Application of Fuel Efficiency and Traffic Efficiency Assessment

Abstract

This chapter presents the application of the fuel assessment methodology developed in the Connecting Austria project. Thereby, a route analysis for an Austrian fleet operator is performed including the assessment of feasible and economic viable routes and scenarios. Furthermore, potential fuel consumption and CO$_{2}$ emission savings are discussed within the given case. The saving potential may be increased via dynamic C-ITS-based truck platoon regulations, instead of statically defined, too restrictive regulations as indicated in the C-ITS assessment section. Finally, the chapter discusses the effect of truck platooning on increasing traffic efficiency.

1 Fuel Efficiency Assessment in a Fleet Operator Case

The evaluation of fuel consumption and its sensitivity on selected parameters is carried out to show the underlying trade-offs with respect to platooning fuel efficiency. These selected parameters are:

• Risk-rated map configuration (how often does the platoon have to be created/dissolved (i.e. number of required formation/dissolution manoeuvres) on a given route.

• Swiftness of the platoon manoeuvres (highly active platoon vs. slow response).

• Intra-platoon distance (time gap between trucks).

The evaluation will take into account the methodology presented in Chap. 11 and investigate given routes of an Austrian fleet operator. Aligned with an Austrian fleet operator, typical inner Austrian routes on highways were defined. These routes have already been illustrated in Fig. 3.4. The routes comprise only Austrian highways, since the assessment focused on truck platooning potentials on highways. The following transport routes (back and forth) were selected:

• Pasching–Guntramsdorf (concerning the Austrian highways A1, A21).

• Pasching–Werndorf (concerning the Austrian highways A1, A9).

• Pasching–Kalsdorf (concerning the Austrian highways A1, A9).

In an initial step, a road infrastructure assessment (compare Fig. 11.1) was conducted. Thereby, a risk-rated map was developed to identify road segments that are eligible and non-eligible for platooning taking into account the defined truck platooning restriction scenarios (compare Fig. 3.3).

Given the resulting scenario-based risk-rated map data, the assessment of the feasibility and economic viability of the routes was conducted. The results for the direction Pasching–Guntramsdorf are depicted in Fig. 12.1. The figure highlights the results based on the scenarios A–F and data for an intra-platoon distance of 0.5 s at a target speed of 80 km/h. For each scenario, the following information is provided:

• Platooning allowed—number of road segments where platooning is allowed with respect to the scenario regulations.

• Platooning feasible—number of road segments where platooning is feasible with respect to the minimum length for platoon formation and dissolution.

• Fast form/diss—number of road segments that are economically viable in case of applying the fast formation and dissolution strategy.

• Medium form/diss—number of road segments that are economically viable in case of applying the medium formation and dissolution strategy.

• Slow form/diss—number of road segments that are economically viable in case of applying the slow formation and dissolution strategy.

Fig. 12.1

Fuel efficiency assessment—example

The results indicate a significant reduction of suitable platooning segments when taking into account feasibility and even more when taking into account economic viability. With respect to economic viability, the medium formation/dissolution strategy results in higher numbers of platooning segments than the slow and fast strategy (medium > slow > fast).

Findings within the data analysis for the route from Pasching to Guntramsdorf are:

• Feasibility assessment

  • Some road segments are not eligible due to falling below the required length for platoon formation and dissolution.

  • The prohibition of platooning near on-/off-ramps significantly affects the delta between “Platooning allowed” and “Platooning feasible”, e.g. Fig. 12.1 delta for number of road segments in scenario A = 18 VS delta in scenario F = 2.

• Economic viability assessment

  • The economic viability assessment additionally decreases the number of road segments suitable for truck platooning.

  • Medium platoon formation and dissolution strategies provide the highest number of economically viable platooning road segments.

Subsequent to the assessment of feasibility and economic viability, an analysis of potential costs and emission savings was conducted. In order to assess the fuel saving potential for the given routes, the fuel saving model presented in Fig. 5.10 was applied. The maximum average fuel savings are depicted in Fig. 12.2 with respect to the direction, the regulation scenario (A-F) and the driving behaviour. This analysis confirmed that in most instances the medium formation/dissolution strategy for a 3-truck platoon driving at an intra-platoon distance between 1 and 1.5 s at a speed of 80 km/h is suitable. The maximum achievable fuel saving is 4.83% for all three trucks in a platoon driving from Pasching to Guntramsdorf in scenario F applying the slow formation/dissolution strategy at an intra-platoon distance of 0.5 s and speed of 80 km/h.

In addition to the maximum fuel savings, configurations that lead to minimum fuel savings were analysed. As depicted in Fig. 12.3, the fast formation/dissolution strategy at an intra-platoon distance of 1 s leads to minimum average fuel savings in a platoon of three trucks. The minimum saving for the route Pasching to Guntramsdorf in scenario F applying the fast formation/dissolution strategy at an intra-platoon distance of 1 s leads to fuel savings of 2.53%. This minimum saving value is 2.3% less than the maximum achievable value of 4.83% given above.

Fig. 12.2

Maximum average fuel savings in 3-truck platoon—fleet operator example

The analysis of the fuel reduction also provides a basis for assessing potential savings in CO$_{2}$ emissions. Based on the consumption values and a factor for translating fuel consumption in emissions, one may illustrate potential CO$_{2}$ savings. In Fig. 12.4, maximum potential CO$_{2}$ savings for a three-truck platoon are depicted for the different regulation scenarios and transport routes. The emissions are calculated based on the emission factor provided by [1].

The depicted values may provide a basis for fleet operators to project the fuel and emission savings for their fleet operations. Doing so, they may assess how truck platooning can contribute to sustainable transport operations. Furthermore, the saving potentials might be used when authoring emission reports related to their logistics operations.

Fig. 12.3

Minimum average fuel savings in 3-truck platoon—fleet operator example

Fig. 12.4

Maximum well-to-wheel savings in 3-truck platoon—fleet operator example

2 Traffic Efficiency Assessment

In this section, the effects of energy-efficient automated convoys on traffic efficiency and traffic control are analysed and measures are derived. Platooning allows groups of trucks to safely drive at small headways, thereby reducing their overall length as a unit. Depending on the exact platoon specifications (availability of C-ITS-equipped trucks, percentage of trucks on the road, ability to form inter-company platoons, exact platoon headway, etc.), the throughput of existing infrastructure can be increased. However, the number of unknown parameters for platooning specifications requires a thorough analysis based on Latin hypercube sampling. The resulting multidimensional data of achievable traffic throughput may then be used for detailed analyses. At the point of writing, many truck platooning parameters are completely unknown and will only become available step by step in future. However, the sampling results are complete in the sense that a result for each conceivable (at the point of writing) combination of parameters is available and can therefore readily be evaluated in detail.

The method to investigate a potential increase in traffic volume has been introduced in Sect. 9.2. The relationship between the input parameters and the attainable increase in traffic volume are presented. Following, the evaluation and interpretation of results are provided.

Two general approaches that are represented by different sets of assumptions are analysed. Therefore, a set of generic assumptions (“mixed platooning”) is proposed, in order to establish the true theoretical upper bound of the influence of platooning, C-ITS measures and their expected impact. Generic assumptions are:

• Any vehicle can be a part of a platoon.

• Platoons require back-to-back driving of vehicles with C-ITS capabilities.

• A platoon can contain an arbitrarily large number of vehicles.

This set of assumptions will not be met in the near foreseeable future. Nevertheless, it is vital to quantify a theoretical upper limit of improvement in order to establish a reasonable roadmap for future research and implementation efforts. In case that the theoretical upper limit represents only an insignificant improvement, one can decide at this point that further research in this area is futile and resources are better spent at issues that provide a significant impact. The second set of assumptions (“truck platooning”) is:

• Only trucks can be a part of a platoon.

• Platoons require back-to-back driving of trucks with C-ITS capabilities.

• A platoon can contain a maximum of three vehicles.

In addition to the parameters mentioned, the vehicle lengths and vehicle type distributions influence traffic efficiency significantly. For this reason, current highway control measurement data provides information on the state of vehicle distributions and lengths on Austrian highways. In order to gauge theoretical limits, the range of these parameters is extended well beyond currently measured values. However, the extension considers physically possible limits, so as to account for possible future development. The described parameters are summarised in Table 12.1.

Table 12.1 Parameter for traffic efficiency assessment

In the following, traffic efficiency assessment cases are detailed. Note that C-ITS capability is a stochastic variable. The state of the art is represented by conventional trucks that have no C-ITS capability. In future, a growing fraction of trucks will be sufficiently equipped, and finally, in the far future, all trucks may be equipped with C-ITS support. Therefore, the range of this particular parameter is between zero and one. At any given time in future, e.g. when a survey shows that a certain fraction of trucks is equipped with C-ITS capabilities, the results of this particular investigation can be applied with the given value for that parameter.

Fig. 12.5

Traffic volume increase for true vehicle length and type distributions and different intra-platoon headways (case 1)

For truck platooning, based on actually measured vehicle lengths and type distributions, the increase in traffic volume is shown in Fig. 12.5. The measured truck fraction is approximately $11 \%$. For fractions of C-ITS capable trucks (represented by platooning fraction) $< 50\%$, the increase of traffic volume is $< 1\%$. The maximum achievable increase in traffic volume of about $7.5\%$ is reached at $100\%$ C-ITS capability and the smallest possible time headway of 0.5 seconds. This is in itself not a very impressive number, given that the imaginary setting is placed well in future. However, it also indicated that the notion of pure truck platoons may be limiting.

For the “mixed platooning” case, traffic volume increase is significantly higher. In Fig. 12.6, the same plot is shown for mixed platooning, representing case 3 sketched in Table 12.1.

Fig. 12.6

Traffic volume increase for true vehicle length and type distributions and different intra-platoon headways (case 3)

Similar result plots are useful for the cases 2 and 4, where the vehicle distribution is sampled and a Monte Carlo approach is utilised to generate results.

Fig. 12.7

Traffic volume increase for constant vehicle length, stochastic type distribution and different intra-platoon headways for truck platooning only (case 2)

For truck platooning in case 2, a particular situation for the truck fraction of $25\%$ and a platooning fraction of $80\%$ is shown in Fig. 12.7. An increase of traffic flow of about $11\%$ can be achieved for a small time headway of 0.5 s and the assumptions stated above.

The results are more promising for mixed platooning as indicated in a similar plot in Fig. 12.8.

Fig. 12.8

Traffic volume increase for constant vehicle length, stochastic type distribution and different intra-platoon headways for mixed platooning (case 4)

Summarising, the achievable increase in traffic volume, i.e. the potential to increase traffic efficiency, depends significantly on the type of platoons that are considered (mixed platoons or truck platoons only), and the fraction of vehicles that have the technical capability to participate in platooning. For all practical purposes in the near future, the fraction of C-ITS capable vehicles in general and trucks in particular may not be large enough to increase efficiency noticeably. However, traffic efficiency is not the only target for platooning as described in detail in Sect. 4.1. The results presented provide an overview of what can be achieved under particular circumstances represented by a set of assumptions and the corresponding range of parameters. Even if traffic efficiency by itself is not the driving force for further truck platooning research and implementation, a positive impact can be expected.

3 C-ITS Assessment for Dynamic Traffic Control

C-ITS enables dynamic truck platooning control on highways and supports further increasing efficiency. Instead of statically defining restrictions for truck platooning on highways, dynamic information regarding current restrictions for a highway segment might be provided via C-ITS. The restrictions may depend on factors such as traffic density, hazardous events, day time or weather conditions. Applying a dynamic C-ITS-based approach for platoon management on highways allows to combine truck platooning regulation scenarios (cf. Fig. 3.3). Such a combination supports increasing the number of “platooning-eligible” highway kilometres as depicted in Fig. 12.9. The figure illustrates the following situation:

• Route: Austrian highway A1, A21, A2 from Linz to Vienna.

  • Highway on-ramp Ansfelden to exit Guntramsdorf.

• Platooning restrictions:

  • Near cities, i.e. Linz, St. Pölten, Vienna, the scenario A “Still safe” rules.

  • In between cities scenario F “Allow ramps” rules.

• Information with respect to the ruling scenario is communicated to vehicles via C-ITS.

Combining both, scenarios A and F, as depicted in Fig. 12.9 allows to increase the number of platooning-eligible kilometres from 116km (exclusively A) to 139km (combination A+F in the given situation). In other words, if on-/off-ramps are allowed on highway segments in between cities from Linz to Vienna, the number of platooning-eligible kilometres may be increased by nearly 20%. As stated above, the dynamic application of different platooning regulations should take into account influencing factors such as the highway topology and the real-time traffic situation.

Fig. 12.9

© OpenStreetMap contributors under the CC-BY-SA license, https://www.openstreetmap.org/copyright

Dynamic C-ITS-based platoon management—example 1, base map and map data from OpenStreetMap,

Another application case could consider dynamic management of tunnel restrictions. In Austria, the A9 from Upper Austria to Styria comprises a high number of tunnels. Therefore, potential savings could be gained in case platooning is considered safe in the given tunnels in certain traffic situations. Figure 12.10 illustrates the following situation:

• Route: Austrian highway A1/A9 from Linz to Graz

  • Highway on-ramp Ansfelden to exit Werndorf.

• Platooning restrictions:

  • In Upper Austria scenario B “Astra Study -no Bridges” rules.

  • In Styria E “Astra Study -no Bridges -no Tunnels” rules.

• Information with respect to the ruling scenario is communicated to vehicles via C-ITS.

Described parameters are summarised.

Fig. 12.10

© OpenStreetMap contributors under the CC-BY-SA license, https://www.openstreetmap.org/copyright

Dynamic C-ITS-based platoon management—example 2, base map and map data from OpenStreetMap,

Combining both, scenarios B and E, as depicted in Fig. 12.10 allows to increase the number of platooning-eligible kilometres from 54 km (exclusively B) to 92 km (combination B+E in the given situation). In other words, if tunnels are allowed on highway segments in Styria on the route from Linz to Graz, the number of platooning-eligible kilometres may be increased by 70% compared to the “exclusively B scenario”. The highest number of platooning-eligible kilometres could be gained in the “exclusively E scenario”. If this scenario would rule in Upper Austria and Styria, the number of platooning-eligible kilometres would be 120km, which is more than half of the overall route.

4 Conclusion

Efficiency represents a crucial element of truck platooning. Efficiency may relate to actual fuel savings in a platoon as well as to traffic efficiency and control. Fuel savings depend on diverse aspects such as the following distances of trucks in platoon, or the additional fuel consumption for platoon formation and dissolution. In this chapter, a typical route of an Austrian fleet operator was assessed with respect to the feasibility of platooning and the economic viability of platooning segments. The results with respect to feasible routes indicate that depending on the scenario the feasibility of allowed platooning road segments varies; e.g. for the the transport route “Pasching–Guntramsdorf”, the share of feasible segments ranges from 92% (scenario F) to 65% (scenario A). The highest share of economic viable road segments is gained when applying a medium formation and dissolution strategy for truck platoons. Restricting truck platoons on on-/off-ramps within the given assessment situation decreases the number of road segments allowed for platooning and leads to a higher number of shorter road segments allowed for platooning that are economically not viable. Therefore, when restricting truck platooning one should also consider the resulting fragmentation of platooning allowed/not allowed road segments. A high number of short segments is considered less efficient compared to a lower number of long platooning segments. Furthermore, applying dynamic C-ITS-based traffic control approaches may increase to number of platooning kilometres compared to static traffic regulations.

Aside from the fuel efficiency assessment, a traffic efficiency and control assessment was conducted in this chapter. The results indicate that the achievable increase in traffic volume depends significantly on the type of platoons that are considered (mixed or truck platoons only), and the amount of vehicles that have the technical capability to participate in platooning. For all practical purposes in the near future, the amount of C-ITS capable vehicles in general and trucks in particular may not be large enough to increase traffic efficiency noticeably.