Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach

Short-term passenger demand forecasting is of great importance to the on-demand ride service platform, which can incentivize vacant cars moving from over-supply regions to over-demand regions. The spatial dependences, temporal dependences, and exogenous dependences need to be considered simultaneously, however, which makes short-term passenger demand forecasting challenging. We propose a novel deep learning (DL) approach, named the fusion convolutional long short-term memory network (FCL-Net), to address these three dependences within one end-to-end learning architecture. The model is stacked and fused by multiple convolutional long short-term memory (LSTM) layers, standard LSTM layers, and convolutional layers. The fusion of convolutional techniques and the LSTM network enables the proposed DL approach to better capture the spatio-temporal characteristics and correlations of explanatory variables. A tailored spatially aggregated random forest is employed to rank the importance of the explanatory variables. The ranking is then used for feature selection. The proposed DL approach is applied to the short-term forecasting of passenger demand under an on-demand ride service platform in Hangzhou, China. Experimental results, validated on real-world data provided by DiDi Chuxing, show that the FCL-Net achieves better predictive performance than traditional approaches including both classical time-series prediction models and neural network based algorithms (e.g., artificial neural network and LSTM). This paper is one of the first DL studies to forecast the short-term passenger demand of an on-demand ride service platform by examining the spatio-temporal correlations.Comment: 39 pages, 10 figure

A General Traffic Flow Prediction Approach Based on Spatial-Temporal Graph Attention

Funding Information: This work was supported in part by the Science and Technology Project of Hunan Provincial Communications Department, China, under Grant 2018037, and in part by the National Nature Science Foundation of China under Grant 61674054. Publisher Copyright: © 2013 IEEE.Accurate and reliable traffic flow prediction is critical to the safe and stable deployment of intelligent transportation systems. However, it is very challenging due to the complex spatial and temporal dependence of traffic flows. Most existing works require the information of the traffic network structure and human intervention to model the spatial-temporal association of traffic data, resulting in low generality of the model and unsatisfactory prediction performance. In this paper, we propose a general spatial-temporal graph attention based dynamic graph convolutional network (GAGCN) model to predict traffic flow. GAGCN uses the graph attention networks to extract the spatial associations among nodes hidden in the traffic feature data automatically which can be dynamically adjusted over time. And then the graph convolution network is adjusted based on the spatial associations to extract the spatial features of the road network. Notably, the information of road network structure and human intervention are not required in GAGCN. The forecasting accuracy and the generality are evaluated with two real-world traffic datasets. Experimental results indicate that our GAGCN surpasses the state-of-the-art baselines on one of the two datasets.Peer reviewe

Recent Advances in Graph-based Machine Learning for Applications in Smart Urban Transportation Systems

The Intelligent Transportation System (ITS) is an important part of modern transportation infrastructure, employing a combination of communication technology, information processing and control systems to manage transportation networks. This integration of various components such as roads, vehicles, and communication systems, is expected to improve efficiency and safety by providing better information, services, and coordination of transportation modes. In recent years, graph-based machine learning has become an increasingly important research focus in the field of ITS aiming at the development of complex, data-driven solutions to address various ITS-related challenges. This chapter presents background information on the key technical challenges for ITS design, along with a review of research methods ranging from classic statistical approaches to modern machine learning and deep learning-based approaches. Specifically, we provide an in-depth review of graph-based machine learning methods, including basic concepts of graphs, graph data representation, graph neural network architectures and their relation to ITS applications. Additionally, two case studies of graph-based ITS applications proposed in our recent work are presented in detail to demonstrate the potential of graph-based machine learning in the ITS domain

Traffic Flow Forecast based on Vehicle Count

Real-time traffic predictions have now become a time-being need for efficient traffic management due to the exponentially increasing traffic congestion In this paper a more pragmatic traffic management system is introduced to address traffic congestion especially in countries such as Sri Lanka where there is no proper traffic monitoring database Here the real-time traffic monitoring is performed using TFmini Plus light detection and ranging LiDAR sensor and vehicle count for next five minutes will be predicted by feeding consecutively collected data into the LSTM neural network More than ten separate prediction models were trained varying both window size and the volume of input data delivered to train the models Since the accuracy results of all prediction models were above 70 it demonstrates that this system can produce accurate predictions even if it is trained using less input data collection Similarly the sensor accuracy test also resulted in 89 7 accurac

Air passenger demand forecast through the use of Artificial Neural Network algorithms

Airport planning depends to a large extent on the levels of activity that are anticipated. To plan the facilities and infrastructures of an airport system and to be able to satisfy future needs, it is essential to predict the level and distribution of demand. This document presents a short- and medium-term forecast of the demand for air passengers carried out through a specific case study (Colombia), in which the impact of the pandemic period due to COVID-19 on air traffic was taken into account. To make the forecast, an algorithm that implements techniques based on Artificial Neural Networks (ANN) (Machine Learning (ML)) was developed. In particular, for the analysis of the available time series, techniques of encoder-decoder networks of the type ConvLSTM2D have been applied. These architectures are a hybrid between Convolutional Neural Networks (CNN), very useful for the extraction of invariant patterns in their spatial position, and Recurrent Neural Networks (RNN), appropriate for the extraction of patterns within their temporal context (time series). The most relevant result of the present research is that the recovery in demand (volume and trend) to the levels reported before the pandemic is forecast for the period between the end of 2022 and the beginning of 2024 (depending on the type of traffic and scenario considered). Finally, the application of the forecasting model based on Machine Learning/Deep Learning (DL) presents, as a metric performance, a Mean Absolute Percentage Error (MAPE) value from 3% to 9% (depending on the scenario), which enables predictions of relative precision and introduces a new alternative technical approach to develop reliable air traffic forecasts, at least in the short and medium term

Learning Informative Representation for Fairness-aware Multivariate Time-series Forecasting: A Group-based Perspective

Performance unfairness among variables widely exists in multivariate time series (MTS) forecasting models since such models may attend/bias to certain (advantaged) variables. Addressing this unfairness problem is important for equally attending to all variables and avoiding vulnerable model biases/risks. However, fair MTS forecasting is challenging and has been less studied in the literature. To bridge such significant gap, we formulate the fairness modeling problem as learning informative representations attending to both advantaged and disadvantaged variables. Accordingly, we propose a novel framework, named FairFor, for fairness-aware MTS forecasting. FairFor is based on adversarial learning to generate both group-independent and group-relevant representations for the downstream forecasting. The framework first leverages a spectral relaxation of the K-means objective to infer variable correlations and thus to group variables. Then, it utilizes a filtering&fusion component to filter the group-relevant information and generate group-independent representations via orthogonality regularization. The group-independent and group-relevant representations form highly informative representations, facilitating to sharing knowledge from advantaged variables to disadvantaged variables to guarantee fairness. Extensive experiments on four public datasets demonstrate the effectiveness of our proposed FairFor for fair forecasting and significant performance improvement.Comment: 13 pages, 5 figures, accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE