Urban Link Travel Time Prediction Based on a Gradient Boosting Method Considering Spatiotemporal Correlations

Zhang, Faming; Zhu, Xinyan; Hu, Tao; Guo, Wei; Chen, Chen; Liu, Lingjia

doi:10.3390/ijgi5110201

Cited by 54 publications

(28 citation statements)

References 48 publications

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“…However, normal distributed data is assumed. Reference [12] utilized the Pearson correlation coefficient to measure the speed correlation of different roads in time and space, respectively. In summary, most of the statistical methods requires specific distributions of empirical traffic data, such as linear or normal distribution, which limits the application and reliability of these methods.…”

Section: B Statistical Methods For Congestion Associationmentioning

confidence: 99%

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A Spatiotemporal Apriori Approach to Capture Dynamic Associations of Regional Traffic Congestion

2020

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Due to the interactions among adjacent roads in urban road networks, traffic congestion gradually propagates to neigboring roads, resulting in regional congestion. To develop advanced regional traffic control strategies, it is necessary to clearly understand the characteristics of regional congestion evolution. To this end, this paper proposes a data-driven approach to mine the spatiotemporal associations of regional traffic congestion. By introducing both time and space attributes, the intra-transaction spatiotemporal Apriori (IntraT-ST-Apriori) algorithm is developed to address the static features of regional traffic congestion; while the inter-transaction spatiotemporal Apriori (InterT-ST-Apriori) algorithm is developed to capture the dynamic characteristics of regional traffic congestion. Case studies are carried out for the urban road network in Tianjin, China, based on empirical data. The results indicate that the Intra-ST-Apriori algorithm can excavate the underlying associations of regional traffic congestion. Furthermore, the congestion propagation trajectories can be clearly revealed based on the InterT-ST-Apriori algorithm. It is expected that the proposed approach can support the regional traffic management and control, significantly relieving traffic congestion.

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Section: B Statistical Methods For Congestion Associationmentioning

confidence: 99%

“…Based on network traffic data, early works employed statistical methods to investigate associations of traffic congestion in terms of adjacent road sections [9]- [12]. However, most of the statistical indicators requires specific distributions of empirical traffic data, such as linear or normal distribution.…”

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confidence: 99%

A Spatiotemporal Apriori Approach to Capture Dynamic Associations of Regional Traffic Congestion

2020

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“…Therefore, it is of great significance to develop an effective framework to model short-term bus passenger flow and make accurate predictions. Traditionally, short-term prediction models were mostly derived from statistical and machine learning (ML) methods, including regression analysis [2], the time-series-based model [3,4], support vector machine [5], artificial neural network prediction model [6], Bayesian method [7], gradient boosting method [8], and KNN-based method [9]. However, these traditional models cannot process datasets in raw format.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Prediction of Bus Passenger Flow Based on a Hybrid Optimized LSTM Network

Han

Wang

Ren

et al. 2019

IJGI

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The accurate prediction of bus passenger flow is the key to public transport management and the smart city. A long short-term memory network, a deep learning method for modeling sequences, is an efficient way to capture the time dependency of passenger flow. In recent years, an increasing number of researchers have sought to apply the LSTM model to passenger flow prediction. However, few of them pay attention to the optimization procedure during model training. In this article, we propose a hybrid, optimized LSTM network based on Nesterov accelerated adaptive moment estimation (Nadam) and the stochastic gradient descent algorithm (SGD). This method trains the model with high efficiency and accuracy, solving the problems of inefficient training and misconvergence that exist in complex models. We employ a hybrid optimized LSTM network to predict the actual passenger flow in Qingdao, China and compare the prediction results with those obtained by non-hybrid LSTM models and conventional methods. In particular, the proposed model brings about a 4%–20% extra performance improvements compared with those of non-hybrid LSTM models. We have also tried combinations of other optimization algorithms and applications in different models, finding that optimizing LSTM by switching Nadam to SGD is the best choice. The sensitivity of the model to its parameters is also explored, which provides guidance for applying this model to bus passenger flow data modelling. The good performance of the proposed model in different temporal and spatial scales shows that it is more robust and effective, which can provide insightful support and guidance for dynamic bus scheduling and regional coordination scheduling.

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“…At present, there are lots of research results in the fields of travel time estimation by using trajectories. Different models and methods have been presented to estimate the travel time, which can be divided into two main categories: one is statistical method based on mass historical data, such as support vector regression (SVR) model for travel-time prediction using real highway traffic data (Wu et al, 2004), a model described probability distributions of travel times (Hofleitner et al, 2011), gradient-boosted regression tree model (Zhang et al, 2016); the other is using low-frequency floating car data and other auxiliary information (for example, points of interest (POI), road network information, weather and so on) to predict the travel time in real time, such as a dynamic travel time prediction models with real-time data collected by probe vehicles on path and its consisting link (Chen et al, 2001), a non-parametric method for route travel time estimation using low-frequency floating car data (FCD) (Rahmani et al, 2013), a model for estimating hourly average of urban link travel times using taxicab origin-destination (OD) trip data (Zhan et al, 2013), three dimension tensor model which includes geospatial, temporal and historical contexts (Wang et al, 2014). Most of the research works are based on such a precondition: the trajectories on a subset of the roads are observed by several vehicles within a short time window.…”

Section: Introductionmentioning

confidence: 99%

Travel Time Estimation Using Spatio-Temporal Index Based on Cassandra

et al. 2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Travel time estimation plays an important role in traffic monitoring and route planning. Taxicabs equipped with Global Positioning System (GPS) devices have been frequently used to monitor the traffic state, and GPS trajectories of taxicabs also used to estimate path travel time in an urban area. However, in most cases, it is difficult to find a trajectory that fits perfectly with the query path, as some road segments may be traveled by no taxicab in present time slot. This makes it hard to estimate the travel time of the query path. This paper proposes a framework to estimate the travel time of a path by using the GPS trajectories of taxicabs as well as map data sources. In this framework, the travel time is represented as a series of residence time in cells (one cell is the gird segmentation unit), thus the key issues of the estimation are: finding the local traffic patterns of frequently shared paths from historical data and computing the stay time in cells. There are three major processes in this framework: trajectories preprocessing, establishing the temporal-spatial index and cell-based travel time estimation. Based on the temporal-spatial index, an algorithm is developed that uses similar route patterns, the cell-based travel time over a period of history and road network information to estimate the travel time of a path. This paper uses GPS trajectories of 10,357 taxicabs over a period of one week to evaluate the framework. The results demonstrate that this paper’s method is effective and feasible in city-wide scenarios.</p>

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Urban Link Travel Time Prediction Based on a Gradient Boosting Method Considering Spatiotemporal Correlations

Cited by 54 publications

References 48 publications

A Spatiotemporal Apriori Approach to Capture Dynamic Associations of Regional Traffic Congestion

A Spatiotemporal Apriori Approach to Capture Dynamic Associations of Regional Traffic Congestion

Short-Term Prediction of Bus Passenger Flow Based on a Hybrid Optimized LSTM Network

Travel Time Estimation Using Spatio-Temporal Index Based on Cassandra

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