Discovering Urban Travel Demands Through Dynamic Zone Correlation in Location-Based Social Networks

Hu, Wangsu; Yao, Zijun; Yang, Sen; Chen, Shuhong; Jin, Peter J.

doi:10.1007/978-3-030-10928-8_6

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…Extracting individual trips and flow data from the traffic link sensors is a non-trivial NP-Hard (non-deterministic polynomial-time hard) and time-dependent problem that requires high computation resources. It has been a subject of intense research for a long time and many approaches and models have been made [ 57 , 58 , 59 ]. But nevertheless, there are some direct ways of obtaining

information: (a) cloud route calculation services that receive directly drivers requests; (b) tracing individual vehicles in the network (using cameras and other mechanisms); and (c) using crowdsensing mechanisms by means of a dedicated app or an embedded measurement rootkit in an app.…”

Section: Mutraff Architecturementioning

confidence: 99%

RETRACTED: MuTraff: A Smart-City Multi-Map Traffic Routing Framework

Paricio

López-Carmona

2019

Sensors

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Urban traffic routing is deemed to be a significant challenge in intelligent transportation systems. Existing implementations suffer from several intrinsic issues such as scalability in centralized systems, unnecessary complexity of mechanisms and communication in distributed systems, and lack of privacy. These imply force intensive computational tasks in the traffic control center, continuous communication in real-time with involved stakeholders which require drivers to reveal their location, origin, and destination of their trips. In this paper we present an innovative urban traffic routing framework and reference architecture (multimap traffic control architecture, MuTraff), which is based on the strategical generation and distribution of a set of traffic network maps (traffic weighted multimaps, TWM) to vehicle categories or fleets. Each map in a TWM map set has the same topology but a different distribution of link weights, which are computed by considering policies and constraints that may apply to different vehicle groups. MuTraff delivers a traffic management system (TMS), where a traffic control center generates and distributes maps, while routing computation is performed at the vehicles. We show how this balance between generation, distribution, and routing computation improves scalability, eases communication complexities, and solves former privacy issues. Our study presents case studies in a real city environment for (a) global congestion management using random maps; (b) congestion control on road incidents; and c) emergency fleets routing. We show that MuTraff is a promising foundation framework that is easy to deploy, and is compatible with other existing TMS frameworks.

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Section: Mutraff Architecturementioning

confidence: 99%

RETRACTED: MuTraff: A Smart-City Multi-Map Traffic Routing Framework

Paricio

López-Carmona

2019

Sensors

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“…Optimal traffic planning in TCS must consider and handle all these factors, implementing new multi-objective cost functions and the corresponding control models. This smart mobility management may be enabled by the use of big data techniques [25], [26] that handle the dynamic generation of tons of information from city sensors and mobile agents on devices.…”

Section: Introductionmentioning

confidence: 99%

Urban Traffic Routing Using Weighted Multi-Map Strategies

Paricio

López-Carmona

2019

IEEE Access

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Urban traffic routing has to deal with individual mobility and collective wellness considering citizens, multi-modal transport, and fleet traffic with conflicting interests such as electric vehicles, local distribution, public transport, and private vehicles. Different interests, goals, and regulations, suggest the development of new multi-objective routing mechanisms which may improve traffic flow. In this work, Traffic Weighted Multi-Maps (TWM) is presented as a novel traffic routing mechanism based on the strategical generation and distribution of complementary cost maps for traffic fleets, oriented towards the application of differentiated traffic planning and control policies. TWM is built upon a centralized control architecture, where a Traffic Management Center generates and distributes customized cost maps of the road network. These maps are used individually to calculate routes. In this research, we present the TWM theoretical model and experimental results based on microscopic simulations over a real city traffic network under multiple scenarios, including traffic incidents management. Experimental evaluation takes into account driver's adherence to the system and considers a multi-objective analysis both for the global network parameters (congestion, travel time, and route length) and for the subjective driving experience. Experimental results deliver performance improvements from 20% to 50%. TWM is fully compatible with existing traffic routing systems and has promising future evolution applying new algorithms, policies and network profiles.

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Predicting and Understanding Human Mobility Based on Social Media Check-in Data

Liu

Chen

et al. 2021

Spatial Data and Intelligence

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Discovering Urban Travel Demands Through Dynamic Zone Correlation in Location-Based Social Networks

Cited by 5 publications

References 26 publications

RETRACTED: MuTraff: A Smart-City Multi-Map Traffic Routing Framework

RETRACTED: MuTraff: A Smart-City Multi-Map Traffic Routing Framework

Urban Traffic Routing Using Weighted Multi-Map Strategies

Predicting and Understanding Human Mobility Based on Social Media Check-in Data

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