Temporal stability of shipment size decisions related to choice of truck type

Figures & data

Table 1. Summary statistics on the combined dataset from the three surveys.

		Combined dataset
Definition	Variable	Mean	Std. dev.
Payload or shipment weight (metric tons)	PL	13.84	13.2
Maximum legal carrying capacity (metric tons), weighted average	M	22.45	15.46
Distance/trip length (km)	L	195.2	280.62
Total operating cost per ton (US dollars), weighted average	CW	24.25	68.13
Dummy variables
Port dummy variable, 1 if origin & destination (either of the OD ends at a seaport or inland dry port)	P_D	569
Special body type, 1 if specialist body truck (dump truck, cement mixer, garbage truck, log carrier truck, refrigerated truck)	BT_D	1037

Table 2. Truck types and number of trips based on origin and destination (OD) location (inter-city trips).

	Inter-city
Vehicle type	Trip number	Mean payload	Std. dev.	Gross weight Limits (metric tons)^a
LT-1	77	1.37	0.81	na
LT-2	258	3.50	1.55	na
T-2	238	5.63	2.19	16
T-3	243	12.80	4.58	24
T-4	443	20.18	5.46	32
ST – 23	308	29.98	7.10	44
ST – 33	395	37.42	6.33	52
Total	1962

^a Based on legal limits for axle loads in Ethiopia (NEGARIT-GAZETA 1990). na = not applicable.

Figure 1. Distribution of commodity groups with truck type for inter-city trips from Addis Ababa.

Table 3. Inter-sector (IS) flows of inter-city trips in the combined datasets.

Sector	Retail	Wholesale	Manufacturing	Other
Retail	89	40	45	1
Wholesale	164	82	84	12
Manufacturing	254	243	202	164
Other	143	178	220	41

Figure 2. Discrete-continuous model framework with integrated choice and latent variable model (adapted from Ben-Akiva and Boccara (1995)).

Table 4. Integrated choice and latent variable (ICLV) model estimates for truck type-shipment size choices.

Shipment size choice model	Estimate	z-stat
Intercept (${\beta }_0$)	2.72	5.89
Agriculture products	0.45	2.69
Coal, crude oil and natural gas	1.22	4.41
Quarrying products: sand and stone	0.47	2.78
Food and beverage products	0.65	3.94
Textile and leather products	−0.16	−0.67
Wood, pulp and paper products	−0.04	−0.22
Chemicals and chemical products	0.27	1.89
Non-metallic mineral products	1.70	6.97
Metals: basic and fabricated	0.84	4.90
Retailer – Retailer	−0.38	−1.06
Retailer – Manufacturer	0.17	0.58
Wholesale – Retail	−0.21	−1.07
Wholesaler – Wholesaler	−0.15	−0.65
Wholesaler – Manufacturer	0.23	1.01
Manufacturer – Retailer	−0.32	−1.53
Manufacturer – Wholesaler	0.63	3.41
Manufacturer – Manufacturer	0.23	1.43
Port dummy (trip start/end at port location)	0.82	4.92
Specialist body type truck dummy	−0.27	−2.47
YD_2: year 2017 (compared with 2015)	−0.76	−5.75
YD_3: year 2019 (compared with 2015)	−1.88	−6.30
Vehicle type choice model (reference group ST-33)	Coefficient	z-stat
ASC for LT-1	−3.05	−6.69
ASC for LT-2	−2.14	−6.19
ASC for T-2	−1.28	−4.02
ASC for T-3	−0.84	−3.69
ASC for T-4	0.49	3.02
ASC for ST-23	0.26	2.33
Average cost per ton, Cwn	−0.023	−4.10
Unused capacity index, Vn	−0.34	−32.17
Parameter α in the measurement equation	0.97	9.31
Rho-square (adj.)	0.52
Log-likelihood choice model	−1727.6
Log-likelihood integrated model	−8409.82
Chi-square test stat. χ2 (df = 24) – Robust (p-val < 0.00)	1696.12
Stand. Root Mean Square Residual (RMSR) – Robust	0.015
Tucker-Lewis Index (TLI) – Robust	0.89

Figure 3. Overall shipment size-trends.

Table 5. Cross-tabulation of the repeated K-fold cross-validation (5-fold, repeated 10×) results.

Cross-validation (training/test sample split 70/30), test sample = 586
	Modelled choice
Actual Choice	LT-1	LT-2	T-2	T-3	T-4	ST-23	ST-33	Total
LT-1	24	0	0	0	0	0	0	24
LT-2	2	59	22	3	0	0	0	86
T-2	0	18	38	3	0	0	0	59
T-3	0	0	8	71	0	0	0	79
T-4	0	0	0	0	128	0	0	128
ST-23	0	0	0	0	0	43	17	60
ST-33	0	0	0	0	0	26	124	150
Total	26	77	68	77	128	69	141	586
Training accuracy = 0.79					Testing accuracy = 0.79
Kappa = 0.75					Kappa = 0.75

Table 6. The elasticity of the truck choice model with changes in operating cost (Cw_n) and unused capacity (V_n).

	Truck type
Variable in the utility function	LT-1	LT-2	T-2	T-3	T-4	ST-23	ST-33
Elasticity of Cwn in the choice
LT-1	−2.21	0.33	0.21	0.075	0.027	0.003	0.23 × 10^−3
LT-2	0.93	−1.30	0.68	0.253	0.088	0.010	0.79 × 10^−3
T-2	0.51	0.59	−1.45	0.36	0.13	0.016	0.001
T-3	0.14	0.17	0.28	−1.10	0.29	0.042	0.004
T-4	0.075	0.090	0.15	0.44	−0.62	0.29	0.039
ST-23	0.004	0.005	0.009	0.030	0.15	−0.50	0.20
ST-33	0.45 × 10^−3	0.55 × 10^−3	0.97 × 10^−3	0.004	0.026	0.26	−0.24
Elasticity of Vn in the choice
LT-1	−0.38	0.051	0.040	0.015	0.006	0.001	0.53 × 10^−4
LT-2	0.34	−0.37	0.17	0.072	0.025	0.003	0.25 × 10^−3
T-2	0.57	0.51	−1.09	0.20	0.076	0.010	0.92 × 10^−3
T-3	0.37	0.38	0.41	−1.26	0.15	0.033	0.48 × 10^−2
T-4	0.35	0.37	0.47	0.67	−1.18	0.30	0.084
ST-23	0.035	0.042	0.059	0.13	0.26	−1.12	0.44
ST-33	0.47 × 10^−2	0.52 × 10^−2	0.008	0.019	0.068	0.34	−0.36

^a Values in bold indicate truck-type choices that are elastic.

Figure 4. Latent growth curve for different truck types.

Table 7. Latent growth (LG) model estimates of shipment size for each truck type.

	Truck type
	LT-1		LT-2		T-2		T-3		T-4		ST-23		ST-33
Parameter	Coeff.	z-value	Coeff.	z-value	Coeff.	z-value	Coeff.	z-value	Coeff.	z-value	Coeff.	z-value	Coeff.	z-value
Mean intercept	1.91	6.99	4.34	18.16	5.56	16.23	14.56	21.58	22.81	68.87	33.59	33.13	39.56	88.67
Mean slope	−0.31	−1.51	–0.72	−3.68	–0.44	−1.41	–0.9	−1.94	– 2.7	−8.1	−4.81	−4.72	–3.47	−5.7
Intercept variance	0.78	1.31	0.85	1.27	4.14	1.37	2.95	1.71	3.2	2.23	64.51	4.65	6.8	1.15
Slope variance	0.67	1.95	0.61	1.48	3.17	1.53	5.38	1.73	2.57	2.15	51.86	3.53	5.24	0.85
Covariance (slope & intercept)	−0.77	−1.75	−0.68	−1.39	−3.59	−3.19	−4.64	−1.89	−3.5	−1.65	–55.03	−4.18	–3.32	−0.71
Error variances across shipment sizes	0.41	3.23	1.98	7.15	4.1	7.06	11.85	7.07	16.78	9.14	19.77	5.03	42.73	7.47
Fit indices: df*, χ2*, p-value	3; 14.38; 0.00		3; 23.74; 0.00		3; 36.73; 0.00		3; 20.35; 0.00		3; 13.69; 0.00		3; 21.64; 0.00		3; 4.31; 0.04

Figure 5. Economic metrics for Ethiopia, 2010/11–2020/21. (a) GDP, inflation, and growth of main sectors, and (b) trade growth and merchandise trade. (Data source: World-Bank (2022)).

Table A1. The distribution of establishment categories in the freight flow (intercity trips).

	2015, Sample = 530				2017, Sample = 1203
Sectors	Retail	Wholesale	Manufacturer	Others	Retail	Wholesale	Manufacturer	Others
Retail	4.8%	1.4%	1.8%	0.0%	3.9%	2.0%	2.3%	0.0%
Wholesale	8.3%	5.4%	4.6%	0.2%	8.7%	3.7%	4.3%	0.8%
Manufacturer	11.7%	13.0%	8.7%	7.0%	13.1%	11.6%	10.6%	8.8%
Others	7.8%	10.0%	12.6%	2.5%	7.6%	9.2%	11.2%	2.2%
Sectors	2019, Sample = 229
	Retail	Wholesale	Manufacturer	Others
Retail	7.1%	4.0%	3.6%	0.8%
Wholesale	6.8%	3.7%	3.3%	0.7%
Manufacturer	14.9%	14.6%	12.2%	9.4%
Others	4.3%	6.2%	7.7%	0.7%

Table A2. Average transport cost per ton-kilometres for each truck type over the years (in US$).

	Transport cost (US$ per ton-kilometres)
Truck types	2015	2017	2019
LT-1	1.39	2.89	2.53
LT-2	1.01	1.50	1.58
T-2	0.85	1.11	0.93
T-3	0.54	0.82	0.85
T-4	0.26	0.58	0.45
ST – 23	0.14	0.15	0.16
ST – 33	0.05	0.11	0.09

Figure A1. Map of Addis Ababa city showing locations used for data collection in the three surveys.

Figure A2. Cumulative distribution of three datasets with travel distance.

Figure A3. Complete path diagram of the shipment size growth curve model.

Figure A4. Commodity-specific shipment size trends with travel distance over data collection years 2015, 2017 and 2019. Note: the bars only indicate the time of major events and do not represent any scale.

Figure A5. Ethiopia’s major socio-political events with a timeline between 2015 and 2020 (own depiction).

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