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

IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture

Multi-Step Subway Passenger Flow Prediction under Large Events Using Website Data

An accurate and reliable forecasting method of the subway passenger flow provides the operators with more valuable reference to make decisions, especially in reducing energy consumption and controlling potential risks. However, due to the non-recurrence and inconsistency of large events (such as sports games, concerts or urban marathons), predicting passenger flow under large events has become a very challenging task. This paper proposes a method for extracting event-related information from websites and constructing a multi-step station-level passenger flow prediction model called DeepSPE (Deep Learning for Subway Passenger Flow Forecasting under Events). Experiments on the actual data set of the Beijing subway prove the superiority of the model and the effectiveness of website data in subway passenger flow forecasting under events

Traffic Time Headway Prediction and Analysis: A Deep Learning Approach

In the modern world of Intelligent Transportation System (ITS), time headway is a key traffic flow parameter affecting ITS operations and planning. Defined as “the time difference between any two successive vehicles when they cross a given point”, time headway is used in various traffic and transportation engineering research domains, such as capacity analysis, safety studies, car-following, and lane-changing behavior modeling, and level of service evaluation describing stochastic features of traffic flow. Advanced travel and headway information can also help road users avoid traffic congestion through dynamic route planning, for instance. Hence, it is crucial to accurately model headway distribution patterns for the purpose of analyzing traffic operations and making subsequent infrastructure-related decisions. Previous studies have applied a variety of probabilistic models, machine learning algorithms (for example, support vector machine, relevance vector machine, etc.), and neural networks for short-term headway prediction. Recently, deep learning has become increasingly popular following a surge of traffic big data with high resolution, thriving algorithms, and evolved computational capacity. However, only a few studies have exploited this emerging technology for headway prediction applications. This is largely due to the difficulty in capturing the random, seasonal, nonlinear, and spatiotemporal correlated nature of traffic data and asymmetric human driving behavior which has a significant impact on headway. This study employs a novel architecture of deep neural networks, Long Short-Term Neural Network (LSTM NN), to capture nonlinear traffic dynamics effectively to predict vehicle headway. LSTM NN can overcome the issue of back-propagated error decay (that is, vanishing gradient problem) existing in regular Recurrent Neural Network (RNN) through memory blocks which is its special feature, and thus exhibits superior capability for time series prediction with long temporal dependency. There is no existing appropriate model for long term prediction of traffic headway, as existing models lack using big dataset and solving the vanishing gradient problem because of not having a memory block. To overcome these critics and fill the gaps in previous works, multiple LSTM layers are stacked to incorporate temporal information. For model training and validation, this study used the USDOT’s Next Generation Simulation (NGSIM) dataset, which contains historical data of some important features to describe the headway distribution such as lane numbers, microscopic traffic flow parameters, vehicle and road shape, vehicle type, and velocity. LSTM NN can capture the historical relationships between these variables and save them using its unique memory block. At the headway prediction stage, the related spatiotemporal features from the dataset (HighwayI-80) were fed into a fully connected layer and again tested with testing data for validation (both highway I-80 & US 101). The predicted accuracy outperforms previous time headway predictions