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Railway platforms are becoming increasingly crowded, especially at peak hours. In this observational study, we investigated how the density of people is perceived by passengers and how this perceived density correlates with safety perception and risk-taking behavior. Risk-taking behavior here means stepping into the danger zone, the area of the platform bordering the tracks where individuals are at risk to their physical integrity by a train passing through, arriving at or leaving the station. The investigation of perceived density and actual behavior on the platform poses methodological challenges. Therefore, we used a stereo sensor technology to collect anonymized behavioral data on a train station platform over two months. Data regarding passenger density and oversteps into the danger zone was collected during rush hours and analyzed for this study. Additionally, subjective data, such as estimation and perception of passenger density and safety perception were collected in a survey with 179 participants. Survey links were distributed during rush hours in three different train stations on platforms over two weeks. While distributing the links for the online survey in the field (two-hour sessions during rush hours), an observation was conducted (i.e., oversteps into the danger zone, general passenger behavior). The results indicate that increased measured passenger density is related to more oversteps. Subjective perception of crowd density, regarding how comfortable someone feels in the given situation, correlates with safety perception and also significantly predicts overstepping into the danger zone. Increased estimated density also correlates with reduced safety perception but is not a predictor of oversteps. We suggest optimizing the passenger distribution on the platform by motivating passengers to move to less crowded areas, e.g. with approaches such as “nudging” so that passengers feel more comfortable on the platform. This can both improve both safety and the customer experience on the platform.

Usually, the economy class has limited space and limited recline possibilities causing discomfort, especially on long haul flights. In this paper, a seat was developed creating more space by using thevertical space in the aircraft. The design is described and 59% preferred this seat based on visual impression, which is promising. Further prototyping, certification studies and manufacturabilityresearch is needed to check the feasibility further.

Usually, the economy class has limited space and limited recline possibilities causing discomfort, especially on long haul flights. In this paper, a seat was developed creating more space by using thevertical space in the aircraft. The design is described and 59% preferred this seat based on visual impression, which is promising. Further prototyping, certification studies and manufacturabilityresearch is needed to check the feasibility further.

Os dados têm desempenhado um papel importante em muitas áreas da sociedade. Eles aumentaram massivamente com o tempo e podem ser uma poderosa fonte de conhecimento. A forma como os dados são tratados, e esse conhecimento é extraído, também deve ser adaptada para suportar essa enorme quantidade de informações vindas de diferentes fontes. A Lambda Architecture vem suprir essa necessidade de ter uma arquitetura Big Data capaz de processar dados históricos e dados em tempo real. Apresentamos o LDAVI, uma implementação da Lambda Architecture baseada na arquitetura Lambda (KIRAN, 2015), uma arquitetura de processamento de dados para manipular uma quantidade massiva de dados decompondo o problema em três camadas: camada de lote - para processamento de dados históricos - camada de veiculação e camada de velocidade - para processamento de streaming. As principais tecnologias usadas para construir essa arquitetura são o Apache Hadoop, o Apache Spark, o Apache Impala e o Apache Kafka. O foco principal é descrever essa arquitetura, bem como sua implementação, pois ela pode ser aplicada a qualquer tipo de problema em que seja necessário armazenar e processar uma grande quantidade de dados - nos modos de fluxo contínuo ou lote. Nosso objetivo neste trabalho é demonstrar o poder, a capacidade e a viabilidade dessa arquitetura e que ela pode ser usada para abordar diferentes tipos de cenários de Big Data. Neste trabalho, abordamos a Mobilidade Inteligente como nosso caso de estudo para avaliar o LDAVI. Analisamos os cartoes de passageiros, GPS de ônibus e paradas de ônibus da cidade de Schenzhen, com o objetivo de extrair a densidade e o fluxo de passageiros. Lambda Architecture é um novo conceito arquitetônico que surgiu com o aumento da area de Big Data Analytics. Neste trabalho, abordamos e fornecemos uma implementação dessa arquitetura, construindo-a com a principal pilha de tecnologia de Big Data. Embora tenha começado a ser usado em algumas áreas, como mecanismos de busca e plataformas que exigem processamento em tempo real - como reprodutores de fluxo de vídeo - demonstramos que essa arquitetura também pode trazer benefícios para a Mobilidade Inteligente, mais precisamente no transporte público. Diferentemente dos trabalhos relacionados, abordamos três tipos diferentes de viagem: viagem simples, viagem de conexão e ida e volta, o que torna a análise completa e mais precisa. Data has been playing an important role in many areas of society. It has massively increased among time and can be a powerful source of knowledge. The way data is handled, and this knowledge is extracted had also to be adapted to support this huge amount of information coming from different sources. Lambda Architecture comes to supply this need of having a Big Data architecture capable of processing both historical data and stream data. We present LDAVI, a Lambda Architecture Driven Implementation based on Lambda Architecture approach (KIRAN, 2015), a data-processing architecture for handling massive amount of data by decomposing the problem into three layers: batch layer – for historical data processing - serving layer and speed layer – for streaming processing. Main technologies used for building this architecture are Apache Hadoop, Apache Spark, Apache Impala and Apache Kafka. The main focus is to this describe this architecture as well as its implementation, as it can apply to any type of problem where one needs to store and process huge amount of data – either in streaming or batch modes. Our objective in this work is to demonstrate the powerful, capacity and feasibility of this architecture and that it can be used to approach different type of Big Data scenarios. In this work we address Smart Mobility are as our case of study to evaluate LDAVI. We analyze passengers smart card and buses GPS and stops location from the city of Schenzhen, aiming to extract passengers density and flow. Lambda Architecture is a new architectural concept that emerged with the raise of Big Data Analytics. In this work we approach and provide an implementation of this architecture, building it with the main Big Data technology stack. Although it has started being used in some areas such as search engines and platforms requiring real-time processing – such as video stream players – we demonstrate that this architecture can also bring benefits for Smart Mobility, more precisely in public transportation. Differently from related works, we approach three different types of trip: simple trip, connection trip and round trip, what makes the analysis complete and more accurate.

Розглянуто питання визначення раціональних сфер використання автобусів різного класу пасажиромісткості як оптимізаційне завдання. Як керовані змінні було застосовано потужність пасажиропотоку та ступінь заповнення салону автобусу на найбільш завантаженому перегоні маршруту. Запропоновано рекомендації щодо застосування автобусів різного класу пасажиромісткості залежно від потужності пасажиропотоку на найбільш завантаженому перегоні маршруту, Problem. One of the key issues of the public transit operating is the choice of vehicles capacity. This parameter has a wide range of variation and the basis arises for the segregation of buses into classes. The use of buses of a certain class of capacity has rational areas, due to a quantity of parameters characterizing the process of demand formation in transit services (passenger flow volume, its spatial and temporal dynamics, etc.). An analysis of existing research of vehicles capacity choice for route operating has shown that they are not well-coordinated and require further research. Goal. The purpose of the article is to determine the rational areas of using the buses of different classes of capacity. Methodology. Mathematical formulations of the problem have been concerned with minimizing of overall costs, that measure as a combination of user and operator costs. As operated variables, the passenger flow and the load factor on the maximum load section of the route were used. Results. As a result, recommendations for using different bus classes of capacity were proposed. For the applied input data, rational areas of using buses of different classes of capacity have the following ranges of passenger flow on the maximum load section of the route: from 50 to 280 pass./hour – small capacity buses; from 280 to 1100 pass./hour – medium capacity buses; from 1100 to 2450 pass./hour – large capacity buses; from 2450 to 5400 pass./hour - especially large capacity buses. Originality. In contrast to the existing, the proposed approach allows two-factor optimization by capacity and passenger density parameters and takes into account various route parameters, in particular, the characteristics of passenger spatial flow dynamics. Practical value. The obtained results can be used for public transit planning and operation tasks solving, in particular, of vehicles capacity choice on the bus routes. The vehicle capacity defined other parameters of transportation such as headway, service frequency, etc., Рассмотрен вопрос определения рациональных сфер использования автобусов различного класса пассажировместимости как оптимизационная задача. В качестве управляемых переменных были использованы мощность пассажиропотока и степень заполнения салона автобуса на наиболее загруженном перегоне маршрута. Предложены рекомендации относительно использования автобусов различного класса пассажировместимости в зависимости от мощности пассажиропотока на наиболее загруженном перегоне маршрута

All metro cities in our country are flooded with population. Though two wheelers and four wheelers are more common, the usage of bus is the single largest mode of land transport next to Indian Railways in modern India. We can see people thronging in queues in all bus stops and groping in the dark without knowing when their expected bus will arrive at their respective bus stops. To facilitate this, we have worked out a method which has a real time update of bus location along with passenger density. This is done with the help of wireless technology using Global system for Mobile Communication (GSM) feature.