Large-scale short-term urban taxi demand forecasting using deep learning

Liao, Siyu; Zhou, Liutong; Di, Xuan; Yuan, Bo; Xiong, Jinjun

doi:10.1109/aspdac.2018.8297361

Cited by 52 publications

(36 citation statements)

References 10 publications

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“…Similarly, another study [83] determined the performance of taxis by selecting the most important feature from a taxi pattern using L1-Norm SVM. Also, another study [84] forecasted taxi travel demand by using deep learning techniques using taxi datasets from New York City, USA. The DNN outperformed other machine learning methods, however, the right architecture must be identified to get accurate results.…”

Section: Predictive Modelsmentioning

confidence: 99%

Applications of Artificial Intelligence in Transport: An Overview

et al. 2019

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The rapid pace of developments in Artificial Intelligence (AI) is providing unprecedented opportunities to enhance the performance of different industries and businesses, including the transport sector. The innovations introduced by AI include highly advanced computational methods that mimic the way the human brain works. The application of AI in the transport field is aimed at overcoming the challenges of an increasing travel demand, CO2 emissions, safety concerns, and environmental degradation. In light of the availability of a huge amount of quantitative and qualitative data and AI in this digital age, addressing these concerns in a more efficient and effective fashion has become more plausible. Examples of AI methods that are finding their way to the transport field include Artificial Neural Networks (ANN), Genetic algorithms (GA), Simulated Annealing (SA), Artificial Immune system (AIS), Ant Colony Optimiser (ACO) and Bee Colony Optimization (BCO) and Fuzzy Logic Model (FLM) The successful application of AI requires a good understanding of the relationships between AI and data on one hand, and transportation system characteristics and variables on the other hand. Moreover, it is promising for transport authorities to determine the way to use these technologies to create a rapid improvement in relieving congestion, making travel time more reliable to their customers and improve the economics and productivity of their vital assets. This paper provides an overview of the AI techniques applied worldwide to address transportation problems mainly in traffic management, traffic safety, public transportation, and urban mobility. The overview concludes by addressing the challenges and limitations of AI applications in transport.

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Section: Predictive Modelsmentioning

confidence: 99%

Applications of Artificial Intelligence in Transport: An Overview

et al. 2019

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“…With the same idea, Zhang et al (2017) has proposed a spatiotemporal Resnet (ST-Resnet) which includes several convolutional layers. Liao et al (2018) has implemented both of these techniques on a New York City taxi record dataset and their comparison has shown that better performance with a faster training time can be achieved using ST-Resnet. The authors suggest two reasons for this.…”

Section: Ride Sharing and Public Transportationmentioning

confidence: 99%

Applications of Deep Learning in Intelligent Transportation Systems

Haghighat

Ravichandra-Mouli

Chakraborty

et al. 2020

J. Big Data Anal. Transp.

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In recent years, Intelligent Transportation Systems (ITS) have seen efficient and faster development by implementing deep learning techniques in problem domains which were previously addressed using analytical or statistical solutions and also in some areas that were untouched. These improvements have facilitated traffic management and traffic planning, increased safety and security in transit roads, decreased costs of maintenance, optimized public transportation and ride-sharing company's performance, and advanced driver-less vehicle development to a new stage. This papers primary objective was to provide a review and comprehensive insight into the applications of deep learning models on intelligent transportation systems accompanied by presenting the progress of ITS research due to deep learning. First, different techniques of deep learning and their state-of-the-art are discussed, followed by an in-depth analysis and explanation of the current applications of these techniques in transportation systems. This enumeration of deep learning on ITS highlights its significance in the domain. The applications are furthermore categorized based on the gap they are trying to address. Finally, different embedded systems for deployment of these techniques are investigated and their advantages and weaknesses over each other are discussed. Based on this systematic review, credible benefits of deep learning models on ITS are demonstrated and directions for future research are discussed.

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“…Later, the ResNet was refined by He et al He et al (2016b) by introducing identity mappings. Liao et al (2018) have succesfully applied ResNet for taxi demand forecasting. In this work, the refined ResNet is treated as a feature extractor for camera images in Waymo Dataset.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

An LSTM-Based Autonomous Driving Model Using a Waymo Open Dataset

et al. 2020

Applied Sciences

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The Waymo Open Dataset has been released recently, providing a platform to crowdsource some fundamental challenges for automated vehicles (AVs), such as 3D detection and tracking. While the dataset provides a large amount of high-quality and multi-source driving information, people in academia are more interested in the underlying driving policy programmed in Waymo self-driving cars, which is inaccessible due to AV manufacturers' proprietary protection. Accordingly, academic researchers have to make various assumptions to implement AV components in their models or simulations, which may not represent the realistic interactions in real-world traffic. Thus, this paper introduces an approach to learn an long short-term memory (LSTM)-based model for imitating the behavior of Waymo's self-driving model. The proposed model has been evaluated based on Mean Absolute Error (MAE). The experimental results show that our model outperforms several baseline models in driving action prediction. Also, a visualization tool is presented for verifying the performance of the model.

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Large-scale short-term urban taxi demand forecasting using deep learning

Cited by 52 publications

References 10 publications

Applications of Artificial Intelligence in Transport: An Overview

Applications of Artificial Intelligence in Transport: An Overview

Applications of Deep Learning in Intelligent Transportation Systems

An LSTM-Based Autonomous Driving Model Using a Waymo Open Dataset

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