Dockless bike use as a last-mile solution: Evidence from Reno and Sparks, Nevada

Dockless bike-sharing schemes have become more prevalent in cities around the world. While interest grows in studying their usage in larger cities with well-established transit systems, their role in expanding mid-sized cities that are more automobile-dependent remains understudied. This paper evaluates how dockless bike-sharing can provide a last-mile solution by connecting existing bus stops with destinations through an analysis of data collected from 111,155 unique trips over a five-month trial period of a dockless bike sharing scheme in Reno-Sparks, Nevada, USA in the summer of 2018. We classify trips by frequency of use and adjacency to bus stops into four categories and apply multivariate models to determine the link between trip occurrence and the immediate environment. Results indicate that repeat users’ trips that begin or end near the same bus stop are more strongly linked with proximity to bicycle lanes, but tend to be farther from parks and casino resorts. Repeat users typically start trips in denser areas of the city with higher shares of renter-occupied units and higher shares of households without a vehicle, and ended them in less dense areas with higher percentages of white, non-Hispanic residents compared to one-time users. We find differences in association of travel with food service, education, health services, and casino resorts. This suggests that some benefits may be realized by deploying bike sharing in mid-sized cities, but must be weighed against findings that show repeat usage in areas that align with common early transportation technology and service adopter profiles

A spatial analysis of demand patterns on a bicycle sharing scheme: Evidence from London

This paper investigates the spatial demand for bikesharing through the application of a series of trip generation models for the London Bicycle Sharing Scheme (LBSS). The production of trips from and the arrival of trips at scheme stations are evaluated in reference to how they connect with features of the built environment, demographics of the resident and workplace populations, and attributes of the scheme's structure. A spatial econometrics approach is taken to specify the models, with four different time windows considered throughout the day for all trips taken during 2016. The built environment features show a consistent pattern of results in the model, indicating that proximity to cycling infrastructure, rail stations, parks, university facilities, as well as the density of shops and conventional roads in the vicinity of stations is linked with trip generation rates. The presence of males and Caucasians are associated with higher station demand, aligning with other work on the introduction of new mobility solutions elsewhere, though we do find that greater distances to work tend to depress use. Trip generation is also reduced at the minority of stations located south of the River Thames, indicating that the presence of natural barriers can affect the operation of schemes. The results carry implications for scheme integration in other cities

Additional file 1: Figure S1. of ghost-tree: creating hybrid-gene phylogenetic trees for diversity analyses

Principal Coordinates comparing unsimulated (real) samples based on (a) unweighted UniFrac distances where trees are computed using ghost-tree, (b) weighted UniFrac distances where trees are computed using ghost-tree, (c) unweighted UniFrac distances where trees are computed using ghost-tree, 0-branch length-foundation, (d) weighted UniFrac distances where trees are computed using ghost-tree, 0-branch-length foundation, (e) unweighted UniFrac distances where trees are computed using ghost-tree, 0-branch-length extensions, (f) weighted UniFrac distances where trees are computed using ghost-tree, 0-branch-length extensions. Blue points are simulated and real human saliva samples, and red points are simulated and real restroom surface samples. Plots were made using EMPeror software [25]. (PDF 522 kb

Sampling Sites

<p>The circles represent the sampling locations in the Sargasso Sea (SAR), Gulf of Mexico (GOM), British Columbia (BBC), and the Arctic Ocean. The number of samples taken at each location and combined for sequencing, as well as the date and depth range, are shown in the boxes.</p

Monte Carlo Simulation of Cross-Contigs between Metagenomic Samples

<p>(A) For the intersample analysis, the maximum likelihood occurred at 35% fraction permuted and 100% fraction shared. (B) The maximum likelihood was between 0% and 0.5% fraction permuted and 85% and 95 % fraction shared for the intrasample controls.</p

Relationship between Geographic and Genetic Distances of Marine Viral Assemblages

<p>In addition to the four metagenomes sequenced for this study, the previous viral metagenomes from the San Diego area (California coast) were also included in this analysis [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0040368#pbio-0040368-b010" target="_blank">10</a>]. There was a significant correlation of 3.28 × 10⁻⁵ Φ_ST / km (Mantel test, <i>Z</i> = ₋78.9, <i>p</i> < 0.017, <i>r</i> = 0.585).</p

Types of Phages in the Four Metagenomes

<p>A new version of the Phage Proteomic Tree (left panel) was constructed from 510 complete phage and prophage genomes using the previously described method [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0040368#pbio-0040368-b023" target="_blank">23</a>]. The metagenomic sequences were compared to the phage on the Phage Proteomic Tree using TBLASTX, and the colored bars on the right represent significant similarities (<i>E</i>-value < 0.0001). Names of prophages are in red and the <i>Prochlorococcus</i> phage genomes are in green. An electronic version of the tree and a FASTA list of phage and prophage genomes used to make the tree are available at the SDSU Center for Universal Microbe Sequencing website (<a href="http://scums.sdsu.edu/phage/Oceans" target="_blank">http://scums.sdsu.edu/phage/Oceans</a>).</p