Emission-based static traffic assignment models

Patil, Gopal R.

doi:10.1007/s10666-015-9498-7

Cited by 22 publications

(12 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When it comes to the level of aggregation of traffic flow and emission models, the use of macroscopic or mesoscopic models to represent traffic flow or emission is a drawback-especially with the advancements in the ICT. For instance, when average speed was utilized in [10,37,38,40], emissions were underestimated. Models that utilized regression models, such as in [18,37,38], were dependent on vehicular speed to estimate emissions produced and/or fuel consumed.…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

“…For instance, when average speed was utilized in [10,37,38,40], emissions were underestimated. Models that utilized regression models, such as in [18,37,38], were dependent on vehicular speed to estimate emissions produced and/or fuel consumed. This means that they are not capable of representing congestion reliably in urban areas.…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Factor Taxonomy of Eco-Routing Models and Future Outlook

Alfaseeh

Farooq

2020

Journal of Sensors

View full text Add to dashboard Cite

Traditionally, routing decisions have been based on minimizing travel time as the associated cost. Eco-routing considers the environmental aspects (e.g., emissions and fuel) as part of the travel cost to mitigate the undesirable impact of transportation systems on the environment. Unlike the existing eco-routing review papers, this research work is aimed at providing a three-factor taxonomy at a more disaggregated level from the optimization perspective and map eco-routing studies to the proposed taxonomy. Furthermore, the strengths and weaknesses of the presented models are summarized. Our main findings include (a) a majority of studies optimized one objective at a time; (b) the microscopic level of aggregation of the flow and emission/fuel models was rarely employed for large case studies, due to the associated complexity; and (c) all of the reviewed studies were applied in a centralized routing system environment. In the near future, when intelligent vehicles will be on the roads, a multi-objective distributed routing framework can be employed with a microscopic level of aggregation for both traffic and emission models, which is capable of operating on largescale networks in real time. Additionally, short-term spatiotemporal prediction of GHG cost is a crucial aspect to be tackled.

show abstract

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Multi-Factor Taxonomy of Eco-Routing Models and Future Outlook

Alfaseeh

Farooq

2020

Journal of Sensors

View full text Add to dashboard Cite

show abstract

“…Furthermore, for the off-peak case, the same scenario showed a slight increase in total travel time but showed a reduction in both total emissions and consumed fuel (Luo et al, 2016). Patil (2016), which belongs to the AS2 classification, used average speed to estimate emissions and fuel based on regression equations. Two scenarios were set in terms of flow, free-flow speed, links length, and links capacity.…”

Section: Macroscopic (A) Modelsmentioning

confidence: 99%

Advancements in multi-objective eco-routing solutions using connectivity

Alfaseeh¹

2021

Preprint

View full text Add to dashboard Cite

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>

show abstract

“…Traffic emissions estimates are increasingly important for environmental policy assessments and infrastructure development [ 2 ]. Toxic gases and fumes emitted by vehicles can cause respiratory and cardiovascular diseases, nitrogen oxide (NO X ) and volatile organic compounds (VOCs) can form ground-level ozone [ 3 , 4 , 5 ], a sign of photochemical smog pollution. Particulate matter (PM), also known as particle contamination, is a complex mixture of very small particles and droplets entering the air.…”

Section: Introductionmentioning

confidence: 99%

“…The relationship between overall emissions and vehicle speed is parabolic. Minimum NO X and CO 2 when the speed is 20–80 km/h, other exhaust emissions show a one-way increase or decrease with speed [ 4 , 31 ]. Therefore, based on the superposition of all exhaust, when the speed is between 20–80 km/h, the total exhaust reaches the minimum.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Trend Forecast of Different Traffic Pollutants in Minnesota Based on Spot Velocity Conversion

Qian

2018

IJERPH

View full text Add to dashboard Cite

Because traffic pollution is a global problem, the prediction of traffic emissions and the analysis of their influencing factors is the key to adopting management and control measures to reduce traffic emissions. Hence, the evaluation of the actual level of traffic emissions has gained more interest. The Computer Program to calculate Emissions from Road Transport model (COPERT) is being downloaded by 100 users per month and is being used in a large number of applications. This paper uses this model to calculate short-term vehicle emissions. The difference from the traditional research was that the input velocity parameter was not the empirical value, but through reasonable conversion of the spot velocity at one point, obtained by the roadside detector, which provided new ideas for predicting traffic emissions by the COPERT model. The hybrid Autoregressive Integrated Moving Average (ARIMA) Model was used to predict spot mean velocity, after converted it to the predicted interval velocity averaged for some period, input the conversion results and other parameters into the COPERT IV model to forecast short-term vehicle emissions. Six common emissions (CO, NOX, CO2, SO2, PM10, NMVOC) of four types of vehicles (PC, LDV, HDV, BUS) were discussed. As a result, PM10 emission estimates increased sharply during late peak hours (from 15:30 p.m.–18:00 p.m.), HDV contributed most of NOX and SO2, accounting for 39% and 45% respectively. Based on this prediction method, the average traffic emissions on the freeway reached a minimum when interval mean velocity reduced to 40 km/h–60 km/h. This paper establishes a bridge between the emissions and velocity of traffic flow and provides new ideas for forecasting traffic emissions. It is further inferred that the implementation of dynamic velocity guidance and vehicle differential management has a controlling effect that improves on road traffic pollution emissions.

show abstract

Emission-based static traffic assignment models

Cited by 22 publications

References 24 publications

Multi-Factor Taxonomy of Eco-Routing Models and Future Outlook

Multi-Factor Taxonomy of Eco-Routing Models and Future Outlook

Advancements in multi-objective eco-routing solutions using connectivity

Short-Term Trend Forecast of Different Traffic Pollutants in Minnesota Based on Spot Velocity Conversion

Contact Info

Product

Resources

About