Leveraging Big Data Analytics to Optimize Route Planning and Assist in Traffic Network Congestion
Quantzig’s current big data analytics assessment on transport network congestion examines the transport demand and supply to optimize routes, reduce costs, and develop network optimization solutions. Big data analytics in traffic management The higher risk of passenger safety, loss of productivity, increase in fuel consumption, and pollution is all effects of urban traffic congestion. Efficient […]
Quantzig’s current big data analytics assessment on transport network congestion examines the transport demand and supply to optimize routes, reduce costs, and develop network optimization solutions.
Big data analytics in traffic management
The higher risk of passenger safety, loss of productivity, increase in fuel consumption, and pollution is all effects of urban traffic congestion. Efficient traffic management will reduce congestion, improve performance measurements for seamless traffic flow, and proficiently manage current roadway assets. Government organizations and administrative authorities are implementing coordinated traffic signals and variable messages to manage traffic network congestion. By implementing big data solutions, administrators can leverage historical trends, a combination of real-time information, and new-age algorithms to improve and traffic networks in urban areas. The growing focus on the development of intelligent network systems and use of big data analytics will assist the traffic management and result in reduced congestions and roadblocks. The big data analytics by Quantzig helps service providers in the mobile services industry to analyze the efficiency of the current transportation system, estimate the transport models, and predict future network scenarios.
The adoption of advanced sensors and GPS signal systems is revolutionizing the urban traffic network. These systems are designed to help reduce network congestion and act as alerts that notify traffic authorities of potential roadblocks and how to avoid them. The sensors are installed in trucks, ships, and airplanes that give real-time insights into the driver’s capabilities and the traffic. GPS signals are utilized for bottlenecks and predict the condition of the transportation network.
Emergence of smart vehicles
The advent of smart vehicles will help reduce the network congestion across several cities in the world. These are connected vehicles that provide a real-time estimation of traffic patterns and help authorities with the deployment of management strategies. These systems are designed to improve the communication vehicle-to-infrastructure (V2X) communications and monitor the traffic network to reduce collisions and accidents. Additionally, the implementation of speed trackers, traffic sensors, and display boards will result in smarter roads and help control speed and traffic issues efficiently.
Telematics is extensively used in traffic management to provide statistics and information data such as weather conditions, traffic conditions, and navigation systems. These systems provide real-time information and authorities leverage predictive analysis to determine the state of the transportation network. Moreover, telematics provides speech-based internet access to the consumers through wireless links that monitor the driver’s state and stress levels and send alerts to systems if there is any issue to avoid or reduce chances of collision.
Outcomes and solutions offered
Quantzig’s big data analytics assessment on traffic network congestion identifies a set of transport indicators that are measured using the mobile phone data available to travel agencies to optimize route planning. Some of the solutions offered are as follows:
- Developed an integrated platform to perform data analysis in a scheduled, easy, and controlled way using a user-friendly interface
- Offered an exhaustive understanding of how traffic demand is distributed in the transportation network and how it varies over time
- Enlisted the different types of bottleneck of the transportation network and enabled enhanced route planning
- Evaluated the travel delay due to congestion based on travel time distribution between peak and non-peak periods
- Provided a comprehensive analysis of total number of trips made to and from each zone based on day, time, month, and holiday
The complete case study that states big data analytics reduces transport network congestion is now available.
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