A Survey on Urban Traffic Optimisation for Sustainable and Resilient Transportation Network

Sang, Koh Song; Zhou, Bo; Yang, Po; Yang, Zaili

doi:10.1109/dese.2016.38

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…The use of automobile vehicles is increasing at a very rapid rate which creates several social problems like traffic congestion, overconsumption of energy, traffic accidents, and high carbon emission (Sang et al (2017)). To solve these problems Intelligent Transportation System (ITS) (Lin et al (2017)) has been considered as a promising solution.…”

Section: Introductionmentioning

confidence: 99%

Multistep traffic speed prediction: A deep learning based approach using latent space mapping considering spatio-temporal dependencies

Modi,

Bhattacharya,

Basak

2021

Preprint

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Traffic management in a city has become a major problem due to the increasing number of vehicles on roads. Intelligent Transportation System (ITS) can help the city traffic managers to tackle the problem by providing accurate traffic forecasts. For this, ITS requires a reliable traffic prediction algorithm that can provide accurate traffic prediction at multiple time steps based on past and current traffic data. In recent years, a number of different methods for traffic prediction have been proposed which have proved their effectiveness in terms of accuracy. However, most of these methods have either considered spatial information or temporal information only and overlooked the effect of other. In this paper, to address the above problem a deep learning based approach has been developed using both the spatial and temporal dependencies. To consider spatio-temporal dependencies, nearby road sensors at a particular instant are selected based on the attributes like traffic similarity and distance. Two pre-trained deep auto-encoders were cross-connected using the concept of latent space mapping and the resultant model was trained using the traffic data from the selected nearby sensors as input. The proposed deep learning based approach was trained using the real-world traffic data collected from loop detector sensors installed on different highways of Los Angeles and Bay Area. The traffic data is freely available from the web portal of the California Department of Transportation Performance Measurement System (PeMS). The effectiveness of the proposed approach was verified by comparing it with a number of machine/deep learning approaches. It has been found that the proposed approach provides accurate traffic prediction results even for 60-min ahead prediction with least error than other techniques.

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Section: Introductionmentioning

confidence: 99%

Multistep traffic speed prediction: A deep learning based approach using latent space mapping considering spatio-temporal dependencies

Modi,

Bhattacharya,

Basak

2021

Preprint

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“…It aims to ensure that environment, social, and economic considerations are factored into decisions. At the same time, a resilient system highlights the capacity to absorb disturbances and adapt to unexpected events in order to improve its safety and availability [14]. The word resilience originates from the Latin word "resiliere," which means to bounce back [15].…”

Section: Introductionmentioning

confidence: 99%

“…In an urban transportation network, sustainability and resilience are the key factors for using the infrastructure efficiently, economically, and environmentally to meet the basic needs of individuals in order to balance the impact on urban environment and natural resources. It can be achieved through minimize emissions and waste, limit fuel consumption, and maximize the capacity to cope with unexpected events and to improve safety on roadways [14]. Therefore, considering the unclear relationship between vulnerability and resilience in aviation, and the significant role of air transport in the provision of basic services, there is a need to study the resilience of ATC sector networks to better achieve sustainability for the ATC system.…”

Section: Introductionmentioning

confidence: 99%

Vulnerability and Resilience Analysis of the Air Traffic Control Sector Network in China

2020

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Sustainability and its component resilience have become an important issue that cannot be neglected in airspace planning and development. Resilience, as an emerging system concept, is critical to sustainability in many fields. With the rapidly growing demand in China’s air transportation sector, airspace congestion and flight delays have become a major issue in the fast development of this sector, and threatens the sustainability and resilience of air traffic control (ATC) systems such as waste of resources, air pollution, etc. Sectors, the basic units of an ATC system, play a significant role in ensuring the safe and smooth operations of day-to-day flights. In this paper, we apply the complex network theory to establish a model of China’s air sector network (CASN) and examine a series of characteristic parameters with an empirical analysis on its vulnerability and resilience. Through a simulation-based approach, the CASN’s resilience was quantitatively assessed with a resilience indicator (RI) in different scenarios to identify the optimal recovery strategy for building higher system resilience. The results show that the CASN has a lengthy average shortest path and a small clustering coefficient, which demonstrates a hybrid topological feature. We have also found that betweenness has the greatest impact on the resilience and has managerial implications to understand the relationship between vulnerability and resilience in CASN, so as to achieve the resilience and sustainability of CASN.

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Optimizing Urban Traffic Management with Machine Learning Techniques: A Systematic Review

Vihurskyi

2024

2024 2nd International Conference on Advancement in Computation &Amp;amp; Computer Technologies (InCACCT)

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A Survey on Urban Traffic Optimisation for Sustainable and Resilient Transportation Network

Cited by 3 publications

References 22 publications

Multistep traffic speed prediction: A deep learning based approach using latent space mapping considering spatio-temporal dependencies

Multistep traffic speed prediction: A deep learning based approach using latent space mapping considering spatio-temporal dependencies

Vulnerability and Resilience Analysis of the Air Traffic Control Sector Network in China

Optimizing Urban Traffic Management with Machine Learning Techniques: A Systematic Review

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