Greenhouse gas emission prediction on road network using deep sequence learning

Alfaseeh, Lama; Tu, Ran; Farooq, Bilal; Hatzopoulou, Marianne

doi:10.1016/j.trd.2020.102593

Cited by 35 publications

(20 citation statements)

References 16 publications

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“…Even when spatial resolution was increased, temporal dimension was not captured at a disaggregated level as in Grote et al (2018), in which five minute updating interval was employed. In terms of predictors used, generally fuel and economical factors were employed (Alfaseeh, Tu, et al, 2020).…”

Section: Literaturementioning

confidence: 99%

“…is the observation i being tested for cluster m, c m is the centroid of cluster m. The number of clusters is dependent on the data and a statistical analysis associated. To specify the optimal number of clusters, the elbow method/sum of squared error (Poucin, Farooq, and Patterson, 2018), which estimates the sum of squared distances between the points within a cluster was the guide (Alfaseeh, Tu, et al, 2020). Figure 6.2b…”

Section: Clusteringmentioning

confidence: 99%

“…The input variables were used to define the optimal cluster they belong to based on the sum of squared distance with the centroid related. Then the response variable was obtained from the optimal cluster (Alfaseeh, Tu, et al, 2020).…”

Section: Clusteringmentioning

confidence: 99%

“…for training to validation, respectively (Alfaseeh, Tu, et al, 2020). Among the proposed approaches to avoid overfitting, adding drop out layers is an effective and easy way in NNs in general (Srivastava et al, 2014).…”

Section: Lstm With Exogenous Variablesmentioning

confidence: 99%

“…For ARIMA model, 4 representative links were considered, which were associated with different characteristics to give an indication of the prediction performance at the network level. Data points of every link have been divided into 80% to 20% for training to validation (Alfaseeh, Tu, et al, 2020). The total number of sequences was 83 for ARIMA.…”

Section: Data Collectionmentioning

confidence: 99%

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Advancements in multi-objective eco-routing solutions using connectivity

Alfaseeh¹

2021

Preprint

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Due to the significant adverse impact of transportation systems on the environment, topics related to alleviating greenhouse gas (GHG) emissions are gaining more attention. As potential solutions to mitigate GHG emissions, several approaches have been proposed to better control traffic and manage transportation systems. The employment of Intelligent Transportation System (ITS), which adopts the advancements in Information and Communication Technology (ICT), has been proposed as the most favourable approach to alleviate the undesirable impact of transportation systems on the environment. ITS can control several aspects of a network, such as speed, traffic signals, and route guidance. For the purpose of routing, this research aims to exploit the advancements in ICT by including connected and automated vehicles (CAVs) and sensing technology in an urban congested network.<div>Anticipatory multi-objective eco-routing in a distributed routing framework was proposed and compared to myopic routing with a large case study on a congested network. The End-to-End Connected Autonomous Vehicles (E2ECAV) dynamic distributed routing framework was examined, and encouraging results were found based on the traffic and environmental perspectives. The impact of different market penetration rates (MPRs) of CAVs was examined for various traffic conditions. E2ECAV was adopted for both the myopic and anticipatory routing strategies in this dissertation. The best GHG costing approach was defined and was among the elements tackled in this research. For a robust anticipatory routing application, predictive models were developed based on Long-Short Term Memory (LSTM), a deep learning approach, while considering a high level of spatial (link level) and temporal (one minute) resolution. With regards to the LSTM predictive models, the impact was illustrated of using a deeper LSTM network and systematically tuning its hyper-parameters. The anticipatory routing strategy significantly outperformed the myopic routing strategy based on the the traffic and environmental perspectives. This research shows that ITS can help significantly reduce GHG emissions produced by transportation systems. The developed predictive models can be used while real-time data are collected from sensors within an urban network. Furthermore, the proposed anticipatory routing framework can be applied in a real-time situation. </div>

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Section: Literaturementioning

confidence: 99%

Section: Clusteringmentioning

confidence: 99%

Section: Clusteringmentioning

confidence: 99%

Section: Lstm With Exogenous Variablesmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

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