In modern urban centers, effective transportation management poses a significant challenge, with traffic jams and inconsistent travel durations greatly affecting commuters and logistics operations. This study introduces a novel method for enhancing urban mobility by combining machine learning algorithms with live traffic information. We developed predictive models for journey time and congestion analysis using data from New York City's yellow taxi trips. The research employed a spatiotemporal analysis framework to identify traffic trends and implemented real-time route optimization using the GraphHopper API. This system determines the most efficient paths based on current conditions, adapting to changes in traffic flow. The methodology utilizes Spark MLlib for predictive modeling and Spark Streaming for processing data in real-time. By integrating historical data analysis with current traffic inputs, our system shows notable enhancements in both travel time forecasts and route optimization, demonstrating its potential for widespread application in major urban areas. This research contributes to ongoing efforts aimed at reducing urban congestion and improving transportation efficiency through advanced data-driven methods.

Promising more timely deliveries for consumers around the globe, NVIDIA's cuOpt real-time route optimization software has set records on a key route optimization benchmark. NVIDIA cuOpt set three new records on the widely followed Li & Lim pickup and delivery benchmark. Last-mile delivery is the most expensive part of the logistics industry, representing over 40% of overall supply chain cost and carbon footprint, according to Gartner. Nearly 150 billion parcels are shipped every year, according to Pitney Bowes. AT&T is using cuOpt to optimize routes for 30,000 technicians.

The Indian logistics industry has traditionally been a fragmented one with a lot of inefficiencies. The cost of logistics in India today amounts to approximately 15 per cent of the nation's GDP, as compared with developed countries where it's around 7-8 per cent of their respective GDPs. This is certainly a significant drain on the economy and negatively impacts sectors such as agriculture and manufacturing, as most Indian companies have had limited visibility into their respective supply chains. However, since 2015, we have noticed a paradigm shift in the government's policies and approach towards this sector, which is enabling seamless integration of operations to make it more organized. Beyond the policy push, today new-age technology is playing a huge role in redefining and revolutionizing various industries in our country, and the logistics industry is no exception.

Taxicab service plays an essential and irreplaceable role in urban traffic system [Ji et al., 2020]. For example, in New York City, there are more than 21,000 taxi drivers and more than 80,000 ride-sharing drivers. Compared to other means of daily transportation, such as bus and subway, taxis usually offers a better trip experience in terms of comfort, convenience, and travel time accommodation. Thus, it has been a long-standing central issue to improve the efficiency of vehicle mobility by optimizing the route recommendation for drivers for taxi services in big cities like New York, Tokyo, and Beijing [Yuan et al., 2011, Zheng et al., 2014]. Based on large-scale taxi trace data, there is an extensive literature on route recommendation systems. Some studies focus on the traditional optimization method. For example, Qu et al. [2014] proposed a cost-efficient objective function and developed a greedy method to maximize the potential net profit. Similar methods can be found in [Ding et al., 2013, Zhou et al., 2016]. Stochastic optimization methods (e.g., simulated annealing -SA-) and parallel computing techniques have also been applied to route recommendation problems to speed up the route searching tasks (see [Ye This manuscript has been accepted by ACM Transactions on Intelligent Systems and Technology on April 25, 2021.

One area of interest in today's end-to-end supply chain is machine learning. And this is certainly a topic that we have written about quite often. Over the last few months, Steve Banker, Clint Reiser, and I have written about artificial intelligence and machine learning in a number of contexts and how it impacts the supply chain. These topics have included transportation management, warehouse management, and supply chain planning, among others. This technology continues to be a hot topic for companies as is evident by how often the Logistics Viewpoints team and others are writing about it.

This study develops an online predictive optimization framework for operating a fleet of autonomous vehicles to enhance mobility in an area, where there exists a latent spatio-temporal distribution of demand for commuting between locations. The proposed framework integrates demand prediction and supply optimization in the network design problem. For demand prediction, our framework estimates a marginal demand distribution for each Origin-Destination pair of locations through Quantile Regression, using counts of crowd movements as a proxy for demand. The framework then combines these marginals into a joint demand distribution by constructing a Gaussian copula, which captures the structure of correlation between different Origin-Destination pairs. For supply optimization, we devise a demand-responsive service, based on linear programming, in which route structure and frequency vary according to the predicted demand. We evaluate our framework using a dataset of movement counts, aggregated from WiFi records of a university campus in Denmark, and the results show that our framework outperforms conventional methods for route optimization, which do not utilize the full predictive distribution.