Sailesh Acharya

Pavement friction is crucial for road safety, especially in adverse weather. This study investigates the relationship between pavement friction—measured as skid number (SN)—and crash frequency on Utah highways. Using data from 2016 to 2019 for I-15 (interstate) and US-89 (non-interstate), negative binomial models were estimated to establish safety performance functions (SPFs) and crash modification factors (CMFs). The models accounted for traffic volume, segment length, and roadway geometric characteristics, examining various crash types, including dry- and wet-weather, property damage only, and injury-related crashes. Results show a significant negative association between SN and crash frequency for all crash types on both highway types. Higher SN values (more friction) were linked to fewer crashes. A 10-unit increase in SN was associated with a 7% to 8% reduction in dry-weather crashes on both highway segments. For wet-weather crashes, the same increase in SN resulted in a 14% decrease on non-interstate and a 21% decrease on interstate highways. The impact of SN on reducing crashes was particularly strong on interstate highways during wet conditions, indicating that pavement friction is vital for safety in these scenarios. Additionally, the safety impact of skid resistance was greater on segments with more curved portions. These findings suggest that enhancing pavement friction through measures such as high friction surface treatments could significantly improve traffic safety. The results support the continued collection of skid data by transportation agencies to identify high-risk locations and prioritize friction improvement efforts to enhance roadway safety.

Based on surveys of 910 long-distance airport access travelers in the United States, we observed early adopters with higher intentions to adopt Electric Air Taxis (EAT) and laggards with lower intentions and notable resistance toward acceptance, compared to majority, thus validating Rogers’ innovativeness-based categorization. The intention to adopt EAT was motivated by perceived ease of use, social validation, and the utility offered by EAT during airport access trips. Performance expectancy emerged as a key predictor of intention to use EAT among the majority and laggards, while social influence had a stronger impact on intention to use EAT among early adopters.

On-demand transit (ODT) systems are increasing in number and size. To evaluate and quantify outcomes, the research team utilizes the mobility energy productivity (MEP) metric, which is a holistic accessibility measure, to analyze and compare the mobility of various transportation modes in Arlington, Texas. The MEP tool is applied to the ODT system in Arlington, Texas, as well as to five existing transportation modes (driving, transportation network company, transit, biking, and walking). Six ODT scenarios are also analyzed and compared. The analysis is focused on the opportunities that an ODT system presents for transportation disadvantaged communities (DACs) with low rates of car ownership. Although driving received the highest MEP score—a finding typical for a U.S.A. city— the results for the ODT system reveal that it serves those in DACs effectively, helping to achieve an equity design goal. ODT improved the average MEP score across the service area by 83% when considering only accessible, nonprivate vehicle modes (biking, transit, and ODT). For the ODT scenarios, decreasing the wait time by 50% compared with the baseline scenario led to a nearly 160% increase in the MEP score, whereas increasing the ODT travel speed by 21% led to an 80% improvement in the MEP score. As analyzed through the MEP tool, this paper demonstrates how ODT can enhance mobility, particularly for DACs. The results of an MEP analysis can be used by researchers and transit agencies to compare transportation modes and improve the effectiveness of transportation systems across a service area.

Cities and communities continue to expand pedestrian and bicycle infrastructure as part of their sustainable mobility and post-pandemic recovery plans. However, the emergence of micromobility (e.g., electric bicycles) has created new challenges for urban transport planning. As the popularity of micro-mobility modes has grown, so have safety concerns due to rising injuries, thus challenging many cities to come up with regulatory infrastructure policies that enable efficient access while minimizing negative impacts from micro-mobility. Leveraging the OpenSource Tool (Mobility Energy Productivity metric), powered by an OpenSource Dataset (OpenStreetMap), and enhanced through the incorporation of perceived discomfort (level-oftraffic stress), this research study focuses on evaluating the accessibility implications of infrastructure planning and regulatory policies for micro-mobility. Scenarios pertaining to level of traffic stress, sidewalk access, traffic calming, and bike lane coverage were tested in the Denver-Aurora region in Colorado. Maximum improvements in energy-efficient access are realized when allowing sidewalks for micro-mobility use. Cities and planning agencies could leverage this information to assess sidewalk use policies for micro-mobility, while ensuring pedestrian safety and ADA access. Results indicate that expansion of bicycle lane coverage yields 11% accessibility benefits for micro-mobility compared to implementing traffic calming measures yielding 3% accessibility improvements. Further, it was observed most of the population in the Denver-Aurora region is experiencing lower accessibility in part due to presence of high-stress connections in the network. Although not generalizable, the use of opensource data and access calculation methodology make this analysis reproducible and transferable for sustainable infrastructure development of any region.

Based on a survey of 696 visitors to US national parks, we found higher preferences for working/studying/reading (+77%), using social media (+63%), and entertaining (+34%) activities when traveling in an autonomous vehicle (AV) compared to a human-driven vehicle (HV). A multinomial logit-based ranked-choice analysis (between HVs, AVs, and teleportation) suggests that while most travelers enjoy spending time traveling by manually driving and/or engaging in activities of interest in HVs and AVs, some would prefer teleportation instead of spending time traveling. The choice of AVs is significantly influenced by preferences for working/studying/reading and eating/caring activities.

With the likely future of autonomous vehicles (AVs) as private, ride-hailing, and pooled vehicles, it is important to consider all forms of AVs when estimating the impacts of automation on travel behavior. To aid this, this study jointly models the public interest in three forms of AVs (owning, ride-hailing, and using pooled services) and compares the interests in owning versus ride-hailing AVs using a combination of structural equation modeling and multivariate ordered probit modeling frameworks. Using the 2019 California Vehicle Survey data, we estimate the impacts of several exogenous and latent variables on all forms of AV adoption. We find that the individual, household, travel-related, and built-environment factors are related to different forms of AV adoption directly and indirectly through attitudes toward human and automated driving. We also report that human and automated driving sentiments have the highest impact on interest in owning an AV compared to interest in ride-hailing and using pooled AVs. We discuss several policy implications by calculating the pseudo-elasticity effects of exogenous variables and the sensitivities of the impacts on latent variables on different forms of AV adoption. For example, public interest in owning private AVs can be increased by more than 7% by making them familiar with autonomous technology.

Purpose: WIth limited research on the effects of variable message sign (VMS) message content and verbiage on revealed driver behavior, this study aims to investigate how different verbiage of crash-related messages are related to the diversion rate. Design/methodology/approach: Using ordered logit models, the associations of message verbiage with diversion rates during crash incidents were assessed using five years of VMS message history within a section of I-15 in the state of Utah. Findings: A significant impact of message verbiage on the diversion rate was observed. Based on the analysis results, the crash message verbiage with the highest diversion was found to be miles to crash + “prepare to stop,” followed by crash location + delay information, miles to crash + “use caution” + lane of the crash, etc. In addition, the diversion rate was found to be correlated to some roadway characteristics (e.g. occupancy in mainline, weather condition and light condition) along with the temporal variations. Research limitations/implications: These findings could be used by transportation agencies (e.g. state department of transportation [DOTs]) to make informed decisions about choosing the message verbiage during future crash incidents. This study also revealed that higher diversion rates are associated with a shorter distance between the crash location and VMS device location, recommending increasing the number of VMS devices, particularly in crash-prone areas.