Identifying Recurring Bottlenecks on Urban Expressway Using a Fusion Method Based on Loop Detector Data

Abstract

e accurate identification of recurrent bottlenecks has been an important assumption of many studies on traffic congestion analysis and management. As one of the most widely used traffic detection devices, loop detectors can provide reliable multidimensional data for traffic bottleneck identification. Although great efforts have been put on developing bottleneck identification methods based on loop detector data, the existing studies are less informative with respect to providing accurate position of the bottlenecks and discussing the algorithm efficiency when facing with large amount of real-time data. is paper aims at improving the quality of bottleneck identification as well as avoiding excessive data processing burden. A fusion method of loop detector data with different collection cycles is proposed. It firstly determines the occurrence and the approximate locations of bottlenecks using large cycle data considering its high accuracy in determining bottlenecks occurrence. en, the small cycle data are used to determine the accurate location and the duration time of the bottlenecks. A case study is introduced to verify the proposed method. A large set of 30 s raw loop detector data from a selected urban expressway segment in California is used. Also, the identification result is compared with the classical transformed cumulative curves method. e results show that the fusion method is valid with bottleneck identification and location positioning. We finally conclude by discussing some future improvements and potential applications.

Urban Traffic Detectors Data Mining for Determination of Variations in Traffic Volumes

“…It made it possible to estimate the seasonal variation in road traffic in urban environments. Furthermore, the average daily volumes for all days of the week and the average daily volumes by specific day of the week were calculated for each group of roads [24,25]. A graph was used to compare the daily traffic volumes by day of the week with the average daily volumes by week.…”

“…4, 2020 Stathopoulos and Karlaftis [23] or Aunet [2]. In their studies, some have identified different variations of traffic in different working weeks relating to the activities of the population in the area [5,10,25]. Due to the fact that traffic volumes on the roads are formed under a great influence of the catchment area, other authors also studied the relationships between traffic variations and catchment areas.…”

A Method for Exploring and Analyzing Spatiotemporal Patterns of Traffic Congestion in Expressway Networks Based on Origin–Destination Data

“…Compared with urban traffic congestion, there are fewer studies that focus on traffic congestion in expressways. The existing studies are based on different data and methods to investigate traffic congestion in expressways [30][31][32][33][34][35][36][37][38][39][40] and analyze the characteristics of traffic congestion in expressways [41][42][43][44][45]. The summary of representative studies about traffic congestion in expressways is listed in Table 1.…”

Statistical Distance-Based Travel-Time Reliability Measurement for Freeway Bottleneck Identification and Ranking

“…However, as a result of the limited spatial coverage of loop detectors and the quality issues with the data (for example, inconsistency and missing values), the identification results are compromised. To improve the identification results, methods to refine loop detector data and the application of new data sources in bottleneck identification have been studied intensively (11)(12)(13)(14)(15)(16)(17)(18)(19)(20). Tang et al (11) proposed a fusing method that identifies bottlenecks based on loop detector data of multiple temporal granularities.…”

“…To improve the identification results, methods to refine loop detector data and the application of new data sources in bottleneck identification have been studied intensively (11)(12)(13)(14)(15)(16)(17)(18)(19)(20). Tang et al (11) proposed a fusing method that identifies bottlenecks based on loop detector data of multiple temporal granularities. Song et al (13) addressed the issues of invalid and inconsistent data in loop detector data by adding a denoising component into the bottleneck identification process.…”