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

Where Do Riders Park Dockless, Shared Electric Scooters? Findings from San Jose, California

Dockless, shared, electric kick-scooters started popping up on U.S. city streets without warning in 2017. Reaction to the shared scooters came swiftly and strongly. On the one hand, the scooters have proven popular with riders, attracting investment capital and expanding service to additional cities. But others have been less enthusiastic, with a central complaint being how shared scooters are parked. This perspective explores the extent to which parked shared scooters pose a problem to others on streets, sidewalks, and public spaces, using empirical evidence documenting where scooters have been parked in downtown San Jose, California

Investigating the Influence of Dockless Electric Bike-share on Travel Behavior, Attitudes, Health, and Equity

Cities throughout the world have implemented bike-share systems as a strategy for expanding mobility options. While these have attracted substantial ridership, little is known about their influence on travel behavior more broadly. The aim of this study was to examine how shared electric bikes (e-bikes) and e-scooters influence individual travel attitudes and behavior, and related outcomes of physical activity and transportation equity. The study involved a survey in the greater Sacramento area of 1959 households before (Spring 2016) and 988 after (Spring 2019) the Summer 2018 implementation of the e-bike and e-scooterservice operated by Jump, Inc., as well as a direct survey of 703 e-bike users (in Fall 2018 & Spring 2019). Among householdrespondents, 3–13% reported having used the service. Of e-bike share trips, 35% substituted for car travel, 30% substituted for walking, and 5% were used to connect to transit. Before- and after-household surveys indicated a slight decrease in self-reported (not objectively measured) median vehicle miles traveled and slight positive shifts in attitudes towards bicycling. Service implementation was associated with minimal changes in health in terms of physical activity and numbers of collisions. The percentages of users by self-reported student status, race, and income suggest a fairly equitable service distribution by these parameters, but each survey under-represents racial minorities and people with low incomes. Therefore, the study is inconclusive about how this service impacts those most in need. Furthermore, aggregated socio-demographics of areas where trips started or ended did not correlate with, and therefore are not reliable indicators of, the socio-demographics of e-bike-share users. Thus, targeted surveying of racial minorities and people with low-incomes is needed to understand bike-share equity

Wheels for All:Ensuring Equitable Access toDockless Mobility in Los Angeles

As the Los Angeles Department of Transportation (LADOT) embarks on a one-year dockless mobility pilot program, both LADOTstaff and the residents they serve have raised concerns over equity and access. Dockless mobility refers to dockless orfree-floating bicycles, electric bicycles, and electric scooters available for short-term rental that have begun to proliferate in citiesacross the country. LADOT staff have permitted eight companies with an accumulated 36,000 vehicles. Because the distributionof scooters across Los Angeles neighborhoods is far from even, LADOT staff are currently using CalEnviroScreen 3.0 to identifydisadvantaged communities where regulations incentivize operators to deploy their scooters. However, CalEnviroScreen 3.0 is ametric developed to identify communities likely affected by environmental injustices and as such prioritizes environmentalexposure factors over those that may affect transportation access.The purpose of this project is to first address the CalEnviroScreen limitations in analyzing dockless mobility equity by developingan access-focused Dockless Equity Map that locates the most socioeconomically and access disadvantaged communities in LosAngeles. LADOT staff could then produce regulations that promote enhanced dockless outreach and service in these areas. Iconstructed this map using data on socioeconomic characteristics (e.g. poverty level, race, etc.) and spatial access indicators (e.g.job accessibility by transit, car ownership, etc.). The Dockless Equity Map includes areas in the San Fernando Valley, East LosAngeles, South Los Angeles, and the Harbor that may be the most appropriate targets for dockless mobility equity policies.While developing an appropriate Equity Map is a crucial step, simply dropping scooters in underserved areas will not translate toequitable access. The final section of this report identifies actions that LADOT staff can take during the one-year pilot and beyondto ensure equitable access in the Dockless Equity Map areas. Through interviews with community-based organizationrepresentatives, I developed the following recommendations: 1) engage with residents in the Dockless Equity Map target areas inorder to educate them on dockless mobility, 2) utilize data collected during the one-year pilot to set specific equity goals, and 3)address infrastructure and safety concerns

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

A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems

Bike sharing provides an environment-friendly way for traveling and is booming all over the world. Yet, due to the high similarity of user travel patterns, the bike imbalance problem constantly occurs, especially for dockless bike sharing systems, causing significant impact on service quality and company revenue. Thus, it has become a critical task for bike sharing systems to resolve such imbalance efficiently. In this paper, we propose a novel deep reinforcement learning framework for incentivizing users to rebalance such systems. We model the problem as a Markov decision process and take both spatial and temporal features into consideration. We develop a novel deep reinforcement learning algorithm called Hierarchical Reinforcement Pricing (HRP), which builds upon the Deep Deterministic Policy Gradient algorithm. Different from existing methods that often ignore spatial information and rely heavily on accurate prediction, HRP captures both spatial and temporal dependencies using a divide-and-conquer structure with an embedded localized module. We conduct extensive experiments to evaluate HRP, based on a dataset from Mobike, a major Chinese dockless bike sharing company. Results show that HRP performs close to the 24-timeslot look-ahead optimization, and outperforms state-of-the-art methods in both service level and bike distribution. It also transfers well when applied to unseen areas

Facing Facts: Facial Injuries from Stand-up Electric Scooters

Background Stand-up electric scooters (SES) are a popular public transportation method. Numerous safety concerns have arisen since their recent introduction. Methods A retrospective chart review was performed to identify patients presenting to the emergency departments in Indianapolis, who sustained SES-related injuries. Results A total of 89 patients were included in our study. The average patient age was 29 ± 12.9 years in a predominantly male cohort (65.2%). No patient was documented as wearing a helmet during the event of injury. Alcohol intoxication was noted in 14.6% of accidents. Falling constituted the leading trauma mechanism (46.1%). Injuries were most common on Saturday (24.7%) from 14h00 to 21h59 (55.1%). Injury types included: abrasions/contusions (33.7%), fractures (31.5%), lacerations (27.0%), or joint injuries (18.0%). The head and neck region (H&N) was the most frequently affected site (42.7%). Operative management under general anesthesia was necessary for 13.5% of injuries. Nonoperative management primarily included conservative orthopedic care (34.8%), pain management with nonsteroidal anti-inflammatory drugs (NSAIDs) (34.8%) and/or opioids (4.5%), bedside laceration repairs (27.0%), and wound dressing (10.1%). Individuals sustaining head and neck injuries were more likely to be older (33.8 vs. 25.7 years, p=0.003), intoxicated by alcohol (29.0% vs. 3.9%, p=0.002), and requiring CT imaging (60.5% vs. 9.8%, p <0.001). Conclusion Although SESs provide a convenient transportation modality, unregulated use raises significant safety concerns. More data need to be collected to guide future safety regulations

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