Asansol, the industrial and cultural hub of West Bengal, is a city filled with diverse attractions and vibrant energy. Whether you’re here for business or leisure, having a reliable mode of transportation can elevate your experience. Motoshare’s newly launched bike and car rental services in Asansol make it easier than ever to explore the city. Here’s how Motoshare can transform your journey in Asansol.

Motoshare Services: Seamless Rentals at Your Fingertips

Motoshare is a user-friendly platform that connects renters with vehicle owners in Asansol. With just a few clicks, you can book a bike or car tailored to your travel needs, ensuring convenience and cost-effectiveness.

• Car Rentals in Asansol: Explore car rental options for comfortable and spacious rides.
• Bike Rentals in Asansol: Discover bike rental options for quick and adventurous commutes.

Why Choose Motoshare?

• Convenience: An intuitive platform that simplifies the booking process.
• Affordability: Competitive pricing by renting directly from vehicle owners.
• Variety: A wide range of vehicles to suit different travel preferences.
• Safety: Trusted platform with secure transactions and well-maintained vehicles.
• Flexibility: Options for short-term and long-term rentals to match your itinerary.

Top 10 Places to Visit in Asansol

Once you’ve secured your rental vehicle, explore these must-visit attractions in Asansol:

1. Kalyaneshwari Temple
   • Best Time to Visit: Morning for spiritual activities.
   • Why Go: A historic temple dedicated to Goddess Kali.
2. Churulia Village
   • Best Time to Visit: Daytime for cultural immersion.
   • Why Go: The birthplace of the famous Bengali poet Kazi Nazrul Islam.
3. Maithon Dam
   • Best Time to Visit: Winter months for picnics.
   • Why Go: A serene spot surrounded by natural beauty, ideal for boating and picnics.
4. Nehru Park
   • Best Time to Visit: Evening for leisure.
   • Why Go: A peaceful park perfect for family outings and relaxation.
5. Panchet Dam
   • Best Time to Visit: Monsoon for lush greenery.
   • Why Go: A scenic destination for nature lovers and photographers.
6. Sengupta Park
   • Best Time to Visit: Morning or evening.
   • Why Go: A well-maintained park for relaxation and recreation.
7. Satabdi Park
   • Best Time to Visit: Evening for a serene atmosphere.
   • Why Go: A quiet spot for leisure activities and picnics.
8. Burnpur Riverside
   • Best Time to Visit: Sunset for picturesque views.
   • Why Go: A scenic riverside destination perfect for walks and tranquility.
9. Joychandi Pahar
   • Best Time to Visit: Winter for trekking.
   • Why Go: A popular trekking spot with breathtaking views.
10. Ghagar Buri Chandi Temple
    • Best Time to Visit: Morning for spiritual peace.
    • Why Go: A revered temple with a tranquil ambiance.

Bonus Recommendations

For unique experiences, consider:

• Baranti: A picturesque village surrounded by hills and lakes.
• Chelidanga Market: A bustling market for local goods and authentic cuisine.
• Biharinath Hill: A serene spot for trekking and picnics.

Advantages of Renting with Motoshare

1. Wide Selection of Vehicles: From bikes to cars, find the perfect ride for your trip.
2. Affordable Prices: Direct rentals from owners ensure budget-friendly options.
3. User-Friendly Platform: Effortless browsing and quick bookings.
4. Flexible Rentals: Adjust rental durations to match your plans.
5. Local Expertise: Gain travel tips and insights from vehicle owners.
6. Secure Payments: Trusted and reliable payment processes.

How to Rent with Motoshare

1. Sign Up: Create an account on Motoshare’s website.
2. Search Vehicles: Browse through the available bikes and cars in Asansol.
3. Book Your Ride: Select your preferred vehicle, confirm dates, and make your booking.
4. Pick Up & Explore: Coordinate with the owner and begin your Asansol adventure.

Plan Your Asansol Journey Today!

With Motoshare, exploring Asansol has never been easier. Whether you’re visiting its historic landmarks, serene parks, or vibrant markets, our bike and car rentals provide the perfect travel solution. Start your journey now by visiting:

• Car Rentals in Asansol
• Bike Rentals in Asansol

Experience Asansol with the freedom and flexibility of Motoshare. Happy exploring!

The post Book Your Ride in Asansol with Motoshare Today appeared first on Artificial Intelligence.

Newswise — The future of commuter traffic probably looks something like this: ride-hailing companies operating fleets of autonomous electric vehicles alongside an increasing number of semi-autonomous EVs co-piloted by humans, all supported by a large infrastructure of charging stations. This scenario is particularly likely in California, which has committed to reducing carbon emissions to 40 percent below 1990 levels by 2030.

Computer scientists at Lawrence Livermore National Laboratory are preparing for this potential future by applying Deep Reinforcement Learning — the same kind of goal-driven algorithms that have defeated video game experts and world champions in the strategy game Go — to determine the most efficient strategy for charging and driving electric vehicles used for ride-hailing services. The goal of the strategy is to maximize service while reducing carbon emissions and the impact to the electrical grid, with an emphasis on autonomous EVs capable of 24-hour service.

In a paper published and presented at the recent NeurIPS 2019 Workshop on Tackling Climate Change with Machine Learning, LLNL computer scientists applied deep reinforcement learning to data gathered from ride-hailing services and utility providers to determine when EV drivers or autonomous electric cars should charge their vehicles and when they should pick up customers. The researchers hope to eventually create a robust tool that could provide ride-hailing drivers or autonomous cars with an optimal driving policy based on surge pricing, wait times at charging stations, carbon emissions released while charging, the current cost of energy and other factors that can change throughout the day.

“This project is a simple environment to train autonomous agents to improve their driving behavior,” said LLNL principal investigator and machine learning researcher Ruben Glatt. “We wanted to build a simulation with input from the ride-sharing and energy data so we could simulate typical rides, including costs and energy implications given a certain location or time. We wanted to know: How can we balance ecological factors like the carbon footprint, which is important for the society, while at the same time optimizing revenue that benefits the individual?”

While EVs are clearly a major step to reducing carbon emissions, there are downsides when compared to combustion engine vehicles, the researchers explained. Currently, drivers who use electric vehicles for ride-hailing companies must constantly weigh numerous options in determining when to offer a ride and when to charge their cars, they said.

“It’s hard to be a ride-share driver with a normal EV because you don’t get as much range with your car when it’s fully charged as you would with a full tank of gas. And waiting times at charging stations can be very high, compared to a couple of minutes to fill your gas car,” said main author and LLNL machine learning researcher Jacob Pettit. “There’s a lot of opportunity cost involved; if you drive for a ride-sharing company you might waste a lot of time just recharging and not providing as much service.”

In training the deep reinforcement learning algorithms, each time the agent (representing an autonomous EV or driver of a shared EV capable of driving 24 hours per day) dropped off a customer, it faced a decision to either charge the vehicle or give a ride to a customer. It was rewarded for successfully completing trips with an expected fare amount and penalized for producing carbon emissions when charging or attempting to provide a ride with insufficient battery power.

The agent learned a beneficial strategy was to charge the vehicle when energy costs are cheap or have low carbon emissions, there is fewer demand for rides and waiting times at charging stations is low. Overall, the agent discovered how to optimize the number of rides it provided (revenue) while at the same time minimizing charging wait times and reducing emissions.

“It learned to look at time of day and extrapolate that, given the time, it wouldn’t have to wait long, and wouldn’t pay much money to charge the car, said co-author and LLNL machine learning researcher Brenden Petersen. “The surprising thing was that although we were mostly optimizing for money, the policy also produced less emissions per mile. Even though the agents were acting selfishly it still helped the environment, which is basically a win-win.”  

The researchers are seeking to collaborate with both ride-hailing companies and energy providers to ensure the infrastructure that will eventually support autonomous EV ride-hailing services will be more stable and increase adoption of EVs generally for such services. They envision a machine-learning based tool that could help utilities and city planners decide where to place future electric vehicle charging stations and build an electrical infrastructure to accommodate autonomous EV traffic.

The team has applied for a Technology Commercialization Fund grant from the Department of Energy to expand the simulation to include multiple agents and alternative scenarios.

“We want to make the simulation closer to real life,” Glatt said. “Here we only investigated for a single agent. We want to see what happens if we can control a fleet of agents and if additional networking effects evolve that we can benefit from.”

The work was funded by the Department of Energy. Former LLNL researcher John Donadee initiated the project and also contributed to the publication.  

The post LLNL computer scientists explore deep learning to improve efficiency of ride-hailing and autonomous electric vehicles appeared first on Artificial Intelligence.