Modeling Time-Varying Variability and Reliability of Freeway Travel Time Using Functional Principal Component Analysis

Chiou, Jeng‐Min; Liou, Han-Tsung; Chen, Wan-Hui

doi:10.1109/tits.2019.2956090

Cited by 20 publications

(13 citation statements)

References 29 publications

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“…Li ( 21 ) used a nonparametric method, kernel density estimation, to measure travel time reliability ratio (the ratio of travel time variability to travel time). Simulation analysis showed that the proposed kernel estimators performed better than the Cornish-Fisher approximation and a “naïve estimator.” Chiou et al ( 22 ) used functional principal component analysis to model travel time reliability on freeways. Nonparametric kernel density estimation was used to estimate the probability density functions of travel times, and the quantiles of travel time distribution were derived from the probability density function.…”

Section: Literature Reviewmentioning

confidence: 99%

Travel Time Reliability Prediction Using Random Forests

Zhao

Zhang²,

Appiah

et al. 2023

Transportation Research Record: Journal of the Transportation R

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The concept of travel time reliability was developed to quantify the variability in travel times. As travel time reliability measures are increasingly used in system planning and performance measurement processes at many transportation agencies, predicting travel time reliability measures has become critical. However, it can be challenging because of the dynamic nature of traffic and the variety of factors contributing to unreliable travel times. This paper developed machine learning models to predict travel time reliability at a planning level. Two random forest algorithms, quantile random forests (QRF) and generalized random forests (GRF), were used to develop prediction models while taking account of a variety of variables from multiple data sources simultaneously. The reliability measures studied are the percentiles of travel times as they are a key component for many commonly used travel time reliability measures. Both QRF and GRF models produced accurate predictions; GRF performed better than QRF at predicting the 50th percentile travel time, and QRF achieved slightly better predictions for the 90th percentile. A case study demonstrated the use of the proposed models for estimating the impact on travel time reliability from an improvement project. The results found both models captured the trend in reliability change, and GRF was preferred over QRF for estimating the level of travel time reliability.

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Section: Literature Reviewmentioning

confidence: 99%

Travel Time Reliability Prediction Using Random Forests

Zhao

Zhang²,

Appiah

et al. 2023

Transportation Research Record: Journal of the Transportation R

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“…There are more than 200 million ETC OBU devices, with average daily ETC transaction data of nearly 1 billion [1]. The ETC transaction data record almost all vehicles' traffic conditions on expressways and can be used for expressway traffic flow prediction [2,3], transit time estimation [4,5], traffic demand visualization [6], etc. The data are expected to provide important information services for intelligent driving on expressways.…”

Section: Introductionmentioning

confidence: 99%

The Fast Detection of Abnormal ETC Data Based on an Improved DTW Algorithm

et al. 2022

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As one of the largest Internet of Things systems in the world, China’s expressway electronic toll collection (ETC) generates nearly one billion pieces of transaction data every day, recording the traffic trajectories of almost all vehicles on the expressway, which has great potential application value. However, there are inevitable missed transactions and false transactions in the expressway ETC system, which leads to certain false and missing rates in ETC data. In this work, a dynamic search step SegrDTW algorithm based on an improved DTW algorithm is proposed according to the characteristics of expressway ETC data with origin–destination (OD) data constraints and coupling between the gantry path and the vehicle trajectory. Through constructing the spatial window of segment retrieval, the spatial complexity of the DTW algorithm is effectively reduced, and the efficiency of the abnormal ETC data detection is greatly improved. In real traffic data experiments, the SegrDTW algorithm only needs 3.36 s to measure the abnormal events of a single set of OD path data for 10 days. Compared with the mainstream algorithms, the SegrDTW performs best. Therefore, the proposal provides a feasible method for the abnormal event detection of expressway ETC data in a province and even the whole country.

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“…ETC data record most of the vehicles driving on the expressway, which basically reflects the traffic status of the expressway section [ 3 ]. Therefore, through ETC data, we can accurately obtain the road utilization rate, traffic rate, traffic speed, travel time, etc., which can help us effectively predict the traffic flow [ 4 ], travel time [ 5 ], and speed [ 6 ] of all vehicle types in each section of the expressway.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, travel time prediction can also provide auxiliary decision-making information for road management and rectification for traffic management departments. A large number of researchers have studied travel time prediction [ 5 , 7 , 8 ], with the deepening of research, the error value is gradually reduced, but there are still some problems. First, there is no separate discussion of different types of vehicles, and different types of vehicles on expressways have different travel characteristics [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-View Travel Time Prediction Based on Electronic Toll Collection Data

Luo

Zou

Zhang

et al. 2022

Entropy

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The travel time prediction of vehicles is an important part of intelligent expressways. It can not only provide the vehicle distribution trend of each section for the expressway management department to assist the fine management of the expressway, but it can also provide owners with dynamic and accurate travel time prediction services to assist the owners to formulate more reasonable travel plans. However, there are still some problems in the current travel time prediction research (e.g., different types of vehicles are not processed separately, the proximity of the road network is not considered, and the capture of important information in the spatial-temporal perspective is not considered in depth). In this paper, we propose a Multi-View Travel Time Prediction (MVPPT) model. First, the travel times of different types of vehicles of each section in the expressway are analyzed, and the main differences in the travel times of different types of vehicles are obtained. Second, multiple travel time features are constructed, which include a novel spatial proximity feature. On this basis, we use CNN to capture the spatial correlation and the spatial attention mechanism to capture key information, the BiLSTM to capture the time correlation of time series, and the time attention mechanism capture key time information. Experiments on large-scale real traffic data demonstrate the effectiveness of our proposal over state-of-the-art methods.

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Modeling Time-Varying Variability and Reliability of Freeway Travel Time Using Functional Principal Component Analysis

Cited by 20 publications

References 29 publications

Travel Time Reliability Prediction Using Random Forests

Travel Time Reliability Prediction Using Random Forests

The Fast Detection of Abnormal ETC Data Based on an Improved DTW Algorithm

Multi-View Travel Time Prediction Based on Electronic Toll Collection Data

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