30 research outputs found
Online Metro Origin-Destination Prediction via Heterogeneous Information Aggregation
Metro origin-destination prediction is a crucial yet challenging time-series
analysis task in intelligent transportation systems, which aims to accurately
forecast two specific types of cross-station ridership, i.e.,
Origin-Destination (OD) one and Destination-Origin (DO) one. However, complete
OD matrices of previous time intervals can not be obtained immediately in
online metro systems, and conventional methods only used limited information to
forecast the future OD and DO ridership separately. In this work, we proposed a
novel neural network module termed Heterogeneous Information Aggregation
Machine (HIAM), which fully exploits heterogeneous information of historical
data (e.g., incomplete OD matrices, unfinished order vectors, and DO matrices)
to jointly learn the evolutionary patterns of OD and DO ridership.
Specifically, an OD modeling branch estimates the potential destinations of
unfinished orders explicitly to complement the information of incomplete OD
matrices, while a DO modeling branch takes DO matrices as input to capture the
spatial-temporal distribution of DO ridership. Moreover, a Dual Information
Transformer is introduced to propagate the mutual information among OD features
and DO features for modeling the OD-DO causality and correlation. Based on the
proposed HIAM, we develop a unified Seq2Seq network to forecast the future OD
and DO ridership simultaneously. Extensive experiments conducted on two
large-scale benchmarks demonstrate the effectiveness of our method for online
metro origin-destination prediction
Gated Ensemble of Spatio-temporal Mixture of Experts for Multi-task Learning in Ride-hailing System
Designing spatio-temporal forecasting models separately in a task-wise and
city-wise manner poses a burden for the expanding transportation network
companies. Therefore, a multi-task learning architecture is proposed in this
study by developing gated ensemble of spatio-temporal mixture of experts
network (GESME-Net) with convolutional recurrent neural network (CRNN),
convolutional neural network (CNN), and recurrent neural network (RNN) for
simultaneously forecasting spatio-temporal tasks in a city as well as across
different cities. Furthermore, a task adaptation layer is integrated with the
architecture for learning joint representation in multi-task learning and
revealing the contribution of the input features utilized in prediction. The
proposed architecture is tested with data from Didi Chuxing for: (i)
simultaneously forecasting demand and supply-demand gap in Beijing, and (ii)
simultaneously forecasting demand across Chengdu and Xian. In both scenarios,
models from our proposed architecture outperformed the single-task and
multi-task deep learning benchmarks and ensemble-based machine learning
algorithms.Comment: arXiv admin note: text overlap with arXiv:2012.0886
Urban Mobility Analytics: Understanding, Inference and Forecasting
Transport systems are the backbones of social and economic activities, which promote industry development and accelerate the process of urbanization. However, the contradiction between the pursuit of travel quality and unbalanced/inadequate development needs the rational construction and operation of transport systems. Owing to the evolution of a massive amount of multi-source data from transport systems, urban mobility analytics, including understanding, inference, and forecasting, support the management and control of transport, which attracts great attention in the long term and becomes more essential in smart transport research. In this thesis, we focus on inferring passenger demographics and predicting passenger demand by understanding travel patterns based on deep spatial-temporal learning algorithms.
We first review the latest state-of-the-art deep learning methods for traffic understanding and attributes inference, traffic forecasting, and demand forecasting to form an overview of the current research progress. Second, we introduce the study public transport dataset collected from the Greater Sydney area and analyze the distributions and similarities of multiple transport modes. Third, we study the investigation of spatial and temporal features in order to infer traveler attributes by proposing a deep-based network with two modules (i.e., a Product-based Spatial-Temporal Module and an Auto-Encoder-based Compression Module). In addition, we study providing confidence interval-based passenger demand forecasting by proposing Probabilistic Graph Convolution Model to help relevant authorities and institutions to better accommodate demand uncertainty/variability. Then, to explore the relations in multimodal transport to boost the demand prediction performance, we propose two deep-based networks for knowledge adaptation between different transport modes by data sharing and model sharing, respectively. Finally, we provide promising directions for future works and conclude the thesis
A spatio-temporal deep learning model for short-term bike-sharing demand prediction
Bike-sharing systems are widely operated in many cities as green transportation means to solve the last mile problem and reduce traffic congestion. One of the critical challenges in operating high-quality bike-sharing systems is rebalancing bike stations from being full or empty. However, the complex characteristics of spatiotemporal dependency on usage demand may lead to difficulties for traditional statistical models in dealing with this complex relationship. To address this issue, we propose a graph-based neural network model to learn the representation of bike-sharing demand spatial-temporal graph. The model has the ability to use graph-structured data and takes both spatial -and temporal aspects into consideration. A case study about bike-sharing systems in Nanjing, a large city in China, is conducted based on the proposed method. The results show that the algorithm can predict short-term bike demand with relatively high accuracy and low computing time. The predicted errors for the hourly station level usage demand prediction are often within 20 bikes. The results provide helpful tools for short-term usage demand prediction of bike-sharing systems and other similar shared mobility systems
How machine learning informs ride-hailing services: A survey
In recent years, online ride-hailing services have emerged as an important component of urban transportation system, which not only provide significant ease for residentsā travel activities, but also shape new travel behavior and diversify urban mobility patterns. This study provides a thorough review of machine-learning-based methodologies for on-demand ride-hailing services. The importance of on-demand ride-hailing services in the spatio-temporal dynamics of urban traffic is first highlighted, with machine-learning-based macro-level ride-hailing research demonstrating its value in guiding the design, planning, operation, and control of urban intelligent transportation systems. Then, the research on travel behavior from the perspective of individual mobility patterns, including carpooling behavior and modal choice behavior, is summarized. In addition, existing studies on order matching and vehicle dispatching strategies, which are among the most important components of on-line ride-hailing systems, are collected and summarized. Finally, some of the critical challenges and opportunities in ride-hailing services are discussed
A Multi-Sensory Stimulating Attention Model for Citiesā Taxi Service Demand Prediction
Taxi demand forecasting is crucial to building an efficient transportation system in a smart city. Accurate taxi demand forecasting could help the taxi management platform to allocate taxi resources in advance, alleviate traffic congestion, and reduce passenger waiting time. Thus, more efforts in industrial and academic circles have been directed towards the citiesā taxi service demand prediction (CTSDP). However, the complex nonlinear spatio-temporal relationship in demand data makes it challenging to construct an accurate forecasting model. There remain challenges in perceiving the micro spatial characteristics and the macro periodicity characteristics from citiesā taxi service demand data. Whatās more, the existing methods are significantly insufficient for exploring the potential multi-time patterns from these demand data. To meet the above challenges, and also stimulated by the human perception mechanism, we propose a Multi-Sensory Stimulus Attention (MSSA) model for CTSDP. Specifically, the MSSA model integrates a detail perception attention and a stimulus variety attention for capturing the micro and macro characteristics from massive historical demand data, respectively. The multiple time resolution modules are employed to capture multiple potential spatio-temporal periodic features from massive historical demand data. Extensive experiments on the yellow taxi trip records data in Manhattan show that the MSSA model outperforms the state-of-the-art baselines
Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities
Traffic prediction plays a crucial role in alleviating traffic congestion
which represents a critical problem globally, resulting in negative
consequences such as lost hours of additional travel time and increased fuel
consumption. Integrating emerging technologies into transportation systems
provides opportunities for improving traffic prediction significantly and
brings about new research problems. In order to lay the foundation for
understanding the open research challenges in traffic prediction, this survey
aims to provide a comprehensive overview of traffic prediction methodologies.
Specifically, we focus on the recent advances and emerging research
opportunities in Artificial Intelligence (AI)-based traffic prediction methods,
due to their recent success and potential in traffic prediction, with an
emphasis on multivariate traffic time series modeling. We first provide a list
and explanation of the various data types and resources used in the literature.
Next, the essential data preprocessing methods within the traffic prediction
context are categorized, and the prediction methods and applications are
subsequently summarized. Lastly, we present primary research challenges in
traffic prediction and discuss some directions for future research.Comment: Published in Transportation Research Part C: Emerging Technologies
(TR_C), Volume 145, 202
A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects
Instant delivery services, such as food delivery and package delivery, have
achieved explosive growth in recent years by providing customers with
daily-life convenience. An emerging research area within these services is
service Route\&Time Prediction (RTP), which aims to estimate the future service
route as well as the arrival time of a given worker. As one of the most crucial
tasks in those service platforms, RTP stands central to enhancing user
satisfaction and trimming operational expenditures on these platforms. Despite
a plethora of algorithms developed to date, there is no systematic,
comprehensive survey to guide researchers in this domain. To fill this gap, our
work presents the first comprehensive survey that methodically categorizes
recent advances in service route and time prediction. We start by defining the
RTP challenge and then delve into the metrics that are often employed.
Following that, we scrutinize the existing RTP methodologies, presenting a
novel taxonomy of them. We categorize these methods based on three criteria:
(i) type of task, subdivided into only-route prediction, only-time prediction,
and joint route\&time prediction; (ii) model architecture, which encompasses
sequence-based and graph-based models; and (iii) learning paradigm, including
Supervised Learning (SL) and Deep Reinforcement Learning (DRL). Conclusively,
we highlight the limitations of current research and suggest prospective
avenues. We believe that the taxonomy, progress, and prospects introduced in
this paper can significantly promote the development of this field
Short-term origin-destination demand prediction in urban rail transit systems: A channel-wise attentive split-convolutional neural network method
Short-term origin-destination (OD) flow prediction in urban rail transit
(URT) plays a crucial role in smart and real-time URT operation and management.
Different from other short-term traffic forecasting methods, the short-term OD
flow prediction possesses three unique characteristics: (1) data availability:
real-time OD flow is not available during the prediction; (2) data
dimensionality: the dimension of the OD flow is much higher than the
cardinality of transportation networks; (3) data sparsity: URT OD flow is
spatiotemporally sparse. There is a great need to develop novel OD flow
forecasting method that explicitly considers the unique characteristics of the
URT system. To this end, a channel-wise attentive split-convolutional neural
network (CAS-CNN) is proposed. The proposed model consists of many novel
components such as the channel-wise attention mechanism and split CNN. In
particular, an inflow/outflow-gated mechanism is innovatively introduced to
address the data availability issue. We further originally propose a masked
loss function to solve the data dimensionality and data sparsity issues. The
model interpretability is also discussed in detail. The CAS-CNN model is tested
on two large-scale real-world datasets from Beijing Subway, and it outperforms
the rest of benchmarking methods. The proposed model contributes to the
development of short-term OD flow prediction, and it also lays the foundations
of real-time URT operation and management.Comment: This paper has been accepted by the Transportation Research Part C:
Emerging Technologies as a regular pape