Analyzing the effects of congestion on planning time index – Grey models vs. random forest regression

Zargari, Shahriar Afandizadeh; Khorshidi, Navid Amoei; Mirzahossein, Hamid; Heidari, Hanif

doi:10.1016/j.ijtst.2022.05.008

Cited by 6 publications

(4 citation statements)

References 18 publications

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“…In recent years, with the advancement of machine learning technology, data-driven machine learning methods have been widely applied in traffic status recognition. Among them, decision trees [13], support vector machines [14], random forests [15], and others are common methods. These approaches learn patterns and rules of traffic status from training data and then apply them to determine new data.…”

Section: Traffic Status Recognitionmentioning

confidence: 99%

“…Depending on the mechanism of data missingness, missing data can be categorized into three types: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR) [3]. In the realm of traffic analysis, MCAR denotes missing traffic data that occur randomly and independently of other observed values, such as the occasional Sustainability 2023, 15, 14671 2 of 20 data point missing due to equipment malfunctions. MAR, on the other hand, signifies that traffic data are missing randomly, but the missingness is related to one or several other observed values, for example, data missing during specific time intervals related to time observations.…”

Section: Introductionmentioning

confidence: 99%

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Traffic Status Prediction Based on Multidimensional Feature Matching and 2nd-Order Hidden Markov Model (HMM)

Li,

Liu,

Chen

2023

Sustainability

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Spatiotemporal data from urban road traffic are pivotal for intelligent transportation systems and urban planning. Nonetheless, missing data in traffic datasets is a common challenge due to equipment failures, communication issues, and monitoring limitations, especially the missing not at random (MNAR) problem. This research introduces an approach to address MNAR-type missing data in traffic status prediction, utilizing a multidimensional feature sequence and a second-order hidden Markov model (2nd-order HMM). First, this approach involves extracting spatiotemporal features for the preset data sections and spatial features for the sections to be predicted based on the traffic spatiotemporal characteristics. Second, using the extracted features, distinctive road traffic features are generated for each section. Furthermore, at specific intervals within the defined time period, nearest distance feature matching is introduced to ascertain the traffic attributes of the road section under prediction. Finally, relying on the matched status results, a 2nd-order HMM is employed to forecast the traffic status for subsequent moments within the defined time period. Experiments were carried out using datasets from Shenzhen City and compared against the hidden Markov models and contrast measure (HMM-C) method to affirm the efficacy of the proposed approach.

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Section: Traffic Status Recognitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Traffic Status Prediction Based on Multidimensional Feature Matching and 2nd-Order Hidden Markov Model (HMM)

Li,

Liu,

Chen

2023

Sustainability

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“…It is an important component of Intelligent Transportation Systems (ITSs). Accurate traffic flow prediction is crucial for urban road planning, traffic control and estimating planning time accurately [2].…”

Section: Introductionmentioning

confidence: 99%

Traffic Flow Prediction Based on Federated Learning and Spatio-Temporal Graph Neural Networks

Feng,

Du,

2024

IJGI

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In response to the insufficient consideration of spatio-temporal dependencies and traffic pattern similarity in traffic flow prediction methods based on federated learning, as well as the neglect of model heterogeneity and objective heterogeneity, a traffic flow prediction model based on federated learning and spatio-temporal graph neural networks is proposed. The model is divided into two stages. In the road network division stage, the traffic road network is divided into subnetworks by the dynamic time warping algorithm and the K-means algorithm, to ensure the same subnetwork has the similar traffic flow pattern. The federated learning stage is divided into two sub-stages. In the local training phase, the spatio-temporal graph neural network with an attention mechanism is utilized to create personalized models and meme models to capture the spatio-temporal dependencies of each subnetwork. At the same time, deep mutual learning is utilized to address model heterogeneity and objective heterogeneity through knowledge distillation. In the global aggregation phase, a multi-factor weighted aggregation strategy is designed to measure the contribution of each local model to the global model, to enhance the fairness of aggregation. Three sets of experiments were conducted on two real datasets, and the experimental results demonstrate that the proposed model outperforms the baseline models in three common evaluation metrics.

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“…This problem has worsened with the increasing traffic in Central Java Province, resulting from population growth, tourist destinations, and the proliferation of culinary establishments (food centers) [8]. Traffic management is among the methods employed based on the analysis of existing traffic flow generation and attraction movements [9][10].…”

Section: Introductionmentioning

confidence: 99%

Estimation of the origin-destination matrix from national road traffic data in Central Java Province using the least squares method

Hermani,

Setyawan,

Syafi’i

2023

E3S Web Conf.

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Central Java Province has experienced increased movement due to economic, social, and cultural developments. This increased activity has caused transportation problems, highlighting the need for effective transportation planning. A commonly used method for transportation planning is the four-step technique. Therefore, this study aimed to estimate the origin-destination (O-D) matrix for Central Java Province in 2022 and 2040. The traffic count method was utilized for calculating the O-D matrix in 2022, while the gravity method was employed for 2040. The results showed that the total movement for 2022 was 50,295 PCU/hour, while the projected amount for 2040 was 139,674 PCU/hour. In 2022, the action was primarily dominated by internal-internal trips, which account for 75% or 37,528 PCU/hour. In conclusion, the proposed approach has good validity, as indicated by a coefficient of determination (R2) of 0.677.

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Analyzing the effects of congestion on planning time index – Grey models vs. random forest regression

Cited by 6 publications

References 18 publications

Traffic Status Prediction Based on Multidimensional Feature Matching and 2nd-Order Hidden Markov Model (HMM)

Traffic Status Prediction Based on Multidimensional Feature Matching and 2nd-Order Hidden Markov Model (HMM)

Traffic Flow Prediction Based on Federated Learning and Spatio-Temporal Graph Neural Networks

Estimation of the origin-destination matrix from national road traffic data in Central Java Province using the least squares method

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