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Abstract

As urban data keeps getting bigger, deep learning is coming to play a key role in providing big data predictive analytics solutions. We are interested in developing a new generation of deep learning based computational technologies that predict traffic congestion and crowd management. In this work, we are mainly interested in efficiently predicting future traffic with high accuracy. The proposed deep learning solution allows the revealing of the latent (hidden) structure common to different cities in terms of dynamics. The data driven insights of traffic analytics will help shareholders, e.g., security forces, stadium management teams, and travel agencies, to take fast and reliable decisions to deliver the best possible experience for visitors. Current traffic data sources in Qatar are incomplete as sensors are not yet permanently deployed for data collection.The following topics are being addressed:Predictive Crowd and Vehicles Traffic Analytics: Forecasting the flow of crowds and vehicles is of great importance to traffic management, risk assessment and public safety. It is affected by many complex factors, including spatial and temporal dependencies, infrastructure constraints and external conditions (e.g. weather and events). If one can predict the flow of crowds and vehicles in a region, tragedies can be mitigated or prevented by utilizing emergency mechanisms, such as conducting traffic control, sending out warnings, signaling diversion routes or evacuating people, in advance. We propose a deep-learning-based approach to collectively forecast the flow of crowds and vehicles. Deep models, such as Deep-Neural-Networks, are currently the best data-driven techniques to handle heterogeneous data and to discover and predict complex data patterns such as traffic congestion and crowd movements. We will focus in particular on predicting inflow and outflow of crowds or vehicles to and from important areas, tracking the transitions between these regions. We will study different deep architectures to increase the accuracy of the predictive model, and explore ways on how to integrate spatio-temporal information into these models. We will also study how deep models can be re-used without retraining to handle new data and better scale to large data sets. What-If Scenarios Modeling: Understanding how congestion or overcrowd at one location can cause ripples throughout a transportation network is vital to pinpoint traffic bottlenecks for congestion mitigation or emergency response preparation. We will use predictive modeling to simulate different states of the transportation network enabling the stakeholder to test different hypotheses in advance. We will use the theory of multi-layer networks to model and then simulate the complex relationship between different but coexisting types of flows (crowd, vehicles) and infrastructures (roads, railways, crossings, passageways, squares…). We will propose a visual analytic platform that will provide necessary visual handles to generate different cases, navigate through different scenarios, and identify potential bottleneck, weak points and resilient routes. This visualization platform connected to the real-time predictive analytic platform will allow supporting stakeholder decision by automatically matching the current situation to the already explored scenarios and possible emergency plans. Safety and Evacuation Planning based on Resilience Analytics: Determining the best route to clear congestion or overcrowded areas, or new routes to divert traffic and people from such areas is crucial to maintain high security and safety levels. The visual analytic platform and the predictive model will enable the test and set up of safety and evacuation plans to be applied in case of upcoming emergency as detected by the predictive analytic platform. Overall, the proposed approach is independent of the type of flows, i.e., vehicles or people, or infrastructures, as long as proper sensors (magnetic loops, video camera, GPS tracking, etc.) provide relevant data about these flows (number of people or vehicles per time unit along a route of some layer of the transportation network). The proposed data-driven learning models are efficient, and they adapt to the specificities of the type of flows, by updating the relevant parameters during the training phase.

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/content/papers/10.5339/qfarc.2018.ICTPD544
2018-03-15
2020-08-14
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http://instance.metastore.ingenta.com/content/papers/10.5339/qfarc.2018.ICTPD544
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