2.1 Study Area Description
Addis Ababa city is the African political capital and the capital city of Ethiopia. The study area was located in the country of a man of origin. detailed location and map description are shown below in table 1 and figure 1 respectively.
Table 1:Location Description
Addis Ababa City
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Coordinates
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38°44′24″E 9°1′48″N
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Climate
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Subtropical highland Climate
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Area(total):
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519.482 km2
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Elevation
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2,355 m
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Time zone
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Universal Time Coordinated (UTC+3)
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2.2 Road transportation in the city
In the city of Addis Ababa, the dominant public transportation modes are automobiles and dry cargo. standard city buses and mini-bus taxis are the dominant and affordable means of public transportation for the majority of the residents. and within a city, there are private limited companies and governmental road freight transportations which give delivery to the customers and a total of 596084 different types of vehicles available until 2019.a great challenge for Addis Ababa city for the establishment of a reliable and sustainable transportation system in the city for proper movement of vehicles.
There is a project in progress in the city for heavy-duty distribution vehicles and public buses to traverse in selected lanes to improve the transport management systems. It is expected that the current research will help in choosing a road network for distribution vehicles in an environmentally friendly manner, which takes into account the gradient and other environmental factors into consideration.
2.3 Data and Data Acquisition
Data used in this study were obtained from different sources. Road network data mainly digitized from google earth image of 2019 on the study area at the spatial resolution of 10m ,and ortho photos were used to georectify the digitized road networks .Advances Land observing satellite (ALOS) Phased array L band synthetic aperture radar (PALSAR) of 12.5 spatial resolution was used to as digital elevation model for the study area obtained from Alaska satellite facility .GPS Coordinates of origin - destination locations were collected using actual measurement in the field. Emission data related data’s were obtained from MEET emission estimation model and other literatures.
2.4 Methods
Primarily this paper establishes methodology whereby to find optimal eco route networks that reduce the consumption of fuel and its related CO2 additionally other air pollutants emission for transporting different types of goods from origin to destination locations and vice versa. the model application was done in four steps: (1) establishment of 3D-RN ;(2) Fuel consumption and pollutant emission estimation by using Emission estimation Model; (3) Road network data set development and (4) optimization for different scenarios and other attribute calculations are done by utilizing visual basic script options.
The following figure 2 shows general Work flow and procedure are adopted in this study and forms the basis for deriving Eco-Route of the study area and subsequently the overall findings.
2.4.1. Connecting 2D road Network with DEM
In this stage the 2D road segment consists of 40886 polyline records with functional class [ motorway, principal arterial, sub arterial, collector and local streets] were preprocessed by adding speed attributes to fit study area speed limit based on Addis Ababa city road authority (AACRA )Standard [ 80 for Motorway,70 for Principal arterial,60 for Sub arterial and collector,30 for Local Streets] and also Street Hierarchy tabulated Based on AACRA Street Hierarchy based on its functional classes Standard [ 1 for Motorway,2 for Principal arterial, 3 for Sub arterial, 4 for collector,5 for Local Streets] also worked on 2D Road Network. Finally, ALOS PALSAR DEM [12.5m resolution] is used to convert 2D road Networks to 3D Road Network. To obtain gradients of a street network ArcMap was used on the first step feature vertices to points on centerline feature class twice once for start and once for end was generated, secondly, values are extracted to points on the start and end features classes finally by adding new fields and gradient was gained by subtracting start elevation from end elevation values and the divided by segment length and multiplied by hundred, The study area faces with many undulating trains especially on local streets. due to DEM resolution on some parts of local street gradient values are unrealistic. To overcome these gradient values for the generated street segments within study boundary higher than permissible maximum restriction for road slopes are reduced according to (Bartlett 2015) however in this study the implemented scenarios cases not exceed the permissible Road Slopes.
2.4.2. Calculation of Fuel Consumption and Pollutants Emission Estimation
for this study, the method proposed in Methodologies to Estimate Emissions from Transport (MEET) Based on (Demir, Bektaş, and Laporte 2014) and (EEA 1999) was used. All calculations and estimations are done using the data available on MEET vehicle weight ranges (i.e. 3.5 up to 32 tones) for three scenarios and each scenario tested with three cases.
The basic fuel consumption and other pollutants expressed for speed only, in (g/ km), which was obtained from MEET Model given below:
Scenario (1):
For V 5 – 60 1425.2V-0.7593 (1)
For V 60 – 100 0.082V2 – 0.0430V + 60.12 (2)
Other Pollutants
CO For V 5 – 100 37.280V-0.6945 (3)
NOx For V 5 – 50 50.305V-0.7708 (4)
For V 50 – 100 0.0014V2 - 0.1737V + 7.5506 (5)
VOC For V 5 – 100 40.120V-0.8774 (6)
PM For V 5 – 100 4.5563V-0.707 (7)
Scenario (2):
For V 5 – 60 1068.4V-0.4905 (8)
For V 60 – 100 0.0126V2 – 0.6589V + 141.2 (9)
Scenario (3):
For V 5 – 60 1595.1V-0.4744 (10)
For V 60 – 100 0.0382V2 – 5.163V + 399.3 (11)
Based on the vehicle’s category, corrections could also be applied to consider the gradient of street and load of vehicles' effects on the emissions.
Gradient factor
Uprising or down rising of the road has the effect of ascending or descending the vehicle's fuel consumption and emission (EEA 1999). For street gradient class, the gradient correction factor calculated using The MEET model provides gradient correction factor for a range of ( -6 % to 6 %) of gradient .to include abroad range of gradient classes in a city road network the given equations are adjusted to fit an exponential function. The equations (12-14) used also for other pollutants as the road gradient correction factor and expressed by the following equation where RG represents the road gradient in percentage.
For scenario 1: GCF = 0.213e0.165RG (12)
For scenario 2: GCF = 0.419e0.180RG (13)
For scenario 3: GCF = 1.0458e0.187RG (14)
Vehicle Load factor
The higher the vehicle weight the higher its fuel consumption and emission and also the same for vice versa (Tavares et al 2009).
50% of the load for emission factor is corrected to tolerate different load conditions, is corrected with the use of the following equation (15).
[Please see the supplementary files section to access this equation.] (15)
where,
Lcf = emission factor corrected of the fuel consumption in [g/km]
lp = 0 for empty load vehicle and lp = 100 for fully load
cf = load correction factor of the FC which is 0.18
for Loaded vehicles Lcf of 1.18 and for unloaded Vehicles Lcf of 0.82 was applied
the above equation (15) also used to consider vehicle load for other pollutants emission estimation calculations. The actual Load factor for pollutants is given in MEET and applied for this study and load correction applied for pollutants is given below in the table (2) load factor and (3) the load correction factor respectively.
Table 2:Pollutants and its Load factor
Pollutant
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Load Factor(lp)
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CO
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0.21
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NOx
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0.18
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VOC
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0
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PM
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0.08
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Table 3:Pollutants Load Correction Factor
Load Correction Factor (Lcf)
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Pollutant
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Empty Load
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Complete Load
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CO
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0.79
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1.21
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NOx
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0.82
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1.18
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VOC
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1
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1
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PM
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0.92
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1.08
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MEET Suggests the Total Fuel Consumption in gram as in equation (16) and for all pollutants, (i.e. CO, NOx, VOC, PM) the total Emission per Road length is calculated as in gram as in equation (17)
FC = Fcv * GCF* Lcf*D (16)
Total Emission of Pollutant = Pv * GCF* Lcf*D (17)
Where: Fcv = Speed factor for Fc , PV = Pollutants speed factor , GCF = Gradient Correction Factor , Lcf = Load Correction Factor , D = Distance in km
For real application, fuels in grams are converted to Liters [L]
Diesel for automotive use is around 832gm/liter
Therefore: [See supplementary files.] (18)
The calculated fuel consumption values are used as an impedance to find eco routes in a network. For comparison, the road networks are completed with time attribute, Length and allowed road functional class velocity expressed as equation 19.
[See supplementary files.] (19)
L[km] = 3D Distance used for Short Routes
Moreover, CO2 emissions rates are estimated based on fuel consumption only for each scenario. The following equation 20 is applied from the MEET methodology.
Weight of CO2 = 44.011 (wight of fuel/(12.011 + 1.008 . rH/C)) (20)
Where: rH/C = hydrogen to carbon ratio in the fuel ( ~2.0 for diesel)
2.4.3. Road Network Data Set Development
Transport networks are mostly done in a GIS environment by the Network dataset. Network datasets are made of network elements. Network elements are created from point or line features to create the network dataset. network elements contain attributes that change navigation over the developed network. Network attributes help to regulate traverse ability over the network. in this study, a one-way restriction was applied while solving the analysis. The hierarchy was given according to the road functional class how drivers generally select the level of a street to travel. More predictable that drivers choose high order roads rather than low order roads. for this study vertex connectivity was selected. And finally, Elevation fields considered that helps to improve the connectivity at line ends. They contain elevation information to consider effectively vehicles to pass overpasses underpasses and normal streets in the network.
2.4.4. Optimization
The route optimization was done with ArcMap Network Analyst. In this study, route optimization primarily focuses on minimizing the fuel to find eco-routes in a network that connects origin-destination points. Additionally, time and distance attributes are added to be used as cost attributes of the network. This study mainly aims to reduce vehicle environmental footprints, therefore, reduction in fuel is not questionable.
In this study, three scenarios were introduced to test different routing conditions, which include different ranges of vehicle weight classes that are available in the city of Addis Ababa. Each scenario contains three cases with different loading and routing conditions. The overall implementations are tabulated in table 4.
Table 4:Scenario Implementation
Scenarios
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Cases
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Engine Type
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Weight Class [Ton]
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Loading Condition
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Routing
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Relationship
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Description
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1
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1
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Diesel Vehicles
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3.5-7.5
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Loaded
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Simple
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One to One
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Loaded Vehicles travels from one origin to one Destination
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2
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Loaded
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Full Distribution
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One to Many
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Loaded Vehicles travels from one origin to Many Destination
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3
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Unloaded
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Back Haul
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Many to One
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Unloaded Vehicles travels from Many Destinations to One Origin
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2
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1
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Diesel Vehicles
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7.5 -16
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Loaded
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Simple
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One to One
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Loaded Vehicles travels from one origin to one Destination
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2
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Loaded
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Full Distribution
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One to Many
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Loaded Vehicles travels from one origin to Many Destination
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3
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Unloaded
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Back Haul
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Many to One
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Unloaded Vehicles travels from Many Destinations to One Origin
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3
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1
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Diesel Vehicles
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16 -32
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Loaded
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Simple
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One to One
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Loaded Vehicles travels from one origin to one Destination
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2
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Loaded
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Full Distribution
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One to Many
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Loaded Vehicles travels from one origin to Many Destination
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3
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Unloaded
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Back Haul
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Many to One
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Unloaded Vehicles travels from Many Destinations to One Origin
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