Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

In this commentary, we describe the current state of the art of points of interest (POIs) as digital, spatial datasets, both in terms of their quality and affordings, and how they are used across research domains. We argue that good spatial coverage and high-quality POI features - especially POI category and temporality information - are key for creating reliable data. We list challenges in POI geolocation and spatial representation, data fidelity, and POI attributes, and address how these challenges may affect the results of geospatial analyses of the built environment for applications in public health, urban planning, sustainable development, mobility, community studies, and sociology. This commentary is intended to shed more light on the importance of POIs both as standalone spatial datasets and as input to geospatial analyses. 

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Points of Interest (POI): a commentary on the state of the art, challenges, and prospects for the future

To increase the sustainability in urban mobility, it is necessary to optimally combine public and shared vehicles throughout a passenger’s trip. In this work, we present a survey on urban mobility based on passengers’ data and machine learning methods. We focus on four applications for urban mobility: public datasets, passenger localization, detection of the transport mode and pattern recognition and generation of mobility models. Public datasets lack data of multimodal trips and are in need of guidelines to facilitate the data collection and documentation processes. Passenger localization is predominantly done through fingerprinting in indoor environments; and fingerprinting relies on unsupervised learning to survey access points. The most common mean of transport detected is the bus, followed by walking and biking, while e-scooters are not included within the detected transport modes. The existing works focus on predicting the travel time of the passenger’s trajectory and no machine learning method stands out to estimate the travel time. There is still a need for works that analyze how passengers make use of the urban infrastructure, which will support municipalities and transport mode operators in resource planning and service design.

Survey of Machine Learning Methods Applied to Urban Mobility

Nowadays, social networks are becoming a popular way of analyzing tourist behavior, thanks to the digital traces left by travelers during their stays on these networks. The massive amount of data generated; by the propensity of tourists to share comments and photos during their trip; makes it possible to model their journeys and analyze their behavior. Predicting the next movement of tourists plays a key role in tourism marketing to understand demand and improve decision support.In this paper, we propose a method to understand and to learn tourists' movements based on social network data analysis to predict future movements. The method relies on a machine learning grammatical inference algorithm. A major contribution in this paper is to adapt the grammatical inference algorithm to the context of big data. Our method produces a hidden Markov model representing the movements of a group of tourists. The hidden Markov model is flexible and editable with new data. The capital city of France, Paris is selected to demonstrate the efficiency of the proposed methodology.

Hidden Markov Model to Predict Tourists Visited Places

The recommendation of taxi pickup locations plays an important role for drivers in carrying passengers efficiently. In addition, the emergence of the Internet of Vehicles provides technical support for it. However, existing recommendation methods do not model dynamic global positioning system information well and in real-time. In this article, we propose a spatio-temporal digraph convolutional network (STDCN) model. First, the pickup and drop-off locations are modeled into a directed spatio-temporal graph as input to the model. The correlation between each node is calculated as a unified edge weight based on the gray relational analysis. Then, the STDCN is used for dynamic spatio-temporal feature extraction. Finally, the edge-cloud collaboration framework is adopted to recommend local taxi pickup locations in real-time. The experimental results show that the proposed method is better than competing methods in terms of effectiveness and efficiency, and it shows good industrial conversion application prospects. 

Spatio-Temporal Digraph Convolutional Network-Based Taxi Pickup Location Recommendation

Nowadays, urban mobility plays an important role in modern cities for city planning, navigation, and other mobility services. Taxicabs are vital public services in large cities that are taken by passengers thousands of times every day. Reducing the number of vacant vehicles on the streets will help service providers to raise drivers' incomes, reduce energy consumption, optimize traffic efficiency, and control air pollution problems in large cities. Since drivers do not have enough information about the location of passengers and other taxis, most of them might drive to the same area. Due to the lack of passenger information, they often end up without picking up any passengers while there are highly demanded areas in their neighborhood. To address these issues, machine learning techniques can be applied to analyze mobility data acquired from the IoT sensors and help companies to organize the taxi fleet or minimize the wait-time for both passengers and drivers in the city. In this paper, an LSTM-based deep sequence learning model is applied to forecast taxi-demand in a particular urban area in a smart city. For this purpose, points of interest (POIs) in the city are extracted from Google Maps and integrated with the mobility data sources. Given a real-world dataset and two evaluation metrics, we observed that taxi-demand in each urban area can be influenced by external factors such as neighborhood locations and the POIs located in that area. The results show that the proposed method outperforms the vanilla LSTM model and has less average error than baseline methods in terms of the Mean Squared Error (MSE) and Symmetric Mean Absolute Percentage Error (SMAPE).

Bahman Askari

Papers

Taxi Demand Prediction using an LSTM-Based Deep Sequence Model and Points of Interest