Commuters who travel by train often feel annoyed due to misunderstanding the causes of delays in train traffic. They oftentimes are unaware of the necessity of performing maintenance to stations, tracks, and trains. MainTrain is a serious game developed to teach commuters about rail-maintenance while simulating the difficulty of keeping passengers happy. It is a fast-paced strategy game with a top-down view in which a player can perform maintenance actions on stations, tracks, and trains. By using commuter happiness as a base metric, MainTrain attempts to elicit empathy from players dissatisfied with scheduled maintenance so that they gain a better appreciation of the need for scheduled maintenance. This is coupled with the need to schedule maintenance for several components of a rail network, encumbering a player while teaching them about different aspects of rail maintenance. To examine the effectiveness of the game, the results of a user study are presented.

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Purpose: The most sustainable forms of urban mobility are walking and cycling. These modes of transportation are the most environmental friendly, the most economically viable and the most socially inclusive and engaging modes of urban transportation. To measure and compare the effectiveness of alternative pedestrianization or cycling infrastructure plans, the authors need to measure the potential flows of pedestrians and cyclists. The paper aims to discuss this issue. Design/methodology/approach: The authors have developed a computational methodology to predict walking and cycling flows and local centrality of streets, given a road centerline network and occupancy or population density data attributed to building plots. Findings: The authors show the functionality of this model in a hypothetical grid network and a simulated setting in a real town. In addition, the authors show how this model can be validated using crowd-sensed data on human mobility trails. This methodology can be used in assessing sustainable urban mobility plans. Originality/value: The main contribution of this paper is the generalization and adaptation of two network centrality models and a trip-distribution model for studying walking and cycling mobility.

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Along with the rise of the smart city movement, Internet of Things is an upcoming phenomenon. Objects and devices are becoming more and more wirelessly interconnected, communicating information between themselves and to human beings. As an addition to static sensor networks that gather real-time environmental data, the feasibility of implementing a dynamic sensor network based on LoRa communication is researched. To achieve such a dynamic system, a self-developed sensor platform was constructed, based on the microcontroller LoPy, measuring temperature and humidity. The emphasis of the research is on the localisation of the sensor platforms. A WiFi fingerprinting radiomap was constructed based on available MAC-addresses, their signal strengths, and GPS coordinates. In this method the GPS module is only used for the composition of the radiomap. The quality of the radiomap methodology was assessed by constructing it of measurements gathered in four days, and testing it for the remaining three days. This test gave a correctness of 50% while another 38% of measurements were localised in a neighbouring cell. The quality of the collected sensor data turned out to be dependent on the weather conditions and the placement location on the carrier vehicle. Another topic of research was LoRa communication, which was deemed as very limited for dynamic implementations, as the sending of location-related data takes up a large part of the already limited message size.@en