Fuel Consumption Information: An Alternative for Congestion Pricing?

Vol 23 No 3 September 2014 Road & Transport Research

52

Fuel consumption information: an alternative for congestion pricing?Omid M. Rouhani and Hossein Zarei

Peer reviewed paperThis paper has been critically reviewed by at least two recognised experts in the relevant field.

Received: November 2012; revised June 2014

AbstractPeople’s poor perception of the amount they spend on fuel is considered to be an important reason for the low price elasticity of gasoline demand. Users’ travel demand can be changed by making them more aware of the fuel costs of different routes. As a result of becoming informed about their petrol consumption cost prior to entering traffic congestion, users may take an alternative route, change their travel schedule, or decide not to travel at all. Without considering the change in the price elasticity of gasoline demand, this study estimates the effects of providing gasoline consumption information (GCI) on people’s route choice behaviour, the influence of fuel price on the effectiveness of the GCI in changing users’ route choice behaviour, and the comparative effectiveness of providing GCI and using congestion pricing schemes. The results from a small network show that providing this information can improve people’s behaviour in terms of route choice and that the results are comparable to those of congestion pricing schemes in terms of total travel time saved, especially for high travel demand and moderate fuel price conditions. The social benefits of providing the GCI are significantly and nonlinearly affected by the pre-policy proportion of uninformed users.

INTRODUCTIONIn urban areas, traffic congestion costs can reach billions of dollars (Schwaab & Thielmann 2001). Over the years, congestion pricing has been developed as a way of harnessing market power to reduce the social costs associated with traffic congestion. When the additional cost of a new user entering a network (additional delay suffered by other users following a new user’s entrance) is ignored, the market fails. Congestion pricing reflects this social cost in a way that can lead to optimal, or closer to optimal, traffic distributions. By changing users’ behaviour, pricing can lead to a more Pareto efficient outcome in terms of the social


53

Fuel consumption information: an alternative for congestion pricing?

welfare of the whole community (Barr 2004). For more in depth discussion about congestion costs, readers are referred to Rouwendal and Verhoef (2006).

Advanced traveller information systems (ATIS) refer to a major field of study that investigates the effects of providing (usually free) information on transportation system performance measures such as congestion (Yang 1999; Zhang & Verhoef 2006; Fernández, De Cea & Valverde 2009). Along with many cities around the world, major cities in Australia have adopted a variety of ATIS projects (Kim & Vandebona 1999; Zhang, Wooley & Stazic 2007).

For instance, a number of strategically located integrated variable message signs (VMS) have been installed in different parts of Australia, including Sydney, Brisbane and Melbourne. The VMS not only display traditional information and warning signs but are also capable of displaying estimated travel times and colour-coded information of traffic conditions. Other ATIS features/projects in Australia include continuous network monitoring through vehicle sensors, adaptive and coordinated ramp metering, lane-based overhead signs to manage speed and lane availability, emergency telephones and real-time traveller information advising motorists of current traffic conditions, and reporting traffic incidents. In addition, traveller information internet applications have been developed in some cities such as Melbourne and Perth. The SUNA transport protocol experts group (TPEG), a range of real-time traffic information services from the SUNA company, has been available to Australian transportation users since 2011 (TISA News 2011). Although these ATIS have been effective in improving transportation system performance, the information provided to users seems to be limited. A number of opportunities exist in expanding ATIS services to incentivise more effective travel behaviour. In-car GPS systems, such as the RACV Connect application, are able to provide fuel consumption of different vehicles (RACV Connect 2014), but not with the detailed information required for more efficient travel behaviour.

On the other hand, congestion pricing has proved to be effective in changing people’s travel behaviour in a range of practical examples (Beevers & Carslaw 2005; Eliasson 2008). Although the effectiveness of the pricing depends heavily on the form of the pricing scheme, generally, people perceive congestion charges as their out-of-pocket costs, which results in charges being effective in reducing the level of congestion. Unlike congestion charges,

gasoline (petrol1) consumption costs barely affect the travel behaviour of road users, partly as a result of the low elasticity of gasoline demand, which can be attributed to the fact that people do not perceive fuel consumption costs directly (Hughes, Knittel & Sperling 2008). Note that in the Australian context, Wang (2009), for instance, found that petrol exhibits extraordinarily high elasticity of demand because of the very prominent discounting price cycle that exists in the Perth area. However, the results cannot be generalised to other cities, and the results are only valid at the station level. Like almost all other countries, the aggregate petrol demand for Australia or New Zealand should be relatively inelastic. Therefore, for road users to become sensitive to fuel costs, as a possible approach, a scheme similar to congestion pricing can be implemented. On-line information from transportation networks can be used to estimate the real-time fuel cost for each possible route and can be provided to users by roadside banners, GPS devices, or online websites, at relatively low cost.

The gasoline consumption information (GCI) scheme can lead to two major changes in people’s travel behaviour. First, the information introduced can change gasoline demand elasticity; however, there is no direct way to to estimate this since we haven’t implemented a GCI scheme. Second, people may change their route and take an alternative route with lower gasoline consumption cost, quite similar to how they react to congestion pricing schemes. The latter has been studied as part of this research work. However, under certain conditions, a GCI scheme cannot effectively change users’ behaviour.

The GCI scheme can be ineffective under two conditions: 1) users are already informed about the fuel costs of using various routes, and their route choices are already based on this information; or 2) the road network is not congested, so the information may not be effective. The first one can be partially true, but it is simply impossible to have real-time information about all possible paths without providing users with the information. To account for this, we divided users into two groups: one group that has complete information about the fuel consumption costs and chooses the routes based on a combination of travel time costs and fuel consumption costs, and a second group that has no clear information about the fuel consumption costs and chooses routes based only on travel time costs.

1 Editor’s note: For the purposes of this paper, the terms ‘gasoline’ and ‘petrol’ are interchangeable and are both used, generally depending on the source or context. ‘Gasoline’ is retained because of its use in the name of the gasoline consumption information (GCI) scheme.

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The second condition, non-congested network, has also been tested, and the results are presented as part of this paper. In addition to developing a new modelling framework to simulate users’ behaviour, we explore the outcomes of using a GCI scheme under various other preconditions such as a specific portion of uninformed users, penetration rate of the scheme, and specific price of petrol.

The rest of the paper is organised as follows. The next section briefly reviews the problem and introduces one way to formulate the pricing problem. The subsequent section describes the modified model for considering GCI. The Gasoline consumption information section estimates what type of function can be used for gasoline consumption estimation. A hypothetical example is introduced, and the results of different problems are analysed to examine the effects of various preconditions. Finally, the conclusion summarises the main points.

MODELLING BACKGROUND Numerous research studies have examined the concept of congestion/road pricing in different contexts (Arnott, De Palma & Lindsey 1993; Dial 1999a, b; De Palma & Lindsey 2002; Rouhani & Niemeier 2011; Rouhani & Niemeier 2014a; Zhang & Levinson 2005). Also, optimising fuel consumption for each vehicle has been studied extensively (Ericsson 2001; Saboohi & Farzaneh 2009; Song & Yu 2009), especially in the eco-driving research area. However, fuel consumption costs as one factor in users’ travel choice behaviour, especially in comparison with time costs, congestion costs and other costs of travel, have not been given much attention.

For few pricing/planning problems, fuel consumption is considered as one of the factors that travellers take into account in their mode/route choice decisions. Qian and Zhang (2011) modelled the morning commute mode choice problem assuming three possible modes for users: taking transit, driving alone, and carpooling. To analyse the results, the fuel costs, in accordance with different fuel prices, were considered as one of the effective factors driving the mode choice decisions. Using a small network, McCord et al. (1997) investigated the value of perfect information on cost, time, fuel consumption and vehicle emissions for traffic assignment problems. The study examined this modification in the context of a decision problem associated with building or not building a highway segment. The results showed the significant value of the information for transportation planning purposes without focusing on how this information can be provided to users.

Some studies have examined the virtue of considering gasoline consumption instead of travel time for route choice decisions, using on-vehicle tools that provide drivers with the information on gasoline consumption. Assuming fuel consumption as the major driver of route choice decisions, Ericsson, Larsson and Brundell-Freij (2006) showed the potential of real-time travel information for fuel savings, especially under congested conditions and traffic distortions. Although the results seem promising, the authors failed to consider that travel time still remains an important component of travel costs incurred by users.

Other studies have examined the problem of minimising fuel consumption. For a vehicle routing problem, Kuo (2010) showed that users may choose routes with lower fuel consumption but with longer travel times and even longer travel distances. Xiao et al. (2012) developed a model based on the classical vehicle routing problem by adding a fuel consumption factor, a load-dependent measure, with the objective of minimising fuel consumption. The study showed that by adding the factor, the fuel consumption can be reduced on a capacitated network. However, the vehicle routing problem is intrinsically different from the urban transportation modelling problems and may not address urban transportation planning problems.

Few studies have analysed the effect of route choice decisions on fuel consumption and air quality factors. Using a combination of microscopic and macroscopic emission estimation tools, Ahn and Rakha (2008) showed that by choosing a shorter but slower route rather than a user-preferred longer but faster route, drivers can save energy and reduce vehicle emissions. Therefore, the study recommends using the emission and energy-optimised traffic assignments without major discussions about how to encourage/incentivise users to follow the energy-optimised routes.

The applications of ATIS in Australia have been the subject of some research studies. Dia and Purchase (1999) modelled drivers’ responses to an ATIS for Brisbane, Australia. Using a microscopic traffic-simulation tool, Dia (2002) developed an agent-based approach to model commuters’ behaviour in response to real-time information along a congested corridor in the same city. Kim and Vandebona (1999) conducted a willingness-to-pay survey of commuters in Sydney, Australia. The survey examined driver attitudes towards radio information systems and found that drivers prefer information on traffic incidents to (static) information on alternative routes. All these studies focus only on online travel time, construction

55



activities, road accidents, and travel distance information rather than fuel consumption.

In this study, we model and estimate the effects of providing fuel consumption information on people’s route choice behaviour in a general transportation network context. Several research questions have not been addressed in the previous studies: What are the impacts of the information for different shares of informed users? Can the provision of the information offer similar benefits to those of congestion pricing schemes? What impact do travel demand and fuel price levels have on the effectiveness of the policy? And how much are users willing to pay for the information in different conditions? This study is an attempt to provide answers to these questions through using a small network example.

We model the vehicle routing problem considering three different formulations corresponding to three different travel behaviour approaches: the user equilibrium (UE), system equilibrium (SE), and GCI problems. The first two problems are based on common models, defined to simulate drivers’ route choice behaviour. The third problem is based on a new formulation proposed by this study. The formulation considers a multi-user equilibrium, in which one group of users chooses their routes based only on travel time (uninformed users) as in the basic UE, and the other group chooses their route based on both time and fuel consumption (informed users).

USER EQUILIBRIUM/SYSTEM EQUILIBRIUMBefore formulating a congestion pricing problem, the UE problem with fixed demand must be defined. UE simulates the traffic assignment problem based on Wardrop’s first principle: the paths connecting an origin–destination (OD) pair will be used only if they have equal and minimal user travel time (Wardrop 1952).

Assume that N(V,A) is the transportation network of concern with V as the set of nodes (intersections) and A the set of links (roads). Let xij be the flow in link (i,j), and x the (row) vector of link flows. Moreover, let be the flow in path p, p ∈Pks, from origin k to destination s, (k,s) ∈P, where P is the set of OD pairs, and Pks the set of paths from origin k to destination s. Such flows are generated by OD demand for travel, dks, from k to s, (k,s) ∈P. Finally, let tij(xij) be the travel-time function of link (i,j), (i,j) ∈A, representing average travel time, which is assumed to be the only cost incurred by users in this model, and to only be a function of flow in link (i,j), defined for xij ∈ 0. x* is the UE flow with

fixed demand in the network N(V,A), where x* is the solution of the following problem:

(1)

(2)

(3)

(4)

where:

is 1 if link (i,j) ∈p, p ∈Pks, otherwise 0.

The first model used in this study is based on the assumption that people behave as assumed in the UE problem.

For the congestion pricing schemes, the cost of congestion (or other externalities) is charged in order to encourage users to adopt a more socially-beneficial behaviour (Rouhani, Knittel & Niemeier 2014). In this new equilibrium, general travel costs, including congestion toll costs as well as travel time costs, drive users’ behaviour.

An important part of the problem is how to determine these costs to increase social welfare. The solution is to charge the marginal social cost of driving that each user imposes on other users. We assumed that charges are set at the optimum charges which lead to the SE flow pattern (second problem). The optimum charge for link a is equal to:

(5)

where:

xa is the volume of link a and ta is the travel-time function for that link (Sheffi 1984).

The optimised charge on each link a equals the flow on the link xa multiplied by the derivative of time function. It can be shown that solving the UE problem considering the total cost of travel (ta + ca) leads to the SE flow pattern. This type of pricing is called first-best pricing, which assumes that all links can be priced (Yang & Huang 1998).

A practical note about the SE/pricing problem is that, in reality, it is impossible to charge all links of a network, at least with present technologies.

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Even if it becomes possible, this can make users indifferent in some aspects; when all links/roads are priced, users may not correctly perceive the price difference as is assumed in the theoretical model. Consequently, an alternative solution called second-best pricing should be used for real-life problems. Researchers have extensively studied second-best pricing schemes, which assume a subset of links in a network is eligible for tolling (Zhang & Ge 2004; Verhoef 2007; Rouhani et al. 2013). Compared to a first-best pricing based on marginal cost pricing, a second-best pricing is easier to implement and can be more cost effective. However, this study compares the fuel consumption information options to first-best congestion pricing schemes.

GASOLINE CONSUMPTION INFORMATION MODELThe GCI model formulates a two-user group equilibrium in which one user group can estimate the exact fuel consumption costs of the possible routes (m = 1), and the other group cannot (do not) estimate the fuel consumption costs and choose routes based on their travel time only (m = 2). This assumption is a simplified simulation of the real-life conditions in which users can also be partially informed (third group).

The first group’s behaviour is based on Wardrop’s first principle; however, their travel choice is based on the generalised cost of travelling on link i–j (cij) rather than just the travel time tij (the UE problem is modified by replacing tij with cij):

cij = tij + gij (6)

where gij is the gasoline consumption expenditure, in terms of time, for using each link i–j, which is assumed to be available for the informed users and all links (through real-time information by GPS devices or on-line websites). The multi-user mathematical formulation of the model is as follows:

(7)

(8)

(9)

(10)

(11)

where:

xij,1 is the traffic volume of the informed, about fuel consumption costs, users on link i–j,

xij,2 is the traffic volume of uninformed users,

is the travel demand of informed users for the k–s OD,

is the travel demand of uninformed users for the k–s OD.

The first-order conditions of the above convex optimisation problem result in the equilibrium equations that guarantee that the uninformed users (xij,1) choose the least travel-time paths, and the informed users (xij,2) choose the least general-cost paths (least cij).

The main assumption is that the fuel consumption information provided to users is exact and in real-time and with the resulting increase in the number of informed users, a more efficient traffic flow pattern can be reached with a GCI scheme.To examine the effects of providing the fuel cost information and the associated benefits, we should solve the GCI problem with different proportions

of informed

and uninformed users .

The effects should be tested under practical and diverse traffic conditions. Here, a hypothetical example has been studied and analysed.

Gasoline consumption for each linkAs one of the steps of modelling, a simple fuel consumption function is needed. The function estimates total gasoline consumption for each link based on different traffic flows. Here, several assumptions are made for simplicity. First, gasoline consumption is the same for all vehicles. An average vehicle, in terms of gasoline consumption, is assumed as a single vehicle/user type. The second assumption is that users drive in urban areas. Thus, their consumption is following the trends of a congested driving gasoline consumption curve. Note that the congested stop-and-go driving conditions (because of constant acceleration and deceleration) increase gasoline consumption in urban areas. Finally, travel-time functions (Table A1 in the Appendix) determine the average time spent on each link, and the average speed (dividing the

57



Figure 1 Gas consumption at different speeds (average vehicle)

average time by the link’s length) is used for gasoline consumption calculations.

The dashed curve on Figure 1 shows the assumed gasoline consumption curve for each vehicle based on the speed on the link (Ahn et al. 2002); these figures are very similar for many OECD countries and are based on average traffic mixture. The function is:

Fuel consumption (litres/100 km) = (12)

To calculate the gasoline consumption function for a link, Equation 12 should be multiplied by the link’s length and divided by 100 to convert it into litres. In addition, the speed should be expressed as the length of each link divided by the time spent on it. Therefore, the fuel consumption (litres) for each link is as follows:

(13)

which is a function of the traffic volume (xij) and the length (Lij) of the link.

The costs used in the UE and other problems are in terms of time. To change the petrol consumption (in litres) to time, Equation 13 should be multiplied by the petrol price (base price: $0.6/litre), and should be divided by the value of time (VOT) for each vehicle (considered at $20 per hour for an average vehicle). Therefore, the gasoline consumption cost function, in terms of time, would be:

gij = 0.0003 × (210 × tij(xij) + 3.5 × Lij) (14)

In fact, users can choose routes (links) based on both travel time and this cost, gij. The idea is that obtaining this information (gij), will guide users to change their routes to avoid extra expenditure on fuel. However, this information might be effective only in the case of high fuel consumption costs relative to travel time costs. The next section will analyse this strategy using a hypothetical example.

Apparently, relaxing the assumption of single VOT for users adds to the complexity of the problem but leads to a more realistic model. The VOT difference is due to different perceived costs of time. For example, a high VOT user is willing to pay more for saving time relative to saving fuel costs. This type of heterogeneity of users is only effective in special conditions and has not been considered in this study.

HYPOTHETICAL EXAMPLEA small network example is used to examine the outcomes of this policy in different conditions. The test network is shown in Figure 2 (the link parameters and OD matrices are presented in the Appendix).

The links’ travel-time functions are considered as the Bureau of Public Roads (BPR) functions with distinct parameters. The test network simulates an infrastructure system that has some empirical qualities. For example, the link 5–6 acts like a freeway (primary road) with high capacity and high free-flow speed. Other links represent lower capacity roads with lower free-flow speeds. The links 1–3 and 2–4 (secondary roads) are alternatives, like

58



Figure 2 Test network with the initial demand (low demand scenario)

arterials, to the freeway. But they can act as passive paths; users may not choose these paths because the free-flow times of those paths are higher than for other available paths. Without any intervention pricing, most users travel on the freeway, link 5–6, and intermediate links such as 1–5 and 6–4, to reach their destinations.

Two sets of OD pair demands are applied to the network: a high travel demand case relative to the assumed links’ capacities and a low demand case (high and low OD pair demands are presented in the Appendix). The problem is solved using a fixed demand assumption with respect to network costs: the demand for travel does not change when users become informed (no change in elasticities). Two different demand levels combined with different fuel price scenarios are used to analyse the effects of fuel consumption information on route choice decisions of users. The Appendix describes the basic settings of the network.

Three different levels of gasoline prices are considered: $0.6, $1.2, $1.8 per litre equivalent to $2.72, $4.55, and $6.82/gallon respectively. For a given time-specific scenario, petrol prices cannot vary dramatically overnight. Nevertheless, these different prices are considered as important possible preconditions (parameters) for cost calculations not as policy levers.

The price elasticity of 0.1 is considered for petrol demand (Hughes et al. 2008; Anas & Hiramatsu 2012). Considering some research studies in the Australian context (e.g. see Wang 2009), a higher elasticity of demand could be applied. As the aggregate travel demand elasticity should be inelastic and the changes in travel demand are small, our assumption is valid under many conditions. Based on the assumed elasticity, travel demand responds to changes in gasoline prices. This means that when gasoline price increases to $1.2, doubled compared to its base price $0.6, travel

demand (Tables A2 and A3 in the Appendix) will decrease by 10 per cent.

RESULTSTable 1 shows the summary results of the UE (0% informed users), GCI (50% informed users), GCI (100% informed users)2 and SE problems solved under different conditions. The first observation is that when travel demand is relatively low (cases 1–12), the GCI scheme is not effective, even for higher gasoline prices (50% and 100% informed versus 0% informed (cases 2 and 3, 6 and 7, and 10 and 11) compared to their similar UE cases 1, 5 and 9). In contrast, when travel demand is higher (more congested conditions), this information can direct the existing flow pattern to a more socially efficient pattern by increasing the number of informed users. In the most effective conditions, fuel consumption information (100% informed users (case 19)) and SE/congestion pricing (case 20) schemes result in nearly the same total travel time and fuel savings, 3% reduction in total travel time and 2% reduction in the total gasoline consumption compared with the do-nothing (UE) case (case 17).

Table 1 can also be used to calculate the marginal social values of providing the information. The value can be calculated by the change in total travel time and total fuel costs resulting from an increase in the proportion of informed users (more informed users). Table 1 shows that the social value is not a linear function of the percentage of informed users. For example, increasing the share of informed users from 0% to 50% (case 1 to case 2) saves travel time by 0.28%, but increasing the share further (case 2 to case 3) results in no more travel time savings. Also, the resulting travel time savings depend on the gasoline price; the information is mainly effective when most users are uninformed (<50%) for high and low gasoline prices (cases 14 and 22 relative to cases 15 and 23 respectively) and is effective when most users are informed (>50%) for a moderate gasoline price (case 19 relative to case 18).

We should examine the results in Table 1 by considering practical constraints. The prices for SE/pricing problems are flexible (i.e. they can be modified to obtain more socially optimal flow patterns), while the realised costs from gasoline information provision schemes are constant because of fixed gasoline prices. In fact, fixed gasoline prices are considered to be one of the main drawbacks of using gasoline taxation instead of road pricing for transportation financing. Using flexible prices,

2 50% informed users means for all k–s and 100%

informed users means that and for all k–s.

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Tab

le 1

Su

mm

ary

of

resu

lts

Low

dem

and

Hig

h de

man

d

Cas

e 1

Cas

e 2

Cas

e 3

Cas

e 4

Cas

e 5

Cas

e 6

Cas

e 7

Cas

e 8

Cas

e 9

Cas

e 10

Cas

e 11

Cas

e 12

Cas

e 13

Cas

e 14

Cas

e 15

Cas

e 16

Cas

e 17

Cas

e 18

Cas

e 19

Cas

e 20

Cas

e 21

Cas

e 22

Cas

e 23

Cas

e 24

Type

of

prob

lem

UE

(0%

)G

CI

(50%

)G

CI

(100

%)

SEU

E (0

%)

GC

I (5

0%)

GC

I (1

00%

)SE

UE

(0%

)G

CI

(50%

)G

CI

(100

%)

SEU

E (0

%)

GC

I (5

0%)

GC

I (1

00%

)SE

UE

(0%

)G

CI

(50%

)G

CI

(100

%)

SEU

E (0

%)

GC

I (5

0%)

GC

I (1

00%

)SE

Petr

ol p

rice

($

/lit)

$0.6

$0.6

$0.6

$0.6

$1.2

$1.2

$1.2

$1.2

$1.8

$1.8

$1.8

$1.8

$0.6

$0.6

$0.6

$0.6

$1.2

$1.2

$1.2

$1.2

$1.8

$1.8

$1.8

$1.8

Tota

l tra

vel

time

(100

ve

h.ho

ur)

27.1

27.1

27.1

24.7

22.8

22.8

22.8

21.8

19.8

19.8

19.8

19.4

46.7

46.6

46.5

45.8

39.6

39.5

38.5

38.4

34.4

33.9

33.6

32.3

% to

tal t

ime

chan

ge

from

UE

(do

noth

ing)

--0

.28

-0.2

8-8

.97

-0.

000.

00-4

.50

-0.

000.

00-1

.91

--0

.25

-0.3

3-1

.81

--0

.39

-2.8

3-3

.06

--1

.61

-2.3

4-6

.04

% p

etro

l co

nsum

. ch

ange

from

U

E (D

o no

thin

g)

--0

.16

-0.1

6-6

.86

-0.

000.

00-4

.43

-0.

000.

00-2

.64

--0

.16

-0.2

1-1

.39

--0

.32

-1.9

9-2

.28

--0

.99

-1.4

3-4

.34

congestion pricing schemes can be more efficient than gasoline information schemes in improving system performance measures.

However, the SE optimised solutions may not be achieved in practice due to difficulties in charging all links/roads, as well as public and political resistance to implementation of congestion pricing schemes. This fact closes the theoretical efficiency gap between the two policy choices: gasoline information and SE/congestion pricing options.

Figure 3 shows reduction in travel time (Figure 3a) and fuel consumption (Figure 3b) relative to the base case (original UE, 0% of informed users) for the high-demand level in the Appendix. The changes in travel time and fuel consumption are calculated considering different proportions of informed users and different fuel price levels by solving the GCI problem. In fact, the marginal social value of the information is the change (decrease) in total travel time and fuel consumption resulting from a small increase in the shares of informed users (the slopes of the curves).

As expected, the curves are decreasing with the highest reductions resulting from the highest proportion of informed users (1 or 100%). However, the change in travel time (fuel consumption) is not a linear function of the share of informed users. Not only is the function non-linear, but the shape of the function differs considerably from one fuel price level to the next. Another important observation is that, for some cases, providing the information many not lead to any decrease in total travel time or fuel consumption; for example, for the high- demand case ($1.8/gallon), total travel time does not decrease going from the 0.25 to 0.5 proportion.

Figure 4 illustrates the average user’s willingness to pay (cents/km) for the fuel consumption information in different settings. The social value of the information was shown in Figure 3. Figure 4 shows the average private value of the information for uninformed users. To find the values, we calculated the weighted average possible generalised cost gap between the used paths and the least-cost paths (only applicable to uninformed users). The weights are based on the traffic volumes on the used paths. However, we assumed that only the uninformed users, who are worse-off without the information, are willing to pay for the information. Uninformed users can also be worse-off with the information3,

3 These users already use the least generalised cost path(s). The information provided for users may direct the other uninformed users to the least cost path(s), which in turn can worsen the generalised cost of these paths.

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Figure 4 Uninformed users’ willingness to pay for the fuel consumption information in different conditions

Figure 3 Travel time and fuel consumption change relative to the base case (UE) for different proportions of informed users: high demand level

and our assumption is that these users are not willing to pay anything for the information.

We can observe several interesting results in Figure 4. First, the willingness to pay is not necessarily decreasing when the proportion of informed users increases; that is, the private value of the information can rise when the share of informed users increases. Second, with a relatively high proportion of informed users (0.75), the information has no value. Third, similar to Figure 3, the curves are different for different gasoline prices, and the willingness to pay is very sensitive to the fuel prices based on both its magnitude and its marginal value (slope of the curves).

Figure 5 shows the flow patterns for the cases 17, 19 and 20. Not only for the cases shown in the table but also for other cases, the values of traffic volumes for the GCI problems (100% informed) are between the UE and SE volumes. The GCI volumes are close to the UE volumes and far from SE volumes. The cases in Figure 5 are chosen from the most effective

conditions for the GCI option, but the theory that GCI and SE volumes would be similar is wrong (although the total travel time of the GCI is very close to the SE case). For the GCI option, the highest change in volumes relative to the UE volumes is 8%. This means that fuel consumption information systems only slightly change the flow pattern from the UE (do nothing) pattern, but this small change can be significant in improving total travel time, as shown in Table 1 (–2.83% change from UE).

The results are encouraging in terms of the potential effects of the GCI on people’s behaviour. Nevertheless, congestion pricing is still a superior policy. The flexibility of congestion pricing schemes (adjusting prices for different demand levels) makes these schemes more efficient options. However, congestion pricing schemes encounter practical difficulties: installing the tracking devices is very expensive, pricing schemes are complicated, the decisive political willingness and public support do not exist, and the required public vision and support is not created (Richards 2008).

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Figure 5 Flow pattern (100 veh/h) for (a) UE, case 17; (b) GCI (100%), case 19; (c) SE, case 20

Finally, congestion pricing and fuel consumption information are intrinsically different problems, and their plain comparison can be misleading in this regard. In addition, these two schemes are not distinct options and can be implemented together.

CONCLUSIONAdvanced traveller information systems (ATIS) have a long history in Australia. A number of cities have implemented a variety of ATIS projects, including Melbourne, Sydney, Brisbane, Perth, and many others. The current ATIS tools in Australia provide road users with real-time data on several pieces of information, including traffic incidents, road conditions and traffic congestion. Major missing pieces of information include the estimates of fuel consumption, emissions, and their associated costs generated from each vehicle (or possibly from a vehicle class). This study models the effects on people’s route choice behaviour of providing fuel consumption information, and offers some basic information needed to develop such systems further.

To account for the constraints of our modelling, we start with the assumptions made for simplifications. First, only two user groups are considered: one group with complete knowledge about the fuel consumption costs of the relevant paths and the other group with no knowledge. The knowledge may not be necessarily in the two extreme poles, and some users may have rough estimates, or even stochastic information, about their fuel costs. Second, the advanced information provided for users is assumed to be exact. As a result, the benefits of providing the information may be overestimated, considering potential errors. Third, the studied SE (congestion pricing) problems are based on the assumption that all links can be priced. This assumption is not realistic; the second-best optimal solution should be considered. In fact, the benefits of the SE schemes in this study can be an overestimation of the benefits of practical congestion pricing schemes.

With all the above considerations, the results from modelling a simple network show that providing GCI can efficiently change people’s route choice

behaviour, at least theoretically. Although the results are encouraging for the use of GCI, especially for congested networks, congestion pricing, if available, might still be a superior policy choice, because of the flexibility in adjusting congestion prices. However, introducing any new pricing scheme to replace or append to a gasoline tax requires political and public acceptance, as well as a thorough cost–benefit analysis, whereas informing users of their fuel cost estimates does not require public or political support, and its implementation costs can be small relative to those of congestion pricing schemes (Mid-Ohio Regional Planning Commission 2009).

Based on our case study, informing users of their fuel costs can decrease total travel time by up to 2.8% and reduce gasoline consumption by up to 2.0%. A one or two per cent system-wide decrease in fuel consumption or total travel time can result in huge amounts of savings for a large-size metropolitan area. However, for low travel demand levels (off-peak hours), the information provided may not be as effective in reducing overall fuel consumption or total travel time. Also, the performance of the system is highly independent of the prevalent fuel prices. Another important policy insight is that GCI systems only slightly change the flow pattern from the UE (do-nothing) pattern, but can be significantly effective in reducing total travel time and fuel consumption.

Our results show that the share of already informed users is an important factor in the successful implementation of this policy. The marginal social value of the information is a function of the informed users’ shares of all users. This function is not linear; under certain traffic conditions, increasing the number of informed users has no social value when most users are not informed and has a significant social value when most users are informed. The opposite relationship is also possible for some other conditions. The users’ willingness to pay for the GCI is estimated at between 0 and 1 cent/km (where 1 cent/km is estimated for high-demand/high gas price conditions when 0% of users are informed prior to providing the information).

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Further, the private value of the information is not a linear function of the percentage of informed users, and can even increase with higher percentages.

Even though introducing the policy can lead to changes in users’ route choice decisions, the potential changes in gas demand elasticity are still hard to estimate. In fact, in this study, the OD travel demand volumes are assumed to be fixed with respect to the fuel consumption information. One important improvement is the estimation of the travel demand elasticities and their addition to the problem. Further work could model the effect of this policy on different income groups of users. Also, the model can be improved by informing users of other variable costs of choosing between routes, such as emissions costs, and the results should be tested to realise the positive or negative effects of adding those costs (Rouhani & Niemeier 2014b).

Finally, the effect of providing fuel consumption information should be tested with practical and diverse traffic conditions. The impacts of providing the information are highly dependent on the network studied and cannot be generalised. Future studies can simulate the outcomes of providing fuel consumption information on a real-size network (see, for example, Rouhani and Gao 2014), which requires using a heuristic approach (Madani et al. 2014) to solve such a complex problem.

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Omid M. Rouhani

Omid M. Rouhani is a postdoctoral research associate at Cornell University, where he works on policy options to improve New York City’s transportation system. Omid’s research focuses on the application of systems analysis, economics, optimisation, simulation, and modelling methods to transportation problems at different scales. His academic articles have appeared in several major journals.

Hossein Zarei

Hossein finished his undergraduate studies in Civil Engineering from Sharif University of Technology in Iran in 1999. He received his Master’s degree in Transportation Engineering from University of Toronto in Canada in 2007 and immediately started working for AECOM. Aside from academic experience, he has over 9 years of practical experience in the fields of transportation and transit planning, traffic and road safety engineering.

CONTACT

Omid M. RouhaniSchool of Civil and Environmental EngineeringCornell UniversityHollister HallIthaca, NY 14853USA.Email: [email protected]: +1 530 204 8576

ACKNOWLEDGMENTS

The authors express their special gratitude to anonymous reviewers for reviewing the paper, sharing thoughts, and insightful comments on the paper.

Fuel Consumption Information: An Alternative for Congestion Pricing?

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