Transportation Research Part A 67 (2014) 254–267

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Transportation Research Part A

journal homepage: www.elsevier .com/locate / t ra

An advanced traveler general information system for Fresno,California

http://dx.doi.org/10.1016/j.tra.2014.07.0110965-8564/� 2014 Elsevier Ltd. All rights reserved.

⇑ Corresponding author.E-mail addresses: om67@cornell.edu (O.M. Rouhani), hg55@cornell.edu (H. Oliver Gao).

1 Tel.: +1 530 204 8576.2 Providing the information is not always free. Some studies explore the use of a priced system. For instance, Yang and Huang (2004) proposed an

time-dependent-service pricing strategy so as to minimize total system cost throughout the time horizon of growth. The goal was to reach a sociallytarget level of ATIS market penetration in a final stationary equilibrium.

Omid M. Rouhani 1, H. Oliver Gao ⇑School of Civil and Environmental Engineering, Cornell University, Hollister Hall, Ithaca, NY 14853, USA

a r t i c l e i n f o a b s t r a c t

Article history:Received 5 April 2013Received in revised form 18 July 2014Accepted 28 July 2014Available online xxxx

Keywords:Advanced traveler information systemFuel consumptionEmissions costsGeneral cost of travelCongestion pricing

This study estimates the effects of an advanced traveler general information system(ATGIS), which includes fuel consumption and health-related emissions cost informationon transportation network users’ travel choice behavior for recurrent congestionconditions. The effects are estimated using four different formulations based on four differ-ent behavioral assumptions. Incorporating stochastic features in link cost estimation ratherthan in route choice, we provide a novel modeling approach that enables us to use trans-portation planning models of major metropolitan areas without a need for major compu-tationally-expensive changes in the existing models. We examined the effects of an ATGISon the Fresno, CA, road network and found several interesting results. First, the ATGISimpact is closely related to pre-system (prior to the implementation of an ATGIS) perceivedfuel and emissions costs. Total travel time in the city can be reduced by 17% (no pre-systemperceived costs) to 1% (accurate pre-system perceived costs), and even increased by 1%(higher-than-actual pre-system perceived costs). Second, the addition of emissions costs,although negligible relative to fuel and time costs, can effectively reduce total system-widetravel time by up to 1% and fuel consumption by up to 0.6% during peak hours. Third, theATGIS can reduce annual social costs by as much as $1053 million (high gas price, nopre-system perception) to $48 million (medium gas price, accurate pre-system perception),which are comparable to social cost savings by a congestion pricing (CP) scheme in thestudy area.

� 2014 Elsevier Ltd. All rights reserved.

1. Introduction

The advanced traveler information system (ATIS) field was developed based on the notion that schemes providing exactand online travel time information would efficiently change users’ travel behavior. Research in ATIS investigates the effectsof providing (usually free)2 information on transportation system users’ travel decisions (Yang, 1999; Zhang and Verhoef, 2006;Fernández et al., 2009; Gubins et al., 2012). However, the emphasis is on travel time information rather than providing fuelconsumption and other travel costs information.

optimal-optimal

O.M. Rouhani, H. Oliver Gao / Transportation Research Part A 67 (2014) 254–267 255

Fuel consumption and emissions costs have a small effect, if any, on road users’ travel choice behavior, partly as a result ofthe low elasticity of gasoline demand. The fundamental reasons for their disinterests are that drivers do not perceive fuelconsumption costs as out-of-pocket costs (Hughes et al., 2008; Anas and Hiramatsu, 2012), and emissions costs are notcharged to users. Therefore, implementing a scheme informing road users of fuel and emissions costs would make themmore sensitive to costs. On-line information about transportation networks can be used to estimate the real-time fuel cost,emissions cost, and other costs of travel for using each possible route and can be provided to users through radio, roadsidebanners, GPS devices, or online websites at a relatively low cost (Mid-Ohio Regional Planning Commission, 2009).

Optimizing fuel consumption for individual vehicles has been the subject of many studies (Ericsson, 2001; Saboohi andFarzaneh, 2009; Song and Yu, 2009), especially in the eco-driving research area. However, fuel consumption costs and emis-sions costs are important elements in users’ travel costs, especially in comparison with time costs and congestion costs, yetthey have been given little attention in the context of influencing motorists’ travel behavior.

In analyzing pricing/planning problems, some researchers have considered gas consumption as one of the factors thatusers take into account in their mode/route choice decision-making. Qian and Zhang (2011) modeled the morning commutemode choice problem and found that fuel costs, in accordance with different fuel prices, were considered one of the factorsdriving mode choice decisions. Using a small network, McCord et al. (1997) investigated the value of perfect information intraffic assignment problems. The information about fuel consumption and vehicle emissions, in addition to time, wasincluded in traffic assignment problems. The study examined this modification in the context of a decision about whetheror not to build a highway segment. The research showed the value of the information for transportation planning purposeswithout focusing on how this information could be provided to users, and under what conditions the provision of theinformation could be useful.

Few studies have considered fuel consumption instead of travel time for route choice decision. The conjecture is that on-vehicle tools would provide information on gas consumption to users. Ericsson et al. (2006) showed that providing real-timetraffic information can lead to fuel savings, especially in more congested areas and when real-time traffic distortions arereported. The main assumption of the study was that, instead of travel time, fuel consumption would be used for routechoice. Although the results seem promising in terms of reducing fuel consumption, the authors failed to consider that traveltime still remains the main travel cost component driving users’ behavior. In spite of the correlation between fuel consump-tion and travel time for some traffic levels, we still need to use both components since the relationship is not linear.

Minimizing fuel consumption is another important area of research. Kuo (2010) proposed a model to minimize fuel con-sumption for a vehicle routing problem. The research showed that users may choose routes involving lower fuel consump-tion but with longer travel times and even longer travel distances. Xiao et al. (2012) developed a model based on the classicalvehicle routing problem by adding a fuel consumption factor with the objective of minimizing fuel consumption. The studyshowed that with the addition of the fuel factor, fuel consumption can be reduced on a capacitated network. However, thevehicle routing problem is intrinsically different from urban transportation modeling problems and cannot be used for sys-tem-wide urban transportation planning problems.

In the context of emissions from transportation networks, studies have developed models to quantify CO2 and standardpollutant emissions (Ahn et al., 2002; Midenet et al., 2004; Beevers and Carslaw, 2005; Nesamani et al., 2007; Cortés et al.,2008). However, these studies have not generally examined the effects of including the emissions costs along with othercosts associated with users’ travel behavior.

Ahn and Rakha (2008), in one of the few studies analyzing the effects of route choice decisions on fuel and emissionscosts, investigated the impacts of route choice on energy consumption and emission rates, taking into consideration vehicletype. Using microscopic and macroscopic emission estimation tools, the study showed that the choice of a shorter but slowerroute can be more energy efficient than choosing a user-preferred longer but faster route. The study recommends usingemissions and energy-optimized traffic assignments without major discussion about how to encourage users to followthe energy-optimized routes.

Building on an earlier effort (Rouhani, 2010), the present study models and estimates the effects of an advanced travelergeneral information system (ATGIS) on people’s travel choice behavior, similar but not limited to FHWA’s applications forenvironment real-time information synthesis (AERIS). In this study, we examine several research questions: (a) what isthe impact of providing the information in various already pre-perceived information conditions?, (b) how effective is theATGIS compared to a congestion pricing (CP) scheme?, (c) what effects do travel demand (peak vs. off-peak), fuel price levels,and emissions cost rates have on the policy’s success?, and (d) what are the results of providing emissions costs informationto users in addition to fuel cost and travel time information? Using the city of Fresno’s road network, this study is an attemptto answer these questions.

2. Model formulations

Simulating different conditions, we model the problem using four different main formulations: the user equilibrium (UE),the multi-user general cost equilibrium (MUGCE), the general cost equilibrium (GCE), and the random general cost equilib-rium (RGCE) problems. The first problem is commonly defined to simulate drivers’ route choice behavior. The last three mod-els are based on new formulations proposed by this study. The second formulation, MUGCE, considers a multi-userequilibrium, in which one group of users chooses routes based only on travel time (uninformed users), as in the basic UE,

256 O.M. Rouhani, H. Oliver Gao / Transportation Research Part A 67 (2014) 254–267

and the other group chooses routes based on main costs of travel (time, fuel, and emissions). The GCE model simplifies theMUGCE model by eliminating the multi-user feature. The last formulation is an extension of the GCE problem, but fuel andemissions costs are assumed to be randomly perceived.

2.1. User equilibrium model

Based on Wardrop’s first principle, the user equilibrium (UE) is a problem in which paths connecting an origin/destination(O/D) pair will be used only if they have equal and minimal user travel time (Wardrop, 1952).

Let N(V,A) be a network of concern with V as the set of nodes and A the set of links (streets). Also, let xij be the flow on link(i,j), and x the (row) vector of link flows. Moreover, let xks

p be the flow in path p, p 2 Pks, from origin k to destination s, ðk; sÞ 2 P,where P is the set of origin–destination pairs, and Pks the set of paths from origin k to destination s. Such flows are generatedby O/D demand for travel, dks, from k to s, ðk; sÞ 2 P. Finally, let tijðxijÞ be the travel time function of link ði; jÞ; ði; jÞ 2 A, repre-senting average travel time, which is assumed to be the only cost incurred by users in this model, and to be only a function offlow on link (i,j), defined for xij � 0. x⁄ is the UE flow in the network N(V,A), where x⁄ is the solution to the following problem:

ðUEÞ Minx

Xði;jÞ

Z xij

0tijðuÞ � du ð1Þ

s:t:Xp2Pks

xksp ¼ dks

; 8ðk; sÞ 2 P ð2Þ

xksp � 0; 8p 2 Pks; 8ðk; sÞ 2 P ð3Þ

xij ¼Xðk;sÞ2p

Xp2Pks

xksp � d

ksij;p; 8ði; jÞ 2 A ð4Þ

where dksij;p is 1 if link ði; jÞ 2 p, p 2 Pks; otherwise it is 0. For the base model, we assume that users behave as modeled in the UE

problem (Poorzahedy and Rouhani, 2007).

2.2. Multi-user general cost equilibrium model

The multi-user general cost equilibrium (MUGCE) model is based on the multi-user equilibrium concept, in which onegroup of users has complete information on the fuel and emissions costs of the possible routes (m = 1), while the other grouphas no clear information on the these costs and chooses routes based only on their travel time (m = 2), as assumed in thebasic UE. This assumption is a simplified simulation of the real-life conditions in which users can also be partially informed(third group).

The informed users (m = 1) also behave based on the basic UE’s principle, except that cij, the generalized cost associatedwith traveling on link i–j, rather than just the travel time tij, drives their behavior. In fact, we replace tij in the UE problemwith cij in the UE problem (Nagurney, 2000):

cijðxijÞ ¼ tijðxijÞ þ gijðxijÞ þ eijðxijÞ ð5Þ

where gij is the fuel (gasoline) consumption expenditure on link i–j, and eij is the emissions cost, both measured in terms oftime to be compatible with the travel time unit (hour). These costs are assumed to be available to the informed users for alllinks (through real-time information or by their experience). The actual gij and eij are calculated using the followingequations:

gijðxijÞ ¼ bg :kgLij

tijðxijÞ

� �� Lij ð6Þ

where Lij is the length of the link i–j, and kg is the unit fuel consumption (gallons per mile) and a function of the link’s speed

level Lij

tijðxijÞ

� �. To find the fuel costs of using each link, kg should be multiplied by the length of the link (Lij). However, the fuel

consumption (in gallons) and travel time cannot be summed because of the difference in units. bg has been added to changethe fuel consumption from gallons to travel costs in terms of hours. So, bg is a combination of two factors: the fuel cost($/gallons) over a general value of time (VOT) measure ($/h).

eijðxijÞ ¼X

k

bk � kkLij

tij xij� �

!� Lij ð7Þ

where k denotes the emission type (CO2, CO emissions, etc.), and kk is the unit emission (grams per mile) and again a functionof speed level Lij

tijðxijÞ

� �. To change the emissions’ units from grams to travel costs in terms of hours, bk has been used (the cost

of each emission type k ($/grams) over a VOT measure).

O.M. Rouhani, H. Oliver Gao / Transportation Research Part A 67 (2014) 254–267 257

The MUGCE mathematical formulation of the model is as follows:

Table 1Main fe

Scen

123456789

10

* Thi+ Em

ðMUGCEÞ Minx

Xði;jÞ

Z xij

0tijðuÞ � duþ

Xði;jÞ

Z xij;1

0ðgij uþ xij;2� �

þ eijðuþ xij;2ÞÞ � du ð8Þ

s:t:Xp2Pks

xksp;m ¼ dks

m ; 8ðk; sÞ 2 P; m ¼ 1;2 ð9Þ

xksp;m � 0; 8p 2 Pks; 8ðk; sÞ 2 P; m ¼ 1;2 ð10Þ

xij;m ¼Xðk;sÞ2p

Xp2Pks

xksp;m � d

ksij;p; 8ði; jÞ 2 A; m ¼ 1;2 ð11Þ

xij ¼ xij;1 þ xij;2 ð12Þ

where xij;1 is the traffic volume associated with the informed users, about fuel consumption and emissions costs, on link i–j,xij;2 is the traffic volume for uninformed users, dks

1 is the travel demand for informed users for the k–s O/D, and dks2 is the travel

demand for uninformed users for the k–s O/D. The first order conditions of the above optimization problem (with well-behaved cost functions) result in the equilibrium equations which guarantee that the uninformed users (m = 2) choosethe paths with minimum travel time and that the informed users (m = 1) choose the paths with minimum general cost(consisting of cij).

Provided that fuel consumption and emissions information is exact and in real-time, the main assumption is that render-ing the information to users can change the existing flow pattern into a more socially efficient pattern by increasing thenumber of informed users.

2.3. General cost equilibrium model

Although informative, the multi-user feature is too complex and time-consuming to apply to real city-size transportationsystems. In addition, some transportation planning models do not provide this feature, as with the Fresno model. Therefore,we assume average users as the sole group of users. The resulting general cost equilibrium (GCE) objective function is asfollows:

ðGCEÞ Minx

Xði;jÞ

Z xij

0ðtijðuÞ þ gijðuÞ þ eijðuÞÞ � du ð13Þ

The GCE model is the same as the UE model, except that instead of using travel time as the only factor in the objectivefunction, the exact fuel and emissions costs are also considered. Note that the emissions costs are not paid out-of-pocket. Infact, we can assume that users take only fuel costs into account. To analyze the effects of this assumption, we considered analternative scenario (case 5, Table 1) in which users choose routes based on ðtijðuÞ þ gijðuÞÞwithout taking the emissions costsinto account.

One important note is that the GCE model is not convex and its solution is not unique if the general cost function in Eq.(13) is not a monotonically increasing function of the link flow. One can argue that the two functions—gijðuÞ andeijðuÞ—should be increasing monotonically as well so that the GCE problem becomes convex. As can be seen later inFig. 2, these two functions are not increasing monotonically with traffic flow (or decreasing with speed). However, whenwe add fuel costs and emissions costs to time costs in our analysis, we always get an increasing general cost function.The main reason for this increase is that the value of time costs is always greater than the fuel and emissions costs added

atures of the analyzed scenarios.

ario number Scenario name Model Price of gasoline ($/gallon) Emissions costs Perceived fuel and emissions costs

Base case UE 4 – –All 2dG GCE 2 Base ExactAll 4dG* GCE 4 Base ExactAll 6dG GCE 6 Base ExactFuel 4dG+ GCE 4 – ExactAll 4dG-2E GCE 4 2 � Base ExactAll 4dG-Norm GCE 4 Base N(l, l2/25)All 4dG-Norm2 RGCE 4 Base N(l, l2/4)All 4dG-Norm3 RGCE 4 Base N(0.9 � l, l2/25)All 4dG-Norm4 RGCE 4 Base N(1.1 � l, l2/25)

s case is assumed as the common case; All other cases are obtained by changing a feature(s) of this case.issions costs are not taken into account by users for this case.

258 O.M. Rouhani, H. Oliver Gao / Transportation Research Part A 67 (2014) 254–267

together. The dominance of time costs can be seen in Fig. 3, even for high levels of fuel and emissions costs. Therefore, thegeneral cost function (the summation of all costs) seems to be increasing with traffic flows, even for other urban transpor-tation studies with relatively low speed levels. Finally, we use approximations of the emissions and fuel cost functions thatare consistent with the travel time (quartic) function, but can be decreasing for some traffic levels. However, the general costfunction is always monotonically increasing with link flows.

To further examine the effects of providing the fuel and emissions information, we analyzed the results using variousassumptions about the perceived random costs of travel. As we mentioned, the MUGCE uses a simplified assumption inwhich users can be either informed or uninformed. However, users can also be partially and randomly informed (thirdgroup). Although it seems reasonable to assume that users can gain actual travel time information through regular commut-ing, the fuel and emissions costs are most probably calculated using random estimations.

2.4. Random general cost equilibrium model

To account for the random effects in perceiving costs, we use the random general cost equilibrium (RGCE) model, which isthe same as the GCE model, except that instead of actual fuel and emissions costs, the random fuel and emissions costs areadded to travel time in the objective function. In fact, users are assumed to choose routes based on exact travel time andrandomly perceived fuel and emissions costs. We assumed that the perceived costs are normally distributed with their meanat the actual cost. The objective function of the RGCE model is as follows (the constraints are the same as UE and GCEmodels):

ðRGCEÞ Minx

Xði;jÞ

Z xij

0ðtijðuÞ þ CijÞ � du ð14Þ

where Cij is a random variable defined as follows:

Cij � Nðlij;r2ijÞ ð15Þ

and lij is the mean, and r2ij is the variance of a normal distribution. For most cases, we assumed that the mean is located at

the actual fuel and emissions cost level:

lij ¼ gijðxijÞ þ eijðxijÞ ð16Þ

For r2ij, we assumed different variance levels (scenarios) relative to lij (refer to Table 1). As an alternative assumption, for

Scenario 9-Table 1, we assumed that the mean, lij, is not at the actual level. However, the RGCE model is based on theassumption that all users have the same random cost distribution. The more realistic model would be the combination ofthe MUGCE and RGCE models, where different user groups have different perceived cost distributions. Finally, the RGCEmodel can be solved by making random draws for the fuel and emissions costs, based on the corresponding distribution.For each case, we solved the RGCE model using random draws and reported the average results.

For each scenario, we assume that the mean is known, as will be shown later in Table 1, which is either at the true value ofcij, lower, or even greater. Therefore, by solving the GCE model, we can find the mean values (exact costs). When we know themean, we provide random draws around the mean based on the assumed distribution. We then provide averages of differentmeasures for each scenario. One important note is that our analysis is based a single-user model. Accordingly, we assumethat users from different O/Ds. behave with a similar mean and variance. However, the current modeling framework can sup-port multi-user equilibrium features as well, but needs some modifications and requires more sets of random draws to pro-vide a robust analysis.

Our approach in incorporating the stochastic feature is slightly different than what was used in most studies (Yang, 1998).The commonly-used assumption is that non-informed users choose routes based on random choice probability functions.We incorporated the stochastic feature into link cost calculations rather than into the route choice decisions.

This modeling deviation helps us to use the existing transportation planning model for Fresno (with a slight modification)to solve for the RGCE problem. Our approach can be efficiently used for exploring mega-city transportation planning modelswithout a need for drastic modifications to the models, which would requires much higher computational efforts and time.Also in our approach, any kind of distribution can be set to simulate users’ behavior, while in the common approach, only thelogistic or probit-based distributions are applied.

However, there is a difference in the implications of the two approaches. The Yang approach (1998) implies that users’route choice is stochastic, and the uncertainty can be caused by choices other than minimizing time (costs), while ourapproach implies that users do not have the exact cost information, but always rely on their own information and choosetheir perceived minimum-cost routes.

One important note about using all the above formulations is that although demand is fixed in each traffic assignmentmethod, the total demand for each k–s O/D pair will be modified iteratively to reflect the changes in the general cost of travelfor the O/D:

dks ¼ mk � Dk � ns � Ds � dðcksÞ ð17Þ

O.M. Rouhani, H. Oliver Gao / Transportation Research Part A 67 (2014) 254–267 259

where Dk and Ds are the travel demand generated from zone k and destined for zone s, mk and ns are the coefficients in thegravity model, and dðcksÞ is the impedance function, which is a function of the minimum general cost of traveling betweenthe zones k and s rather than only the minimum travel time between the zones.

3. Case study

3.1. Fresno network

The city of Fresno is located in the Central Valley of California. With a population of about 480,000, Fresno is the fifthlargest city in California (City of Fresno website, 2012). The city maintains a standard four-step transportation planningmodel with a calibrated base year of 2003 and future year network of 2030. The Fresno network consists of 20,865 linksand 1852 traffic zones (Fig. 1).

3.2. Assumptions

The applied planning model is a static deterministic user equilibrium model (Sheffi, 1984). It is reasonable to assume sta-tic conditions, given that our interest is not in analyzing real-time implications. The deterministic feature is accompanied bystochastic perceptions about fuel and emissions costs. Other major assumptions are as follows:

– An average user values time at $14/h. Using the load factor of 1.4 persons/vehicle, the value of time (VOT) for each vehicleis about $20/h (14 � 1.4). So, to calculate general costs of travel, a $1 charge on a link is valued at 3 min (60 min per hour/$20 per hour).

– To account for different effects, three different gasoline (as the only fuel option) price levels are considered: $2, $4, and $6/gallon. For a given time-specific scenario, gas prices cannot vary dramatically overnight. Nevertheless, these differentprices are considered to be important possible preconditions (parameters) for cost calculations, not policy levers.

– Fig. 2 shows the assumed emission and fuel consumption factors (kk’s and kg) for CO2, CO, total organic gases (TOG), NOx,PM2.5, PM10 emissions, and fuel consumption. Based on EMFAC (2011), the emission and fuel consumption factor valuesare the weighted averages of 2030 emission factors (only running emissions) for different vehicle classes at differentspeeds. The weights are the vehicle miles traveled (VMT) by different vehicle classes. Considering a representativevehicle, the average kk’s and kg are assumed to be the same for all vehicles.

– The assumed base unit emission costs are as follows: $25/ton of CO2 (using average carbon market values—Rouhani,2013), $250/ton of CO, $7000/ton of NOx, $3000/ton of TOG, $30,000/ton of PM10, and $300,000/ton of PM2.5. These costs

Fig. 1. Road network of Fresno, California (Source: Transportation planning model, city of Fresno).

Fig. 2. Weighted average curves for (a) CO2; (b) CO; (c) TOG; (d) NOx; (e) PM2.5; (f) PM10; (g) Gasoline (Data source: EMFAC, 2011, for Fresno 2030).

260 O.M. Rouhani, H. Oliver Gao / Transportation Research Part A 67 (2014) 254–267

are inflation-adjusted averages of what various studies have found using associated health costs in urban areas (Wanget al., 1994; McCubbin and Delucchi, 1999; AEA Technology Environment, 2005).

– For simplicity, we only considered the AM peak results. Another reason for using only these results is that PM peak vol-umes are noisier than those of AM peak. Modeling the PM peak is more challenging because of the lower estimationpower.

– Fuel consumption is the same for all vehicles. An average vehicle in terms of fuel consumption is assumed to be a singlevehicle/user type.

– For the congestion pricing (CP) scheme, policy-makers/road-owners apply a constant mileage-based toll rate to a smallnumber of road segments with the goal of reducing congestion (social cost pricing cases). For more information aboutthe (CP) scheme, refer to Rouhani et al. (2013) and Rouhani and Niemeier (2014a).

4. Results

Before looking at the results of implementing ATGIS systems under different conditions, we should examine the relativesignificance of various travel cost types in users’ decision-making. Knowledge about the relative value of travel cost compo-nents can help policy makers to better estimate the effects of informing users about some of these costs.

Fig. 3 shows the monetary shares of various cost components at different volume per capacity (V/C) levels for a typicalfreeway or arterial. Fig. 3 also shows the shares of travel time in total costs (the double lines). The first observation is thattravel time comprises a large part of the total cost of travel. However, the travel time share is not constant: at low V/C (highspeed) levels, the share is approximately 70%, and the time cost’s share increases with V/C, reaching over 80%. The costs forarterials and freeways follow the same patterns, but the costs are higher for arterials as a result of lower speeds.

For relatively low cost parameters (VOT = $20/h, gas price = $4/gallon, base unit emissions costs), time costs for freewaysand for arterials range from $0.31 per mile to $3.08 and from $0.57 to $5.71, respectively. Fuel costs range from $0.2 to $0.6and from $0.19 to $0.68, respectively, and emissions cost ranges from $0.01 per mile to $0.04 and from $0.01 to 0.05, respec-tively. The time cost can reach $20/mile for very congested conditions and high VOTs. Emissions costs are relatively verysmall, while CO2 emissions costs comprise the main portion of the total emissions costs. Although fuel consumption costs,and especially emissions costs, are small relative to time costs, providing information on these costs will affect users’ travelchoice behavior and consequently the transportation system performance as a whole, as can be seen in Figs. 4–7.

Fig. 3. Travel cost components as functions of volume per capacity for a typical (a) freeway and (b) arterial.

O.M. Rouhani, H. Oliver Gao / Transportation Research Part A 67 (2014) 254–267 261

After examining the relative significance of travel cost components, we should examine the effects of implementing anATGIS on the transportation system of interest. To that end, we determined and then analyzed the scenarios shown in Table 1.The base case (Scenario 1) simulates users’ decision-making when travel time is the only factor in choosing routes (UE). Sce-narios 2 through 4 examine the inclusion of actual fuel and emissions costs in users’ decision-making when the price of gas-oline varies. Scenario 5 is the same as Scenario 3, but instead of considering both fuel costs and emissions costs, users takeonly fuel costs into account. Scenario 6 mimics Scenario 3 but with twice the base unit emissions costs. Scenarios 7 through10 simulate the do-nothing conditions where users’ choices are based on randomly-perceived fuel and emissions costs, con-sidering different distributions (changing standard error—Scenario 8 or changing the mean—Scenario 9 and 10).

Solving for the above-mentioned scenarios, we determined the effects of an ATGIS on various transportation system indi-ces. For the first set of results, we assumed that users do not consider fuel and emissions costs in their travel decision-makingprior to implementation of this system. Therefore, the results of applying an ATGIS should be compared to the base case (Sce-nario 1), in which users make decisions based only on travel time. Considering an ATGIS which effectively provides fuel andemissions costs to users, Fig. 4 shows how much the system reduces total travel time and fuel consumption under differentfuel prices for peak (4a) and off-peak (4b) hours.

We can observe in Fig. 4 that (1) the system’s effects on travel time and fuel consumption increases with higher fuel prices(going from Scenario 2-$2/gallon to Scenario 4-$6/gallon), (2) the travel time can be reduced from 9% to 20% for peak andfrom 7% to%16 for off-peak hours, (3) the percentage decrease in fuel consumption is lower than that for time and rangesfrom 6% to 14% for peak and 5% to 11% for off-peak hours, and (4) the effects of the system are significantly higher for peakthan for off-peak hours, mainly because of higher reductions in travel demand (congestion) for peak periods.

Fig. 5 shows the effects of employing an ATGIS under different emissions cost assumptions. Choosing Scenario 3 as thebenchmark, we examined the results of two alternative conditions. First, either users do not take the emissions costs intoaccount, or the information about the emissions costs are not provided to them (Scenario 5). Second, the unit emissions costsare twice the assumed base unit costs (Scenario 6). As shown in Fig. 5, the inclusion of emissions costs has significant effectson total travel time (1% decrease for peak and 0.6% decrease for off-peak periods which translates into a saving of millions ofdollars) and total fuel consumption (0.6% decrease for peak and 0.4% decrease for off-peak periods) going from Scenarios 5 to3. Also, doubling unit emissions costs dramatically reduces total travel time and fuel consumption (Scenario 3 to 6). A per-centage point decrease in these measures is commensurate with hundreds of dollars of travel cost savings. The significanteffects of including (and increasing) emissions costs happen despite the fact that emissions costs comprise a negligible shareof total costs of travel (0.5% to 1%, Fig. 3).

Fig. 4. ATGIS under different fuel prices relative to the base case (Scenario 1) for (a) AM peak and (b) off-peak.

Fig. 5. ATGIS under different emissions cost assumptions relative to Scenario 3 for (a) AM peak and (b) off-peak.

262 O.M. Rouhani, H. Oliver Gao / Transportation Research Part A 67 (2014) 254–267

For the second set of results, we assumed that users have a rough (random) estimate of their fuel and emissions costsprior to using an ATGIS (Scenarios 7 to 10). Then we calculated the effects of implementing an ATGIS (Scenario 3—full infor-mation) compared to Scenarios 7 to 10—random information. Fig. 6 shows the average decrease in total travel time and fuelconsumption resulting from an ATGIS, under different pre-system perceived fuel and emissions cost (perceived costs beforethe implementation of an ATGIS) assumptions. The first observation is that the effects of the ATGIS are much lower whenusers estimate these costs than when they do not consider these costs at all. Implementing an ATGIS reduces total travel timebetween 1% to 3% for peak and 0.5% to 1.5% for off-peak hours (much lower than the Fig. 4 values). Even when users perceivehigher costs than the actual costs (Scenario 10), implementing an ATGIS can lead to adverse effects: increase in total traveltime and total fuel consumption relative to the do nothing case (Scenario 3).

Fig. 6 also shows that the higher the variations in the perceived costs (higher standard error-r), going from Scenario 7 toScenario 8, the higher the ATGIS effects on reducing travel time and fuel consumption. Compatible with what was expectedand found by other studies (Yang, 1998), this result shows that the ATGIS can provide more valuable information when thetraffic volumes and road supply vary dramatically day by day; i.e., users’ perceptions also vary but cannot precisely reflectthe actual costs due to the uncertainty.

Also, when users’ average perceived fuel and emissions cost is lower than the actual cost (Scenario 9 vs. Scenario 7), theATGIS reduces travel time and fuel consumption significantly more than when average perceived cost is the same as theactual cost. However, when the perceived cost is higher than the actual cost (Scenario 10 vs. Scenario 7), the ATGIS resultsin higher travel time and fuel consumption. In fact, the positive effects of more precise cost signals are being offset by thenegative effects of increasing travel demand as a result of lower realized costs. The discussed results confirm the importanceof users’ ability to estimate the fuel consumption and emissions costs for the success of the system. A thorough ATIS analysisshould be accompanied by comprehensive studies on users’ fuel and emissions cost estimation power.

Fig. 7 shows the percentage change in various emissions types resulting from applying an ATGIS under different pre-sys-tem cost perceptions. As with time and fuel, emissions mitigation is the highest when users do not consider fuel and emis-sions costs in their decision- making prior to the ATGIS application (Scenario 3(1): Scenario 3 relative to Scenario 1), followedby a non-accurate estimation (Scenario 3(9)), a high standard error estimation (Scenario 3(8)), and an accurate estimation ofthe fuel and emissions costs prior to system adoption (Scenario 3(7)), which leads to the lowest benefits from an ATGIS. Fig. 7also shows that the mitigation pattern is different for peak (7a) than for off-peak periods (7b); while TOG has the highestreduction in peak periods, CO has the highest decrease in off-peak periods. The difference in mitigation patterns results fromthe difference in speed in the two periods. Finally, although the changes in CO2 emissions are not the highest in terms of

Fig. 6. ATGIS under different pre-policy perceived cost assumptions relative to Scenario 3 for (a) AM peak and (b) off-peak.

Fig. 7. Effects of ATGIS on total emissions under different pre-policy perceived cost assumptions for (a) AM peak and (b) off-peak.

Table 2System performance under various scenarios for (a) AM peak and (b) off-peak.

Scenario Total CBD*

Totalvehicle-hour

TotalVMT

Total Fuel(gallons)

Speed(mph)

LengthV/C > 1

Totalvehicle-hour

TotalVMT

Total Fuel(gallons)

Speed(mph)

LengthV/C > 1

(a)1 Base case 94,175 3,387,303 170,697 36.4 263.4 1402 29,486 2012 23.5 1.42 All 2dG 85,584 3,222,005 159,418 37.7 200.0 1203 27,228 1800 24.4 1.03 All 4dG** 78,899 3,094,585 150,898 38.7 177.9 1087 25,709 1646 25.0 1.04 All 6dG 75,392 2,987,494 145,736 39.9 159.9 989 24,079 1513 25.9 0.85 Fuel 4dG 79,753 3,107,587 151,830 38.6 185.7 1113 26,049 1664 25.0 0.86 All 4dG-2E 78,719 3,079,020 150,236 39.1 171.2 1029 25,132 1579 25.3 0.57 All 4dG-Norm*** 79,857 3,105,161 151,891 38.9 179.7 1075 25,683 1638 25.1 0.88 All 4dG-Norm2*** 80,478 3,116,384 152,767 38.9 184.5 1081 25,759 1647 25.2 0.89 All 4dG-Norm3*** 81,115 3,134,085 153,479 38.6 185.8 1101 26,049 1674 24.9 0.910 All 4dG-Norm4*** 78,383 3,065,298 149,702 39.0 173.5 1043 25,195 1596 25.2 0.7

(b)1 Base case 31,300 1,391,710 64,595 39.8 80.4 372 9972 587 26.3 0.22 All 2dG 28,965 1,317,548 61,358 41.4 69.0 360 9801 567 27.1 0.13 All 4dG** 27,475 1,263,977 59,175 42.5 45.9 350 9745 558 27.6 0.14 All 6dG 26,369 1,220,766 57,497 43.8 58.7 351 9870 561 27.8 0.05 Fuel 4dG 27,629 1,269,714 59,389 42.4 53.7 351 9749 559 27.4 0.16 All 4dG-2E 27,298 1,258,252 58,919 42.7 45.7 351 9747 558 27.4 0.17 All 4dG-Norm*** 27,586 1,268,498 59,344 42.6 53.3 354 9813 562 27.5 0.18 All 4dG-Norm2*** 27,696 1,272,980 59,518 42.6 56.5 356 9853 565 27.4 0.19 All 4dG-Norm3*** 27,932 1,280,739 59,815 42.3 56.3 354 9799 562 27.4 0.110 All 4dG-Norm4*** 27,142 1,251,760 58,605 42.6 50.8 352 9790 560 27.4 0.1

* A small section of downtown Fresno is chosen as the CBD.** For the RGCE (random) problems, Scenario 3 (full information) has been compared to Scenarios 7 to 10 (random information).

*** Because of using a random distribution, we only reported the average effects of several runs (based on their probability).

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percentage, the cost of CO2 emissions is dominant in the total emissions cost. Therefore, most benefits of mitigating emis-sions come from reducing CO2 emissions.

Table 2 reports the resulting important hourly performance measures of the various problems solved both for total sys-tem and for the central business district (CBD). Note that these measures should be compared to their corresponding basescenarios in order to capture the effects of implementing an ATGIS. Two different forms of base scenarios are assumed. First,users are not aware of their fuel and emissions costs (base case-Scenario 1). Second, users perceive an approximate amountof these costs (Scenario 7 to 10). For these scenarios (7–10), the average measures of several runs have been reported to cap-ture the random feature. Subsequently, we will be able to compare the effects of providing the exact information to userswith these base scenarios.

When compared to Scenario 1 (the first assumption), two distinct patterns can be recognized. For the peak hours, theeffects of ATGIS is higher on the CBD area than on the system as a whole; e.g., going from Scenario 1 to Scenario 4, the totalaverage speed increases from 36.4 to 39.9 mph (9.4%), while the CBD average speed increases from 23.5 to 25.9 mph (10.1%).The opposite holds true for the off-peak hours; e.g., going from Scenario 1 to Scenario 4, the total average speed increasesfrom 39.8 to 43.8 mph (10%) while the CBD average speed increases from 26.3 to 27.8 mph (5.7%). This pattern can beobserved for all the other measures. The reason behind this can be attributed to the difference in the volume per capacity

Table 3Social welfare analysis for various scenarios.

Scenario number Scenario name Do-nothingScenario

(1) Change in total travel time(Veh-h)*

(2) Change in total fuelconsumption (gallons)*

(3) Change in total emissionscosts ($)*,**

(4) Change in total traveldemand (VMT)*

Total change insocial costs(million $)*

Percentagechange (%)

Annual costchange (million $)

Percentagechange (%)

Annual costchange (million $)

Percentagechange (%)

Annual costchange (million $)

Percentagechange (%)

Annual costchange (million $)

2(1) All 2dG Base case �7.88 �467.9 �5.41 �125.9 �5.41 �7.8 �4.88 39.8 �561.93(1) All 4dG Base case �13.22 �802.6 �9.19 �216.4 �9.19 �13.4 �8.64 120.7 �911.74(1) All 6dG Base case �16.80 �1007.3 �11.90 �277.5 �11.90 �17.2 �11.80 249.1 �1053.05(1) Fuel 4dG Base case �12.63 �763.1 �8.81 �206.9 �8.82 �12.9 �8.26 111.4 �871.56(1) All 4dG-2E Base case �13.69 �823.9 �9.59 �224.9 �9.59 �14.0 �9.10 143.9 �918.9

3(7) All 4dG All 4dG-Norm1 �0.60 �38.8 �0.38 �9.0 �0.38 �0.6 �0.35 1.1 �47.33(8) All 4dG All 4dG-Norm2 �1.09 �67.3 �0.74 �17.4 �0.74 �1.1 �0.71 2.8 �83.03(9) All 4dG All 4dG-Norm3 �1.91 �107.6 �1.22 �27.0 �1.23 �1.7 �1.30 6.5 �129.8

3(10) All 4dG All 4dG-Norm4 1.08 45.4 0.93 17.4 0.93 1.1 0.97 �4.6 59.3

– Cong. Pric. Base case �2.95 �186.0 �1.97 �47.7 �1.97 �3.0 �0.70 1.4 �235.3

* For the purpose of comparing different scenarios, we assumed the following general parameters to calculate social costs: VOT of $20/h, fuel price of $4/gallon, base emissions cost rates, and 250 working days.** As a result of the CO2 costs dominance in total emissions costs, the percentage changes in fuel consumption (which contains certain amount of CO2) and emissions costs are very closely related.

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(V/C) levels. When V/C is high (peak), the information about the higher costs of travel (adding emissions and fuel costs)would compel motorists not to use the CBD area and use longer routes. But when V/C is low (off-peak), motorists wouldbe willing to travel on more congested but shorter CBD routes.

Another observation is that when the emissions costs are higher (going from Scenario 3 to Scenario 6), the ATGIS is muchmore effective in improving the performance measure for the peak than for the off-peak hours; e.g., the average CBD speedincreases from 25 to 25.3 mph for peak hours while the average CBD speed decreases from 27.6 to 27.4 mph for off-peakhours (although the total travel time decreases). Also, this policy seems to be effective in reducing the length of roads withV/C’s over 1 (very congested).

For the second assumption, the effects are much smaller, and in some cases (going from Scenario 7, 8, or 9 to Scenario 3),the total vehicle-hour or total VMT would increase for the CBD. But the ATGIS is still an efficient scheme to improve the sys-tem-wide performance measures, especially in reducing the length of the very congested links (V/C > 1). Finally, most of theATGIS’s positive effects could be attributed to its induced demand (VMT) reductions rather than to more efficient routechoice behavior. In fact, an ATGIS can provide better information on the fuel and emissions costs which are usually perceivedlower (if any) without it. However, if the perceived costs prior to the ATGIS implementation are higher than the actual costs,an ATGIS might degrade system performance (Scenario 10 to Scenario 3).

Table 3 presents a social welfare analysis of various scenarios, using a number of typical system-level measures. The fig-ures in Table 2 represent the combination of off-peak (18 h) and peak (6 h) values over 250 working days. The annual totalchange in welfare is the summation of the change in total travel time (Column 1), in total fuel consumption (Column 2), intotal emissions cost (Column 3), and in welfare resulting from the travel demand decrease (Column 4). The decrease in wel-fare due to the travel demand reduction is calculated based on the rule of half: the welfare loss is calculated by multiplyinghalf the average change in travel cost by the change in travel demand. For more information about the rule of half, refer toRouhani and Niemeier (2011) and Victoria Transport Policy Institute (2007).

To capture the two pre-system cost perceptions, the welfare analysis has been categorized into two sections. For the firstsection, we assumed that users do not consider fuel and emissions costs in their travel choices prior to implementing anATGIS. For the second section, we assumed that users roughly perceive these costs, based on random distributions in Table 1.In addition, we compare the results to those of a congestion pricing case (last row in Table 3) explored in Rouhani et al.(2013).

Table 3 shows that an ATGIS can potentially achieve reduction in social costs ranging from $562 million to $1053 millionwhen users do not perceive fuel and emissions costs and from $48 million to $131 million when users perceive these costs byapproximation. These benefits are comparable to the $235 million in cost savings that can be gained from a congestion pric-ing (CP) scheme. In all scenarios, travel time reduction accounts for most of the total change in social costs (�80% to �95%),followed by fuel consumption reduction (�19% to �26%), emissions mitigation (�1% to �2%), and increase in social costs as aresult of travel demand decrease (+4% to +24%). Note that the welfare effects are the summation of the above factors.Although the change in social costs from mitigating emissions is very small (�1% to �2%), the welfare effects of includingemissions costs in users’ travel behavior are substantial: an approximate 5% decrease in social costs (going from Scenario5—no emissions cost to Scenario 3).

Table 3 also shows that the social costs associated with the change in travel demand (Column 4) can be substantial insome cases; this factor should be fully considered in analyzing ATISs. An ATGIS is more effective in terms of reducing socialcosts when the gasoline price is high (Scenario 4), when emissions costs are high (Scenario 6), and when prior to implemen-tation, users do not consider fuel and emissions costs (first section of Table 3) or do not accurately estimate these costs(Scenario 3[9]). Finally, the numbers in Table 3 show the maximum potential benefits gained from an ATGIS, assuming thatthe system affects all users’ travel choices (full market penetration). In practice, only a portion of users would adopt/usethese systems, and the effects of an ATGIS would be smaller. In addition, when users perceive higher general travel costs thanthe actual costs prior to implementing an ATGIS, the ATGIS could worsen system performance (Scenario 3[10]) because theATGIS could induce higher travel demand. In fact, the higher travel demand could offset the accurate signal costs forchoosing least travel cost routes.

As discussed, the social cost savings of an ATGIS are comparable to those of a CP scheme. In addition, an ATGIS has a com-parative advantage over CP because informing users of their fuel costs does not require public/political support, and itsimplementation costs can be small relative to those for CP schemes (Mid-Ohio Regional Planning Commission, 2009).Nevertheless, CP and ATGIS are intrinsically different, and simple comparisons of the two schemes can be misleading. Inaddition, the CP and ATGIS are not distinct options and can be implemented together. However, to make a decision aboutimplementing an ATGIS either in replace of or together with a CP, we need to provide a detailed cost/benefit analysis. Finally,the results from this case study cannot be generalized to other cases since users’ behavior is highly dependent on thetopology of the network.

5. Conclusions

Our modeling and consequently our results are subject to many simplifications and limitations. First, only one averageuser group is considered in this study. The fuel and emissions cost information might be perceived differently by differentgroups of users. A more realistic model should combine the MUGCE and RGCE models, in which different user groups have

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different perceived cost distributions. Second, the advanced information provided for users is assumed to be exact and tofully penetrate the market. As a result, the benefits of providing information might be overestimated considering potentialerrors of these systems and limits in penetration. Third, a more complicated model will add to users’ heterogeneity types. Weused an average VOT of $20 per vehicle and average curves for fuel consumption and emissions. However, the VOT’s are dif-ferent for different income groups and/or different trip types. Also, the vehicle type can drastically change the fuel consump-tion and emissions curves. We did not address these in our calculations. Fourth, although by incorporating stochasticfeatures in the link cost estimation rather than in the route choice, we will save computational/modeling efforts, the estima-tion lacks the practical proof showing that users behave accordingly. Fifth, the same practical proof-related criticism holdstrue for the choice of the specific normal distributions. Finally, we did not include the effects of an ATGIS scheme on travelmode choice since Fresno has no significant public transportation system. Generally, the analysis should incorporate theperceived emissions and fuel costs into the mode choice models as well.

With all the above considerations, the results from a Fresno network modeling show that an ATGIS can efficiently changepeople’s travel behavior, at least theoretically. The results are encouraging for the implementation of ATGISs, especiallyunder high fuel price conditions and the users’ poor perceptions about the fuel and emissions costs. Informing users of theirfuel and emissions costs (using an ATGIS) could reduce total travel time, fuel consumption, and emissions from a road net-work. However, the ATGIS impact is closely related to users’ pre-system perceptions of fuel and emissions costs. Travel timecould be reduced by 16% (no pre-system perceived costs) down to 1% (accurate pre-system perceived costs). The more exactand informed the user, the lower the effects of implementing an ATGIS. Furthermore, when users initially perceive highergeneral travel costs than actual costs, an ATGIS could even worsen system performance by inducing higher travel demand.

The ATGIS effects significantly depend on the level of demand and the resulting flow pattern. For peak hours, the effects ofan ATGIS would be higher on the CBD than on the whole system. The opposite holds true for the off-peak hours. This resultstems from the fact that in highly congested conditions (peak hours), information about the higher costs of travel wouldcompel motorists not to use the CBD area. But when the congestion is low (off-peak), motorists would be willing to travelon more (though still less-than-peak) congested but shorter CBD-based routes than the outside-CBD routes.

The addition of emissions costs, although they are negligible relative to fuel and time costs, could effectively reduce totalsystem-wide travel time by up to 1% and fuel consumption by up to 0.6% for peak hours in the case of no perceived costsprior to ATGIS. These seemingly small changes could save millions of dollars in a major metropolitan area. Even thoughwe should assume that not all users might respond to the information since the emissions are not charged from users(Rouhani and Niemeier, 2014b), a small change in behavior could still effectively save system-wide costs of travel.

From the social welfare perspective, the application of an ATGIS could reduce total social costs by as much as $1053 mil-lion (high gas price, no pre-system perceived cost) to $48 million (medium gas price, precise pre-system perceived cost).These figures are comparable to social cost savings by a congestion pricing (CP) scheme of about a $200 million saving. Inaddition, an ATGIS would have a comparative advantage over a CP because informing users of their fuel cost estimates wouldnot require public/political support. In addition, the CP schemes can become very complicated practically, and the ATGISimplementation/transaction costs could be small relative to those of congestion pricing schemes (Richards, 2008).Nevertheless, congestion pricing might still be a superior policy. The flexibility of congestion pricing schemes (adjustingprices for different demand levels) makes the CP a more efficient option.

Finally, in searching for more robust modeling of urban travel behavior, our results confirm that we should think beyondthe common assumption that users’ behavior is based only on their travel time. Not only is the inclusion of fuel consumptionsignificant for travel demand calculations (trip generation and trip distribution), but its inclusion is also important for routechoice behavior. The shortest time paths are not the same as the least general cost paths. Although we might assume that thefuel consumption cost is not comprehensively perceived by users, we should reconsider our transportation planning modelsin accordance with some sort of perceived fuel costs.

6. Caveat

The transportation planning model used in this study was employed for research purposes only, and not for developingregional transportation plans or transportation improvement programs.

Acknowledgments

The authors express their special gratitude to Emily Rhoads Johnson of Emily’s Editing Services for reviewing the paperand editorial help. In addition, anonymous reviewers are acknowledged for their useful suggestions.

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