Market Outlook:

Transportation data analytics increasingly power mobility information and insights – transforming transportation planning by making it easier, faster, cheaper, and safer to collect and understand critical information.  

Leveraging AI in transportation helps the sector increase passenger safety, reduce traffic congestion and accidents, lessen carbon emissions, and minimize overall financial expenses.


Delay Prediction:

Using data lake techniques and computer vision, the mobility industry can offer exceptional service to customers by cutting their waiting times and enhancing their journey experience.

Smart Mobility:

Data science successfully enhances how vehicles go from point A to B, optimizing fuel and time to ensure they are punctual and reliable. Passengers should have more confidence and expect more efficiency when they are utilizing them.

Traffic Analysis:

Big data uses vast amounts of data generated by cars that contain the necessary information to understand and optimize traffic flow. Using that data, traffic insights can be extracted which helps in traffic management.

Road Intelligence:

Safe road conditions are the foundational building blocks for intelligent transportation systems that ensure safety and optimize traffic flow. Data Analytics gathers road insights and provides the necessary information regarding how the driving experience is linked to all kinds of road conditions.


Better Vehicle Utilization

More Efficiency

Enhanced Customer Experience

Increased Profitability and Revenue

Case Study


The objective of this project was to utilize the deep reinforcement learning method to find a route from point A to B considering the EV state of charge, temperature, wait time at the charging station and distance as well.


We have successfully implemented the reinforcement learning method called DQN on Google Maps. The DQN algorithm predicts the action (direction) that should be taken on coordinates. The project utilizes Google’s Maps APIs to get steps to reach the next position on the map, and the time it will take to reach that position. The EV would move in the direction predicted by the DQN algorithm and APIs would be used to navigate. At any point, if the state of charge is below a certain threshold, the EV would start looking for nearby charging stations through Maps API and would move towards that as its second objective; after recharging it will continue to its main endpoint. The battery discharge rate is based on the temperature and the EV would dynamically calculate the time required for charging to full capacity. Also, the random wait time on the charging station has been added.


The model has been trained on a fraction of episodes of what reinforcement learning algorithms require it to because of the cost and time of APIs but still, it shows the potential of reaching its goal. The Red pointers are the steps taken by the agent to find its way to the endpoint, Green represents the start position, Dark Blue is the end, and Light Blue are the points where it was charged. The Blue line is for comparison with the graph-based approach (Dijkstra) to find the shortest path.