Transportation modes converge at intersections resulting in conflicts between bicyclists and motor vehicles. A common conflict and crash type involving bicycles at intersections is the right-hook, where a right-turning vehicle collides with a through bicyclist. While various geometric and signal control treatments have been used in attempts to mitigate bicycle–vehicle crashes and conflicts, agencies often face questions about optimal treatment selection. To date, limited research has been conducted to analyze how certain treatments (bike boxes, mixing zones, and bicycle signals) along with traffic characteristics (e.g., vehicle, bicycle, and pedestrian volumes) affect the frequency of bicycle–vehicle conflicts, as well as the severity of such conflicts. To address this research need, this study provides an analysis of bicycle–vehicle conflicts extracted from field-collected videos at 12 intersections; three with bike boxes, three with mixing zones, three with bicycle signals, and three control sites with no specific bicycle treatment. From these videos, bicycle–vehicle conflicts were identified and their severity measured using post-encroachment time (PET), along with conflict-involved road user speeds and hourly road user volumes. From these data, a series of Poisson regression models were estimated to assess hourly predicted conflicts as a function of bicycle and vehicle volumes across different treatment types. Additionally, differences in conflict severity across treatments were analyzed, including use of a novel measure which incorporates both PET and vehicle speed. Ultimately, several differences in conflict frequency and severity were observed between the treatment types, providing new data-driven guidance to assist practitioners in treatment selection.

Public health concerns of the Covid-19 pandemic and the popularity of on-demand mobility services have led to a recent and prominent increase in online food delivery (OFD) service adoption. While app-based services that offer restaurant customers the convenience of a freshly prepared meal delivered to any location have existed, their present and future impacts to urban transportation networks and landscapes have become ever more apparent since the pandemic’s onset and subsequent restrictions on restaurant dining. By analyzing route-level data collected by a ridehailing driver assistant app between October 2015 and October 2019, this study informs a baseline understanding of where and when these on-demand food delivery services were used within the Phoenix metro area prior to the pandemic. This identification of the spatiotemporal patterns of OFD service is accompanied by the estimation of traditional and spatially lagged negative binomial models of delivery counts using a robust set of predictors of the built environment and socioeconomic context found at the trip destination. Study results indicate that on-demand food delivery services were popular during dinner hours corresponding with evening peak travel and in neighborhoods characterized by higher activity density, greater drinking establishment access, and increased shares of residents under 45 years old.

This study aims to develop a hybrid approach based on backpropagation artificial neural network (ANN) and spatial analysis techniques to predict particulate matter of size 2.5 µm (PM2.5) from vehicle exhaust emissions in the State of California using aerosol optical depth (AOD) and several meteorological indicators (relative humidity, temperature, precipitation, and wind speed). The PM2.5 data were generated using the Motor Vehicle Emission Simulator (MOVES). The measured meteorological variables and AOD were obtained from the California Irrigation Management Information System (CIMIS) and NASA’s Moderate Resolution Spectroradiometer (MODIS), respectively. The data were resampled to a seasonal format and downscaled over grids of 10 by 10 to 150 by 150. Coefficient of determination (${\mathrm{R}}^2$), mean absolute percentage error (MAPE), and root mean square error (RMSE) were used to assess the quality of the ANN prediction model. The model peaked at winter seasons with ${\mathrm{R}}^2$ = 0.984, RMSE = 0.027, and MAPE = 25.311, whereas it had the lowest performance in summer with ${\mathrm{R}}^2$ = 0.920, RMSE = 0.057, and MAPE = 65.214. These results indicate that the ANN model can reasonably predict the PM2.5 mass and can be used to forecast future trends.