Improving Probe-Based Congestion Performance Metrics Accuracy by Using Change Point Detection

Abstract

Probe-based speed data provide great value to agencies; especially, in areas which are not feasibly covered by traffic sensors. However, as with sensors, probe data are not without nuance and issues like latency prevent alignment between calculated metrics by data source. In recent years, there has been a strong impetus on using data-driven decision making. Data-driven insights have become critical for smart mobility. To support data-driven decision making, Federal Highway Administration has procured probe data feeds and provides free access to state and local agencies as National Performance Measures Research dataset (NPRMDS). In addition to the NPRMDS, several state agencies subscribe to a paid probe data provider for obtaining real-time streams of high-resolution probe data. These datasets are used to generate nationwide urban mobility reports as well as reports focusing on certain jurisdiction. These mobility reports are integrated in several Transportation System Management and Operations (TSMO) plans which are often used to drive several resource allocation projects. This paper examines accuracy of methodology used to derive two frequently used performance measures in the mobility reports; namely, number of congested hours and number of congestion events. An improve methodology is then proposed to find accurate estimates for number of congested hours and number of congested incidents.

Exploring the Efficacy of Large-Scale Connected Vehicle Data in Real-Time Traffic Applications

“…The benchmarked or ground reference data set for the present study is obtained from infrastructure-mounted traffic sensors (Wavetronix smart sensors) using side-fire radars, which collect data for both directions of travel for individual lanes. The sensors collect real-time traffic data such as vehicle count, occupancy, and vehicle speeds, along with timestamps every 60 s. Although the traffic sensor data are observed to have minor inherent flaws ( 10 , 11 ) caused by technical malfunctions, weather disturbances, and human errors, they have been used in many studies as ground reference ( 4, 12–14 ). This study used the data from 48 accurately installed operational traffic sensors with fewer flaws.…”

Unlocking Metaverse-as-a-Service The three pillars to watch: Privacy and Security, Edge Computing, and Blockchain

“…Another challenge is the variety of data produced by metaverse apps, which can make it difficult and time-consuming to collect and organize the necessary data for consumers. Additionally, the rapid development of big data technology [128]- [133] can make it challenging to stay up to date with technical advancements in the metaverse.…”

Using Probe-Based Speed Data and Interactive Maps for Long-Term and COVID-Era Congestion Monitoring in San Francisco

“…Numerous studies have been performed on assessing the quality of probe data in relation to accuracy and bias (10)(11)(12)(13)(14). These studies have also proposed methods to detect and address biases that might be present in the data.…”

“…Probe data that provide roadway speeds and travel times are increasingly being used for a variety of purposes in the transportation domain (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15). A key use of these datasets has been roadway performance monitoring.…”

“…Despite considerable savings in time and effort for data collection, several challenges associated with using probe data still exist. One set of challenges relates to the quality of the underlying data, and several studies have proposed methods to detect bias and improve overall accuracy (10)(11)(12)(13)(14). The other set of challenges relates to the cumbersome process of cleaning and processing the data to derive the required performance metrics on many roadway links.…”