In a period of increasingly constrained budgets, traffic engineers must develop more cost-effective maintenance strategies for traffic signal systems. Existing literature suggests that many agencies lack methods to systematically identify signal maintenance opportunities, and even fewer measure the impact of maintenance activities. This paper presents a method of using high-resolution signal controller data to identify intersections with failed inductive loop detection, which can result in substantial misallocation of green times and unnecessary vehicle delay. An application of this methodology is explored on a major urban arterial, with the resulting repairs translating to approximately $900,000 in annual user benefits. The results presented in this paper are easily implementable, given the increasing adoption of high-resolution signal controller data. Such methods can also be used to more clearly communicate maintenance success stories with the general public and elected officials.

This article proposes a methodology that can be applied to evaluate the environmental and energy impacts of a de novo technology, the automated electric highway systems (AEHS). The authors used the Motor Vehicle Emission Simulator (MOVES) program to estimate the impacts on emissions and energy use associated with a hypothetical implementation of this technology on the Interstate 70 corridor in Missouri during the period 2011–2040. The estimation results suggest that application of AEHS on the study corridor would decrease fossil fuel energy use by more than 25%, while emissions would decrease by up to 27%, depending on the pollutant being considered. A sensitivity analysis was also performed, in order to assess the impact of different demand estimates for the system; a 10% reduction in AEHS vehicle miles traveled would have an effect of 5% or less on the criteria of interest. This article represents one of the first attempts to quantify the environmental benefits of this widely discussed technology, and can serve as a guiding methodology for evaluation of upcoming intelligent transportation system technologies.

Pedestrian and bicyclist safety in school zones is critical because of the vulnerability of children and adult pedestrians to vehicle crashes. This paper explores vehicle–pedestrian/bike crash severity within a 15-min walking time buffer around schools in Detroit, Michigan, and San Jose, California—cities with high pedestrian/bike fatality rates. Using 2016–2020 crash data, we employed random-parameter multinomial logit models with heterogeneity in means and variances to understand unobserved relationships between variables. Key random parameters identified include the number of buffer zones that each crash falls into, daylight conditions, and the number of units involved in a crash, all significantly affecting injury severity. Spatial stability was investigated to see if variable effects were consistent across locations. Results revealed spatial instability across Detroit and San Jose. Factors such as Covid lockdown, dark lighting, arterial road presence, bicycle crashes, and the number of units involved showed stable effects with varying magnitudes in both cities. Network buffer zones highlighted that crash proximity to multiple schools affects injury severity. Additionally, the study found that various behavioral, roadway, weather, lighting, and school-related factors influence injury severity in school zones. These findings provide valuable insights for policymakers and planners to develop countermeasures, making school areas safer for children, adult pedestrians, and bicyclists.