Long-term travel time prediction using gradient boosting

Chen, Che-Ming; Liang, Chia-Ching; Chu, Chih‐Peng

doi:10.1080/15472450.2018.1542304

Cited by 32 publications

(19 citation statements)

References 33 publications

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“…Although the literature evaluated different NN models by considering some factors affecting traffic, this research will consider several factors together, GA-MENN model, and google maps data to predict traffic velocity. Most of the research considered temporal [61,62] and spatial-temporal [18,56,57,58,60,63] features of the vehicle velocity in the short term traffic prediction, but they lacked consideration of atypical conditions including the weather conditions, weekdays, hour and holidays. Some research addressed these factors; however, they have not been considered all together to predict the vehicle velocity.…”

Section: Literature Reviewmentioning

confidence: 99%

Short-Term Traffic Flow Prediction Using the Modified Elman Recurrent Neural Network Optimized Through a Genetic Algorithm

et al. 2020

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Traffic stream determining is an essential part of the intelligent transportation management system. Precise prediction of traffic flow provides a basis for other tasks, like forecasting travel time. While traditional methods have some merits for improving traffic prediction precision in some ways, high precision, considering different circumstances, is still difficult to achieve. This paper presents a short-term traffic flow prediction model based on the Modified Elman Recurrent Neural Network model (GA-MENN) to deal with this practical problem. In GA-MENN, the algorithm of Elman Recurrent Neural Network is modified, optimized through the Genetic Algorithm (GA) and considered weather conditions, weekday, hour and day's classification to forecast the vehicle velocity in Tehran streets and highways. The traffic data were collected from the online Google Map API service for 139 routs in 7 districts in Tehran. The method improves prediction precision and also lowers the prediction error rate, according to experimental results. Exploratory outcomes verify the superior performance of the proposed traffic condition prediction model over Regression Multi-layer Perceptron, Linear Regression, Logistic Regression, Probabilistic Neural Network, Regression Generalized Feedforward, Time-lag Recurrent Network, Support Vector Machine model, Elman neural network, K-NN model, ARIMA, Kalman filter model, Convolutional Neural Networks (CNNs), SARIMA, and Long Short-Term Memory (LSTM) model. To the best of our knowledge, this is the first occasion when that traffic stream is gauged in urban roads and avenues in this specific way.

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Section: Literature Reviewmentioning

confidence: 99%

Short-Term Traffic Flow Prediction Using the Modified Elman Recurrent Neural Network Optimized Through a Genetic Algorithm

et al. 2020

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“…Uncertainty may also be considered to compute a route with a given latest time of arrival (Lee et al, 2019). While long-term prediction algorithms exist in the literature (Chen et al, 2019), short-term predictions are still challenging. Tra c jams are complicated to predict (Hu et al, 2017), and their impact on travel time depends on various local factors (e.g., speed limit, type of street, neighboring local roads).…”

Section: Introductionmentioning

confidence: 99%

To re-route, or not to re-route: Impact of real-time re-routing in urban road networks

Falek

Gallais

Pelsser

et al. 2021

Journal of Intelligent Transportation Systems

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Route planning represents a major challenge with a substantial impact on safety, economy, and even climate. An ever-growing urban population caused a significant increase in commuting times, therefore, stressing the prominence of e cient realtime route planning. In essence, the goal is to compute the fastest route to reach the target location in a realistic environment where tra c conditions are time-evolving. Consequently, a large volume of tra c data is potentially required and the route continuously updated. We thereby address the re-routing problem to answer questions such as when, how often, and where is re-routing worthwhile. We base our study on a real dataset, comprising the travel times of the road segments of New York, London, and Chicago, collected over three months. By exploiting this dataset, we implement an optimal algorithm, able to mimic ideal predictions of road segment speeds in the network. Thereby, allowing us to compute the lower bound of traveltime to serve as a reference against other routing techniques. Mainly, we quantify the achieved travel-time gain of a static, no re-routing, and continuous re-routing strategies. Surprisingly, we find that tra c conditions are su ciently stable for short time windows, and re-routing a vehicle is very seldom useful when exploiting accurate statistics at departure time. Typically, real-time re-routing should only be triggered during rush hours, for long routes, passing through well-identified road segments.

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“…Modified reinforcement algorithm is introduced with mathematical function to improve the performance of reinforcement learning in the parameters like average objective function and average no of learning episodes with various dimensions [8]. For large distance travel, time prediction is an effective method for managing the traffic, in [9] Gradient Boosting [GB] method is introduced in this work for time prediction. Actually GB is used for smaller distance time prediction, here additional parameters like time, day, week and tolls etc were considered and Fourier filtering is used to avoid noise and applied for larger distance.…”

Section: Introductionmentioning

confidence: 99%

Effective Routing in Vehicular Adhoc Network (VANET) using an Bio-inspired Algorithm: Enhanced Deep Reinforcement Learning (EDRL) for Secure Wireless Communication

Ravikumar¹,

Thiyagarajan²

2021

Preprint

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For improving the performance of city wide-ranging lane networks through the optimized control signal, we proposed a framework in Vehicular Adhoc Network (VANET). Node which reduces the traffic efficiency drastically is identified as critical node, with the help of defined framework. Tripartite graph is used for identifying critical node through vehicle trajectory in the over-all viewpoint. Enhanced Deep Reinforcement Learning (EDRL) method is introduced to control the traffic signal and gives appropriate decision for routing the data from Road Side Unit (RSU) to intermediate or destination node. Various experiments were done with proposed model and the result shows considerable efficiency in delay and travelling time of the node in VANET.

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Long-term travel time prediction using gradient boosting

Cited by 32 publications

References 33 publications

Short-Term Traffic Flow Prediction Using the Modified Elman Recurrent Neural Network Optimized Through a Genetic Algorithm

Short-Term Traffic Flow Prediction Using the Modified Elman Recurrent Neural Network Optimized Through a Genetic Algorithm

To re-route, or not to re-route: Impact of real-time re-routing in urban road networks

Effective Routing in Vehicular Adhoc Network (VANET) using an Bio-inspired Algorithm: Enhanced Deep Reinforcement Learning (EDRL) for Secure Wireless Communication

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