This article specifies and estimates a multinomial logit model (MNL) to explain the purpose of renting a vehicle for short-term use. The model, which predicts the probability of renting a vehicle for business, leisure, temporary replacement, or other purposes, is estimated using a random sample of approximately 1,000 individuals from 10 Canadian provinces. The records used in the analysis were collected in 2016 via an online survey. The findings suggest that the purpose for renting could be predicted through factors associated with the sociodemographic characteristics of the renters and their rental plans, as well as attributes associated with the rented vehicle.

In this paper we examine the gender split in 76,981,561 bicycle share trips made from 2014-2018 for three of the largest public bicycle share programs in the U.S.: Bluebikes (Boston), Citi Bike (New York), and Divvy Bikes (Chicago). Overall, women made only one-quarter of all bicycle share trips from 2014-2018. The proportion of trips made by women increased over time for Citi Bike from 22.6% in 2014 to 25.5% in 2018, but hovered steady around 25% for Bluebikes and Divvy Bikes. Across programs, the gender gap was wider for older bicycle share users.

This article specifies and estimates a multinomial logit model (MNL) to explain the purpose of renting a vehicle for short-term use. The model, which predicts the probability of renting a vehicle for business, leisure, temporary replacement, or other purposes, is estimated using a random sample of approximately 1,000 individuals from 10 Canadian provinces. The records used in the analysis were collected in 2016 via an online survey. The findings suggest that the purpose for renting could be predicted through factors associated with the sociodemographic characteristics of the renters and their rental plans, as well as attributes associated with the rented vehicle.

When designing bicycle count programs, it can be difficult to know where to locate counters to generate a representative sample of bicycling ridership. Crowdsourced data on ridership has been shown to represent patterns of temporal ridership in dense urban areas. Here we use crowdsourced data and machine learning to categorize street segments into classes of temporal patterns of ridership. We used continuous signal processing to group 3,880 street segments in Ottawa, Ontario into six classes of temporal ridership that varied based on overall volume and daily patterns (commute vs non-commute). Transportation practitioners can use this data to strategically place counters across these strata to efficiently capture bicycling ridership counts that better represent the entire city.

This paper presents results from a 2018 survey of local planners (n = 77) about an impending transition in California’s environmental review law, which will require planners to evaluate land development projects for their effects on vehicle miles traveled (VMT) rather than automobile level-of-service (LOS). We find that most planners view VMT as an appropriate metric to measure environmental impacts from transportation, both generally and in their own jurisdictions. Outside of environmental review, some jurisdictions will likely continue to use LOS to assess development impact fees. But LOS may not be as ingrained in local planning practice as generally assumed.

We build on established inequality methods to evaluate (in)equities in accessibility to advertised jobs in Portland, Seattle and Vancouver. We demonstrate the value of using postings from the world’s largest classifieds platform, Indeed.com, to complement existing employment datasets in accessibility studies.

Bicycles, scooters, Segways, skateboards, and more are placing new demands on urban streets in big cities. These modes, termed ”little vehicles” (LVs) here, share common characteristics. This research leverages the 2017 National Household Travel Survey to understand and benchmark key characteristics for current users and trip characteristics of LVs. Our findings show that young men are the predominant users of these modes for a wide range of purposes, including recreational and social activities, shopping and errands, and work travel. In the biggest metro areas in the United States, three-quarters of the trips by LVs are less than 4 km (2.5 mi), and half are shorter than 2 km (1.2 mi) (median)—a distance band that is often considered too far to walk, but shorter than most trips currently taken by car.

This article uses piecewise regression to explore diminishing returns to population density and identifies the density thresholds in the three most populous Canadian metropolitan areas beyond which any increase in population density is associated with declining marginal returns in transit mode split. An improved understanding of diminishing returns will enable planners and transit operators to focus on specific transit modes and service areas where increasing population densities are more likely to correlate with higher transit mode splits.

This article uses one year’s worth of daily travel distance data for 252 Seattle households’ vehicles to ascertain that one day’s distance (plus day of week and month of year information) accounts for 10.7% of the variability in that vehicle’s annual (total) distance traveled, while two and seven consecutive days’ distance values predict 16.7% and 33.6%, respectively. In analyzing Gini coefficients (which average 0.546 + / − 0.117 across these instrumented vehicles), one finds that full-time employed females have the most stable day-to-day driving patterns, allowing for shorter-duration surveys of such households.

We compute a series of benchmarks and key performance indicators (KPIs) that describe the state of transport equity in the Greater Toronto and Hamilton Area (GTHA). These measures are designed to be simple to interpret, have clear normative interpretations, and be easily replicable in future survey waves or for other regions.