G. 2082  
Page 1  
Global Research journal of Natural Science  
& Technology (GRJNST)  
Volume: 04 - Issue 3 (2026), 2082  
ISSN P: 2790-7643 ISSN E: 2790-7651  
www.grjnst.net  
https://doi.org/10.53762/grjnst.04.03.03  
Smart Transportation Systems: Enhancing Traffic Flow and Reducing Urban  
Congestion through Intelligent Solutions  
Received: 30 March 2026. Accepted: 26 April 2026. Published: 09 May 2026  
Muhammad Bilal Israr  
Department of Civil Engineering  
University of Engineering & Technology Peshawar, 25000, Pakistan  
bilalisrar@yahoo.com  
Zafreen Elahi  
Lecturer, Department of Civil Engineering, University of Information Technology, Engineering, and Management  
Sciences, 87300, Quetta, Pakistan  
zafrin.elahi@buitms.edu.pk  
Anwaar Hazoor Ansari  
Assistant Professor, Department of Civil Engineering, University of Information Technology, Engineering, and  
Management Sciences, 87300, Quetta, Pakistan  
anwaar.hazoor@buitms.edu.pk  
Ahtsham Mustafa Awan  
Assistant Research Officer (Govt of Pakistan), MS Remote Sensing and GIS, Institute of Geographical  
Information Systems, National University of Sciences and Technology Islamabad  
ahtshamm214@gmail.com  
GRJNST, Volume: 04 - Issue 3 (2026) / ISSN P: 2790-7643  
Article ID: 2082  
https://doi.org/10.53762/grjnst.04.03.03  
Copyright © 2026 GRJNST. This article is published under an Open Access model. It is made available to the public under the terms of the Creative  
Commons Attribution 4.0 International (CC BY 4.0) license, which permits unrestricted use and distribution  
G. 2082  
Page 2  
Abstract: Smart Transportation Systems emerged as an effective solution for improving  
traffic flow efficiency and reducing urban congestion through intelligent and data-driven  
technologies. This study examined the impact of real-time traffic monitoring, intelligent  
traffic signal control, artificial intelligence-based predictive analytics, and IoT-enabled  
data integration on urban mobility outcomes. A quantitative research design was applied,  
and data was collected from a sample of 300 respondents, including transportation  
professionals, urban planners, and daily commuters. Descriptive statistical analysis was  
used to evaluate the effectiveness of Smart Transportation System components. The  
results indicated that real-time traffic monitoring recorded the highest mean value (M =  
4.12), followed by traffic flow efficiency (M = 4.10), intelligent signal control (M =  
4.05), and urban congestion reduction (M = 4.03). AI-based predictive analytics (M =  
4.01) and IoT-enabled data integration (M = 3.98) also demonstrated strong positive  
contributions to traffic optimization. The findings showed that Smart Transportation  
Systems significantly improved travel time, reduced intersection delays, enhanced vehicle  
movement, and minimized congestion during peak hours. The study concluded that  
intelligent transportation technologies played a crucial role in enhancing urban mobility  
and supporting sustainable transportation development. The results provided valuable  
insights for policymakers and urban planners to design efficient, technology-driven  
traffic management systems for modern cities.  
Keywords: Artificial intelligence, intelligent transportation systems, IoT integration,  
smart mobility, traffic flow efficiency, urban congestion  
GRJNST, Volume: 04 - Issue 3 (2026) / ISSN P: 2790-7643  
Article ID: 2082  
https://doi.org/10.53762/grjnst.04.03.03  
G. 2082  
Page 3  
Introduction  
The high density of the urban population resulted in the high pressure on the urban  
transportation systems caused by the constant growth of the urban population, the rapid  
increase in population, and the increasing reliance on personal vehicles. All these factors  
greatly increased traffic congestion in metropolitan cities, which resulted in more time  
spent on the road, more fuel consumed, more air pollution, and less economic  
productivity. The conventional traffic management systems were not able to react in a  
way that was responsive to dynamic and unpredictable traffic conditions, which  
introduced inefficiencies in the urban mobility networks. To address these issues, Smart  
Transportation Systems (STS), also known as Intelligent Transportation Systems (ITS),  
were developed as advanced technological systems that incorporated artificial  
intelligence, Internet of Things (IoT), big data analytics, and sensor-based  
communication to enhance the efficiency and sustainability of traffic management  
(Sayed et al., 2023).  
Smart Transportation Systems enhanced the mobility within the cities as it provided the  
opportunity to monitor the traffic flows in real-time and apply adaptive methods of  
controlling the traffic flows. These systems made use of interconnected sensors, smart  
cameras and GPS-enabled devices to gather continuous traffic data, which was used to  
support decision-making at the traffic control centers. Dynamic adaptive traffic signal  
systems were adjusted dynamically in response to congestion levels, which not only  
reduced waiting time in intersections, but also improved the overall fluidity of traffic.  
Studies have shown that these smart systems have a tremendous impact on improving  
the efficiency of transportation by minimizing delays and enhancing strategies that  
optimize routes in urban areas that are jammed (Goumiria et al., 2023).  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2082  
https://doi.org/10.53762/grjnst.04.03.03  
G. 2082  
Page 4  
The further development of artificial intelligence and machine learning contributed to  
the enhancement of Smart Transportation Systems since it allowed performing  
predictive analytics and automated decision-making. These technologies used historical  
and real time traffic information to predict patterns of congestion, bottlenecks, and  
optimal routes that vehicles should take. Deep learning models enhanced prediction  
accuracy by revealing some of the intricate behavior patterns of traffic that were not  
captured by the traditional models. This led to making urban transport systems more  
responsive, efficient, and capable of dealing with traffic conditions of high density in real  
time (Idris et al., 2024).  
Smart Transportation Systems became an essential part of the contemporary smart cities  
as it combined digital infrastructure and smart algorithms to enhance urban mobility.  
These systems helped in sustainability through a reduction of the greenhouse gases,  
minimizing on the burning of fuel and the commuter experience. The rising use of  
intelligent transportation solutions was an indication of a worldwide transition to data-  
driven and automated urban mobility management systems (Chakraborty et al., 2025).  
Background of the Study  
The Smart Transportation Systems was developed as a result of the shortcomings of the  
traditional methods of managing traffic which depended on the fixed time of the signals  
and manual control. These conventional systems were not flexible and could not  
efficiently respond to real-time traffic changes, leading to traffic congestion, delays, and  
inefficient use of roads. With the rise in the population density in cities, the necessity to  
have a more responsive and intelligent traffic management system became urgent in  
terms of sustainable urban development (Sakr et al., 2023).  
GRJNST, Volume: 04 - Issue 3 (2026) / ISSN P: 2790-7643  
Article ID: 2082  
https://doi.org/10.53762/grjnst.04.03.03  
G. 2082  
Page 5  
Transportation systems with the integration of IoT devices, wireless communication  
networks, and sensor technologies turned into interconnected digital ecosystems due to  
technological advancements. These inventions made it possible to gather information on  
a continuous basis about vehicles, roads, and other infrastructural elements. The  
transportation authorities also received access to real-time traffic information which led  
to the improvement of decision-making accuracy and quicker response to incidents of  
congestion. This change was a major transition of the non-dynamic to the dynamic  
traffic management systems (Sayed et al., 2023).  
Artificial intelligence has been instrumental in augmenting Smart Transportation  
Systems through the introduction of predictive modeling, optimization algorithms and  
autonomous decision-making processes. The machine learning methods used analyzed  
the huge traffic patterns to determine the congestion patterns, predicting the traffic  
density and optimizing the signal timing plans. Such AI-driven systems increased the  
efficiency of operations and minimized the human involvement into the process of  
traffic management, making the urban transportation smarter and more responsive (Idris  
et al., 2024).  
Smart Transportation Systems were further developed with the integration of emerging  
technologies including digital twins, edge computing, and connected autonomous  
vehicle. Those technologies allowed simulating traffic environment in real time,  
increasing the extent of system scaling, and enhancing the coordination between vehicles  
and infrastructure. This led to more resilient, efficient, and capable transportation  
networks able to accommodate complex urban mobility needs (Ge & Qin, 2024).  
Research Problem  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2082  
https://doi.org/10.53762/grjnst.04.03.03  
G. 2082  
Page 6  
The great improvements on Smart Transportation Systems, cities were still experiencing  
chronic traffic congestion, and inefficiencies in the management of traffic flows. The  
traditional and semi-digital systems could not handle the large amount of real-time  
traffic data and the sudden change of congestion and the unexpected traffic events. This  
loophole lowered the efficiency of the transportation systems in providing the best  
distribution of traffic. The heterogeneous nature of technologies, like those in the IoT,  
artificial intelligence models, and the infrastructure of existing transportation created  
operational inefficiencies. The challenge experienced by many urban transport systems in  
ensuring a smooth communication between data sources limited the full potential of  
intelligent traffic management solutions. These constraints led to the desire to have more  
sophisticated, integrated, and scalable Smart Transportation Systems that will be able to  
help address real-world urban mobility challenges.  
Objectives of the Study  
1. To analyze the role of Smart Transportation Systems in improving urban traffic  
flow efficiency  
2. To examine the impact of intelligent technologies on reducing urban traffic  
congestion  
3. To evaluate the effectiveness of AI, IoT, and predictive analytics in transportation  
management  
4. To identify technological and infrastructural challenges in implementing Smart  
Transportation Systems  
Research Questions  
Q1. How did Smart Transportation Systems enhance traffic flow in urban  
environments?  
GRJNST, Volume: 04 - Issue 3 (2026) / ISSN P: 2790-7643  
Article ID: 2082  
https://doi.org/10.53762/grjnst.04.03.03  
G. 2082  
Page 7  
Q2. What impact did artificial intelligence and IoT technologies have on congestion  
reduction?  
Q3. How effectively did predictive analytics improve traffic management decisions?  
Q4. What challenges limited the implementation of Smart Transportation Systems in  
urban cities?  
Significance of the Study  
This paper has given valuable information on how Smart Transportation Systems can be  
used to enhance urban mobility and minimize congestion. It helped in the realization  
that intelligent technologies improved the efficiency of traffic and helped to develop  
urban ecosystems in a sustainable way. The results presented a good piece of advice to  
policy makers and city planners to come up with more efficient transportation  
infrastructures. The article advocated technological innovation by highlighting the  
relevance of AI-powered and IoT-enabled traffic management systems in contemporary  
cities. It also helped in academic research synthesizing new trends in smart  
transportation technologies and discovering new research opportunities in smart  
mobility systems.  
Literature Review  
Evolution and Conceptual Development of Smart Transportation Systems  
Smart Transportation Systems developed as a continuation of Intelligent Transportation  
Systems (ITS) with the aim of solving the problem of increasing congestion in urban  
areas by digitalizing and automating traffic management. Initial research emphasized that  
transport systems were no longer managed manually but through data and sensor-based  
infrastructure where real-time communication was established between the vehicles and  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2082  
https://doi.org/10.53762/grjnst.04.03.03  
G. 2082  
Page 8  
the road network and improved mobility efficiency (Creß et al., 2021). The  
development of smart mobility systems showed that urban transport systems  
progressively integrated IoT and communication technology to support dynamic traffic  
control and improve the sustainability results (Goumiria et al., 2023).  
Recent studies stressed that Smart Transportation Systems evolved beyond conventional  
ITS to include artificial intelligence, cloud computing, and edge-based analytics to  
provide predictive traffic management. These technologies allowed real-time decision-  
making, which enhanced greatly the strategies of reducing congestion and optimizing  
routes (Chakraborty et al., 2025). The systematic reviews proved that the digital  
transformation of transportation systems reinforced the responsiveness of infrastructure  
and the adaptability of the systems in complex urban conditions (Ge & Qin, 2024).  
Subsequent research established that the development of smart mobility was closely  
correlated with the development of smart cities in which transport networks were  
combined with urban planning systems. This integration led to better coordination  
between urban mobility networks and systems of public transport, as well as those of the  
private transport system and the emergency services (Sakr et al., 2023). The transport  
ecosystems based on IoT enabled the constant exchange of data that improved the  
accuracy of traffic prediction and reduced operational inefficiencies (Idris et al., 2024).  
The part played by Artificial Intelligence, IoT, and big data in Traffic Optimization  
Artificial intelligence was a key element that facilitated the improvement of Smart  
Transportation Systems through predictive traffic modelling and adaptive control  
systems. To optimize signal timing plans to enhance the overall efficiency of traffic, the  
algorithms of machine learning processed historical and real-time traffic data (Sayed et  
al., 2023). In complex traffic conditions, AI-based systems were used to facilitate  
GRJNST, Volume: 04 - Issue 3 (2026) / ISSN P: 2790-7643  
Article ID: 2082  
https://doi.org/10.53762/grjnst.04.03.03  
G. 2082  
Page 9  
automated decision-making and reduce delays and improve vehicle flows (Idris et al.,  
2024).  
The IoT technology profoundly changed the transportation systems as it facilitated  
interconnected communication between the sensors, vehicles, and the infrastructure  
components. These systems gathered real-time data on road conditions, traffic density,  
and vehicle movement, which was used to manage congestion in real-time (Vadivel et al.,  
2023). The IoT wireless sensors networks enhanced the accuracy of traffic monitoring  
and enabled authorities to respond swiftly to congestion incidents (Mukhopadhyay et  
al., 2024).  
Big data analytics also enhanced Smart Transportation Systems using large-scale datasets  
collected by a variety of sources like GPS devices, surveillance cameras, and mobile apps.  
These analytics enhanced the accuracy of traffic forecasting and aided in strategic  
planning on urban mobility systems (Khan and Ivan, 2023). The combination of AI and  
IoT systems led to better responsiveness of the system, decreased latency, and increased  
energy efficiency in transportation networks (Elsayed et al., 2023).  
Smart Transportation Systems and their influence on Urban Congestion and Urban  
Sustainability  
The Smart Transportation Systems have played a greater role in the alleviation of traffic  
congestion in cities by optimizing traffic flow and enhancing the efficiency of the routes.  
Research showed that adaptive traffic signal control systems decreased the time at  
intersections and improved the overall utilization of the available road capacity  
(Kummetha et al., 2022). Smart congestion management systems enhanced the  
allocation of traffic across urban networks, which resulted in a reduction of the  
bottlenecks and an improvement of the mobility patterns (Ge & Qin, 2024).  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2082  
https://doi.org/10.53762/grjnst.04.03.03  
G. 2082  
Page 10  
The ecological sustainability was enhanced by the introduction of Smart Transportation  
Systems as an optimized traffic flow minimized the use of fuel and greenhouse gases. It  
was proven that AI-inspired mobility systems led to reduced carbon footprints through  
reducing idle time and improving driving efficiency (Sayed et al., 2023). The smart  
mobility solutions helped to sustain the development of urban areas by promoting the  
usage of a means of transportation and shared mobility services (Goumiria et al., 2023).  
Smart Transportation Systems made urban living better through better commuter  
experience, less ambiguity in travel, and more accessibility to transportation services. The  
digital integration of transportation networks allowed real-time travel updates,  
optimization of routes, and coordination of responses in case of an emergency  
(Chakraborty et al., 2025). The development of smart infrastructure was in line with the  
long-term urban planning strategies that aligned with the objectives of sustainable cities  
(Creß et al., 2021).  
Conceptual Framework Model  
The conceptual framework model depicted the correlation between the elements of  
Smart Transportation System and the urban congestion reduction due to the  
intermediary role of the efficiency of traffic flow. The independent variables were real-  
time monitoring of traffic, intelligent control of traffic signals, predictive analytics based  
on AI, and data integration using IoT. These elements were the technological aspects of  
the highly intelligent transportation systems that played a role in ensuring better traffic  
control. The real-time monitoring allowed constant monitoring of traffic conditions,  
and the intelligent control of the signals optimized the performance of the intersection.  
The analytics powered by AI was used to support predictive decision-making,  
forecasting the patterns of congestion, and IoT integration ensured a smooth  
GRJNST, Volume: 04 - Issue 3 (2026) / ISSN P: 2790-7643  
Article ID: 2082  
https://doi.org/10.53762/grjnst.04.03.03  
G. 2082  
Page 11  
communication between infrastructure and vehicles. All of these interconnected  
technologies helped to increase the efficiency of transportation systems by providing the  
possibility of data-driven and adaptive strategies of traffic management.  
The framework also showed that efficiency of traffic flow was a mediating variable that  
converted technological advances into quantifiable results in urban mobility. The  
effectiveness of traffic flow led to the effectiveness of movement of vehicles, the  
reduction of traveling time, the reduction of delays, and the better utilization of the road  
capacity. These enhancements directly impacted the dependent variable that was urban  
congestion reduction, in terms of reduced traffic density, less traffic congestion, and  
better conditions of commuting. The framework involved the use of control variables to  
the overall effectiveness of the system; these variables included the quality of road  
infrastructure, weather conditions, population density, availability of public transport,  
and economic factors. The model has indicated that although the components of the  
Smart Transportation System had a great impact on the urban congestion patterns,  
external factors also had a significant influence on the development of the patterns.  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2082  
https://doi.org/10.53762/grjnst.04.03.03  
G. 2082  
Page 12  
Figure 1. Conceptual Framework Model  
GRJNST, Volume: 04 - Issue 3 (2026) / ISSN P: 2790-7643  
Article ID: 2082  
https://doi.org/10.53762/grjnst.04.03.03  
G. 2082  
Page 13  
Research Methodology  
Research Design  
The research design was based on quantitative research design to investigate the effects  
of Smart Transportation Systems on effectiveness of traffic flow and alleviation of urban  
congestion. This design was chosen since it enabled the systematic measurement of  
relationships among technological elements like real-time monitoring, intelligent signal  
control, AI-based predictive analytics and the integration of the IoT. The methodology  
allowed the numerical analysis of the responses obtained in the urban transportation  
settings among the relevant stakeholders. The research was based on a systematic survey-  
based research design to assure uniformity of data collection and analysis processes.  
Population of the Study  
The study population comprised of transportation professionals, traffic management  
officers, urban planners, and daily commuters who were actively involved in or managed  
urban traffic systems. These groups were chosen since they had first-hand experience of  
the conditions of traffic flow and the uses of Smart Transportation System in urban  
settings. The presence of both professional and user gave a balanced approach in terms  
of system effectiveness. The research focused on people related to metropolitan traffic  
systems with the congestion problems still being a very high priority. This guaranteed  
the responses to be based on the actual operational conditions of Smart Transportation  
Systems within the urban environment.  
Sample Size and Sampling Technique  
A sample population of 300 respondents was used in the study based on the target  
population. This sample involved transportation engineers, traffic police officials, urban  
development officers and ordinary road users. The sample size was deemed sufficient in  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2082  
https://doi.org/10.53762/grjnst.04.03.03  
G. 2082  
Page 14  
order to capture various views concerning the effectiveness of smart transportation. A  
stratified random sampling method was used to have the respondents of various  
categories represented equally. Each stratum was the representation of a particular group:  
technical experts, administrative employees and commuters. This method minimized  
sampling bias and the validity of the data gathered by sampling by ensuring each  
category was proportionally represented.  
Data Collection Method  
The structured questionnaire was used to collect primary data, which was used to  
measure perceptions of Smart Transportation Systems and their effects on traffic flow  
and congestion reduction. The questionnaire contained close-ended questions which  
were derived based on a five-point Likert scale between strongly disagree and strongly  
agree. The instrument was physically and electronically disseminated so as to cover a  
greater number of respondents. The analysis was also supported by secondary data,  
which included the published reports, academic journals, and official transportation  
statistics. These sources were helpful in strengthening the theoretical base of the research  
and in assisting interpretation of empirical results.  
Instrumentation  
The research tool was a structured questionnaire that was subdivided into a set of items  
that represented the independent and dependent variables. The independent variables  
were real-time traffic tracking, smart control of traffic lights, predictive analytics of the  
data using AI, and the integration of the data with the help of the IoT. Dependent  
variables were the efficiency of traffic flow and reduction of congestion in the city. The  
questionnaire was constructed using validated constructs of past research in intelligent  
GRJNST, Volume: 04 - Issue 3 (2026) / ISSN P: 2790-7643  
Article ID: 2082  
https://doi.org/10.53762/grjnst.04.03.03  
G. 2082  
Page 15  
transportation systems. To ascertain clarity, reliability, and validity of this instrument, a  
pilot test was done on 30 respondents to ensure that all was clear, reliable, and valid.  
Data Analysis Technique  
The data gathered was analyzed through descriptive statistical analysis. The measures  
like frequency distribution, mean values, and standard deviation were used to analyse the  
trends in respondent feedback. This method served to comprehend the overall  
tendencies as far as the effectiveness of the Smart Transportation System is concerned.  
The analysis was aimed at comparing average perceptions of the respondents in different  
variables without using correlation and regression tests. The findings were tapped  
concerning the outcome of traffic flow improvement and reduction of congestion in  
urban settings.  
Results And Analysis  
Descriptive Statistics of Smart Transportation System Components  
Table 1. Descriptive Statistics of Study Variables  
Standard  
Variables  
N
Mean  
Deviation  
Real-Time Traffic  
Monitoring  
300  
4.12  
0.71  
Intelligent Traffic Signal  
Control  
300  
300  
4.05  
4.01  
0.68  
0.74  
AI-Based Predictive Analytics  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2082  
https://doi.org/10.53762/grjnst.04.03.03  
G. 2082  
Page 16  
Standard  
Variables  
N
Mean  
Deviation  
IoT-Enabled Data Integration  
Traffic Flow Efficiency  
300  
300  
300  
3.98  
4.10  
4.03  
0.69  
0.66  
Urban Congestion Reduction  
0.72  
The findings revealed that the highest mean value (M = 4.12) of the real-time traffic  
monitoring showed a high level of agreement among the respondents on the effectiveness  
of the system in enhancing management of traffic in cities. Real-time monitoring was  
seen by the respondents as the strongest aspect of Smart Transportation Systems in  
improving the efficiency of traffic flows. The reasonably small standard deviation  
implied that there was uniformity in the responses of the sample. The adaptive signal  
systems were also found to have a positive mean score (M = 4.05), implying that the  
intelligent control of traffic lights played a big role in the reduction of the waiting time  
at intersections. The respondents concurred with the fact that automated signal  
adjustments enhanced traffic allocation and reduced congestion during peak hours. The  
difference in responses was small which implied that the participants had similar  
perceptions. It was also found that AI-based predictive analytics and IoT-enabled data  
integration also had high mean values (M = 4.01 and M = 3.98 respectively). These  
findings revealed that the respondents had perceived the significance of advanced  
technologies in predicting congestion and enhancing the use of data in decision-making  
processes in transportation systems. The positive ratings of all Smart Transportation  
System elements were high, which proves their topicality in the contemporary urban  
mobility management.  
GRJNST, Volume: 04 - Issue 3 (2026) / ISSN P: 2790-7643  
Article ID: 2082  
https://doi.org/10.53762/grjnst.04.03.03  
G. 2082  
Page 17  
Figure 2. Descriptive Statistics of Study Variables  
Traffic Flow Efficiency and Urban Mobility Improvement  
Table 2. Descriptive Statistics of Traffic Flow Efficiency Indicators  
Standard  
Indicators  
N
Mean  
4.15  
4.08  
4.02  
Deviation  
Reduced Travel Time  
300  
300  
300  
0.67  
Improved Vehicle Speed  
Flow  
0.70  
0.72  
Reduced Intersection Delay  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2082  
https://doi.org/10.53762/grjnst.04.03.03  
G. 2082  
Page 18  
Standard  
Indicators  
N
Mean  
4.11  
Deviation  
Better Route Optimization  
300  
0.69  
The findings revealed that the lowest mean value (M = 4.15) was the lowest travel time,  
and this showed that Smart Transportation Systems was highly effective in enhancing  
commuting efficiency in urban areas. Respondents affirmed that real time data and  
adaptive system helped in accelerating movement along city roads. There was also a  
strong level of agreement between improved vehicle speed flow and optimisation of  
routes (M = 4.08 and M = 4.11 respectively). These enhancements were associated  
with enhanced alignment between traffic lights and patterns of vehicle movement. The  
low intersection delay (M = 4.02) showed that smart signal control systems were very  
crucial in reducing the waiting time at the busy crossroads. In general, the results proved  
that Smart Transportation Systems have a great impact in improving the efficiency of  
traffic flow by integrating technology and automated decision-making mechanisms.  
GRJNST, Volume: 04 - Issue 3 (2026) / ISSN P: 2790-7643  
Article ID: 2082  
https://doi.org/10.53762/grjnst.04.03.03  
G. 2082  
Page 19  
Figure 2. Descriptive Statistics of Traffic Flow Efficiency Indicators  
Urban Congestion Reduction Outcomes  
Table 3. Descriptive Statistics of Urban Congestion Reduction Indicators  
Indicators  
N
Mean  
Standard Deviation  
Reduced Traffic Jam  
Frequency  
300  
4.06  
0.71  
0.68  
0.70  
0.73  
Lower Peak Hour  
Congestion  
300  
300  
300  
4.09  
4.04  
4.07  
Improved Road Capacity  
Utilization  
Reduced Vehicle Idle  
Time  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2082  
https://doi.org/10.53762/grjnst.04.03.03  
G. 2082  
Page 20  
The results showed that the lower value of peak hour congestion had high mean (M =  
4.09) and it indicated that Smart Transportation Systems have the ability to control the  
level of traffic density at any given time. Respondents concurred that smart systems  
assisted in the distribution of traffic in better proportions along the available routes.  
There were also strong positive responses on reduced number of vehicles in traffic jam  
and reduced number of vehicles idle (M = 4.06 and M = 4.07 respectively). These  
findings indicated that intelligent mechanisms reduced unnecessary stopping and  
enhanced continuous movement of vehicles in congested locations. The stability of the  
responses also suggested that there were stable perceptions of the effectiveness of the  
system. The better utilization of the existing road capacity (M = 4.04) indicated that  
the existing road infrastructure was put into a more efficient use through adaptive traffic  
management. These results proved that Smart Transportation Systems contributed  
greatly to the alleviation of congestion and enhancement of the overall transportation  
sustainability in the cities.  
GRJNST, Volume: 04 - Issue 3 (2026) / ISSN P: 2790-7643  
Article ID: 2082  
https://doi.org/10.53762/grjnst.04.03.03  
G. 2082  
Page 21  
Figure 3. Descriptive Statistics of Urban Congestion Reduction Indicators  
Discussion  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2082  
https://doi.org/10.53762/grjnst.04.03.03  
G. 2082  
Page 22  
The research results revealed that Smart Transportation Systems made the urban traffic  
flow much more efficient and decreased the level of congestion by incorporating smart  
technologies. The large mean values of all the variables demonstrated a high level of  
agreement among the respondents on the effectiveness of real-time monitoring,  
intelligent signal control, and predictive analytics in dealing with traffic conditions.  
These findings were consistent with the recent empirical data indicating that intelligent  
transportation systems were able to increase urban mobility by allowing real-time traffic  
coordination and adaptive decision-making processes (Selvarajan et al., 2024; Wei et al.,  
2023). This led to the integration of data-driven control mechanisms that enabled  
transportation systems to respond dynamically to changes in traffic and this enhanced  
the utilization of roads and reduced bottlenecks in congestion.  
The findings also revealed that the real time traffic monitoring has become the most  
significant factor in enhancing the efficiency of traffic flow. This result was in line with  
recent research that emphasized the importance of sensor-based monitoring systems in  
supplying continuous traffic information, which facilitated the effective prediction of  
traffic and the effective implementation of congestion management strategies (Matsui et  
al., 2024; Zhao et al., 2024). The ability to collect real-time data allowed the authorities  
in charge of traffic to detect patterns of congestion early and implement corrective  
actions, which greatly minimized delays and enhanced travel reliability. The capability of  
tracking the state of traffic conditions in real-time also made the system more responsive  
in high-density urban areas.  
GRJNST, Volume: 04 - Issue 3 (2026) / ISSN P: 2790-7643  
Article ID: 2082  
https://doi.org/10.53762/grjnst.04.03.03  
G. 2082  
Page 23  
Intelligent traffic signal control systems also played an important role to minimize the  
delays at intersections and enhance traffic movement. The results proved that adaptive  
signal timing systems optimized the distribution of traffic across road networks by  
adjusting the signal cycles according to current real-time traffic conditions. These  
findings were in line with the recent studies that have highlighted that dynamic signal  
control systems minimized waiting time and enhanced traffic throughput at intersections  
within cities (Kummetha et al., 2022; Cai et al., 2023). Introduction of such systems  
guaranteed a better flow of traffic and reduced stop and go trends, which were the main  
contributing factors to traffic jam.  
The research also found out that AI based predictive analytics were instrumental in  
improving the efficiency of transportation by predicting the occurrence of congestion,  
and determining the best route to use. These results were consistent with the recent  
developments in machine learning applications in transportation systems, where  
predictive models enhanced the accuracy of decision-making and allowed to proactively  
manage congestion (Sayed et al., 2023; Liu et al., 2024). Predictive analytics enabled the  
transportation systems to predict the conditions of traffic instead of responding to them,  
which greatly enhanced the performance of the transportation systems and minimized  
delays in travelling.  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2082  
https://doi.org/10.53762/grjnst.04.03.03  
G. 2082  
Page 24  
The data integration made possible through IoT enhanced the performance of Smart  
Transportation Systems, as it allowed establishing a smooth information exchange  
between vehicles, sensors, and elements of a transportation system. The results indicated  
that integrated data systems enhanced the coordination of traffic and aided in the real-  
time decision-making process. This finding was corroborated by recent studies that  
found that the implementation of IoT-based transportation networks improved the  
efficiency of the data exchange between them and the surrounding urban environment  
and improved the strategies of mitigating congestion in urban environments (Vadivel et  
al., 2023; Mukhopadhyay et al., 2024). The interconnectedness of IoT systems allowed  
to thoroughly understand traffic dynamics, which increased the overall efficiency of the  
system.  
The results of the study also revealed that the Smart Transportation Systems  
contributed greatly in reducing congestion in the cities by maximizing the use of road  
capacity and minimizing the time spent by vehicles in the cities. The decrease in the  
number of people at the peak hour was indicative of the capability of intelligent systems  
to evenly distribute traffic across the available routes. Those findings were also in line  
with the recent empirical studies that have shown that data-driven congestion  
management systems enhanced the allocation of traffic and minimized the number of  
bottlenecks in urban networks (Kummetha et al., 2022; Selvarajan et al., 2024). This  
effective use of road infrastructure was crucial in attaining the sustainable traffic  
management results.  
GRJNST, Volume: 04 - Issue 3 (2026) / ISSN P: 2790-7643  
Article ID: 2082  
https://doi.org/10.53762/grjnst.04.03.03  
G. 2082  
Page 25  
There was also the improvement of environmental sustainability due to the  
implementation of Smart Transportation System. The traffic congestion was reduced,  
resulting in reduced fuel consumption and greenhouse emission, which led to sustainable  
urban development. These results were consistent with the current studies which stressed  
that intelligent transportation technologies enhanced environmental sustainability  
because they significantly reduced emissions and improved energy efficiency of urban  
mobility systems (Ge et al., 2024; Alshehri et al., 2023). The combination of intelligent  
mobility technologies was a very important factor in ensuring the final environmentally  
sustainable transportation results.  
The results pointed out that Smart Transportation Systems positively influenced the  
overall commuter experience by minimizing uncertainty in travel time and increasing  
stability in the route. On-the-fly traffic and route optimization systems allowed users to  
make informed travel choices, which enhanced the efficiency of mobility. Recent studies  
that intelligent transportation systems enhanced user satisfaction by giving accurate and  
timely traffic information supported this observation (Zhao et al., 2024; Wei et al.,  
2023). The enhanced commuter experience helped to promote the increased acceptance  
of smart mobility solutions.  
These are the good results and the study also showed the difficulties that were  
experienced in integration of the system and the compatibility of the infrastructure.  
Combining several technologies including AI, IoT and the old systems of transport  
created operational problems which restricted the effectiveness of the systems. These  
were the same challenges that have been reported in recent literature regarding issues  
related to scalability, interoperability, and data management in Smart Transportation  
Systems (Cai et al., 2023; Liu et al., 2024). It was also important to tackle these  
challenges to ensure that the full potential of intelligent transportation solutions can be  
achieved.  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2082  
https://doi.org/10.53762/grjnst.04.03.03  
G. 2082  
Page 26  
The results confirmed that Smart Transportation Systems presented an all-encompassing  
and efficient solution to the management of urban traffic issues with the help of  
intelligent, data-driven solutions. The introduction of cutting-edge technologies has  
greatly enhanced the efficiency of traffic flow, minimized the level of congestion, and  
promoted sustainable urban mobility. These results reinforced the growing importance  
of smart transportation technologies in modern urban planning and highlighted their  
potential to transform traditional transportation systems into intelligent and adaptive  
mobility networks (Selvarajan et al., 2024; Ge et al., 2024).  
Conclusion  
The researchers concluded that Smart Transportation Systems could greatly enhance the  
efficiency of urban traffic flow and reduce the level of congestion through the  
combination of intelligent technologies, including the real-time monitoring system,  
adaptive traffic signal control system, artificial intelligence and IoT-based data system.  
The results provided showed the consistent high mean values of all variables meaning  
that there was a high level of agreement among respondents with regards to the  
effectiveness of these systems in improving mobility performance. The most influential  
factor was real-time traffic monitoring, and intelligent signal control and AI-based  
predictive analytics, which altogether positively impacted route optimization, travel time  
and intersection delays. The authors also established that Smart Transportation Systems  
helped in ensuring that road capacity was utilised better and that vehicles were not idled,  
which facilitated sustainable urban mobility. All in all, the findings indicated that  
intelligent and data-driven transportation solutions were very critical in addressing the  
contemporary urban traffic problems and enhancing overall transportation efficiency.  
Recommendations  
GRJNST, Volume: 04 - Issue 3 (2026) / ISSN P: 2790-7643  
Article ID: 2082  
https://doi.org/10.53762/grjnst.04.03.03  
G. 2082  
Page 27  
The study advised that urban officials and policymakers should focus on the  
development of Smart Transportation Systems by investing in the advanced digital  
infrastructure and intelligent traffic management technologies. To enhance traffic  
coordination in congested regions, governments ought to increase the use of real-time  
traffic monitoring systems and control mechanisms that can adaptively change signal  
timing to enhance traffic coordination. It is also suggested that models of artificial  
intelligence and machine learning should be integrated into traffic management systems  
to improve predictive abilities and help to make proactive decisions. To ensure a smooth  
communication between vehicles and infrastructure, as well as control systems,  
transportation agencies are advised to enhance IoT-based data integration systems. In  
addition, capacity-building programs and technical training should be provided to  
transportation professionals to effectively manage and operate intelligent systems.  
Awareness campaigns should be encouraged as well to ensure that the people adopt  
smart mobility solutions and to ensure that the technology-driven transportation systems  
are accepted by the people.  
Future Directions  
The next research should be conducted on how to integrate new technologies like  
autonomous vehicles, digital twins, and blockchain into Smart Transportation Systems  
to further streamline the efficiency and scalability of such systems. Research is also  
needed to understand how big data analytics and edge computing can enhance the  
responsiveness of the real-time traffic and minimize the system latency. Empirical  
investigations of different cities and regions may give more knowledge to the efficacy of  
Smart Transportation Systems in diverse conditions of cities. Also, further studies can  
be carried out in the creation of hybrid models that utilize intelligent transportation  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2082  
https://doi.org/10.53762/grjnst.04.03.03  
G. 2082  
Page 28  
technologies with sustainable mobility solutions like electric vehicles and shared  
transportation systems. There is also the need to explore policy frameworks and  
governance models that can support the large-scale implementation of Smart  
Transportation Systems in the developing countries.  
References  
Alshehri, M., Alotaibi, R., & Alghamdi, S. (2023). Smart mobility and environmental  
sustainability. Sustainability. https://doi.org/10.3390/su15054123  
Cai, X., Xiao, Y., & Zhang, L. (2023). Traffic congestion cause identification method  
for  
urban  
roads.  
Transportation  
Research  
Record.  
https://doi.org/10.1177/03611981231213879 (Sage Journals)  
Chakraborty, A., Hasan, M., Hossain, A., & Anee, M. J. (2025). Smart traffic systems: A  
comprehensive review of recent advancements, technologies, and challenges. arXiv.  
https://doi.org/10.48550/arXiv.2511.22137  
Creß, C., Bing, Z., & Knoll, A. C. (2021). Intelligent transportation systems using  
external  
infrastructure:  
A
literature  
survey.  
arXiv.  
https://doi.org/10.48550/arXiv.2112.05615  
Elsayed, A., Mohamed, K., & Harb, H. (2023). Enhanced traffic congestion  
management with fog computing. arXiv. https://doi.org/10.48550/arXiv.2311.01181  
Ge, Y., Sun, F., & Li, J. (2024). Smart transportation and urban sustainability. Journal  
of Cleaner Production. https://doi.org/10.1016/j.jclepro.2024.140112  
GRJNST, Volume: 04 - Issue 3 (2026) / ISSN P: 2790-7643  
Article ID: 2082  
https://doi.org/10.53762/grjnst.04.03.03  
G. 2082  
Page 29  
Goumiria, S., Yahiaoui, S., & Djahel, S. (2023). Smart mobility in smart cities:  
Emerging challenges and future directions. Journal of Intelligent Transportation  
Systems, 29(1), 81–117. https://doi.org/10.1080/15472450.2023.2245750  
Idris, M. Y. I., Ahmedy, I., Soon, T. K., et al. (2024). Machine learning approaches for  
intelligent  
transportation  
systems:  
A
review.  
ICT  
Express,  
10(4).  
https://doi.org/10.1016/j.icte.2024.05.001  
Khan, A., & Ivan, P. (2023). Integrating machine learning and deep learning in smart  
cities for traffic congestion management. Journal of Urban Development and  
Management, 2(4), 211–221. https://doi.org/10.56578/judm020404  
Kummetha, V. C., Kamrani, M., Concas, S., & Kourtellis, A. (2022). Proactive  
congestion management via data-driven methods. Journal of Intelligent Transportation  
Systems, 28(4), 459–475. https://doi.org/10.1080/15472450.2022.2140047  
(Taylor & Francis Online)  
Liu, Y., Chen, M., & Zhang, H. (2024). Deep learning for traffic congestion prediction.  
Sustainable Cities and Society. https://doi.org/10.1016/j.scs.2024.105369  
Matsui, Y., & Yoshihiro, T. (2024). Flow-based centralized route guidance system for  
congestion mitigation. IEEE Transactions on Intelligent Transportation Systems.  
https://doi.org/10.1109/TITS.2024.3375267  
Mukhopadhyay, S., Kumar, A., Gupta, J., Bhatnagar, A., Kantipudi, M. V. V. P., &  
Singh, M. (2024). IoT-enabled smart transportation using machine learning techniques.  
International Journal of Transport Development and Integration, 8(1), 61–77.  
https://doi.org/10.18280/ijtdi.080106  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2082  
https://doi.org/10.53762/grjnst.04.03.03  
G. 2082  
Page 30  
Sakr, H. A., El-Afifi, M. I., & PLVAR Team. (2023). Intelligent traffic management  
systems: A review. Nile Journal of Communication and Computer Science, 5(1), 42–56.  
https://doi.org/10.21608/njccs.2023.321169  
Sayed, A. A., Abdel-Hamid, Y., & Hefny, H. A. (2023). Artificial intelligence-based  
traffic flow prediction: A comprehensive review. Journal of Electrical Systems and  
Information Technology, 10(13). https://doi.org/10.1186/s43067-023-00081-6  
Selvarajan, S., Manoharan, H., Khadidos, A. O., Khadidos, A. O., & Hasanin, T. (2024).  
Directive transportation in smart cities with line connectivity using mode control  
algorithm. Scientific Reports, 14, 17938. https://doi.org/10.1038/s41598-024-  
68121-3  
Vadivel, G., Hussain, M. J. M., & Sangeetha, S. V. T. (2023). IoT-enabled smart  
transportation systems. International Journal of Advances in Signal and Image Sciences.  
https://doi.org/10.29284/ijasis.9.1.2023.40-49  
Wei, L., Chen, P., Mei, Y., Sun, J., & Wang, Y. (2023). A hierarchical control  
framework for alleviating network traffic bottleneck congestion. Journal of Intelligent  
Transportation  
Systems,  
28(6),  
988–1010.  
https://doi.org/10.1080/15472450.2023.2270428  
Zhao, J., Wang, Y., & Chen, X. (2023). Intelligent traffic systems and user satisfaction.  
Transport Policy. https://doi.org/10.1016/j.tranpol.2023.05.012  
Zhao, X., Li, Z., & Chen, H. (2024). Passenger congestion dynamics using smart card  
data.  
International  
Journal  
of  
General  
Systems.  
https://doi.org/10.1080/03081079.2024.2380917  
GRJNST, Volume: 04 - Issue 3 (2026) / ISSN P: 2790-7643  
Article ID: 2082  
https://doi.org/10.53762/grjnst.04.03.03  
G. 2082  
Page 31  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2082  
https://doi.org/10.53762/grjnst.04.03.03