In traffic operations, the aim of transportation agencies and researchers is typically to reduce congestion and improve safety. To attain these goals, agencies need continuous and accurate information about the traffic situation. Level-of-Service (LOS) is a beneficial index of traffic operations used to monitor freeways. The Highway Capacity Manual (HCM) provides analytical methods to assess LOS based on traffic density and highway characteristics. Generally, obtaining reliable density data on every road in large networks using traditional fixed location sensors and cameras is expensive and otherwise unrealistic. Traditional intelligent transportation system facilities are typically limited to major urban areas in different states. Crowdsourced data are an emerging, low-cost solution that can potentially improve safety and operations. This study incorporates crowdsourced data provided by Waze to propose an algorithm for LOS assessment on an hourly basis. The proposed algorithm exploits various features from big data (crowdsourced Waze user alerts and speed/travel time variation) to perform LOS classification using machine learning models. Three categories of model inputs are introduced: Basic statistical measures of speed; travel time reliability measures; and the number of hourly Waze alerts. Data collected from fixed location sensors were used to calculate ground truth LOS. The results reveal that using Waze crowdsourced alerts can improve the LOS estimation accuracy by about 10% (accuracy = 0.93, Kappa = 0.83). The proposed method was also tested and confirmed by using data from after coronavirus disease 2019 (COVID-19) with severe traffic breakdown due to a stay-at-home policy. The proposed method is extendible for freeways in other locations. The results of this research provide transportation agencies with a LOS method based on crowdsourced data on different freeway segments, regardless of the availability of traditional fixed location sensors.

Researchers are increasingly aware of the importance of crowdsourced data and consider them as possible complementary data sources to existing traffic monitoring systems. In this study, we investigated the coverage, timeliness, and location accuracy of Waze user reports over the years (2018–2021) by comparing their reports with official abnormal event records. The results indicate that the coverage of Waze reports is not constant over time and space. The matching rate of Waze crashes to official records was affected by traffic volumes. It dropped at the start of the COVID-19 pandemic (March 2020) and recovered gradually in 2021, coinciding with the decline and recovery of traffic during the COVID-19 period. Other factors affecting the matching rate include incident duration and location. In addition, Waze reports were timely. On average, 55% of the crashes were reported sooner by Waze users than by the official records. Location accuracy was evaluated as well, and the average absolute distance difference was 0.40 mi for crashes and 0.29 mi for stopped vehicles. Nearly all (90%) of the distance differences between Waze reports and Tennessee Department of Transportation (TDOT) records were between −0.85 and 0.80 mi for crashes and −0.67 to 0.62 mi for stopped vehicles. No obvious change in the spatiotemporal accuracy over time could be observed. Moreover, those Waze reports that could not be matched to TDOT records were analyzed, and the number of crashes covered by those reports was estimated. The results suggest that Waze reports have potential value in identifying missed/unreported crash records and complementing the existing incident database.

The General Transit Feed Specification (GTFS) is an open standard format for recording transit information, utilized by thousands of transit agencies worldwide. In this study, a new tool named GTFS2STN for converting GTFS data into the spatiotemporal networks is introduced. To analyze the travel time variability, it is important to transform a transit network to a spatiotemporal network to enable a comprehensive analysis of transit system accessibility. GTFS2STN is a new tool that converts General Transit Feed Specification (GTFS) data into spatiotemporal networks, addressing the lack of open-source solutions for transit analysis. The tool includes a web application that generates isochrone maps and calculates travel time variability between locations. Validation against Google Maps APIs shows that journey time (i.e., the summation of the transit time, walking time, and waiting time) differences in the Mean Absolute Percentage Error are typically within 12%. A before–after analysis shows that for the transit journey time in 2024 in Nashville, Tennessee, 8 out of 10 pivotal bus stops showed a significantly decreased journey time compared with the case of 2019. A further set of before–after analyses shows that although journey time between transit sites significantly dropped on May 2020 during COVID-19 emergencies, the journey time almost totally recovered to the before-COVID-19 level by November 2020. By supporting any valid GTFS schedule, GTFS2STN enables the analysis of historical and planned transit systems, making it valuable for long-term accessibility assessment and travel time variability studies.