Ambulance Emergency Response Optimization in Developing Countries

The lack of emergency medical transportation is viewed as the main barrier to the access of emergency medical care in low and middle-income countries (LMICs). In this paper, we present a robust optimization approach to optimize both the location and routing of emergency response vehicles, accounting for uncertainty in travel times and spatial demand characteristic of LMICs. We traveled to Dhaka, Bangladesh, the sixth largest and third most densely populated city in the world, to conduct field research resulting in the collection of two unique datasets that inform our approach. This data is leveraged to develop machine learning methodologies to estimate demand for emergency medical services in a LMIC setting and to predict the travel time between any two locations in the road network for different times of day and days of the week. We combine our robust optimization and machine learning frameworks with real data to provide an in-depth investigation into three policy-related questions. First, we demonstrate that outpost locations optimized for weekday rush hour lead to good performance for all times of day and days of the week. Second, we find that significant improvements in emergency response times can be achieved by re-locating a small number of outposts and that the performance of the current system could be replicated using only 30% of the resources. Lastly, we show that a fleet of small motorcycle-based ambulances has the potential to significantly outperform traditional ambulance vans. In particular, they are able to capture three times more demand while reducing the median response time by 42% due to increased routing flexibility offered by nimble vehicles on a larger road network. Our results provide practical insights for emergency response optimization that can be leveraged by hospital-based and private ambulance providers in Dhaka and other urban centers in LMICs

Analysis of last mile transport pilot: Implementation of the model and its adaptation among local citizens

When we contemplate an ideal city's constituents such as social environment, medical facilities, law enforcement and education, the one that is religiously used by its inhabitants is public transport. An ideal public transport, if not impossible, is hard to achieve as each individual possesses unique needs. Mobility as a service focuses on achieving the desired by combining multiple transport systems (bus, metro, tram, taxi, car sharing, bicycle sharing) into one single platform. One of the biggest challenges faced by MaaS is to solve the first/last mile problem. This research focuses on understanding stakeholders' collaboration, required business model, user behaviour and experiences for a new first/last mile transport service by analysing a three-month kick-scooter sharing pilot concluded in the city of Espoo, Finland by Samocat Sharing Oy. Collected data for the exploratory research was in the form of 54 user survey responses, 2 in depth user interviews, interviews with Samocat co-founders, overall trip data, user helpline data, emails and documents. The qualitative and quantitative data was analysed with the help of generated hierarchical MaaS framework. The findings highlight essential elements needed for a successful stakeholders' collaboration and what is the required business model to make the kick scooter service sustainable by benchmarking existing MaaS business model and analysed data. Findings also highlight the modifications required in the service for a better user experience by analysing user behaviour during the pilot. Overall, findings prove the hypotheses "Samocat kick-scooter sharing solves the last mile problem" right. Samocat sharing service shows great potential to be an ideal MaaS candidate providing true door to door and a natural mode of transport for shorter distances. The service shows potential to generate synergistic effects with other mode of transport, opening doors for new research fields at the same time

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity