LRN 2016 SPECIAL – high capacity vehicles and modal shift from rail to road: combining macro and micro analyses

Figures & data

Figure 1. Research design and input data for the modal shift calculations in the macro and micro studies.

Table 1. Data sources and their role in the studies.

Data source	Source	Role of data
Statistics on average load factor	National Statistics Bureau	Included in cost calculations
Freight transport scenario	The Swedish Transport Administration	Tonne-kilometres on roads in Sweden by 2030
Expert workshops	Seven senior researchers in transport, logistics, economics, and policy science; two representatives from the Swedish Transport Administration; one representative from the Swedish Transport Agency	Checked tonne-kilometre data. Estimated the proportion of each product group that is limited by weight and volume. Divided road operations into (1) terminal transport: transport that ends in a freight terminal; (2) direct transport: no use of freight terminals; (3) distribution transport: transport to a receiver. Estimated the proportion of tonne-kilometres for each product group in road operations 1, 2, and 3. Infrastructure details about modal shift are possible.
SÅ Calc	The Swedish Association of Road Transport Companies	Cost structure for lorries
Interviews^a	Chief technologist, truck manufacturer	Input on data from HCV trials
	CEO, bulk tank operator	Cost input for bulk transports
	CEO and owner, SME general cargo	Cost data from the haulier
	CEO and owner, SME mixed bulk haulier	Detailed cost data on various types of vehicles
	Owner-operator of a forestry haulier	Detailed revenue and cost data on forestry haulage
	Project manager at a haulier participating in HCV trial	Detailed input from the haulier’s ongoing HCV trial
	Driver of an HCV trial combination at a haulier (same as previous)	Details on the execution of the HCV trial and some cost data
	Driver of an HCV trial combination at a transport laboratory	Details on the execution of the HCV trial and some cost data
Transport and road data	The Swedish Transport Administration, SAMGODS model (Trafikverket, 2016)	Transport flows per product group and geographical area

^aThe respondents were interviewed in private meetings and over the phone, lasting from 10 min to 3 h.

Table 2. Cost efficiency per product group for increased weight limits from 60 t/25.25 m to 74 t/25.25 m, and for increased weight and length limits from 60 t/25.25 m to 74 t/34 m.

Product group	Lorry	Average fill rate	Cost efficiency per tonne- km 74 t/25.25 m	Cost efficiency per tonne- km 74 t/34 m	Cost efficiency per m^3km 74 t/34 m	Relation 74 t/25.25 m vs. 74 t/34m	Cost eff. per tonne-km* 74 t/25.25  m and 74 t/34 m
Food	Triaxle truck for long-dist. haul with lightweight cabinet and trailer	75%	19.9%	20.2%	22.0%	23%	20.1%
Agriculture	Raw material body	50%	14.4%	20.2%	22.0%	90%	15.0%
Forestry	Forrest truck alt. chip truck	60%	14.4%	20.2%	22.0%	100%	14.4%
Wood raw materials, paper	Raw material body alt. DUO2 (long)	50%	14.0%	20.2%	22.0%	80%	15.2%
Crude oil and petroleum products	Tank truck	50%	14.0%	0%	0.0%	100%	14.0%
Ore and other raw metal	Raw material body	50%	14.0%	0%	0.0%	100%	14.0%
Steel and metal materials	Raw material body	50%	14.0%	0%	0.0%	100%	14.0%
Construction materials	Construction truck	50%	14.0%	0%	0.0%	100%	14.0%
Chemicals	Tank truck	50%	14.0%	0%	0.0%	100%	14.0%
Other manufactured goods	Triaxle truck for long-dist. haul with lightweight cabinet and trailer	75%	14.0%	20.2%	22.0%	22%	18.8%

Table 3. Tonne-kilometres for different types of trains in 1,000,000 tonne-kilometres (Tabell D:10, Trafikanalys 2015).

Train type	1000 tonne-km	Share (excl. Iron Ore Line)
Wagonload	11,747	70%
Iron Ore Line	4504	–
Combi goods	5046
- of containers and swap bodies	2679	16%
- of lorries and semitrailers	2367	14%
Total	21,296
- of which are system trains (excl. Iron Ore Line)	4211

Table 4. Estimated allocation of tonne-kilometres per product group to different types of trains.

	Allocation of total railway goods		Allocation of combi goods		Wagonload	Combi trains with containers/ swap bodies	Combi trains with lorries/ semitrailers
	Share wagonload and system trains	Share combi trains	Share containers/ swap bodies	Share lorries/ semitrailers	(1000,000 tonne-km)	(1000,000 tonne-km)	(1000,000 tonne-km)
Food	55%	45%	20%	80%	1103	479	423
Agriculture	100%	0%			84	0	0
Forestry	100%	0%			1172	0	0
Wood raw materials, paper	85%	15%	90%	10%	3052	286	252
Crude oil and petroleum products	100%	0%			688	0	0
Ore and other raw metal	100%	0%			9528	0	0
Steel and metal materials	90%	10%	90%	10%	5433	320	283
Construction materials	100%	0%			769	0	0
Chemicals	90%	10%	90%	10%	858	50	44
Other manufactured goods	20%	80%	50%	50%	1467	3117	2754
Total					24,160	4254	3758

Table 5. Characteristics of COOP’s distribution network.

Case company characteristics	Amount
Central terminals	3
Regional terminals	10
Urban stores	80
Rural stores	500
Inbound flow (pallets/month)^a	>87,000
Inter-facility flow (pallets/month)^a	>80,000
Distribution flow, urban stores (pallets/month)	>18,000
Distribution flow, rural stores (pallets/month)	>200,000

^aFor goods that use load carriers other than pallets (e.g. distribution of chilled goods which use roll containers), the distribution flows are translated into ‘pallet-equivalents’.

Table 6. Modal shift from rail to road in 2030 when implementing 74 t/25.25 m vehicles (in 1,000,000 tonne-km).

Product type	Wagonload	Modal shift wagonload	Combi train containers/ swap bodies	Modal shift combi train containers/ swap bodies	Combi train with lorries/ semitrailers	Modal shift combi train with lorries/ semitrailers
Food	1070	93	175	15	700	61
Agriculture	81	5	0	0	0	0
Forestry	1138	72	0	0	0	0
Wood raw materials, paper	2963	182	470	29	52	3
Crude oil and petroleum products	668	41	0	0	0	0
Ore and other raw metal	782	48	0	0	0	0
Steel and metal materials	5273	324	527	32	59	3
Construction materials	746	45	0	0	0	0
Chemicals	833	51	83	5	9	0.6
Other manufactured goods	1424	87	2849	175	2849	175
Total	14,983	952	4105	257	3670	244
Modal shift	6.4%		6.3%		6.7%
Modal shift average	6.4%

Table 7. Modal shift from rail to road in 2030 when implementing 74 t/34 m vehicles (in 1,000,000 tonne-km).

Product type	Wagonload	Modal shift wagonload	Combi train containers/swap bodies	Modal shift combi train containers/swap bodies	Combi train with lorries/semitrailers	Modal shift combi train with lorries/semitrailers
Food	1071	95	175	16	701	126
Agriculture	82	5	–	–	–	–
Forestry	1138	72	–	–	–	–
Wood raw materials, paper	2963	199	471	32	52	9
Crude oil and petroleum products	669	41	–	–	–	–
Ore and other raw metal	782	48	–	–	–	–
Steel and metal materials	5274	325	527	32	59	11
Construction Materials	746	46	–	–	–	–
Chemicals	833	51	83	5	9	2
Other manufactured goods	1425	118	2849	236	2849	514
Total	14,983	1001	4106	321	3670	662
Modal shift	6.7%		7.8%		18.0%
Modal shift average	8.7%

Table 8. Cost and CO₂ effects of HCVs in three scenarios.

	Baseline scenario (1)	HCVs and intermodal transport (2)	HCVs replace intermodal transport (3)
Inbound cost (m€)	12	11	11
Inter-Facility cost (m€)	31	26	28
Distribution (m€)	48	48	49
Handling (rail) (m€)	1.1	1.1	0
Total cost (m€)	92	86	89
Inbound & Inter-Facility CO2 (t)	14,522	10,702	15,397
Distribution CO2 (t)	9421	9397	10,048
Total CO2 (t)	23,943	20,099	25,445
Cost difference vs. As-Is Scenario	–	−6.1%	−3.3%
CO2 difference vs. As-Is Scenario	–	−16.1%	6.3%

Figure A1. Transport flows on road, rail and sea in Sweden (courtesy of the magazine Transportnytt 2017).

Figure A2. A screenshot from SÅ Calc provided by the Swedish Association of Road Transport Companies and used to calculate the cost parameters for the macro and micro studies.

Trafikverket. 2016. Description of Samgods. Accessed 15 June 2016. www.trafikverket.se/tjanster/system-och-verktyg/Prognos–och-analysverktyg/Samgods/.

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Trafikanalys. 2015. Rail Traffic 2014. Östersund: Trafikanalys.

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