Network routing is a distributed decision problem which naturally admits numerical performance measures, such as the average time for a packet to travel from source to destination. OLPOMDP, a policy-gradient reinforcement learning algorithm, was successfully applied to simulated network routing under a number of network models. Multiple distributed agents (routers) learned co-operative behavior without explicit inter-agent communication, and they avoided behavior which was individually desirable, but detrimental to the group's overall performance. Furthermore, shaping the reward signal by explicitly penalizing certain patterns of sub-optimal behavior was found to dramatically improve the convergence rate.

Among the major public transportation systems in cities, bus transit has its problems, including more accuracy and reliability when estimating the bus arrival time for riders. This can lead to delays and decreased ridership, especially in cities where public transportation is heavily relied upon. A common issue is that the arrival times of buses do not match the schedules, resulting in latency for fixed schedules. According to the study in this paper on New York City bus data, there is an average delay of around eight minutes or 491 seconds mismatch between the bus arrivals and the actual scheduled time. This research paper presents a novel AI-based data-driven approach for estimating the arrival times of buses at each transit point (station). Our approach is based on a fully connected neural network and can predict the arrival time collectively across all bus lines in large metropolitan areas. Our neural-net data-driven approach provides a new way to estimate the arrival time of the buses, which can lead to a more efficient and smarter way to bring the bus transit to the general public. Our evaluation of the network bus system with more than 200 bus lines, and 2 million data points, demonstrates less than 40 seconds of estimated error for arrival times. The inference time per each validation set data point is less than 0.006 ms.

When it comes to getting from A to B on foot, it turns out we're wired not to take the shortest route but rather the'pointiest path'. The reason for this, researchers say, is because human brains are not optimised to navigate cities. Instead, pedestrians appear to choose paths that seem to point most directly toward their destination, even if those routes end up being longer. Researchers at MIT, who based their study on a dataset of more than 14,000 people going about their daily lives, called this the'pointiest path'. An MIT study suggests our brains are not optimized to calculate the shortest possible route when navigating on foot.

As Public Transport (PT) becomes more dynamic and demand-responsive, it increasingly depends on predictions of transport demand. But how accurate need such predictions be for effective PT operation? We address this question through an experimental case study of PT trips in Metropolitan Copenhagen, Denmark, which we conduct independently of any specific prediction models. First, we simulate errors in demand prediction through unbiased noise distributions that vary considerably in shape. Using the noisy predictions, we then simulate and optimize demand-responsive PT fleets via a commonly used linear programming formulation and measure their performance. Our results suggest that the optimized performance is mainly affected by the skew of the noise distribution and the presence of infrequently large prediction errors. In particular, the optimized performance can improve under non-Gaussian vs. Gaussian noise. We also obtain that dynamic routing can reduce trip time by at least 23% vs. static routing. This reduction is estimated at 809,000 EUR per year in terms of Value of Travel Time Savings for the case study.

As traffic congestion continues growing in urban areas, more and more cities have realized that investment priority should be given to public transport modes, such as bus transit systems (BRT) instead of personal vehicles. Simply put, in congested cities, public transport modes are more efficient than personal vehicles in terms of carrying and moving people around. As city populations grow and as their economic bases shift and evolve, their housing sector adjusts, even more vehicles are expected to enter the roads each day, creating more traffic congestion. The 2012 Urban Mobility Report states that, the lack of public transportation services would have cost commuters an additional 865 million hours of delay. With growing urban population numbers, this number undoubtedly stands higher today (National Express). On average, expanding and optimizing transit services produced an economic benefit of roughly $45 million a year by connecting urban areas in the US. There is no doubt that expanding public transportation use is key to reducing traffic congestion.

Advances in sensor technology have enabled the collection of large-scale datasets. Such datasets can be extremely noisy and often contain a significant amount of outliers that result from sensor malfunction or human operation faults. In order to utilize such data for real-world applications, it is critical to detect outliers so that models built from these datasets will not be skewed by outliers. In this paper, we propose a new outlier detection method that utilizes the correlations in the data (e.g., taxi trip distance vs. trip time). Different from existing outlier detection methods, we build a robust regression model that explicitly models the outliers and detects outliers simultaneously with the model fitting. We validate our approach on real-world datasets against methods specifically designed for each dataset as well as the state of the art outlier detectors. Our outlier detection method achieves better performances, demonstrating the robustness and generality of our method. Last, we report interesting case studies on some outliers that result from atypical events.

Modern vehicle fleets, e.g., for ridesharing platforms and taxi companies, can reduce passengers' waiting times by proactively dispatching vehicles to locations where pickup requests are anticipated in the future. Yet it is unclear how to best do this: optimal dispatching requires optimizing over several sources of uncertainty, including vehicles' travel times to their dispatched locations, as well as coordinating between vehicles so that they do not attempt to pick up the same passenger. While prior works have developed models for this uncertainty and used them to optimize dispatch policies, in this work we introduce a model-free approach. Specifically, we propose MOVI, a Deep Q-network (DQN)-based framework that directly learns the optimal vehicle dispatch policy. Since DQNs scale poorly with a large number of possible dispatches, we streamline our DQN training and suppose that each individual vehicle independently learns its own optimal policy, ensuring scalability at the cost of less coordination between vehicles. We then formulate a centralized receding-horizon control (RHC) policy to compare with our DQN policies. To compare these policies, we design and build MOVI as a large-scale realistic simulator based on 15 million taxi trip records that simulates policy-agnostic responses to dispatch decisions. We show that the DQN dispatch policy reduces the number of unserviced requests by 76% compared to without dispatch and 20% compared to the RHC approach, emphasizing the benefits of a model-free approach and suggesting that there is limited value to coordinating vehicle actions. This finding may help to explain the success of ridesharing platforms, for which drivers make individual decisions.

This paper introduces AntNet, a novel approach to the adaptive learning of routing tables in communications networks. AntNet is a distributed, mobile agents based Monte Carlo system that was inspired by recent work on the ant colony metaphor for solving optimization problems. AntNet's agents concurrently explore the network and exchange collected information. The communication among the agents is indirect and asynchronous, mediated by the network itself. This form of communication is typical of social insects and is called stigmergy. We compare our algorithm with six state-of-the-art routing algorithms coming from the telecommunications and machine learning fields. The algorithms' performance is evaluated over a set of realistic testbeds. We run many experiments over real and artificial IP datagram networks with increasing number of nodes and under several paradigmatic spatial and temporal traffic distributions. Results are very encouraging. AntNet showed superior performance under all the experimental conditions with respect to its competitors. We analyze the main characteristics of the algorithm and try to explain the reasons for its superiority.