Effects of on-ramp on the fuel consumption of the vehicles on the main road under car-following model

Tang, Tie-Qiao; Li, Jingang; Yang, Shichun; Shang, Hongyan

doi:10.1016/j.physa.2014.10.051

Cited by 59 publications

(14 citation statements)

References 48 publications

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“…Some studies have demonstrated that the driver's awareness of headway and traffic density helps to save the fuel and consequently, reduces emission of hazardous gases. Vaezipour et al [71] stated that reducing headway by maximizing the speed, rapid acceleration or driving in the highest possible gear leads to a disruption in the traffic pattern, which may increase traffic density. Vaezipour et al [71] suggested that drivers should stay in a fuel-efficient gear and use in-vehicle systems (e.g., intelligent speed adoption, congestion assistants, satellite navigation systems, Intelligent Transport Systems, etc.)…”

Section: Literature Content Analysis and Synthesizementioning

confidence: 99%

“…At an 80-m initial headway, a lower car density (20 nos) results in 10.28 L/100 km fuel consumption compared to a higher car density (80 nos), which consumes 10.46 L/100 km under the influence of ramps. [71]. Moreover, according to De Vlieger [50], the fuel consumption rate and tailpipe emission in rural and motorway driving modes were determined to be 30-40% lower compared to the urban area, which is normally 10 L/100 km.…”

Section: Literature Content Analysis and Synthesizementioning

confidence: 99%

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Green Driver: Travel Behaviors Revisited on Fuel Saving and Less Emission

et al. 2018

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Road transportation is the main energy consumer and major contributor of ever-increasing hazardous emissions. Transportation professionals have raised the idea of applying the green concept in various areas of transportation, including green highways, green vehicles and transit-oriented designs, to tackle the negative impact of road transportation. This research generated a new dimension called the green driver to remediate urgently the existing driving assessment models that have intensified emissions and energy consumption. In this regard, this study aimed to establish the green driver's behaviors related to fuel saving and emission reduction. The study has two phases. Phase one involves investigating the driving behaviors influencing fuel saving and emission reduction through a systematic literature review and content analysis, which identified twenty-one variables classified into four clusters. These clusters included the following: (i) FE f1 , which is driving style; (ii) FE f2 , which is driving behavior associated with vehicle transmission; (iii) FE f3 , which is driving behavior associated with road design and traffic rules; and (iv) FE f4 , which is driving behavior associated with vehicle operational characteristics. The second phase involves validating phase one findings by applying the Grounded Group Decision Making (GGDM) method. The results of GGDM have established seventeen green driving behaviors. The study conducted the Green Value (GV) analysis for each green behavior on fuel saving and emission reduction. The study found that aggressive driving (GV = 0.16) interferes with the association between fuel consumption, emission and driver's personalities. The research concludes that driver's personalities (including physical, psychological and psychosocial characteristics) have to be integrated for advanced in-vehicle driver assistance system and particularly, for green driving accreditation.

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Section: Literature Content Analysis and Synthesizementioning

confidence: 99%

Section: Literature Content Analysis and Synthesizementioning

confidence: 99%

Green Driver: Travel Behaviors Revisited on Fuel Saving and Less Emission

et al. 2018

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“…We explore the effect of strong wind on vehicle's fuel consumption because the existing studies show that fuel consumption can be affected by driving behavior [51][52][53]. Ahn [51] proposed a VT-micro model to formulate the vehicle's fuel consumption in 1998.…”

Section: Vehicle's Fuel Consumptionmentioning

confidence: 99%

An Improved Car‐Following Model Accounting for Impact of Strong Wind

Liu

Shi

2017

Mathematical Problems in Engineering

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In order to investigate the effect of strong wind on dynamic characteristic of traffic flow, an improved car-following model based on the full velocity difference model is developed in this paper. Wind force is introduced as the influence factor of car-following behavior. Among three components of wind force, lift force and side force are taken into account. The linear stability analysis is carried out and the stability condition of the newly developed model is derived. Numerical analysis is made to explore the effect of strong wind on spatial-time evolution of a small perturbation. The results show that the strong wind can significantly affect the stability of traffic flow. Driving safety in strong wind is also studied by comparing the lateral force under different wind speeds with the side friction of vehicles. Finally, the fuel consumption of vehicle in strong wind condition is explored and the results show that the fuel consumption decreased with the increase of wind speed.

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“…To investigate the trait of traffic flow, some new traffic models were proposed recently by considering different traffic conditions [1][2][3][4][5][6][7][8][9][10][11]. In particular, Perc et al [12,13] and Szolnoki et al [14] have investigated the subject of lattice modeling of social phenomena by introducing coevolutionary games rules on the evolution of cooperation, which might affect the interaction network.…”

Section: Introductionmentioning

confidence: 99%

Impact of the traffic interruption probability of optimal current on traffic congestion in lattice model

Peng

2015

Physica A: Statistical Mechanics and its Applications

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Effects of on-ramp on the fuel consumption of the vehicles on the main road under car-following model

Cited by 59 publications

References 48 publications

Green Driver: Travel Behaviors Revisited on Fuel Saving and Less Emission

Green Driver: Travel Behaviors Revisited on Fuel Saving and Less Emission

An Improved Car‐Following Model Accounting for Impact of Strong Wind

Impact of the traffic interruption probability of optimal current on traffic congestion in lattice model

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