Modeling urban traffic dynamics in coexistence with urban data streams

2013 ◽  
Author(s):  
Vahid Moosavi ◽  
Ludger Hovestadt
Keyword(s):  
Data Streams ◽  
Urban Traffic ◽  
Traffic Dynamics ◽  
Urban Data

scholarly journals Integrative analysis of multimodal traffic data: addressing open challenges using big data analytics in the city of Lisbon

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Carlos Lemonde ◽  
Elisabete Arsenio ◽  
Rui Henriques
Keyword(s):  
Big Data ◽  
City Council ◽  
Urban Traffic ◽  
Integrative Analysis ◽  
Context Aware ◽  
Traffic Data ◽  
Situational Context ◽  
Transport Modes ◽  
The City ◽  
Urban Data

AbstractWorldwide cities are establishing efforts to collect urban traffic data from various modes and sources. Integrating traffic data, together with their situational context, offers more comprehensive views on the ongoing mobility changes and supports enhanced management decisions accordingly. Hence, cities are becoming sensorized and heterogeneous sources of urban data are being consolidated with the aim of monitoring multimodal traffic patterns, encompassing all major transport modes—road, railway, inland waterway—, and active transport modes such as walking and cycling. The research reported in this paper aims at bridging the existing literature gap on the integrative analysis of multimodal traffic data and its situational urban context. The reported work is anchored on the major findings and contributions from the research and innovation project Integrative Learning from Urban Data and Situational Context for City Mobility Optimization (ILU), a multi-disciplinary project on the field of artificial intelligence applied to urban mobility, joining the Lisbon city Council, public carriers, and national research institutes. The manuscript is focused on the context-aware analysis of multimodal traffic data with a focus on public transportation, offering four major contributions. First, it provides a structured view on the scientific and technical challenges and opportunities for data-centric multimodal mobility decisions. Second, rooted on existing literature and empirical evidence, we outline principles for the context-aware discovery of multimodal patterns from heterogeneous sources of urban data. Third, Lisbon is introduced as a case study to show how these principles can be enacted in practice, together with some essential findings. Finally, we instantiate some principles by conducting a spatiotemporal analysis of multimodality indices in the city against available context. Concluding, this work offers a structured view on the opportunities offered by cross-modal and context-enriched analysis of traffic data, motivating the role of Big Data to support more transparent and inclusive mobility planning decisions, promote coordination among public transport operators, and dynamically align transport supply with the emerging urban traffic dynamics.

Building a Data Pipeline for the Management and Processing of Urban Data Streams

2018 ◽  
pp. 379-395
Author(s):  
Elarbi Badidi ◽  
Nouf El Neyadi ◽  
Meera Al Saeedi ◽  
Fatima Al Kaabi ◽  
Muthucumaru Maheswaran
Keyword(s):  
Data Streams ◽  
Data Pipeline ◽  
Urban Data

scholarly journals Enabling the use of a planning agent for urban traffic management via enriched and integrated urban data

2019 ◽  
Vol 98 ◽  
pp. 284-297 ◽  
Author(s):  
Grigoris Antoniou ◽  
Sotiris Batsakis ◽  
John Davies ◽  
Alistair Duke ◽  
Thomas L. McCluskey ◽  
...  
Keyword(s):  
Traffic Management ◽  
Urban Traffic ◽  
Urban Data

scholarly journals Macroscopic Traffic Dynamics in Urban Networks during Incidents

2020 ◽  
Author(s):  
Sasan Amini ◽  
Gabriel Tilg ◽  
Fritz Busch
Keyword(s):  
Hysteresis Loop ◽  
Network Performance ◽  
Route Choice ◽  
Urban Traffic ◽  
Incident Management ◽  
Fundamental Diagram ◽  
Traffic Dynamics ◽  
Urban Networks ◽  
Wide Range ◽  
Traffic Incident Management

The degradation of road network performance due to incidents is a major concern to traffic operators. The development of urban traffic incident management systems requires a comprehensive understanding of traffic dynamics during incidents. Recently, the concept of the macroscopic fundamental diagram (MFD) contributed to such an understanding and has been used in a wide range of applications. However, the MFD is merely reproducible under recurring traffic patterns. Motivated by a few studies which argue the existence of the MFD with a clockwise hysteresis loop during incidents, we tackle this limitation of the MFD and propose a framework to study the characteristics of the MFD under non-recurring congestion. More specifically, we introduce a criticality score (CS) which represents network redundancy and postulate that links with a higher level of CS impose a larger hysteresis loop on the MFD. We design an experiment in a microscopic traffic simulation to study the relation of closed links and the resulting MFDs. The results confirm our postulation and we observe that links with similar CS have a comparable impact on the shape of the MFD. The main contribution of this paper is the possibility to develop a framework for incident detection in urban networks under limited sensor coverage. However, the findings of the study may strongly rely on the assumptions, for instance, the network structure, the OD pairs, and drivers route choice during incidents. Thus, future studies are required to study other network topologies as well as more realistic driver route choice during incidents.

scholarly journals Modelling urban traffic dynamics based upon the variational formulation of kinematic waves

2015 ◽  
Vol 3 (3) ◽  
pp. 169-191 ◽  
Author(s):  
Andy H.F. Chow ◽  
Shuai Li ◽  
W.Y. Szeto ◽  
David Z.W. Wang
Keyword(s):  
Variational Formulation ◽  
Urban Traffic ◽  
Traffic Dynamics ◽  
Kinematic Waves

scholarly journals Macroscopic Traffic Dynamics in Urban Networks during Incidents

2020 ◽  
Author(s):  
Sasan Amini ◽  
Gabriel Tilg ◽  
Fritz Busch
Keyword(s):  
Hysteresis Loop ◽  
Network Performance ◽  
Route Choice ◽  
Urban Traffic ◽  
Incident Management ◽  
Fundamental Diagram ◽  
Traffic Dynamics ◽  
Urban Networks ◽  
Wide Range ◽  
Traffic Incident Management

The degradation of road network performance due to incidents is a major concern to traffic operators. The development of urban traffic incident management systems requires a comprehensive understanding of traffic dynamics during incidents. Recently, the concept of the macroscopic fundamental diagram (MFD) contributed to such an understanding and has been used in a wide range of applications. However, the MFD is merely reproducible under recurring traffic patterns. Motivated by a few studies which argue the existence of the MFD with a clockwise hysteresis loop during incidents, we tackle this limitation of the MFD and propose a framework to study the characteristics of the MFD under non-recurring congestion. More specifically, we introduce a criticality score (CS) which represents network redundancy and postulate that links with a higher level of CS impose a larger hysteresis loop on the MFD. We design an experiment in a microscopic traffic simulation to study the relation of closed links and the resulting MFDs. The results confirm our postulation and we observe that links with similar CS have a comparable impact on the shape of the MFD. The main contribution of this paper is the possibility to develop a framework for incident detection in urban networks under limited sensor coverage. However, the findings of the study may strongly rely on the assumptions, for instance, the network structure, the OD pairs, and drivers route choice during incidents. Thus, future studies are required to study other network topologies as well as more realistic driver route choice during incidents.

Urban Traffic Dynamics Prediction—A Continuous Spatial-temporal Meta-learning Approach

2022 ◽  
Vol 13 (2) ◽  
pp. 1-19
Author(s):  
Yingxue Zhang ◽  
Yanhua Li ◽  
Xun Zhou ◽  
Jun Luo ◽  
Zhi-Li Zhang
Keyword(s):  
Urban Traffic ◽  
Traffic Prediction ◽  
Traffic Dynamics ◽  
Transportation Management ◽  
Learning Framework ◽  
Rolling Window ◽  
Meta Learning ◽  
Testing Algorithms ◽  
Traffic Speed ◽  
Temporal Dependencies

Urban traffic status (e.g., traffic speed and volume) is highly dynamic in nature, namely, varying across space and evolving over time. Thus, predicting such traffic dynamics is of great importance to urban development and transportation management. However, it is very challenging to solve this problem due to spatial-temporal dependencies and traffic uncertainties. In this article, we solve the traffic dynamics prediction problem from Bayesian meta-learning perspective and propose a novel continuous spatial-temporal meta-learner (cST-ML), which is trained on a distribution of traffic prediction tasks segmented by historical traffic data with the goal of learning a strategy that can be quickly adapted to related but unseen traffic prediction tasks. cST-ML tackles the traffic dynamics prediction challenges by advancing the Bayesian black-box meta-learning framework through the following new points: (1) cST-ML captures the dynamics of traffic prediction tasks using variational inference, and to better capture the temporal uncertainties within tasks, cST-ML performs as a rolling window within each task; (2) cST-ML has novel designs in architecture, where CNN and LSTM are embedded to capture the spatial-temporal dependencies between traffic status and traffic-related features; (3) novel training and testing algorithms for cST-ML are designed. We also conduct experiments on two real-world traffic datasets (taxi inflow and traffic speed) to evaluate our proposed cST-ML. The experimental results verify that cST-ML can significantly improve the urban traffic prediction performance and outperform all baseline models especially when obvious traffic dynamics and temporal uncertainties are presented.

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