Appendix B: Detailed Example of VEIN

A.3 Network

inventory(file.path(tempdir(), "YourCity"))
setwd("YourCity")
data(net) # Traffic demand simulation for west São Paulo
data(net)
data(pc_profile)
pc_week <- temp_fact(net$ldv+net$hdv, pc_profile)
speed <- netspeed(pc_week, net$ps, net$ffs,
                  net$capacity, net$lkm)
saveRDS(net, file = "network/net.rds")
saveRDS(speed, file = "network/speed.rds")

A.4 Vehicular composition (CETESB 2015)

The vehicular composition vehcomp has 5 types of vehicles: Passenger Carss (PC), Light Commercial Vehicles (LCV), Heavy Good Vehicles or trucks (HGV), Buses (BUS), and Motorcycles (MC). The default value of this argument is: c(PC = 4, LCV = 5, HGV = 5, BUS = 3, MC = 9), which means that there are 4 types of PC, 5 of LCV, 5 of trucks, 3 of buses and 9 types of motorcycles. This composition comes from the vehicular emissions inventory of the Environmental Agency of São Paulo, Brazil (CETESB 2015). In Brazil, the fuel used in vehicles is blended with ethanol with and biodiesel. The user can use any vehicular composition that representes its fleet with up-to 99 type of vehicles per category. The default composition according Brazilian emissions inventory is:

Passenger Cars (PC)

  1. Passenger Cars using Gasoline blended with 25% of ethanol (E25).
  2. Passenger Cars with Flex engine using Gasoline blended with 25% of ethanol (FE25).
  3. Passenger Cars with Flex engine using 100% of ethanol (FE100).
  4. Passenger Cars with engines that uses only ethanol (E100).

Light Commercial Vehicles (LCV)

  1. Light Commercial Vehicles with gross weight (GW) less than 3.8 t using Gasoline blended with 25% of ethanol (E25).
  2. Light Commercial Vehicles with GW less than 3.8 t with Flex engine using Gasoline blended with 25% of ethanol (FE25).
  3. Light Commercial Vehicles with GW less than 3.8 t with Flex engine using 100% of ethanol (FE100).
  4. Light Commercial Vehicles with GW less than 3.8 t with engines that uses only ethanol (E100).
  5. Light Commercial Vehicles with GW less than 3.8 t with engines that uses only ethanol (E100).

Heavy Good Vehicles (HGV) or Trucks

  1. Semi Light Trucks (SLT) with 3.8 t < GW < 6 t using Diesel blended with 5% of biodiesel (B5).
  2. Light Trucks (LT) with 6 t < GW < 10 t using Diesel blended with 5% of biodiesel (B5).
  3. Medium Trucks (MT) with 10 t < GW < 15 t using Diesel blended with 5% of biodiesel (B5).
  4. Semi Heavy Trucks (SHT) with 15 t < GW on a Rigid Truck (RT) and GW < 40 on an Articulated Truck (AT) using Diesel blended with 5% of bodiesel (B5).
  5. Heavy Trucks (HT) with 15 t < GW on a RT and GW >= 40 on an AT using Diesel blended with 5% of bodiesel (B5).

Buses

  1. Urban Bus (UB) using Diesel blended with 5% of biodiesel (B5).
  2. Small Urban Bus (SUB) using Diesel blended with 5% of biodiesel (B5).
  3. Motorway Bus (MB) or Coach using Diesel blended with 5% of biodiesel (B5).

Motorcycles (MC)

  1. Motorcycles with engine cc < 150 using Gasoline blended with 25% of ethanol (E25).
  2. Motorcycles with engine 150 < cc < 500 using Gasoline blended with 25% of ethanol (E25).
  3. Motorcycles with engine cc > 500 using Gasoline blended with 25% of ethanol (E25).
  4. Motorcycles with engine cc < 150 with Flex engine using Gasoline blended with 25% of ethanol (FE25).
  5. Motorcycles with engine 150 < cc < 500 with Flex engine using Gasoline blended with 25% of ethanol (FE25).
  6. Motorcycles with engine cc > 500 with Flex engine using Gasoline blended with 25% of ethanol (FE25).
  7. Motorcycles with engine cc < 150 with Flex engine using 100% of ethanol (FE100).
  8. Motorcycles with engine 150 < cc < 500 with Flex engine using 100% of ethanol (FE100).
  9. Motorcycles with engine cc > 500 with Flex engine using 100% of ethanol (FE100).

A.5 Traffic data

The vehicular composition will split the traffic simulation as shown on next table. The simulation has traffic for Light Duty Vehicles and Heavy Good Vehicles, therefore, the vehicular composition must split this categories and considerations:

  • Passenger Cars (PC) = 75% of Light Duty Vehicles (LDV) of traffic simulation.
  • Light Commercial Vehicles (LCV) = 10% of LDV of traffic simulation.
  • Motorcycles (MC) = 15% of LDV on traffic simulation.
  • Heavy Good Vehicles (HGV) = 75% of Heavy Duty Vehicles (HDV).
  • Buses = = 25% of Heavy Duty Vehicles (HDV).
  • LCV and PC have vehicles with flex engines capable to run with any mixture of gasoline and ethanol (Giroldo et al. 2005).
  • Type of fuel consumed consists in gasoline blended with 25% of ethanol (E25).
  • The vehicular composition consists in type of vehicles and type of fuel. Optionally, the user could have a wider vehicular composition considering more sizes, gross weight, etc.
  • The percentages in composition apply for each type of vehicle PC, LCV, HGV, BUS and MC.

Passenger Cars

The vehicular composition consisted in PC using E25, PC with Flex engines using E25 and E100, and OC with engines for consuming only E100. The PC with Flex engines entered into Brazilian market in 2003, therefore, at year 2015 flex vehicles has 13 years of use. On the other case, PC with engines for E100 went out of the market in 2007, therefore, the newest PC with E100 engine has 9 years of use.

Table 10.2: Vehicular composition of PC for applied in this book
Vehicles Composition
PC_E25 37.25%
PC_FE25 22.26%
PC_FE100 37.97%
PC_E100 2.44% 
PC_E25 <- age_ldv(x = net$ldv,name = "PC_E25",
                  k = 75/100*37.25)
PC_FE25 <- age_ldv(x = net$ldv, name = "PC_FE25", 
                   agemax = 13,
                   k = 75/100*22.26)
PC_FE100 <- age_ldv(x = net$ldv, name = "PC_FE100",
                    agemax = 13,
                    k = 75/100*37.97)
PC_E100 <- age_ldv(x = net$ldv, name = "PC_E100",
                   agemin = 9,
                   k = 75/100*2.44)
saveRDS(PC_E25, file = "veh/PC_E25.rds")
saveRDS(PC_FE25, file = "veh/PC_FE25.rds")
saveRDS(PC_FE100, file = "veh/PC_FE100.rds")
saveRDS(PC_E100, file = "veh/PC_E100.rds")

Light Commercial Vehicles

The engine/fuel type affects in the same way to LCV with PC vehicles. However, this category has a fraction of vehicles being driven with diesel. In Brazil, all diesle is blended with approximatly 5% of biodiesel, then it is named as B5. The categorization of the names in LCV is similar with PC.

Table 10.3: Vehicular composition of LCV for applied in this book
Vehicles Composition
LCV_E25 39.13%
LCV_FE25 15.21%
LCV_FE100 25.90%
LCV_E100 1.18%
LCV_B5 18.58% 
LCV_E25 <- age_ldv(x = net$ldv,name = "LCV_E25",
                   k = 10/100*39.13)
LCV_FE25 <- age_ldv(x = net$ldv, name = "LCV_FE25", 
                    agemax = 13, k = 10/100*15.21)
LCV_FE100 <- age_ldv(x = net$ldv, name = "LCV_FE100",
                     agemax = 13, k = 10/100*25.90)
LCV_E100 <- age_ldv(x = net$ldv, name = "LCV_E100",
                    agemin = 9, k = 10/100*1.18)
LCV_B5 <- age_ldv(x = net$ldv, name = "LCV_B5",
                  k = 10/100*18.58)
saveRDS(LCV_E25, file = "veh/LCV_E25.rds")
saveRDS(LCV_FE25, file = "veh/LCV_FE25.rds")
saveRDS(LCV_FE100, file = "veh/LCV_FE100.rds")
saveRDS(LCV_E100, file = "veh/LCV_E100.rds")
saveRDS(LCV_B5, file = "veh/LCV_B5.rds")

Heavy Good Vehicles

HGV uses diesel which is blended with approximatly 5% of biodiesel from sugar- cane.

Table 10.4: Vehicular composition of HGV for applied in this book
Vehicles Composition
SLT 8.38%
LT 25.50%
MT 15.28%
SHT 24.98%
HT 25.85% 
HGV_SLT_B5 <- age_hdv(x = net$hdv,name = "HGV_SLT_B5",
                      k = 75/100*8.38)
HGV_LT_B5 <- age_hdv(x = net$hdv, name = "HGV_SLT_B5",
                     k = 75/100*25.50)
HGV_MT_B5 <- age_hdv(x = net$hdv, name = "HGV_SLT_B5",
                     k = 75/100*15.28)
HGV_SHT_B5 <- age_hdv(x = net$hdv, name = "HGV_SLT_B5", 
                      k = 75/100*24.98)
HGV_HT_B5 <- age_hdv(x = net$hdv, name = "HGV_SLT_B5",
                     k = 75/100*25.85)
saveRDS(HGV_LT_B5, file = "veh/HGV_SLT_B5.rds")
saveRDS(HGV_SLT_B5, file = "veh/HGV_LT_B5.rds")
saveRDS(HGV_MT_B5, file = "veh/HGV_MT_B5.rds")
saveRDS(HGV_SHT_B5, file = "veh/HGV_SHT_B5.rds")
saveRDS(HGV_HT_B5, file = "veh/HGV_HT_B5.rds")

Buses

In Brazil there are many type of buses, but in this book we are focussing in the most abundandt: Urban Buses, Small Urban Buses and Motoreway Buses ro Coach. According to the Secretary or Urban Mobility of São Paulo (Sptrans, http://www.sptrans.com.br/), the fleet has an average age of use of 5 years and 5 monthts. To achieve this average age, the agemax of this vehicles is 10 years of use.

Table 10.5: Vehicular composition of BUS for applied in this book
Vehicles Composition
UB 77.43%
SUB 9.07%
MB 13.5% 
UB_B5 <- age_hdv(x = net$ldv,name = "UB_B5", agemax = 10,
                 k = 25/100*77.43)
SUB_B5 <- age_hdv(x = net$ldv, name = "SUB_B5", 
                  k = 25/100*9.07)
MB_B5 <- age_hdv(x = net$ldv, name = "MB_B5",
                 k = 25/100*13.5)
saveRDS(HGV_LT_B5, file = "veh/HGV_SLT_B5.rds")
saveRDS(HGV_SLT_B5, file = "veh/HGV_LT_B5.rds")
saveRDS(HGV_MT_B5, file = "veh/HGV_MT_B5.rds")

Motorcycles

The vehicular composition consisted in Motorcycles using E25, and recnetly, in year 2010, entered into the market flex motorcycles, which can use gasoline E25 or ethano E100. Therefore, the oldest flex MC is 6 years old.

Table 10.6: Vehicular composition of MC for applied in this book
Vehicles Composition
MC_150_E25 72.97%
MC_150_500_E25 11.28%
MC_500_E25 3.15%
MC_150_FE25 3.93%
MC_150_500_FE25 0.57%
MC_500_FE25 0.16%
MC_150_FE100 6.69%
MC_150_500_FE100 0.98%
MC_500_FE100 0.27% 
MC_150_E25 <- age_hdv(x = net$ldv,name = "MC_150_E25",
                      k = 15/100*72.97)
MC_150_500_E25 <- age_hdv(x = net$ldv, name = "MC_150_500_E25", 
                          k = 15/100*11.28)
MC_500_E25 <- age_hdv(x = net$ldv, name = "MC_500_E25",
                      k = 15/100*3.15)

MC_150_FE25 <- age_hdv(x = net$ldv,name = "MC_150_FE25",
                       agemax = 6, k = 15/100*3.93)
MC_150_500_FE25 <- age_hdv(x = net$ldv, name = "MC_150_500_FE25",
                           agemax = 6, k = 15/100*0.57)
MC_500_FE25 <- age_hdv(x = net$ldv, name = "MC_500_FE25",
                       agemax = 6, k = 15/100*0.16)

MC_150_FE100 <- age_hdv(x = net$ldv,name = "MC_150_FE100",
                        agemax = 6, k = 15/100*6.69)
MC_150_500_FE100 <- age_hdv(x = net$ldv,
                            name = "MC_150_500_FE100",
                            agemax = 6, k = 15/100*0.98)
MC_500_FE100 <- age_hdv(x = net$ldv, name = "MC_500_FE100",
                        agemax = 6, k = 15/100*0.27)

saveRDS(MC_150_E25, file = "veh/MC_150_E25.rds")
saveRDS(MC_150_500_E25, file = "veh/MC_150_500_E25.rds")
saveRDS(MC_500_E25, file = "veh/MC_500_E25.rds")

saveRDS(MC_150_FE25, file = "veh/MC_150_FE25.rds")
saveRDS(MC_150_500_FE25, file = "veh/MC_150_500_FE25.rds")
saveRDS(MC_500_FE25, file = "veh/MC_500_FE25.rds")

saveRDS(MC_150_FE100, file = "veh/MC_150_FE100.rds")
saveRDS(MC_150_500_FE100, file = "veh/MC_150_500_FE100.rds")
saveRDS(MC_500_FE100, file = "veh/MC_500_FE100.rds")

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