DeepUrbanMomentum: An Online Deep-Learning System for Short-Term Urban Mobility Prediction

Jiang, Renhe; Song, Xuan; Fan, Zipei; Xia, Tianqi; Chen, Quanjun; Miyazawa, Satoshi; Shibasaki, Ryosuke

doi:10.1609/aaai.v32i1.11338

Cited by 37 publications

(5 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[171,172] to predict the movements of a group of people or crowds. Jiang et al applied the RNN for city mobility prediction from actual data collected from operational networks and achieved better accuracy than the Markovian model [51]. Lin et al applied the hidden Markov model to wireless CDR data for user route profile generation to support urban transport assessment [172].…”

Section: Applications In Mobility Assessmentmentioning

confidence: 99%

See 1 more Smart Citation

Scope of machine learning applications for addressing the challenges in next‐generation wireless networks

Samanta¹,

Sadhukhan²,

Samaddar³

et al. 2022

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

The convenience of availing quality services at affordable costs anytime and anywhere makes mobile technology very popular among users. Due to this popularity, there has been a huge rise in mobile data volume, applications, types of services, and number of customers. Furthermore, due to the COVID‐19 pandemic, the worldwide lockdown has added fuel to this increase as most of our professional and commercial activities are being done online from home. This massive increase in demand for multi‐class services has posed numerous challenges to wireless network frameworks. The services offered through wireless networks are required to support this huge volume of data and multiple types of traffic, such as real‐time live streaming of videos, audios, text, images etc., at a very high bit rate with a negligible delay in transmission and permissible vehicular speed of the customers. Next‐generation wireless networks (NGWNs, i.e. 5G networks and beyond) are being developed to accommodate the service qualities mentioned above and many more. However, achieving all the desired service qualities to be incorporated into the design of the 5G network infrastructure imposes large challenges for designers and engineers. It requires the analysis of a huge volume of network data (structured and unstructured) received or collected from heterogeneous devices, applications, services, and customers and the effective and dynamic management of network parameters based on this analysis in real time. In the ever‐increasing network heterogeneity and complexity, machine learning (ML) techniques may become an efficient tool for effectively managing these issues. In recent days, the progress of artificial intelligence and ML techniques has grown interest in their application in the networking domain. This study discusses current wireless network research, brief discussions on ML methods that can be effectively applied to the wireless networking domain, some tools available to support and customise efficient mobile system design, and some unresolved issues for future research directions.

show abstract

Section: Applications In Mobility Assessmentmentioning

confidence: 99%

“…Jiang et al. applied the RNN for city mobility prediction from actual data collected from operational networks and achieved better accuracy than the Markovian model [51]. Lin et al.…”

Section: Intelligent Next‐generation Wireless Networkmentioning

confidence: 99%

Scope of machine learning applications for addressing the challenges in next‐generation wireless networks

Samanta¹,

Sadhukhan²,

Samaddar³

et al. 2022

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

show abstract

“…Compared with our fine-grained predictor, such coarse To predict the fine-grained level human mobility, we need to generate a realistic trajectory, which is a long timestamped sequence, on the transportation network. [12] applied the seq2seq model to human trajectories and predicted future movements at the coarse level (only a few steps with continuous location representation), without considering the transportation network. [19] predicted the rest-of-the-day trajectory of the user in a predicting-by-retrieving paradigm, which is similar to the node-level prediction phase in our work.…”

Section: Related Workmentioning

confidence: 99%

Online Trajectory Prediction for Metropolitan Scale Mobility Digital Twin

Fan¹,

Yang²,

Wang³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Knowing "what is happening" and "what will happen" of the mobility in a city is the building block of a data-driven smart city system. In recent years, mobility digital twin that makes a virtual replication of human mobility and predicting or simulating the fine-grained movements of the subjects in a virtual space at a metropolitan scale in near real-time has shown its great potential in modern urban intelligent systems. However, few studies have provided practical solutions. The main difficulties are four-folds. 1) The daily variation of human mobility is hard to model and predict; 2) the transportation network enforces a complex constraints on human mobility; 3) generating a rational fine-grained human trajectory is challenging for existing machine learning models; and 4) making a fine-grained prediction incurs high computational costs, which is challenging for an online system. Bearing these difficulties in mind, in this paper we propose a two-stage human mobility predictor that stratifies the coarse and fine-grained level predictions. In the first stage, to encode the daily variation of human mobility at a metropolitan level, we automatically extract citywide mobility trends as crowd contexts and predict long-term and long-distance movements at a coarse level. In the second stage, the coarse predictions are resolved to a fine-grained level via a probabilistic trajectory retrieval method, which offloads most of the heavy computations to the offline phase. We tested our method using a real-world mobile phone GPS dataset in the Kanto area in Japan, and achieved good prediction accuracy and a time efficiency of about 2 min in predicting future 1h movements of about 220K mobile phone users on a single machine to support more higher-level analysis of mobility prediction.

show abstract

“…historic blizzard, COVID-19 pandemic). There is another line of research (Fan et al 2015;Jiang et al 2018Jiang et al , 2019 attempting to capture anomalous mobility tendency under events in an online fashion based on low-order Markov assumption and fine-grained time slot setting. These practices are arguably circumventing the inherent difficulty of the task instead of truly tackling it.…”

Section: Introductionmentioning

confidence: 99%

Event-Aware Multimodal Mobility Nowcasting

Wang

Jiang

Han

et al. 2022

AAAI

Self Cite

View full text Add to dashboard Cite

As a decisive part in the success of Mobility-as-a-Service (MaaS), spatio-temporal predictive modeling for crowd movements is a challenging task particularly considering scenarios where societal events drive mobility behavior deviated from the normality. While tremendous progress has been made to model high-level spatio-temporal regularities with deep learning, most, if not all of the existing methods are neither aware of the dynamic interactions among multiple transport modes nor adaptive to unprecedented volatility brought by potential societal events. In this paper, we are therefore motivated to improve the canonical spatio-temporal network (ST-Net) from two perspectives: (1) design a heterogeneous mobility information network (HMIN) to explicitly represent intermodality in multimodal mobility; (2) propose a memory-augmented dynamic filter generator (MDFG) to generate sequence-specific parameters in an on-the-fly fashion for various scenarios. The enhanced event-aware spatio-temporal network, namely EAST-Net, is evaluated on several real-world datasets with a wide variety and coverage of societal events. Both quantitative and qualitative experimental results verify the superiority of our approach compared with the state-of-the-art baselines. Code and data are published on https://github.com/underdoc-wang/EAST-Net.

show abstract

DeepUrbanMomentum: An Online Deep-Learning System for Short-Term Urban Mobility Prediction

Cited by 37 publications

References 15 publications

Scope of machine learning applications for addressing the challenges in next‐generation wireless networks

Scope of machine learning applications for addressing the challenges in next‐generation wireless networks

Online Trajectory Prediction for Metropolitan Scale Mobility Digital Twin

Event-Aware Multimodal Mobility Nowcasting

Contact Info

Product

Resources

About