A simulation platform for computing energy consumption and emissions in transportation networks

Cortés, Cristián E.; Vargas, Luis; Corvalán, Roberto M.

doi:10.1016/j.trd.2008.07.006

Cited by 20 publications

(6 citation statements)

References 17 publications

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“…The main factors affecting vehicle emissions are summarized in Litman (2011), including vehicle type and model, load, fuel type, and operating conditions including speed and acceleration, road type, weather, and engine temperature. Various researches have attempted to improve the modeling of emission from transportation (Cortés et al, 2008;Rentziou et al, 2012) and emphasize the need for accurate data to achieve reliable emission estimates (Armstrong & Khan, 2004). Emission modeling can be done either by a bottom-up approach (Colvice et al, 2001) which needs to tackle several problems: to collect real local data regarding traffic conditions at different hours and days, to accurately estimate the emissions generated by the actual fleet according to these conditions, to estimate the composition of the fleet, and to estimate the mileage driven by the fleet, spatially and temporally resolved.…”

Section: Related Workmentioning

confidence: 99%

Augmented Betweenness Centrality for Environmentally Aware Traffic Monitoring in Transportation Networks

Puzis

Altshuler

Elovici³

et al. 2012

Journal of Intelligent Transportation Systems

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Section: Related Workmentioning

confidence: 99%

Augmented Betweenness Centrality for Environmentally Aware Traffic Monitoring in Transportation Networks

Puzis

Altshuler

Elovici³

et al. 2012

Journal of Intelligent Transportation Systems

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“…Contreras, et al (2009) analyzed the change in hydrogen energy vehicles' market share in road traffic using the MAR-KAL model. Cortés et al (2008) developed an object-oriented simulation platform using a Java program to analyze urban traffic network energy consumption and emissions. On the whole, bottom-up methods perform economic analyses poorly, providing useful detailed descriptions of technology.…”

Section: Introductionmentioning

confidence: 99%

A system dynamics approach to scenario analysis for urban passenger transport energy consumption and CO 2 emissions: A case study of Beijing

et al. 2015

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The creation of a Beijing urban transport carbon model using system dynamics. The effect of different policies on energy conservation and emission reductions. The cumulative effect of different individual policies. The optimal sequence of individual policy implementation in comprehensive policy. a b s t r a c tWith the accelerating process of urbanization, developing countries are facing growing pressure to pursue energy savings and emission reductions, especially in urban passenger transport. In this paper, we built a Beijing urban passenger transport carbon model, including an economy subsystem, population subsystem, transport subsystem, and energy consumption and CO 2 emissions subsystem using System Dynamics. Furthermore, we constructed a variety of policy scenarios based on management experience in Beijing. The analysis showed that priority to the development of public transport (PDPT) could significantly increase the proportion of public transport locally and would be helpful in pursuing energy savings and emission reductions as well. Travel demand management (TDM) had a distinctive effect on energy savings and emission reductions in the short term, while technical progress (TP) was more conducive to realizing emission reduction targets. Administrative rules and regulations management (ARM) had the best overall effect of the individual policies on both energy savings and emission reductions. However, the effect of comprehensive policy (CP) was better than any of the individual policies pursued separately. Furthermore, the optimal implementation sequence of each individual policy in CP was TP-PDPT-TDM-ARM.

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“…The method to estimate the emissions differs by agency or rail class. The third group of models includes simulation-based approaches [14][15][16]. For example, the train energy model (TEM) that is widely used in the railway community is an effective tool to estimate the energy consumption of trains [17].…”

Section: Research Overviewmentioning

confidence: 99%

A Green Transportation Planning Approach for Coal Heavy-Haul Railway System by Simultaneously Optimizing Energy Consumption and Capacity Utilization

Chen

2021

Sustainability

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Coal heavy-haul railway has been aiming at maximizing capacity utilization, but ignoring energy consumption for a long time. With the focus on green production, heavy-haul railways need transportation organization plans that can balance energy consumption and capacity utilization. Based on this, this paper proposes a data mining + optimization framework that uses train trajectory data to estimate train energy consumption and then uses a mixed integer programming model to simultaneously optimize plans from energy and capacity aspects. We use Gaussian distribution to describe features of energy consumption under different situations, and build a multi-dimensional cube to store these features to connect with the optimization model. In addition, a branch-and-bound algorithm is design to solve the optimization model. From the sensitivity analyses we can conclude that (1) shortening the departure interval from 13 min to 9 min will generate more energy consumption, about 3.6%; (2) combining short-form trains (50 units) with long-form trains (100 units) while increasing the carrying capacity will generate more energy consumption, about 5~14%; and (3) by controlling weights of the optimization model, capacity–energy-balanced plans can be obtained. The results can contribute to improving the sustainability of railways.

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A simulation platform for computing energy consumption and emissions in transportation networks

Cited by 20 publications

References 17 publications

Augmented Betweenness Centrality for Environmentally Aware Traffic Monitoring in Transportation Networks

Augmented Betweenness Centrality for Environmentally Aware Traffic Monitoring in Transportation Networks

A system dynamics approach to scenario analysis for urban passenger transport energy consumption and CO 2 emissions: A case study of Beijing

A Green Transportation Planning Approach for Coal Heavy-Haul Railway System by Simultaneously Optimizing Energy Consumption and Capacity Utilization

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