A Multitier Approach to Estimating the Energy Efficiency of Urban Passenger Mobility
Abstract
:1. Introduction
2. Literature Review
3. Energy Efficiency Model
3.1. Data Requirements
3.2. Top-Down Approach
3.3. Bottom-Up Approach
4. Energy Efficiency in Urban Mobility
4.1. Aggregate Results
4.2. Disaggregate Results
4.2.1. Energy Use by Source
4.2.2. Energy Efficiency
5. Conclusions and Policy Implications
Author Contributions
Funding
Conflicts of Interest
References
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Author | Activity | Modes | Approach | Data Base | Case Study | Input | Output |
---|---|---|---|---|---|---|---|
Szász (1982) [13] | Passenger | Road | Bottom-up | - | Hypothetical | Energy consumption: distance traveled; consumption coefficient by kilometer and round hour; average speed and average vehicle occupancy | Energy consumption |
Bose and Srinivasachary (1997) [21] | Passenger | Road and Rail | Top-down and Bottom-up | Database (National) | New Delhi (India) | Transport activity by mode; vehicle occupancy; total energy demand by mode of transport and type of energy; energy efficiency by type of vehicle and emission factors | Energy consumption, atmospheric pollutant |
Hillman and Rawaswami (2010) [24] | Passenger and freight | Road and Air | Top-down | Origin–destination (OD) matrices, Database (National) | Denver, Portland,
Seattle, Minneapolis and Austin (USA) | Regional travel volume per year | Energy consumption, CO2e |
He et al. (2011) [15] | Passenger | Road | Bottom-up | OD matrices (Company), Database (Municipal) | Jinan (China) | Modal split; travel distance by mode; vehicle occupancy; EE by mode and emission factor | Energy consumption, CO2e |
Tartakovsky et al. (2013) [22] | Passenger | Road | Bottom-up | Survey (Company) | Hypothetical | Fleet; number of passengers; distance and vehicle occupancy | EE, atmospheric pollutant |
Giordano et al. (2014) [26] | Passenger | Road | Top-down | Database (Continental) | Barcelona (Spain) and Lugano (Switzerland) | Petrol consumption; distance; % of the mileage traveled on urban roads | Energy consumption, CO2, and atmospheric pollutant |
Aggarwal and Jain (2014) [27] | Passenger | Road | Bottom-up | Survey, Database (State) | New Delhi (India) | Travel demand; modal split; distance traveled per vehicle; per mode and per fuel CO2 emission factors | Energy consumption, CO2e |
Jiang et al. (2014) [25] | Passenger | Road | Top-down | Database (National) | Barcelona (Spain), Amsterdam (Netherlands), London (UK) | Energy consumption; travel frequency; distance per trip; vehicle occupancy; energy intensity factor; consumption coefficient and energy factor by fuel | EE |
Guimarães and Leal Junior (2016) [28] | Passenger | Road and Water | Bottom-up | Research (Company) | Rio de Janeiro (Brazil) | Total passengers transported; distance and EE | Energy consumption, CO2, and atmospheric pollutant |
Saujot and Lefèvre (2016) [14] | Passenger | Road and Rail | Top-down | Database (National) and Survey | Grenoble (France) | Transport activity by mode; Energy by source, mode, and emission factor | Energy consumption, CO2e |
Yang et al. (2017) [29] | Passenger | Road and Rail | Bottom-up | Survey, Database (National and Municipal) | Beijing (China) | Daily displacement data (time; reason) and attributes of each mode (distance; speed; time) | Transport activity, energy consumption, and CO2 |
Alonso et al. (2017) [30] | Passenger | Road | Bottom-up | (OD) matrices, Database (Municipal) | Madrid (Spain) | Travel distance; speed and travel time; automotive operating costs and vehicle occupancy. | Energy consumption, CO2, and atmospheric pollutant |
Menezes et al. (2017) [16] | Passenger and freight | Road and Rail | Bottom-up | Database (Municipal, State and Federal) | São Paulo (Brazil) | Fleet inventory by type of vehicle and fuel; new registered vehicles; vehicle kilometers traveled; age; fuel economy; average number of passengers, tons transported per mode; fuel prices and taxes; GHG emission factors by type of fuel | Transport activity, energy consumption, CO2e |
Gerboni et al. (2017) [23] | Passenger and freight | Road, Rail, Air, and Water | Bottom-up | Database (National forecasting or Regional) | Unspecified city (Italy) | Mobility demand and energy by source and mode | Energy consumption, CO2 |
Input | Total | Bottom-Up | Top-Down |
---|---|---|---|
Number of passengers transported | 11 | 7 | 4 |
Modal split (%) | 8 | 5 | 3 |
Distance traveled (km) | 9 | 6 | 3 |
Energy source | 8 | 8 | 0 |
Category of vehicles | 7 | 7 | 0 |
Number of trips by mode | 6 | 6 | 0 |
Fuel economy (km/L) | 5 | 5 | 0 |
Transport activity passenger-kilometers (pass-km) | 5 | 1 | 4 |
Vehicle occupancy (pass/vehicle) | 5 | 5 | 0 |
Inputs | Top-Down | Bottom-Up | |
---|---|---|---|
Tier 1 | Tier 2 | ||
Energy use by source | • | • | • |
Modal split | • | • | • |
Average trip distance | • | • | • |
Fuel economy 1 | • | ||
Vehicle stock | • | • | |
Vehicle kilometers traveled (VKT) 1 | • | ||
Average occupancy 1 | • |
Vehicle | Technology | Stock | Average Age |
---|---|---|---|
Cars | Ethanol | 1377 | 15 |
NGV | 1352 | 12 | |
Flexible-fueled 1 | 150,976 | 7 | |
Gasoline | 42,084 | 14 | |
Hybrid | 172 | 1.4 | |
Light commercials | Flexible-fueled | 21,889 | 7 |
Diesel | 623 | 7 | |
Gasoline | 8314 | 10 | |
Motorcycles | Flexible-fueled | 13,117 | 5 |
Gasoline | 39,872 | 9 | |
Micro buses | Diesel | 709 | 6 |
Buses | Diesel | 793 | 7 |
Vehicle | Technology | Annual VKT |
---|---|---|
Cars | Alcohol | 13,595 |
Natural Gas Vehicle | 13,595 | |
Flexible-fueled | 15,208 | |
Gasoline | 14,309 | |
Hybrid | 15,227 | |
Light commercials | Flexible-fueled | 18,255 |
Diesel | 24,142 | |
Gasoline | 14,624 | |
Motorcycles | Flexible-fueled | 13,293 |
Gasoline | 12,781 | |
Micro buses | Diesel | 61,215 |
Buses | 124,735 | |
Buses (school and chartered) | 76,880 | |
Articulated buses | 42,977 |
Type of Vehicle | Type of Energy | Fuel Economy (km/L) |
---|---|---|
Cars | Alcohol | 10.9 |
Natural Gas Vehicle | 12.0 | |
Flexible-fueled | 8.3/12.2 | |
Gasoline | 11.3 | |
Hybrid | 16.5 | |
Light commercials | Flexible-fueled | 6.2/8.6 |
Diesel | 9.5 | |
Gasoline | 11.3 | |
Motorcycles | Flexible-fueled | 29.3/43.2 |
Gasoline | 37.3 | |
Micro buses | Diesel | 4.3 |
Buses | 2.9 | |
Buses (school and chartered) | 2.6 | |
Articulated buses | 1.7 |
Type of Vehicle | Average Occupancy 1 |
---|---|
Car | 1.3 |
Light commercial | 1.0 |
Motorcycle | 1.0 |
Micro bus | 14.5 |
Basic city bus | 32.6 |
Special and standard buses | 44.9 |
Articulated bus | 41.8 |
Mode of Transport | 2013 | 2014 | 2015 | 2016 | 2017 |
---|---|---|---|---|---|
On foot | 4.81 | 4.81 | 4.81 | 4.81 | 4.81 |
Bicycles | 8.93 | 8.93 | 8.93 | 8.93 | 8.93 |
Motorcycles | 1.41 | 1.37 | 1.28 | 1.36 | 1.45 |
Cars | 0.43 | 0.48 | 0.42 | 0.42 | 0.44 |
Buses | 2.32 | 2.32 | 2.63 | 2.68 | 2.89 |
Bus (suburban and municipal) | 2.31 | 2.39 | 2.35 | 2.42 | 2.54 |
Total | 2013 | 2014 | 2015 | 2016 | 2017 |
EE (pass-km/MJ) | 0.67 | 0.72 | 0.65 | 0.66 | 0.70 |
Energy intensity (kJ/pass-km) | 1499 | 1398 | 1544 | 151 | 1429 |
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Gonçalves, D.N.S.; Bandeira, R.A.d.M.; Costa, M.G.d.; Goes, G.V.; Assis, T.F.d.; D’Agosto, M.d.A.; Almeida, I.R.P.L.d.; Freitas, R.R.d. A Multitier Approach to Estimating the Energy Efficiency of Urban Passenger Mobility. Sustainability 2020, 12, 10263. https://doi.org/10.3390/su122410263
Gonçalves DNS, Bandeira RAdM, Costa MGd, Goes GV, Assis TFd, D’Agosto MdA, Almeida IRPLd, Freitas RRd. A Multitier Approach to Estimating the Energy Efficiency of Urban Passenger Mobility. Sustainability. 2020; 12(24):10263. https://doi.org/10.3390/su122410263
Chicago/Turabian StyleGonçalves, Daniel Neves Schmitz, Renata Albergaria de Mello Bandeira, Mariane Gonzalez da Costa, George Vasconcelos Goes, Tássia Faria de Assis, Márcio de Almeida D’Agosto, Isabela Rocha Pombo Lessi de Almeida, and Rodrigo Rodrigues de Freitas. 2020. "A Multitier Approach to Estimating the Energy Efficiency of Urban Passenger Mobility" Sustainability 12, no. 24: 10263. https://doi.org/10.3390/su122410263