe issue. Further analysis revealed that the majority of the respondents, including 72.3% of drivers, 76.2% of car passengers, 82.9% of bus riders, 69.8% of subway riders, 83.3% of minibus riders, and 70.6% of pedestrians marked traffic congestion as one of the primary problems in Tashkent. Consequently, the survey results suggested that the problem of traffic congestion is a concern for both bus and minibus passengers.

The participants were further requested to mark the three main situations that they believed were the primary causes of traffic congestion among ten circumstances. A significant proportion of the respondents, 62.2%, noted irregular drivers, driving violations, and improper parking as the major contributors, followed by road defects (57.6%), school commuters (46.8%), and commuters (41.2%). Additionally, more than 20% of the participants considered traffic light timing, traffic rules, and inadequacies in the installation of road signs as some of the leading causes of traffic congestion. The traffic congestion also results in additional commuting time for the Tashkent residents, concerning work and daily errands, as depicted in Table 3.

Table 3. Results of survey.

Furthermore, the data revealed that 9.3% of the surveyed students make a living by driving a car, with 90.9% of them being students who come from other regions. Among the students, the majority of them preferred to use bus services (54.2%) and subway services (21.2%) as their primary mode of transportation.

Based on the findings of the survey, an assessment was undertaken to determine the excess time that car drivers in Tashkent spend on the road due to traffic congestion, taking into consideration realistic and optimistic scenarios. The realistic scenario involved utilizing the average values of the excess time intervals that drivers experience while commuting on days with traffic, whereas the optimistic scenario was premised on the smallest values of these intervals. The results indicate that, on average, Tashkent drivers encounter an additional 33.2 minutes per day of travel time resulting from traffic gridlock. Conversely, the optimistic scenario reveals a slightly lower average of 25.4 minutes per day. 

Environmental impact of traffic congestion

The study conducted an analysis to determine the quantity of CO2 (carbonic anhydride) emitted into the atmosphere due to the surge in traffic during educational hours, and calculated the corresponding area of green space needed to mitigate this effect, as outlined in Table 4.

Table 4. Environmental impact of increased traffic during education period.

The outcomes of measurement of the length of congested roads suggest that the disparity between the length of roads with traffic speed that ranges from 10-25 km/h at point 9 and point 7 traffic densities amounts to 2.8 km. Furthermore, the findings demonstrate that the onset of the study period resulted in an increase in traffic by 1.43 points. Based on these observations, the length of the distance corresponding to the impact of traffic during the study period was determined to be approximately 2.0 kilometres.

The subsequent analysis of these data revealed that under the realistic scenario, an additional 506.9 tons of CO2 will be released into the atmosphere in 1 year due to the surge in traffic during the study period. Conversely, the optimistic scenario resulted in a lower amount of CO2 with 387.8 tons emitted. Moreover, implementing the realistic scenario would necessitate planting an additional 19,031 trees or establishing 52.9 hectares of green space to mitigate the environmental impact caused by the emissions.