Truck Equivalence Factors for Divided, Multilane Highways in Brazil

Cunha, André Luiz; Setti, José Reynaldo

doi:10.1016/j.sbspro.2011.04.447

Cited by 11 publications

(6 citation statements)

References 9 publications

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“…Keller and Saklas developed and tested a procedure using a macroscopic traffic simulation model on an urban network and found that PCE estimates increase as traffic volume approaches restricted flow conditions, vehicles get larger, and the signal system approaches optimum (20). Cunha and Setti used the recalibrated CORSIM model to derive new PCEs using density as the measure of effectiveness for scenarios of different grade magnitudes, grade lengths, and truck percentages (21). Fu et al found that the PCE increases sharply with a decrease in truck speed; the PCE also increases with increases in mixed traffic flow and truck percentage before decreasing (22).…”

Section: Factors Affecting Pcementioning

confidence: 99%

Developing Bicycle Equivalents for Mopeds in Shanghai, China

Chen

Han

Lin

2012

Transportation Research Record

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The bicycle equivalent (BE) of the moped is a key nonmotorized traffic flow parameter when bicycles and mopeds share the same lanes. However, this parameter should not be considered a constant, because it may be affected by factors such as the mixed proportion of the nonmotorized traffic flow. This study estimated the effects of traffic flow characteristics and road design parameters on the BE. Basic traffic flow parameters (i.e., flow, density, and speed) were extracted from video data collected at three observation sites in Shanghai, China. A concept method was used to calculate the BE of mopeds in terms of their time–space resource occupancy on traffic facilities. The free-flow speed characteristics of bicycles and mopeds were investigated, and relationships between speeds and densities in the mixed traffic flow were analyzed. The BEs were estimated for a range of characteristics related to traffic condition or road design (i.e., moped percentage, traffic flow density, width of nonmotorized traffic lanes, and nonmotorized traffic lane grades). The effects of these characteristics on the BE and the variation trends of the BE were observed, and reasons for these trends are discussed. The study findings provide valuable support for the measurement of nonmotorized traffic mixed with mopeds and bicycles in different traffic and road design conditions and could support future studies for capacity and level of service of dedicated nonmotorized lanes.

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Section: Factors Affecting Pcementioning

confidence: 99%

Developing Bicycle Equivalents for Mopeds in Shanghai, China

Chen

Han

Lin

2012

Transportation Research Record

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“…This was especially true for heavy vehicles on long and steep grades (Webster and Elefteriadou, 1999). Studies in Brazil also confirmed that the impact of a heavy vehicle varies by heavy vehicle type (Setti and Neto, 1998;Cunha and Setti, 2011). These studies showed that the underpowered heavy vehicles in Brazil had larger PCE values than the more powerful heavy vehicles in the United States.…”

Section: Pce Versus Heavy Vehicle Typementioning

confidence: 79%

Improved Analysis of Two-Lane Highway Capacity and Operational Performance

Washburn

Al-Kaisy

Luttinen³

et al. 2018

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“…This paper use the average travel times as the evaluation index of each crossroad; and then set up a corresponding mathematical model. Delay time in HCM2000 average delay model [4] is composed of delay time of lane group, average delay times of crossroad can be obtained by the sum of lane group delay times. In fact, HCM2000 model only need simple computing, it is easy to be understood.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…With the expansion of urban, the insular crossroad control mode and trunk mode are not enough to meet with real traffic scene. Therefore, the regional traffic control model [2,4] has become a research hotspot.…”

Section: Introductionsmentioning

confidence: 99%

“…In addition, in the most famous American Road Traffic Capacity Manual, HCM2000 model about the traffic capacity and the evaluation model of the delay can describe delay time of the real crossroad more accurately [4,13]. At home, scholar Zhang hui and some others proposed urban traffic coordination control based on distributed Q-learning, and also proved that this control method is more effective than the timing control method [5].…”

Section: Introductionsmentioning

confidence: 99%

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