Peer-to-peer ride-sharing platforms like Uber, Lyft, and DiDi have revolutionized the transportation industry and labor market. At its essence, these systems tackle the bipartite matching problem between two populations: riders and drivers. This research paper comprises two main components: an initial literature review of existing ride-sharing platforms and efforts to enhance driver satisfaction, and the development of a novel algorithm implemented through simulation testing to allow us to make our own observations. The core algorithm utilized is the Gale-Shapley deferred acceptance algorithm, applied to a static matching problem over multiple time periods. In this simulation, we construct a preference-aware task assignment model, considering both overall revenue maximization and individual preference satisfaction. Specifically, the algorithm design incorporates factors such as passenger willingness-to-pay, driver preferences, and location attractiveness, with an overarching goal of achieving equitable income distribution for drivers while maintaining overall system efficiency. Through simulation, the paper compares the performance of the proposed algorithm with random matching and closest neighbor algorithms, looking at metrics such as total revenue, revenue per ride, and standard deviation to identify trends and impacts of shifting priorities. Additionally, the DA algorithm is compared to the Boston algorithm, and the paper explores the effect of prioritizing proximity to passengers versus distance from city center. Ultimately, the research underscores the importance of continued exploration in areas such as dynamic pricing models and additional modeling for unconventional driving times to further enhance the findings on the effectiveness and fairness of ride-sharing platforms.

We design a concept for an autonomous underground freight transport system for Hanover, Germany. To evaluate the resulting system changes in overall traffic flows from an environmental perspective, we carried out an agent-based traffic simulation with MATSim. Our simulations indicate comparatively low impacts on network-wide traffic volumes. Local CO2 emissions, on the other hand, could be reduced by up to 32 %. In total, the shuttle system can replace more than 18 % of the vehicles in use with conventional combustion engines. Thus, an autonomous underground freight transportation system can contribute to environmentally friendly and economical transportation of urban goods on the condition of cooperative use of the system.

Machine learning can seem like a daunting concept to learn and apply, but with the right framing and understanding of the process and algorithms, it can be interesting, useful, and fun. Let's explore apartment prices in a city. Suppose that you're looking for a new apartment. You speak to friends and family, and do some online searches for apartments in the city. You notice that apartments in different areas are priced differently.

To see exactly how the networks are performing, what speeds to expect and the extent of coverage, I toured the UK to test 5G in five major cities: London, Cardiff, Birmingham, Manchester and Edinburgh. The next-generation wireless technology promises a big boost in speed and responsiveness, bringing not just a faster connection to your phone, but also enabling advancements like telemedicine and self-driving cars. The UK deployment is among several happening worldwide from the US to South Korea, as 5G slowly turns from hype to reality. EE and Vodafone have the largest UK networks so far, while O2 and Three are ramping up. I visited the cities across the course of a week, seeking out a variety of locations in each place that showed as 5G-enabled zones on network coverage maps.

Transportation systems can be conceptualized as an instrument of spreading people and resources over the territory, playing an important role in developing sustainable cities. The current rationale of transport provision is based on population demand, disregarding land use and socioeconomic information. To meet the challenge to promote a more equitable resource distribution, this work aims at identifying and describing patterns of urban services supply, their accessibility, and household income. By using a multidimensional approach, the spatial inequalities of a large city of the global south reveal that the low-income population has low access mainly to hospitals and cultural centers. A low-income group presents an intermediate level of accessibility to public schools and sports centers, evidencing the diverse condition of citizens in the peripheries. These complex outcomes generated by the interaction of land use and public transportation emphasize the importance of comprehensive methodological approaches to support decisions of urban projects, plans and programs. Reducing spatial inequalities, especially providing services for deprived groups, is fundamental to promote the sustainable use of resources and optimize the daily commuting.

An estimated 4 billion people in the world lack a physical address. Without one, residents lose access to important services like package deliveries, medical care, and disaster relief, as well as the ability to register to vote or obtain a driver's license. Cities also have trouble planning new infrastructure, such as schools, water pipes, and electricity lines. "As you move into a more global economy and more people order and get goods delivered at a distance, you need a more specific address than'the house with the red door across from the cathedral,'" says Merry Law, the president of a company that provides international addressing information. Researchers at the MIT Media Lab and Facebook are now proposing a new way to address the unaddressed: with machine learning.

In October, the largest self-driving car project backed by the British government wrapped up three years worth of testing aimed at getting autonomous vehicles onto roads by 2021. Many of the autonomous car and pod tests took place in Milton Keynes, a town built for cars that represents one of the fastest-growing city or town economies in the United Kingdom. Originally founded as a new "model town" in 1967, Milton Keynes is a city in all but name after having grown to 280,000 people in 50 years. But the same economic success means that Milton Keynes--built in a grid layout and suburban style--faces a number of growing pains that it's looking to ease with the help of autonomous vehicle technology. The recent UK Autodrive tests were designed to test the capabilities of both self-driving cars and smaller autonomous pod vehicles made by Coventry, UK-based Aurrigo, a division of RDM Group, with an eye toward easing traffic congestion and possibly even eliminating the need for cars in the city center.

Abstract--Handling the tremendous amount of network data, produced by the explosive growth of mobile traffic volume, is becoming of main priority to achieve desired performance targets efficiently. Opportunistic communication such as Floating Content (FC), can be used to offload part of the cellular traffic volume to vehicular-to-vehicular communication (V2V), leaving to the infrastructure the task of coordinating the communication. Existing FC dimensioning approaches have limitations, mainly due to unrealistic assumptions and on a coarse partitioning of users, which results in over-dimensioning. Shaping the opportunistic communication area is a crucial task to achieve desired application performance efficiently. In this work, we propose a solution for this open challenge. In particular, the broadcasting areas called Anchor Zone (AZ), are selected via a deep learning approach to minimize communication resources achieving desired message availability. No assumption required to fit the classifier in both synthetic and real mobility. A numerical study is made to validate the effectiveness and efficiency of the proposed method. The predicted AZ configuration can achieve an accuracy of 89.7% within 98% of confidence level. By cause of the learning approach, the method performs even better in real scenarios, saving up to 27% of resources compared to previous work analytically modeled. I NTRODUCTION New offloading techniques to cope with the explosive growth in mobile traffic volumes, are a fundamental component of the next generation radio access network (5G). Part of the cellular traffic volume can be offloaded to vehicular-to- vehicular communication (V2V), leaving to the infrastructure the task of managing and coordinating the communication. In this context, of special interest are communication paradigms such as Floating Content (FC), an opportunistic communication scheme for the local dissemination of information [1]. FC as an infrastructure-less communication model, enables probabilistic contents storing in geographically constrained locations - denoted as Anchor Zone (AZ) - and over a limited amount of time based on the application requirements.

New rules of the road for robot cars coming out of Washington this week could lead to the eventual extinction of one of the defining archetypes of the past century: the human driver. While banning people from driving may seem like something from a Kurt Vonnegut short story, it's the logical endgame of a technology that could dramatically reduce -- or even eliminate -- the 1.25 million road deaths a year globally. Human error is the cause of 94 percent of roadway fatalities, U.S. safety regulators say, and robot drivers never get drunk, sleepy or distracted. Autonomous cars already have "superhuman intelligence" that allows them to see around corners and avoid crashes, said Danny Shapiro, senior director of automotive at Nvidia Corp., a maker of high-speed processors for self-driving cars. "Long term, these vehicles will drive better than any human possibly can," Shapiro said.