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

Bikesharing and Bicycle Safety

The growth of bikesharing in the United States has had a transformative impact on urban transportation. Major cities have established large bikesharing systems, including Boston, Chicago, Denver, Minneapolis-Saint Paul, New York City, Salt Lake City, the San Francisco Bay Area, Seattle, Washington DC, and others. These systems began operating as early as 2010, and no fatalities have occurred within the US as of this writing. However, three have happened in North America—two in Canada and one in Mexico. Bikesharing has some qualities that appear inherently unsafe for bicyclists. Most prominently, helmet usage is documented to be quite low in most regions. Bikesharing is also used by irregular bicyclists who are less familiar with the local terrain. In this study, researchers take a closer look at bikesharing safety from qualitative and quantitative perspectives. Through a series of four focus groups, they discussed bikesharing usage and safety with bikesharing members and nonmembers in the Bay Area. They further engaged experts nationwide from a variety of fields to evaluate their opinions and perspectives on bikesharing and safety. Finally, researchers conducted an analysis of bicycle and bikesharing activity data, as well as bicycle and bikesharing collisions to evaluate injury rates associated with bikesharing when compared with benchmarks of personal bicycling. The data analysis found that collision and injury rates for bikesharing are lower than previously computed rates for personal bicycling. Experts and focus group participants independently pointed to bikesharing rider behavior and bikesharing bicycle design as possible factors. In particular, bikesharing bicycles are generally designed in ways that promote stability and limited speeds, which mitigate the conditions that contribute to collisions. Data analysis also explored whether there was evidence of a “safety in numbers benefit” that resulted from bikesharing activity. However, no significant impact from bikesharing activity on broader bicycle collisions could be found within the regions in which they operate. Discussion and recommendations are presented in the conclusion

Feasibility Study of a Campus-Based Bikesharing Program at UNLV

Bikesharing systems have been deployed worldwide as a transportation demand management strategy to encourage active modes and reduce single-occupant vehicle travel. These systems have been deployed at universities, both as part of a city program or as a stand-alone system, to serve for trips to work, as well as trips on campus. The Regional Transportation Commission of Southern Nevada (RTCSNV) has built a public bikesharing system in downtown Las Vegas, approximately five miles from the University of Nevada, Las Vegas (UNLV). This study analyzes the feasibility of a campus-based bikesharing program at UNLV. Through a review of the literature, survey of UNLV students and staff, and field observations and analysis of potential bikeshare station locations, the authors determined that a bikesharing program is feasible at UNLV

Public Bikesharing in North America During a Period of Rapid Expansion: Understanding Business Models, Industry Trends & User Impacts, MTI Report 12-29

Public bikesharing—the shared use of a bicycle fleet—is an innovative transportation strategy that has recently emerged in major cities around the world, including North America. Information technology (IT)-based bikesharing systems typically position bicycles throughout an urban environment, among a network of docking stations, for immediate access. Trips can be one-way, round-trip, or both, depending on the operator. Bikesharing can serve as a first-and-last mile connector to other modes, as well as for both short and long distance destinations. In 2012, 22 IT-based public bikesharing systems were operating in the United States, with a total of 884,442 users and 7,549 bicycles. Four IT-based programs in Canada had a total of 197,419 users and 6,115 bicycles. Two IT-based programs in Mexico had a total of 71,611 users and 3,680 bicycles. (Membership numbers reflect the total number of short- and long-term users.) This study evaluates public bikesharing in North America, reviewing the change in travel behavior exhibited by members of different programs in the context of their business models and operational environment. This Phase II research builds on data collected during our Phase I research conducted in 2012. During the 2012 research (Phase I), researchers conducted 14 expert interviews with industry experts and public officials in the United States and Canada, as well as 19 interviews with the manager and/or key staff of IT-based bikesharing organizations. For more information on the Phase I research, please see the Shaheen et al., 2012 report Public Bikesharing in North America: Early Operator and User Understanding. For this Phase II study, an additional 23 interviews were conducted with IT-based bikesharing organizations in the United States, Canada, and Mexico in Spring 2013. Notable developments during this period include the ongoing expansion of public bikesharing in North America, including the recent launches of multiple large bikesharing programs in the United States (i.e., Citi Bike in New York City, Divvy in Chicago, and Bay Area Bike Share in the San Francisco Bay Area). In addition to expert interviews, the authors conducted two kinds of surveys with bikesharing users. One was the online member survey. This survey was sent to all people for whom the operator had an email address.The population of this survey was mainly annual members of the bikesharing system, and the members took the survey via a URL link sent to them from the operator. The second survey was an on-street survey. This survey was designed for anyone, including casual users (i.e., those who are not members of the system and use it on a short-term basis), to take “on-street” via a smartphone. The member survey was deployed in five cities: Montreal, Toronto, Salt Lake City, Minneapolis-Saint Paul, and Mexico City. The on-street survey was implemented in three cities: Boston, Salt Lake City, and San Antonio

A simulation model for public bike-sharing systems

Urban areas are in need of efficient and sustainable mobility services. Public bicycle sharing systems stand out as a promising alternative and many cities have invested in their deployment. This has led to a continuous and fast implementation of these systems around the world, while at the same time, research works devoted to understand the system dynamics and deriving optimal designs are being developed. In spite of this, many promoting agencies have faced the impossibility of evaluating a system design in advance, increasing the uncertainty on its performance and the risks of failure. This paper describes the development of an agent-based simulation model to emulate a bike-sharing system. The goal is to obtain a tool to evaluate and compare different alternatives for the system design before their implementation. This tool will support the decision-making process in all the stages of implementation, from the strategical planning to the daily operation. The main behavioral patterns and schemes for all agents involved are designed and implemented into a Matlab programming code. The model is validated against real data compiled from the Barcelona’s Bicing system showing good accuracy.Postprint (published version

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

Incentives and Redistribution in Homogeneous Bike-Sharing Systems with Stations of Finite Capacity

Bike-sharing systems are becoming important for urban transportation. In such systems, users arrive at a station, take a bike and use it for a while, then return it to another station of their choice. Each station has a finite capacity: it cannot host more bikes than its capacity. We propose a stochastic model of an homogeneous bike-sharing system and study the effect of users random choices on the number of problematic stations, i.e., stations that, at a given time, have no bikes available or no available spots for bikes to be returned to. We quantify the influence of the station capacities, and we compute the fleet size that is optimal in terms of minimizing the proportion of problematic stations. Even in a homogeneous city, the system exhibits a poor performance: the minimal proportion of problematic stations is of the order of (but not lower than) the inverse of the capacity. We show that simple incentives, such as suggesting users to return to the least loaded station among two stations, improve the situation by an exponential factor. We also compute the rate at which bikes have to be redistributed by trucks to insure a given quality of service. This rate is of the order of the inverse of the station capacity. For all cases considered, the fleet size that corresponds to the best performance is half of the total number of spots plus a few more, the value of the few more can be computed in closed-form as a function of the system parameters. It corresponds to the average number of bikes in circulation