| dc.description.abstract | Traffic congestion is a widespread problem that plagues urban transportation systems, causing
delays, increased fuel consumption, and environmental pollution. Addressing this issue
requires accurate prediction of traffic congestion, enabling proactive management strategies
and real-time information dissemination. Deep learning algorithms have emerged as powerful
tools for traffic prediction, offering the potential to forecast congestion patterns effectively.
The development of a model for predicting traffic congestion that is capable of accurately
detecting and reducing the overall density of traffic in most urban areas frequented by
motorists, such as offices, downtown, and establishments, has become one of the main
challenges for engineers and designers in recent years. Traffic prediction models in use today
are based on several modern technologies, including wireless sensor networks and surveillance
cameras. In Kenya, the Nairobi Metropolitan Area has greatly felt the impacts of traffic
congestion due to ever growing urban population. This is primarily because the number of
vehicles has rapidly increased as compared to the infrastructure growth. This study presented
a platform for addressing the traffic congestion through the establishment of Intelligent Traffic
Management model using Deep Learning Algorithm. The study utilized observation checklist
and questionnaire as the source of data for the study. An observation data collection sheet was
used in collecting the data from the four main roads. To obtain data from the traffic officers,
questionnaires was used. SPSS version 28 were used to analyze the data. Further from the
correlation analysis, all the variables including High cost of travel/fares (r=.494), High vehicle
maintenance (r=.206), Environmental pollution (r=.359), Staff fatigue (drivers and conductors)
(r=.488), Accidents (r=.310), Poor road design (r=.308), Poor Traffic control system (r=.410),
Road construction and maintenance works (r=.353), Vehicle break downs (r=.179), Roadside
parking/obstruction (r=.452), High number of private cars (r=.233), High number of public
transport vehicles (r=.071), Behavior of road usage (r=.228) Accidents (r=-.042), Poor road
use (r=-.042) and Poor traffic management (r=-.209) had positive correlation with traffic
congestion in Nairobi Metropolitan Area. Regression analysis further found that poor traffic
management by traffic officers, a high number of public transport vehicles, poor road design,
accidents, a high number of private cars, poor road use, poor traffic control systems, driver
behavior, vehicle breakdowns, road construction and maintenance, and roadside parking
explained up to 34.4% of the variation in travel time. In comparison, factors such as driver
behavior, roundabout type, time of day, number of lanes, vehicle type, weather conditions, and
travel rate explained 13.7% of the variation in road travel rates. Therefore, improved
infrastructure, traffic management practices, and enhanced driver behavior are concluded to
reduce travel time and improve transportation efficiency in the region. The study recommends
that traffic engineering and urban planning practices should prioritize the optimization of road
networks. The study recommends that local authorities and law enforcement agencies should
collaborate to enforce traffic rules and regulations rigorously. The study also recommends that
implementation of robust traffic management strategy by improving traffic signal
synchronization, implementing intelligent traffic management systems, and investing in
technology-driven solutions like real-time traffic monitoring and congestion alerts. Adequate
and efficient traffic management by officers should also be ensured, as this factor has been
found to play a substantial role in congestion mitigation. Additionally, policymakers should
consider congestion pricing mechanisms during peak hours. This will incentivize drivers to use
alternative routes or modes of transportation, thus reducing traffic congestion during high demand periods. Revenues generated from congestion pricing can be reinvested in
transportation infrastructure and improvements. | en_US |
