Smart Sustainable Mobility: Analytics and Algorithms for Next-Generation Mobility Systems

To this date, mobility ecosystems around the world operate on an uncoordinated, inefficient and unsustainable basis. Yet, many technology-enabled solutions that have the potential to remedy these societal negatives are already at our disposal or just around the corner. Innovations in vehicle technology, IoT devices, mobile connectivity and AI-powered information systems are expected to bring about a mobility system that is connected, autonomous, shared and electric (CASE). In order to fully leverage the sustainability opportunities afforded by CASE, system-level coordination and management approaches are needed. This Thesis sets out an agenda for Information Systems research to shape the future of CASE mobility through data, analytics and algorithms (Chapter 1). Drawing on causal inference, (spatial) machine learning, mathematical programming and reinforcement learning, three concrete contributions toward this agenda are developed. Chapter 2 demonstrates the potential of pervasive and inexpensive sensor technology for policy analysis. Connected sensing devices have significantly reduced the cost and complexity of acquiring high-resolution, high-frequency data in the physical world. This affords researchers the opportunity to track temporal and spatial patterns of offline phenomena. Drawing on a case from the bikesharing sector, we demonstrate how geo-tagged IoT data streams can be used for tracing out highly localized causal effects of large-scale mobility policy interventions while offering actionable insights for policy makers and practitioners. Chapter 3 sets out a solution approach to a novel decision problem faced by operators of shared mobility fleets: allocating vehicle inventory optimally across a network when competition is present. The proposed three-stage model combines real-time data analytics, machine learning and mixed integer non-linear programming into an integrated framework. It provides operational decision support for fleet managers in contested shared mobility markets by generating optimal vehicle re-positioning schedules in real time. Chapter 4 proposes a method for leveraging data-driven digital twin (DT) frameworks for large multi-stage stochastic design problems. Such problem classes are notoriously difficult to solve with traditional stochastic optimization. Drawing on the case of Electric Vehicle Charging Hubs (EVCHs), we show how high-fidelity, data-driven DT simulation environments fused with reinforcement learning (DT-RL) can achieve (close-to) arbitrary scalability and high modeling flexibility. In benchmark experiments we demonstrate that DT-RL-derived designs result in superior cost and service-level performance under real-world operating conditions

The market economy of trips

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 59-62).Mobility on Demand (MOD) systems allow users to pick-up and drop-off vehicles (bikes, automobiles) ubiquitously in a network of parking stations. Asymmetric demand patterns cause unbalanced fleet allocation decreasing level of service. Current redistribution policies are complex to plan and typically cost more that the usage revenues of the system. The Market Economy of Trips (MET) explores a new operation model based on a double auction market where cost-minimizing users are both buyers and sellers of trip rights while profit-maximizing stations are competing auctioneers that trade them. Trip rights are priced relatively to the inventory needs of origin and destination stations. A theory, a game, and a model are presented to explore equilibrium and limits of efficiency of MET under different demand patterns and income distribution.by Dimitris Papanikolaou.S.M

Investigating the temporal differences among bike-sharing users through comparative analysis based on count, time series, and data mining models

Bike-sharing services provide easy access to environmentally-friendly mobility reducing congestion in urban areas. Increasing demand requires highly service planning methods based on bike-sharing user behavior. Negative Binomial, Poisson Regression, and Time Series models were elaborated considering the weather to reveal the differences between the members, occasional users, and visitor bike-sharing user groups. The negative Binomial approach is found to be superior to Poisson. Weather effects were varied in their influence on bike-sharing user classifications. In general, good weather conditions lead to more usage of bike-sharing. Weekends attract more occasional users and visitors than weekdays. In time series models, the seasonal trend of bike-sharing trips conducted by members was predicted without weather impact. According to the comparison, Random Forest performed better than SARIMA when the number of observations was low. Visitors are more influenced by temperature, wind and type of day. Occasional users are more subjected to precipitation. For members, it is found that the temperature, type of day are the most significant factors. The least factors for all are varied as well: precipitation for visitors, humidity for occasional users, precipitation and wind for members. The results help decision-makers predict the daily usage of bike-sharing for various user groups

Advances on Smart Cities and Smart Buildings

Modern cities are facing the challenge of combining competitiveness at the global city scale and sustainable urban development to become smart cities. A smart city is a high-tech, intensive and advanced city that connects people, information, and city elements using new technologies in order to create a sustainable, greener city; competitive and innovative commerce; and an increased quality of life. This Special Issue collects the recent advancements in smart cities and covers different topics and aspects

Identifying the gaps between needs, expectations, and views of different stakeholders related to car-sharing, bike-sharing, and scooter-sharing systems

L'abstract è presente nell'allegato / the abstract is in the attachmen

Prescribe a bike: reducing income-based disparities in bike access for health promotion and active transport through primary care

Low-income groups have greater potential to gain from incorporating health promotion into daily living using bike-share to increase physical activity and expand transport options. The potential is unmet because of socioeconomics and access. Disproportionate uptake of bike-share by higher income individuals widens the gaps in health equity and transportation equity as bike-share use over-represents males with more resources, less need, and lower health risk. The Prescribe a Bike (RxBike) program, a key focus of this study, is a partnership between primary care providers (PCPs) at an urban safety net hospital and the city’s existing income-based, subsidized bike-share membership. Three studies using quantitative and qualitative methods were performed to: examine utilization of bike-share by Boston residents among subsidized and non-subsidized members; examine perceived attributes of the RxBike program by Boston Medical Center (BMC) PCPs; and evaluate BMC patient referrals. The overarching conceptual model uses elements of theories from health services and organizational behavior, in a public health framework. Analysis of Boston resident utilization at the trip-level (2012-2015) demonstrated overall ridership was increasingly by males and residents of more advantaged neighborhoods. Subsidized members had significantly higher likelihood of living in neighborhoods with socioeconomic and health disadvantage, and less gender disparity when compared to non-subsidized members. The impact was minimal because subsidized members made only 7.17% of trips. The survey of PCPs revealed mismatch between highly favorable opinion of RxBike appropriateness and lower intent to refer. Female gender and not being an urban biker predicted lower likelihood of intent to refer. Examination of open-ended survey comments mirrored quantitative data and expanded on the range of provider biking safety concerns in Boston. From 2013-2015, 27 BMC providers made only 72 referrals to RxBike. Patients referred had high cardiovascular health risk, resided in neighborhoods with extremely high levels of disadvantage, and in neighborhoods without meaningful access to bike-share kiosks. Overall, the subsidized membership extends reach of bike-share to residents of neighborhoods with more health and socioeconomic risk than the rest of the city; RxBike has strong potential to impact this vulnerable population. The most critical matters for program success are safety and neighborhood access

Designing an On-Demand Dynamic Crowdshipping Model and Evaluating its Ability to Serve Local Retail Delivery in New York City

Nowadays city mobility is challenging, mainly in populated metropolitan areas. Growing commute demands, increase in the number of for-hire vehicles, enormous escalation in several intra-city deliveries and limited infrastructure (road capacities), all contribute to mobility challenges. These challenges typically have significant impacts on residents’ quality-of-life particularly from an economic and environmental perspective. Decision-makers have to optimize transportation resources to minimize the system externalities (especially in large-scale metropolitan areas). This thesis focus on the intra-city mobility problems experienced by travelers (in the form of congestion and imbalance taxi resources) and businesses (in the form of last-mile delivery), while taking into consideration a measurement of potential adoption by citizens (in the form of a survey). To find solutions for this mobility problem this dissertation proposes three distinct and complementary methodological studies. First, taxi demand is predicted by employing a deep learning approach that leverages Long Short-Term Memory (LSTM) neural networks, trained over publicly available New York City taxi trip data. Taxi pickup data are binned based on geospatial and temporal informational tags, which are then clustered using a technique inspired by Principal Component Analysis. The spatiotemporal distribution of the taxi pickup demand is studied within short-term periods (for the next hour) as well as long-term periods (for the next 48 hours) within each data cluster. The performance and robustness of the LSTM model are evaluated through a comparison with Adaptive Boosting Regression and Decision Tree Regression models fitted to the same datasets. On the next study, an On-Demand Dynamic Crowdshipping system is designed to utilize excess transport capacity to serve parcel delivery tasks and passengers collectively. This method is general and could be expanded and used for all types of public transportation modes depending upon the availability of data. This system is evaluated for the case study of New York City and to assess the impacts of the crowdshipping system (by using taxis as carriers) on trip cost, vehicle miles traveled, and people travel behavior. Finally, a Stated Preference (SP) survey is presented, designed to collect information about people’s willingness to participate in a crowdshipping system. The survey is analyzed to determine the essential attributes and evaluate the likelihood of individuals participating in the service either as requesters or as carriers. The survey collects information on the preferences and important attributes of New York citizens, describing what segments of the population are willing to participate in a crowdshipping system. While the transportation problems are complex and approximations had to be done within the studies to achieve progress, this dissertation provides a comprehensive way to model and understand the potential impact of efficient utilization of existing resources on transportation systems. Generally, this study offer insights to decisions makers and academics about potential areas of opportunity and methodologies to optimize the transportation system of densely populated areas. This dissertation offers methods that can optimize taxi distribution based on the demand, optimize costs for retail delivery, while providing additional income for individuals. It also provides valuable insights for decision makers in terms of collecting population opinion about the service and analyzing the likelihood of participating in the service. The analysis provides an initial foundation for future modeling and assessment of crowdshipping

CITIES: Energetic Efficiency, Sustainability; Infrastructures, Energy and the Environment; Mobility and IoT; Governance and Citizenship

This book collects important contributions on smart cities. This book was created in collaboration with the ICSC-CITIES2020, held in San José (Costa Rica) in 2020. This book collects articles on: energetic efficiency and sustainability; infrastructures, energy and the environment; mobility and IoT; governance and citizenship