A Framework for Integrating Transportation Into Smart Cities

In recent years, economic, environmental, and political forces have quickly given rise to “Smart Cities” -- an array of strategies that can transform transportation in cities. Using a multi-method approach to research and develop a framework for smart cities, this study provides a framework that can be employed to: Understand what a smart city is and how to replicate smart city successes; The role of pilot projects, metrics, and evaluations to test, implement, and replicate strategies; and Understand the role of shared micromobility, big data, and other key issues impacting communities. This research provides recommendations for policy and professional practice as it relates to integrating transportation into smart cities

Micromobility evolution and expansion: Understanding how docked and dockless bikesharing models complement and compete – A case study of San Francisco

Shared micromobility – the shared use of bicycles, scooters, or other low-speed modes – is an innovative transportation strategy growing across the United States that includes various service models such as docked, dockless, and e-bike service models. This research focuses on understanding how docked bikesharing and dockless e-bikesharing models complement and compete with respect to user travel behaviors. To inform our analysis, we used two datasets from February 2018 of Ford GoBike (docked) and JUMP (dockless electric) bikesharing trips in San Francisco. We employed three methodological approaches: 1) travel behavior analysis, 2) discrete choice analysis with a destination choice model, and 3) geospatial suitability analysis based on the Spatial Temporal Economic Physiological Social (STEPS) to Transportation Equity framework. We found that dockless e-bikesharing trips were longer in distance and duration than docked trips. The average JUMP trip was about a third longer in distance and about twice as long in duration than the average GoBike trip. JUMP users were far less sensitive to estimated total elevation gain than were GoBike users, making trips with total elevation gain about three times larger than those of GoBike users, on average. The JUMP system achieved greater usage rates than GoBike, with 0.8 more daily trips per bike and 2.3 more miles traveled on each bike per day, on average. The destination choice model results suggest that JUMP users traveled to lower-density destinations, and GoBike users were largely traveling to dense employment areas. Bike rack density was a significant positive factor for JUMP users. The location of GoBike docking stations may attract users and/or be well-placed to the destination preferences of users. The STEPS-based bikeability analysis revealed opportunities for the expansion of both bikesharing systems in areas of the city where high-job density and bike facility availability converge with older resident populations

MIRAI Architecture for Heterogeneous Network

One of the keywords that describe next-generation wireless communications is "seamless." As part of the e-Japan Plan promoted by the Japanese Government, the Multimedia Integrated Network by Radio Access Innovation project has as its goal the development of new technologies to enable seamless integration of various wireless access systems for practical use by 2005. This article describes a heterogeneous network architecture including a common tool, a common platform, and a common access. In particular, software-defined radio technologies are used to develop a multiservice user terminal to access different wireless networks. The common platform for various wireless networks is based on a wireless-supporting IPv6 network. A basic access network, separated from other wireless access networks, is used as a means for wireless system discovery, signaling, and paging. A proof-of-concept experimental demonstration system is available

Chapter 13 - Sharing strategies: carsharing, shared micromobility (bikesharing and scooter sharing), transportation network companies, microtransit, and other innovative mobility modes

Shared mobility—the shared use of a vehicle, bicycle, or other mode—is an innovative transportation strategy that enables users to gain short-term access to transportation modes on an “as-needed” basis. It includes various forms of carsharing, bikesharing, scooter sharing, ridesharing (carpooling and vanpooling), transportation network companies (TNCs), and microtransit. Included in this ecosystem are smartphone “apps” that aggregate and optimize these mobility options, as well as “courier network services” that provide last mile package and food delivery. This chapter describes different models that have emerged in shared mobility and reviews research that has quantified the environmental, social, and transportation-related impacts of these services

A life cycle thinking approach applied to novel micromobility vehicle

While the production of cars has high environmental costs, producing and maintaining micromobility vehicles might consume fewer resources. Likewise, replacing the car with active mobility transportation modes reduces noise and air pollution. The Life Cycle Assessment (LCA) methodology contributes to study such environmentally sustainable solutions. We present a "cradle-to-grave" analysis by tracking the activity from the extraction of raw materials until the product's life ends. The goal is to carry out an LCA of a novel micromobility vehicle under a life cycle thinking perspective. The LCA tool - Good to Go? Assessing the Environmental Performance of New Mobility, developed by the International Transport Forum - was used to model the baseline and alternative scenarios. The vehicle’s materials, primary energy sources for battery charging, use of the vehicle as a shared mobility mode, among other factors, were changed to assess the energy use and greenhouse gases (GHG) emissions during the entire life cycle chain. The LCA results at the baseline scenario for the micromobility device, the Ghisallo vehicle, are similar to the values of other micromobility vehicles. Energy consumption (Mega Joule [MJ]) and GHG emissions (grams of equivalent CO2) per vehicle-kilometer are 0.36 [MJ/v-km] and 29 [g CO2 eq/v-km], respectively. For this personal mobility vehicle, it is a conclusion that most GHG emissions are due to production (42% of the total). Air transport from production to sales site increases the impact by 10%. Finally, we present measures to decrease the energy and GHG emissions impact of a micromobility device life cycle.in publicatio

Mobile Networking

We point out the different performance problems that need to be addressed when considering mobility in IP networks. We also define the reference architecture and present a framework to classify the different solutions for mobility management in IP networks. The performance of the major candidate micro-mobility solutions is evaluated for both real-time (UDP) and data (TCP) traffic through simulation and by means of an analytical model. Using these models we compare the performance of different mobility management schemes for different data and real-time services and the network resources that are needed for it. We point out the problems of TCP in wireless environments and review some proposed enhancements to TCP that aim at improving TCP performance. We make a detailed study of how some of micro-mobility protocols namely Cellular IP, Hawaii and Hierarchical Mobile IP affect the behavior of TCP and their interaction with the MAC layer. We investigate the impact of handoffs on TCP by means of simulation traces that show the evolution of segments and acknowledgments during handoffs.Publicad

Chapter 3 - Mobility on demand (MOD) and mobility as a service (MaaS): early understanding of shared mobility impacts and public transit partnerships

Technology is changing the way we move and reshaping cities and society. Shared and on-demand mobility represent notable transportation shifts in the 21st century. In recent years, mobility on demand (MOD)—where consumers access mobility, goods, and services on-demand by dispatching shared modes, courier services, public transport, and other innovative strategies—has grown rapidly due to technological advancements; changing consumer preferences; and a range of economic, environmental, and social factors. New attitudes toward sharing, MOD, and mobility as a service (MaaS) are changing traveler behavior and creating new opportunities and challenges for public transportation. This chapter discusses similarities and differences between the evolving concepts of MaaS and MOD. Next, it characterizes the range of existing public transit and MOD service models and enabling partnerships. The chapter also explores emerging trends impacting public transportation. While vehicle automation could result in greater public transit competition in the future, it could also foster new opportunities for transit enhancements (e.g., microtransit services, first- and last-mile connections, reduced operating costs). The chapter concludes with a discussion of how MOD/MaaS partnerships and automation could enable the public transit industry to reinvent itself, making it more attractive and competitive with private vehicle ownership and use