Impact of TNC on travel behavior and mode choice: a comparative analysis of Boston and Philadelphia.

We compare responses from an online survey among 700 customers of transportation network companies (TNC) in Boston and Philadelphia to investigate TNC's impact on vehicle ownership, trip making, and mode choice. We first use a qualitative comparative analysis to examine changes in respondents' travel behavior and vehicle ownership after adopting TNC. We then use a random parameter logit regression analysis to investigate customers' preferences between transit and TNC based on a choice experiment. We find that in both cities, TNC allows customers, including those who currently do not own a car, to either delay purchasing a car or forgo a car altogether. TNC enables customers across income levels to take trips that they otherwise would not have taken. Meanwhile, TNC substitutes for more than complementing transit. The random parameter logit analysis indicates that when choosing between TNC and transit, individuals in both cities consider waiting time and overall travel time for transit to be more burdensome than those for TNC. Bostonians perceive the time spent walking to and from transit to be less burdensome, and the time spent traveling in vehicle to be more burdensome than do Philadelphians. Differences in built environment, mode share within transit systems, and income likely contribute to respondents' different values of time between the two cities. Our paper is the first to compare individual trade-off between transit and TNC in two cities with different urban settings and transit services. The findings have implications on transit service planning, station area improvements, parking regulations, and traffic management. Supplementary Information: The online version contains supplementary material available at 10.1007/s11116-021-10220-5.

Crash risk, crash exposure, and the built environment: A conceptual review.

This paper reviews the literature on the relationship between the built environment and roadway safety, with a focus on studies that analyse small geographical units, such as census tracts or travel analysis zones. We review different types of built environment measures to analyse if there are consistent relationships between such measures and crash frequency, finding that for many built environment variables there are mixed or contradictory correlations. We turn to the treatment of exposure, because built environment measures are often used, either explicitly or implicitly, as measures of exposure. We find that because exposure is often not adequately controlled for, correlations between built environment features and crash rates could be due to either higher levels of exposure or higher rates of crash risk per unit of exposure. Then, we identify various built environment variables as either more related to exposure, more related to risk, or ambiguous, and recommend further targeted research on those variables whose relationship is currently ambiguous.

Where do bike lanes work best? A Bayesian spatial model of bicycle lanes and bicycle crashes.

US municipalities are increasingly introducing bicycle lanes to promote bicycle use, increase roadway safety and improve public health. The aim of this study was to identify specific locations where bicycle lanes, if created, could most effectively reduce crash rates. Previous research has found that bike lanes reduce crash incidence, but a lack of comprehensive bicycle traffic flow data has limited researchers' ability to assess relationships at high spatial resolution. We used Bayesian conditional autoregressive logit models to relate the odds that a bicycle injury crash occurred on a street segment in Philadelphia, PA (n = 37,673) between 2011 and 2014 to characteristics of the street and adjacent intersections. Statistical models included interaction terms to address the problem of unknown bicycle traffic flows, and found bicycle lanes were associated with reduced crash odds of 48% in streets segments adjacent to 4-exit intersections, of 40% in streets with one- or two-way stop intersections, and of 43% in high traffic volume streets. Presence of bicycle lanes was not associated with change in crash odds at intersections with less or more than 4 exits, at 4-way stop and signalized intersections, on one-way streets and streets with trolley tracks, and on streets with low-moderate traffic volume. The effectiveness of bicycle lanes appears to depend most on the configuration of the adjacent intersections and on the volume of vehicular traffic. Our approach can be used to predict specific street segments on which the greatest absolute reduction in bicycle crash odds could occur by installing new bicycle lanes.