Low emission zones: Effects on alternative-fuel vehicle uptake and fleet CO2 emissions

Peters, Joachim; Burguillo, Mercedes; Muñoz, José María Arranz

doi:10.1016/j.trd.2021.102882

Cited by 27 publications

(9 citation statements)

References 32 publications

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“…11 to 13. The advanced predictive effect of DL-STFEE plays a vital role in shaping pertinent environmental policies, e.g., net-zero transitions [42] and traffic network management [43], and designing vehicle-environment cooperation methods, e.g., connected automated vehicle control considering driving behaviours [44] and cooperative perception of supported infrastructure-vehicle system [45]. Consideration of regional transportation conditions is critical for urban development planning and effective macro-adjustment and control of urban transportation, e.g., traffic event prediction [46] and intelligent speed system designing for the traffic management [47].…”

Section: Fig 12 the Results Of Ablation Analysis Based On Temporal Re...mentioning

confidence: 99%

“…Despite the slower inference speed of the DL-STFEE model compared to the machine learning method, its inference speed, which achieves more than 50Hz inference frequency, can still meet the requirement for online implementation such as vehicleenvironment cooperation in the onboard vehicle control unit [40] or mobile edge computing devices [41]. The relevant studies demonstrate that the real-time application of vehicleenvironment cooperation requires a control frequency larger than 50Hz [42,43]. Therefore, the proposed DL-STFEE traffic condition predictor has the potential to be implemented in realtime operational deployment..…”

Section: Experiments 2: Comparison With Baselinesmentioning

confidence: 99%

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Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction

Chu,

Hou,

Jiang

et al. 2024

IEEE Trans. Intell. Veh.

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Traffic condition prediction is crucial for executing traffic control and scheduling tasks within intelligent transportation systems. With diversified data sources, effectively modeling the complex spatial-temporal dependencies in the whole traffic network and predicting nonlinear general traffic condition changes become primary challenges for intelligent transportation systems. In this paper, a double-layer spatial-temporal feature extraction and evaluation (DL-STFEE) model is proposed, aimed at accurately predicting the traffic condition transferring of the whole traffic network. Firstly, a public traffic dataset is processed to extract the spatial and temporal features of vehicles, as well as the clusters of traffic conditions of the entire traffic network. Secondly, a double-layer deep learning traffic condition predictor is proposed. The model incorporates a spatial-temporal feature extraction layer, which leverages the graph convolution network (GCN) and attention mechanism to obtain spatiotemporal features across the whole traffic network. Additionally, a spatial-temporal combination layer employs a high-dimensional self-attention mechanism to integrate features across spatial-temporal combinations, bolstering prediction accuracy. Finally, through rigorous experiments, the contributions of neural network structures on the spatial-temporal feature extraction are comprehensively analyzed and the experiments also validate the effectiveness of traffic condition prediction by the proposed DL-STFEE model.

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Section: Fig 12 the Results Of Ablation Analysis Based On Temporal Re...mentioning

confidence: 99%

Section: Experiments 2: Comparison With Baselinesmentioning

confidence: 99%

Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction

Chu,

Hou,

Jiang

et al. 2024

IEEE Trans. Intell. Veh.

View full text Add to dashboard Cite

show abstract

“…First, most of the LEZs to date have been implemented around major trip attractions, such as city centers and ports. This type of implementation aims to reduce vehicle emissions inside the LEZ primarily by encouraging the turnover of vehicles entering the LEZ toward cleaner technologies ( 18 , 19 ). In contrast, this study evaluates a different type of LEZ implementation that aims to reduce vehicle emissions inside the LEZ primarily by diverting high-emitting pass-through vehicles away from the LEZ.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling the Impacts of Low Emission Zone on Route Diversion and Emissions of Heavy-Duty Trucks: A Southern California Case Study

Boriboonsomsin

Tanvir

Barth

2023

Transportation Research Record: Journal of the Transportation R

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Heavy-duty diesel trucks (HDDTs), the majority of which are used for freight movement, are major contributors of nitrogen oxides (NO x) and fine particulate matter (PM2.5) emissions. As a result, communities close to freight hubs such as ports, railyards, and distribution centers are disproportionately affected by diesel-related air pollution. This study evaluates the potential for emission-based access restriction and pricing strategy to reduce HDDT emissions in one such community in Southern California, U.S. Using a regional travel demand model with a dedicated heavy-duty truck submodel, it is assumed that a $10 emission fee is collected from HDDTs that do not meet the 2007 emission standards (i.e., model years 2009 and older) when they enter the community, which is considered to be a low emission zone (LEZ). The modeling results show that the emission fee diverts 60% of the pass-through trips made by HDDTs that do not meet the required emission standards away from the LEZ. The emission fee is found to reduce NO x, PM2.5, and carbon dioxide (CO2) emissions from HDDTs inside the LEZ by 37%, 38%, and 25%, respectively. Also, it is found that the emission fee has minimal impacts on the total emissions in the modeling area. The total NO x and CO2 emissions remain unchanged, while the total PM2.5 emission increases by 1%. These results demonstrate that emission-based access restriction and pricing can be an effective strategy for reducing truck emissions in communities heavily affected by truck traffic without resulting in adverse impacts on the regional emission inventory.

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“…This measure will reduce CO 2 e emissions from a more global perspective since passenger and commercial vehicles generate 11.9% of global greenhouse gas emissions (Ritchie and Roser, 2020). Further, low- or zero-emission zones can support the transformation towards e-mobility (Peters et al. , 2021).…”

Section: Managerial and Research Implicationsmentioning

confidence: 99%

The impact of optimal parcel locker locations on costs and the environment

Peppel

Spinler

2022

IJPDLM

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PurposeLast-mile delivery is associated with a negative environmental impact and high costs. The purpose of this paper is to develop an approach to designing stationary parcel locker (SPL) networks while minimizing both CO2 equivalent (CO2e) emissions and costs during delivery and pick-up.Design/methodology/approachThis study uses a multinomial logit model to evaluate recipients' willingness to use SPLs based on their availability at home and travel distance. To determine optimal SPL locations, this study formulates a mixed-integer linear programming model.FindingsThe empirical study of different regional clusters reveals that optimal SPL locations can generate cost savings of up to 11.0%. SPLs have a positive impact on total CO2e emission savings in urban areas (i.e. up to 2.5%), but give rise to additional emissions (i.e. 4.6%) in less populated areas due to longer travel distances during the pick-up process.Originality/valueThis paper optimizes SPL locations and the ecological effect of SPLs by minimizing emissions and costs simultaneously. Furthermore, it extends existing discrete choice models by also including recipients' availability at home, increasing the accuracy of recipients' preferences. So far, the effect of SPLs has been studied for metropolitan areas only. A global logistics service provider shared a real dataset which allows us to study seven different regional clusters ranging from rural areas to large cities. Thus, this study contributes to the field of sustainable urban logistics.

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Low emission zones: Effects on alternative-fuel vehicle uptake and fleet CO2 emissions

Cited by 27 publications

References 32 publications

Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction

Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction

Modeling the Impacts of Low Emission Zone on Route Diversion and Emissions of Heavy-Duty Trucks: A Southern California Case Study

The impact of optimal parcel locker locations on costs and the environment

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