A MULTI-OBJECTIVE STUDY ON THE IMPROVEMENT OF THE SECONDARY ROAD NETWORK OF THE CENTRO REGION OF Santos, Bruno F.; Antunes, António P.; Miller, Eric J.

A MULTI-OBJECTIVE STUDY ON THE IMPROVEMENT OF THE SECONDARY ROAD NETWORK OF THE CENTRO REGION OF PORTUGAL

Bruno Filipe Santos, University of , Portugal – [email protected] António Pais Antunes, University of Coimbra, Portugal – [email protected] Eric John Miller, University of Toronto, Canada - [email protected]

ABSTRACT

In the last 25 years, very large investments have been made in Portugal to improve its main road network. In contrast, during the same period, little has been done with regard to the secondary roads. In this paper, we present a study aimed at determining the best way of improving the secondary road network of the Centro Region of Portugal. This is one of the regions of the country where some accessibility problems persist. The study was made using a multi-objective road network planning model. In relation to the road network planning models available in the literature, the one we applied has a number of important, distinctive features. The objectives considered were efficiency (evaluated by four different efficiency measures: average travel speed; weighted travel cost; consumers’ surplus gains; and weighted aggregated accessibility), equity (evaluated by the Gini Index), and energy (evaluated by fuel consumption). An overview of the measures used in the past to evaluate road network efficiency and a discussion of the implication of adopting the four measures of efficiency considered are also provided. The results indicate that solutions can be significantly different when using each of the efficiency measures used.

Keywords: road network planning, multi-objective optimization, efficiency measures, Gini index

INTRODUCTION

Very large investments were made in the transformation of the main road network of Portugal over the last 25 years within the framework of a new road network plan – Plano Rodoviário Nacional 2000. In contrast, during the same period, little has been done with regard to the secondary roads. The improvement of some of these roads will allow a significant increase of the accessibility to many of the population centers that have gained less from the investments already made.

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1 A MULTI-OBJECTIVE STUDY ON THE IMPROVEMENT OF THE SECONDARY ROAD NETWORK OF THE CENTRO REGION OF PORTUGAL Santos, Bruno F.; Antunes, António P.; Miller, Eric J.

In this paper, we present a study aimed at determining the best way of improving the secondary road network of the Centro Region of Portugal. This is one of the regions of the country where some accessibility problems persist. The analysis is carried out assuming that solutions are established by solving a road network design problem (RNDP). The RNDP is a widely studied optimization problem that consists in determining the best way of improving a road network according to some objective or objectives (Yang and Bell, 1998). In the paper, we assume that the RNDP is handled through the multi-objective approach proposed in Santos et al. (2009), which is consistent with the planning framework adopted in the Highway Capacity Manual (TRB 2000). Several objectives are usually considered in the evaluation of road network planning solutions. One of the most important ones is efficiency – that is, in a simple definition for a complex concept, the ability of making the maximum possible benefits with the minimum possible costs (or given benefits at minimum costs, or maximum benefits at given costs). As it often happens with complex concepts, efficiency can be difficult to assess. This certainly is the reason why there is no single, standardized measure to assess the efficiency of a road network. Instead, there are several measures, highlighting different attributes of the road network. In this paper, we provide an overview of the measures used in the past to evaluate road network efficiency and discussion the implication of adopting the four measures of efficiency considered in the analysis (average travel speed; weighted travel cost; consumers’ surplus gains; and weighted aggregated accessibility). In addition to the efficiency of the network, in our case study we added the maximization of equity (measured by the Gini Index) and the minimization of energy (measured by fuel consumption) to take into account other important features of regional road network planning. The structure of the paper is as follows. In the next section, we describe the optimization model used to represent the RNDP under consideration. Then, we provide an overview of the efficiency measures used in the past and specify the efficiency measures considered in our study. Afterward, we present and discuss the implications of adopting different efficiency measures for a real-world case study: the improvement of the road network of the Centro Region of Portugal. In the last section, we offer some concluding remarks.

OPTIMIZATION MODEL

The approach presented in Santos et al. (2009) upon which our study of efficiency measures is based relies on a multi-objective optimization model. The model has a number of particular features. First, as already mentioned, it is consistent with the planning framework of the Highway Capacity Manual. Second, it takes into account the fact that road capacity increases are discrete, defined according to a set of road levels (whereas most RNDP models assume them to be continuous). Third, it relies on an assignment principle different from the traditional user equilibrium principle, which we believe to be more appropriate when dealing with interurban trips: drivers will follow least-cost paths if the minimum level of service is verified for every link of the paths. Fourth and last, it assumes that travel demand is elastic in respect to both trip distribution and traffic induction. The model can be formulated as follows:

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2 A MULTI-OBJECTIVE STUDY ON THE IMPROVEMENT OF THE SECONDARY ROAD NETWORK OF THE CENTRO REGION OF PORTUGAL Santos, Bruno F.; Antunes, António P.; Miller, Eric J.

i i Z  Z V V 0 maxOF  w  0  w  (1) Z  Vi i i ZB  Z0 iG V B V 0 subject to

Z (y) (2)

V i  i (y) (3)

 Tjk  Pj Pk Cjk (y) ,j,kN (4)

Ql   Tjk  xljk ,lL (5) jN kN

 ylm 1, l  L (6) mMl

Q  Q  y ,l L (7) l  maxm lm mMl

 elm  ylm b (8) lL mMl

Tjk ,Ql  0, j,k N,l L (9)

xljk , ylm 0,1,j,k  N,l  L, m Ml (10)

where (in order of appearance): OF is the normalized value of a solution; wZ is the weight attached to the efficiency objective; Z is the value of a solution in terms of the efficiency objective; ZB is the best value obtained for the efficiency objective; Z0 is the worst value obtained for the efficiency objective; G is the set of objectives considered in addition to the efficiency objective (e.g., maximization of equity, maximization of robustness, and V i minimization of fuel consumption); w i is the weight attached to objective i; V is the value of a i i solution in terms of objective i; V B is the best value obtained for objective i; V 0 is the worst value obtained for objective i; η is the efficiency measure; y = {ylm} is a matrix of binary variables equal to one if link l is set at road level m and equal to zero otherwise; ξi is the measure for assessing objective i; Tjk is the estimated traffic flow from center j to center; θ and  are statistical calibration parameters; Pj is the population of center j (or any other measure of the size of the center); Cjk is the (generalized) cost of traveling between centers j and k; N is the set of traffic generation centers; Ql is the estimated traffic flow on link l; xljk are binary variables equal to one if link l belongs to the least-cost route between centers j and k and equal to zero otherwise (their values are obtained by solving a lower-level optimization model, see Yang and Bell, 1998); L is the set of links; Ml is the set of possible road types for link l; Q is the maximum service flow for a link of road type m; e is the expenditure maxm lm required to set link l at road type m; and b is the budget.

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3 A MULTI-OBJECTIVE STUDY ON THE IMPROVEMENT OF THE SECONDARY ROAD NETWORK OF THE CENTRO REGION OF PORTUGAL Santos, Bruno F.; Antunes, António P.; Miller, Eric J.

The objective-function (Equation 1) of this combinatorial non-linear optimization model represents the maximization of the normalized value of the road network planning solution. This solution is obtained through the application of weights to the normalized values of the solutions. The weights are included to reflect the relative importance of the different objectives under consideration. The normalization of solution values is made considering the range of variation of solutions, but other normalization procedures could be used. The values of the solutions for the three objectives, as well as the normalized values, depend on the decisions made with regard to road types (which are represented with variables y). Constraint (2) defines the efficiency measure as being dependent on the road type assigned to the various links of the network. Constraints (3) specify that the measures used to assess other objectives are also dependent on the road type assigned to the various links of the network. Traffic demand is calculated according to constraints (4) and the number of trips on each link is calculated according to constraints (5). Constraints (6) are included to guarantee that each link will be set at one, and only one, road type. For some links, it may be impossible or, particularly because of environmental concerns, undesirable to choose some road types. This is the reason why the set of road types (Ml) is indexed in the link. Constraints (7) are used to ensure that the maximum service flow is not exceeded by the traffic flow estimated for each link. Constraint (8) is included to guarantee that the cost of improving the network does not exceed a given budget. Expressions (9) and (10) define the domain for the decision variables.

EFFICIENCY MEASURES

Literature overview

Throughout time, a vast number of efficiency measures have been used for the assessment of transportation efficiency. These measures are surveyed in Levinson (2003), being classified there according to the perspective of engineers, economists, managers, and planners. Below, we focus on measures that have been used within the context of RNDP. Travel time is one of the efficiency measures considered more often. In particular, this is the measure retained in Leblanc (1975), the first article devoted to the RDNP. In this article, congestion is identified as the major cause for the poor performance of a (urban) road network and a model is proposed to define the optimum set of links that should be added to some network in order to minimize travel time. The same objective is used, for instance, in Abdulaal and Leblanc (1979) and, more recently, in Solanki et al. (1998), Poorzahedy and Abulghasemi (2005), and Kim et al (2008). The latter addresses the RNDP from a multi- period perspective. In other articles, the minimization of travel time is combined with other objectives. Feng and Wu (2003) includes an equity objective together with the efficiency objective. The equity objective is to minimize the intraregional and interregional differences in the average travel speed at which trips between cities and their respective regional capitals take place. Chen and Alfa (1991) and Chiou (2005) consider the minimization of investment costs, measured in equivalent time units, together with the minimization of travel time. Cantarella and Vitetta (2006) addresses a multi-modal RNDP where CO emissions and travel time are both to be minimized. Ukkusuri et al. (2007) considers the minimization of

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4 A MULTI-OBJECTIVE STUDY ON THE IMPROVEMENT OF THE SECONDARY ROAD NETWORK OF THE CENTRO REGION OF PORTUGAL Santos, Bruno F.; Antunes, António P.; Miller, Eric J. travel time as the efficiency objective in a robustness approach to the RNDP with demand uncertainty. Another efficiency measure widely present in the RDNP literature is travel costs (or user costs). The minimization of travel costs is the objective retained in Boyce and Janson (1980) under a budget constraint on investment costs. Janson et al. (1991) considers travel costs (shipping costs) within a multi-period planning approach developed to define future expansions and improvements of the U.S. interstate highway network. The studies described in Friesz et al. (1992), Tzeng and Tsaur (1997), and Meng et al. (2001) take investment costs not as a constraint but as a minimization objective, simultaneously with travel costs. In Ben- Ayed et al. (1992) and Friesz et al. (1993), other costs are added. The former contemplates the minimization of operating costs and accident costs, and the latter the minimization of travel distance and property expropriation. A recent study described in Kim and Kim (2006) presents a comprehensive cost approach to the multi-modal RNDP. This study assumes construction costs as a constraint, and considers the minimization of travel costs, vehicle- operating costs, accident costs, environmental costs, and maintenance costs as the objective. In relatively recent years, consumers’ surplus has often been used as an efficiency measure within the RNDP, following ideas first proposed in (Jara-Diaz and Friesz, 1982;Kocur and Hendrickson, 1982). It expresses the amount of money by which consumers value a good or service (trips) over and above its purchase price (travel cost). Jara-Diaz and Farah (1988) and William and Lam (1991) were the first to use it within a RNDP model. Chen and Subprasom (2007) and Szeto and Lo (2008) consider the maximization of consumers’ surplus together with the maximization of operator profits in the analysis of government policies to improve road networks. Yang and Bell (1997) and Szeto and Lo (2006) consider consumers’ surplus as the efficiency measure to deal with the problem of defining the optimum toll pattern for a road network (the latter present an interesting intergeneration equity approach to define tolling strategies and road investments). Other efficiency measures have been used more rarely within an RNDP. These include accessibility (Antunes et al., 2003) and connectivity (Scaparra and Church, 2005). The concept of accessibility can be defined as the “potential of opportunities for interaction” (Hansen, 1959) or, more precisely, as a "measure of spatial separation of human activities, which denotes the ease with which activities may be reached using a particular transportation system" (Morris et al., 1979). The concept of connectivity can be defined as the easiness with which it is possible of reaching all travel destinations from all travel origins (by a given type of road).

Efficiency measures considered

For our study, we considered the following four efficiency measures: average travel speed; weighted travel cost; consumers’ surplus gains; and weighted aggregate accessibility. These measures are explained below in separate subsections.

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5 A MULTI-OBJECTIVE STUDY ON THE IMPROVEMENT OF THE SECONDARY ROAD NETWORK OF THE CENTRO REGION OF PORTUGAL Santos, Bruno F.; Antunes, António P.; Miller, Eric J.

Average travel speed

One of the main reasons to improve a road network is to increase the average travel speed at which trips are made. The average travel speed for a road network (ATS) is the ratio between the total travel length and the corresponding total travel time, and can be calculated through the following expression:

 T jk ( y) d jk ATS ( y)  jN kN (11)  T jk ( y)t jk ( y) jN kN

where djk is the travel distance by the least-cost route between centers j and k; and tjk is the travel time by the least-cost route between centers j and k.

Weighted travel cost

Travel costs are one of the main road network efficiency measures. However, the importance of these costs may be different depending on the travel destinations. In particular, it is important to distinguish the trips made from the different population centers of some study area to the respective national and regional capitals, because this is where the more specialized public services are provided. The weighted travel cost for a road network (WTC) is the weighted sum of travel costs involved in traveling to major population centers, considering their functional hierarchic level. Weights are used to represent the relative importance of hierarchic levels. The WTC can be calculated through the following expression:

P j   (12) WTC( y)     wm C jm ( y) jN P mH 

where P is the total population of the study area; H is the set of hierarchic levels; wm is the weight assigned to hierarchic level m; Cjm is the (generalized) cost of traveling by the least- cost route between center j and its respective capital at hierarchic level m.

Consumers’ surplus gains

Consumers’ surplus is widely considered in the economic literature as the measure upon which the assessment of the net social benefits derived from public investments should be based. The consumers’ surplus gains (CSG) associated with the improvement of a road network is the sum of the consumers’ surplus gains obtained for each center of the network considering the trips made to all other centers. The gain for each O/D pair is the shaded area in Figure 1. The CSG can be calculated as follows:

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6 A MULTI-OBJECTIVE STUDY ON THE IMPROVEMENT OF THE SECONDARY ROAD NETWORK OF THE CENTRO REGION OF PORTUGAL Santos, Bruno F.; Antunes, António P.; Miller, Eric J.

Tjk    1 0  0 0  (13) CSG( y)      D jk (v)dv C jk ( y)Tjk ( y) T jk  T jk C jk ( y)  C jk  jN kN  0   Tjk   

0 -1 where T jk is the initial traffic flow from center j to center k (before improvement); Djk is the -1 1/β 0 inverse demand function for O/D pair j/k (Djk =[ θPjPkTjk] ); and C jk is the initial cost of traveling between centers j and k.

Travel cost

0 C jk

Cjk

-1 Djk

0 Trips T jk Tjk

Figure 1 - Consumers' surplus gain for each O/D pair

Weighted aggregate accessibility

Accessibility is a measure used very often in geographic studies involving the improvement of road networks (Hansen, 1959;Keeble et al., 1982;Vickerman et al., 1999;Holl, 2007). The accessibility of a population center is a gravity-based measure that increases with the population of neighboring centers (or any other suitable indicator of the importance of the activities carried out there), and decreases with the travel time or cost needed to reach the centers. The weighted aggregate accessibility (WAA) is the sum of the accessibilities of the centers weighted by their population and divided by the total population of the study area, and can be calculated as follows:

Pj Pk ACC( y)   Aj  , with Aj    (14) jN P kN \ j C ( y) jk where Aj is the accessibility of center j.

CASE STUDY

Presentation

An academic example based on the road network of the Centro Region of Portugal is used to illustrate the implications of adopting different efficiency measures. The Centro Region of

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7 A MULTI-OBJECTIVE STUDY ON THE IMPROVEMENT OF THE SECONDARY ROAD NETWORK OF THE CENTRO REGION OF PORTUGAL Santos, Bruno F.; Antunes, António P.; Miller, Eric J.

Portugal is one of the five planning regions of Continental Portugal. The main road network of the region was significantly improved in the last 20 years within the framework of PRN 2000 (PRN are the initials of Plano Rodoviário Nacional). The plan defined the national (and international) road corridors connecting the major Portuguese cities, borders and ports. However, the Centro Region secondary network, which is necessary to ensure the connection of smaller cities to the main road network and is deemed to be of great importance for the economic vitality of the region, has not been changed accordingly. The case study consists in discussing the road investments that should be done to enhance the quality of the region’s secondary network. Possible improvements of the main road network are also discussed. The Centro Region of Portugal involves eight administrative districts. The districts, their capitals, and the other population centers are depicted in Figure 2. Some population centers located in the south of the Centro Region have their district capital outside the Centro Region (Santarém and Lisboa). This happens because the regional borders, and in particular the Centro Region border, are not coincident with the borders of administrative districts. The reference road network adopted in this study is shown in Figure 3. The figure depicts the main roads connections between the municipalities of the Centro Region (represented by their main towns), the rest of Portugal (represented by the main cities of the other regions), Spain (represented by the capitals of autonomous communities, except for País Vasco, La Rioja, and Navarra, which are represented by a single centre), and the rest of Europe (represented by Paris). This network comprises 196 nodes (124 population centers plus 72 nodes corresponding to road intersections) and 334 links (261 within the Centro Region and 73 outside). The size of the population centers was assumed to be equal to their population forecasts for 2015 (Ine-P, 2004;Ine-S, 2005;Eurostat, 2006), multiplied by 0.15 in the case of foreign population centers to reflect cross-border traffic effects. Detailed information about centers of the network is given in Appendix 1. The current Centro Region road network (2007) has a total length of 3,794.2 kilometers (km) – 2,429.0 km of slow two-lane roads, 429.4 km of fast two-lane roads, and 935.8 km of four- lane freeways. The reference road network includes an additional 82.8 km, corresponding to projected national roads (which are included in PRN 2000 but are yet to be built). Detailed information about links of the network is given in Appendix 2. A synthesis of the design characteristics of the different road levels is presented in Table 1. The budget for road infrastructure investment was taken to be 3,500 monetary units, which is the cost of building 1,750 km of new fast two-lane highways on flat terrain. The costs per kilometer for road construction and upgrading in flat terrain are presented in Table 3.2. In mountainous areas (that is, all the region except for a stretch of land of approximately 50 km along the Atlantic coastline) these unit costs were doubled to represent the additional difficulties of construction. Travel costs were calculated assuming average vehicle costs of 0.36 Euros per kilometer and average time costs of 6.0 Euros per hour. Traffic volumes were calculated assuming an impedance parameter () of 1.5, as proposed by Antunes et al (2003) for the Portuguese national road network. The other statistical calibration parameter (α) was taken equal to 0.35.

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8 A MULTI-OBJECTIVE STUDY ON THE IMPROVEMENT OF THE SECONDARY ROAD NETWORK OF THE CENTRO REGION OF PORTUGAL Santos, Bruno F.; Antunes, António P.; Miller, Eric J.

#S #S

S# S# #S # S# S S# S# # S S# S# S# S# S# S# S# S# Viseu S# Aveiro S# S# #Y #Y S# N S# S# Guarda S# S# #Y S# S# S# S# S# Centro Region S# S# S# S# S# S# S# S# S# S# S# S# S# S# Centers Coimbra S# #Y S# S# S# S# S# S# S# Centro Region: S# S# S# District capitals S# S# S# # Other population centers S# S S# S# S# S# #Y Leiria S# Outside Centro Region: S# Castelo Branco S# #Y S# NUTS III main cities S# S# # S# S# S S# S# S# S# # S# S# S S# S# S# S# S# #S S# Santarém S# #Y

S#

S# S#

Lisboa #Y #S

Figure 1 - Centro Region districts #S #S

#S #S

S# #S S# # S# S S# S# S# # # S# S# S S# S S# S# S# S# S# S# #S #Y #Y S# Paris S# S# S# S# #Y S# S# S# S# S# S# S# S# # S# S# S S# S# S# S# S# S# #S S# S# S# #Y S# Paris S# S# S# S# S# S# S# S# S# S# S#

S# S# S# S# S# S#

S# S# #Y S# S# Zaragoza #S Barcelona #Y S# #S S# S# #S # Porto S# S# S S# S# S# #S S# Madrid # S# S# S S# S# S# S# #S S# ValencBiarcelona #S Zaragoza #S S# #S S# #Y #S Porto #S Lisboa S# #S Madrid S# Sevilla S# #S Valencia #S

#S Lisboa #Y #S

Road Type:Sevilla Projected Road Slow 2-lane highway Fast 2-lane highway#S 4-lane freeway#S 6-lane freeway #S Figure 2 - Reference road network

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9 A MULTI-OBJECTIVE STUDY ON THE IMPROVEMENT OF THE SECONDARY ROAD NETWORK OF THE CENTRO REGION OF PORTUGAL Santos, Bruno F.; Antunes, António P.; Miller, Eric J.

Table 1 - Design characteristics for the different road type Free-flow Maximum Maximum Capacity Road type speed Level of service service flow service speed [km/h] [pcu/h/lane] [pcu/h/lane] [km/h] Slow two-lane highway 70 1700 D 255 55 Fast two-lane highway 90 2100 C 1428 90 Four-lane freeway 120 2400 B 1320 120 Six-lane freeway 120 2400 B 1320 120

Table 2 - Road construction and upgrading costs per kilometer To From Slow two-lane Fast two-lane Four-lane Six-lane highway highway freeway freeway Projected road 1 2 3 4 Slow two-lane highway - 1.5 2.5 3.5 Four-lane highway - - 2 3 Six-lane highway - - - 1 .

The discussion of road network investments in the Centro Region is made considering not only efficiency objectives (assessed through the four measures presented in the previous section, that is, average travel speed, weighted travel costs, consumers’ surplus gains, and weighted aggregate accessibility), but also equity objectives and environmental objectives. Half of the priority is given to the efficiency objective and the other half is given to the two other objectives (weights of 50/100 for efficiency objective and 25/100 for the other objectives). The weighted travel costs were calculated considering the following weights: 40 percent for the district capital, 25 percent for the Centro Region capital (the city of Coimbra), 25-percent for the Portuguese capital (the city of Lisboa), and 10 percent for the closest population center located abroad. The equity measure used in the study was the Gini Index, one of the measures used more often in social and economic inequality studies (Xu, 2004; Santos et al., 2008). This coefficient takes values in the interval [0, 1], with zero corresponding to a fully equitable situation. It can be calculated through the following expression:

 Z j ( y)  Z k ( y)  1( y)  jN kN (15) 2n2 Z

1 where ξ is the Gini Index value (objective 1); Zj is the efficiency solution value for center j (e.g., the average speed for travel between center j and the other centers of the network); n is the number of centers that belong to N; and is the average efficiency value of the centers that belong to N. The environmental measure used in the study was the average fuel consumption in the network, given through the following expression:

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10 A MULTI-OBJECTIVE STUDY ON THE IMPROVEMENT OF THE SECONDARY ROAD NETWORK OF THE CENTRO REGION OF PORTUGAL Santos, Bruno F.; Antunes, António P.; Miller, Eric J.

Fl ( y)Ql ( y) Ll  2 ( y)  lL (16) Ql ( y) Ll lL

2 where ξ is the average fuel consumption in the network (objective 2); Fl is the average fuel consumption for link l; and Ll is the length of link l. The average fuel consumption for each link was calculated using the well-known COPERT model (European Commission, 1999). We considered the current composition of the Portuguese fleet and run COPERT assuming different average (steady) speeds in highways and rural roads. With the values of fuel consumption obtained for the different speeds, we calibrated a quadratic function to measure the relationship between fuel consumption and speed. The result was as follows:

F  97.055688  1.273995 S 0.008036  S 2 (R2  0.938 ) (17) where F is the fuel consumed (expressed in grams of oil equivalent per kilometer); S is the average speed; and R² is the correlation coefficient (note that, according to this expression the lowest fuel consumption is achieved for a travel speed of 79.3 km/h).

Results

The optimization model presented above was applied to the case study. For solving the model we used the enhanced genetic algorithm (EGA) presented in Santos et al (2005). The computation of the optimum solution for each one of the four efficiency measures under consideration took 3 to 6 days on an Intel Dual Core 6700 microprocessor running at 2.66 GHz. The optimum solutions are depicted in Figure 4. Some similarities can be found in the solutions. For example, the fast two-lane highway between Leiria and Lisboa is upgraded to a four-lane freeway (and even to a six-lane freeway in the southern part of the Centro Region). The four-lane freeway connection between Lisboa to Porto is widened to a six-lane freeway to the north of Aveiro and next to Coimbra. Other similarity is the construction of the road projected near Aveiro as a four-lane freeway. None of the solutions involves the construction of the projected road in the south of Viseu. The projected road located in the edge of the districts of Guarda, Castelo Branco, and Coimbra, would be built as a fast two- lane highway if accessibility is selected as the efficiency measure (Figure 4b).

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11 A MULTI-OBJECTIVE STUDY ON THE IMPROVEMENT OF THE SECONDARY ROAD NETWORK OF THE CENTRO REGION OF PORTUGAL Santos, Bruno F.; Antunes, António P.; Miller, Eric J.

#S #S #S #S

# S# S # S# #S S #S # # S# S# S S # S# S # S# S S# S# # # S# S# S# S# S# S S# S S# S# S# S# # S# S# S# S# S# S # S# S# S# S #Y #Y #Y # S# S# #Y S# S# S# S S# S# S# S# #Y # #Y S# S# S# S # S# S# S# S # S# S # # S# S# S# S S S# # S# S# S# # S S# S# S# S # S# S# S# S# S # S# S# S # S# S # S# S# S# S # S# #Y S #Y S# # S# # S# S# S# S S# S S# S# S# S# S# S# # S# S S# S# S# S# # S# S# S# S

S# # S# S# S# S# S S# S# S# S# S# S#

S# S# #Y S# #Y S# S# S# S# S# #Y #Y S# S# # S# S# S# S # S# S# S# S# S# S S# S# S# S# S# S# # S# S S# # S# S# S# # S# S S# S# S S# S# S# S# S# S# S# #S #S S# S# S# #Y S# #Y

S# S#

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#Y #S #Y #S (a) Average speed, Gini Index, and fuel consumption (b) Weighted travel costs, Gini Index, and fuel consumption #S #S #S #S

#S #S #S #S

S# S# S# S# #S # #S # S# S # S S# S S# S# S# S# S# # # S# # # S# S# S S# S S# S S# S S# S# S# S# S# S# S# S# S# S# S# S# #Y # #Y #Y S# S# S #Y S# S# S# S# S# #Y S# S# #Y S# S# S# S# S# S# S# S# S# S# S# S# S# S# S# S# S# S# # S# S# S# S# S S# S# S# S# S# S# S# S# S# S# S# S# S# S# S# S# S# #Y # #Y # # S# S S# S S# S # S# S# S# S S# S# S# S# S# S# # S S# S# S# S# S# S# S#

S# S# S# S# S# S# S# S# S# S# S# S#

S# S# S# #Y S# #Y S# S# S# S# #Y S# #Y S# S# S# S# S# # S# # S# S S# S# S# S S# S# S# S# S# S# S# # S# S# S # S# S# S# S# S S# S# S# S# S# S# S# S# #S #S S# S# S# #Y S# #Y

S# S#

S# S# S# S#

#Y #S #Y #S (c) Consumers’ surplus, Gini Index, and fuel consumption (d) Accessibility, Gini Index, and fuel consumption #S #S #S #S Road Type: Projected Road Slow 2-lane highway Fast 2-lane highway 4-lane freeway 6-lane freeway

Figure 3 - Best solution for the different efficiency objectives

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12 A MULTI-OBJECTIVE STUDY ON THE IMPROVEMENT OF THE SECONDARY ROAD NETWORK OF THE CENTRO REGION OF PORTUGAL Santos, Bruno F.; Antunes, António P.; Miller, Eric J.

A summary of the results obtained for the four efficiency measures is presented in Table 3. The solution that maximizes average speed is the one with largest length of freeways– 1,314.8 km of four-lane freeways and 60.1 km of six-lane freeways –, closely followed by solution that minimizes weighted travel costs – 1,296.1 km of four-lane freeways and 70.7 km of six-lane freeways. The solution with the largest length of two-lane freeways is, by far, the one that maximizes consumers’ surplus – 1,419.7 km, more 225.4 km than the solution that maximizes accessibility (which is the one with the second largest length of two- lane freeways).

Table 3 - Summary of results for different efficiency measures Solution Reference 1 2 3 4 Network Value Variation Value Variation Value Variation Value Variation

Projected road 82.8 64.8 -21.7% 64.8 -21.7% 64.8 -21.7% 19.8 -76.1% Slow two-lane highway 2,429.0 1,451.6 -40.2% 1,448.4 -40.4% 1,222.5 -49.7% 1,400.8 -42.3% Road type length Fast two-lane highway 429.4 985.7 129.5% 997.1 132.2% 1,419.7 230.6% 1,194.3 178.1% Four-lane freeway 935.8 1,314.8 40.5% 1,296.1 38.5% 1,099.4 17.5% 1,191.5 27.3% Six-lane freeway 0.0 60.1 ---- 70.7 ---- 70.7 ---- 70.7 ---- Average speed 111.24 115.64 4.0% 113.86 2.4% 114.52 2.9% 114.30 2.8% Efficiency Weighted travel cost 66.75 64.39 -3.5% 64.11 -4.0% 64.35 -3.6% 64.38 -3.6% measures Consumers' surplus gains 0.0 38.91 ---- 33.90 ---- 41.65 ---- 39.41 ---- Accessibility 7.91 8.49 7.3% 8.33 5.3% 8.52 7.7% 8.69 9.8% Other Equity (Gini Index) ------60.2% ---- -6.8% ------3.2% objectives Fuel consumption 57.75 58.28 0.9% 57.93 0.3% 57.53 -0.4% 57.58 -0.3% Note: Solution 1 – average speed, Gini Index, and fuel consumption; Solution 2 – weighted travel costs, Gini Index, and fuel consumption; Solution 3 – consumers’ surplus gains, Gini Index, and fuel consumption; Solution 4 – weighted aggregated accessibility, Gini Index, and fuel consumption.

The maximum possible gain for average speed is approximately 4.0 percent (obtained when the efficiency measure is the average speed). The gain reduces to 2.9 percent when the objective is to maximize consumers’ surplus. With regard to weighted travel costs, the maximum possible gain is also 4.0 percent. The gain falls down to 3.6 percent when the efficiency measures are consumers’ surplus or accessibility. The maximum possible consumers’ surplus gain is 41.65 units. This gain reduces to 39.41 when the efficiency measure is accessibility. Finally, with regard to accessibility, the maximum possible gain is 9.8 percent. This gain shrinks to 7.7 percent when the objective is to maximize consumers’ surplus. From these results, it can be concluded that, for this particular case study, consumers’ surplus was the more comprehensive measure, with good results regardless of the measures used to express the efficiency objective. The largest gains on the equity objective are obtained when the efficiency measure is average speed – the Gini Index decreases by 60.2 percent. Comparatively, the weighted travel cost and the accessibility measures lead to very small gains. In terms of the environmental objective, the two solutions with the largest length of freeways are the worst ones – fuel consumption increases by 0.3 percent in the weighted travel cost solution and by 0.9 percent in the average speed solution. In contrast, for the consumers’ surplus and the accessibility solutions, fuel consumption decreases by 0.4 and 0.3 percent, respectively. A comparison of the gains obtained for the six district capitals, with regard to the different solutions, is presented in Figure 5. The cities of Castelo Branco and Leiria are the district capitals which most benefit when average speed is the efficiency measure – the average

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13 A MULTI-OBJECTIVE STUDY ON THE IMPROVEMENT OF THE SECONDARY ROAD NETWORK OF THE CENTRO REGION OF PORTUGAL Santos, Bruno F.; Antunes, António P.; Miller, Eric J. speed increases by 4.4 and by 4.2 percent, respectively, when compared with the reference network. The city of Castelo Branco is also the city that has the maximum gain in terms of weighted travel costs. The weighted travel costs decrease by 5.5 percent. The city of Aveiro is the one that most benefits when accessibility and consumers’ surplus are the efficiency measures – gains of 9.7 and 14.3 percent, respectively. In general, the district capital that less benefits is Guarda. When average speed is the efficiency measure, the city of Guarda has a very small benefit, of only 0.6 percent. For the weighted travel costs and the accessibility solutions, Guarda only gains 1.8 and 2.6 percent, respectively. With regard to the consumers’ surplus solution, Guarda is the second capital with lower gains (4.6 units) closely followed by the city of Castelo Branco (4.3 units).

Figure 5 – Gains for the district capitals with regard to the different solutions (log scale)

In order to compare pairs of road network solutions, we defined the following similarity index:

uv  Ll  Δl S(u,v)  lL (18)  Ll lL where S is the similarity index, u and v are a pair of solutions (the optimum networks obtained uv for two different efficiency measures) and ∆l is the difference of levels for link l in solutions u

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14 A MULTI-OBJECTIVE STUDY ON THE IMPROVEMENT OF THE SECONDARY ROAD NETWORK OF THE CENTRO REGION OF PORTUGAL Santos, Bruno F.; Antunes, António P.; Miller, Eric J. and v. The value of this index is always higher or equal to zero. The lower its value is, the closer the two solutions are of being exactly similar. The similarity indexes for the Centro Region case study are presented in Table 4. The solutions more different from the initial network is the one obtained for the accessibility measure, which is quite similar to the one obtained for the consumers’ surplus measure. The two less similar solutions are obtained for the weighted travel cost and the average speed measures (despite being two solutions with similar lengths for roads of the same type).

Table 4 - Similarity index for each pair of solutions Current Solution Network 1 2 3 4 Current Network ---- 0.390 0.392 0.399 0.400 1 0.390 ---- 0.400 0.272 0.279 2 0.392 0.400 ---- 0.368 0.348 Solution 3 0.399 0.272 0.368 ---- 0.201 4 0.400 0.279 0.348 0.201 ---- Note: Solution 1 – average speed, Gini Index, and fuel consumption; Solution 2 – weighted travel costs, Gini Index, and fuel consumption; Solution 3 – consumers’ surplus gains, Gini Index, and fuel consumption; Solution 4 – weighted aggregate accessibility, Gini Index, and fuel consumption.

CONCLUSION

In this paper, we analyzed the sensitivity of road network planning solutions to four measures that can be adopted for assessing their efficiency: average travel speed; weighted travel costs; consumers’ surplus gains; and weighted aggregate accessibility. The analysis was carried out assuming that solutions are established by solving a road network design problem. The road network of the Centro Region of Portugal was used as case study. Equity and fuel consumption objectives were added to the efficiency objective to take into account other important features of road network planning. The results show that, depending on the efficiency measure used, the optimum solution can be considerably different. The solutions obtained when average speed and weighted travel costs were used as efficiency measures included the largest length of freeways. However, the roads improved in the two cases are quite different and, in fact, these are the less similar of the solutions obtained for the four measures. The accessibility solution is the more different in comparison with the initial network. The consumers’ surplus solution was characterized with the best results with regard to the other efficiency measures. In summary, caution is required when defining road network efficiency. The choice of the measure should reflect the goals and the concerns for the specific network and territory under analysis. The simultaneous consideration of more than one efficiency measure can be recommendable. This issue will be addressed in future works

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ACKNOWLEDGMENTS

The authors are grateful to Tiago Farias and Ana Vasconcelos at the Instituto Superior Técnico (IST) for their help in fuel consumption modeling. The participation of the first author in the study has been supported by Fundação para a Ciência e Tecnologia through grant SFRH/BD/16407/2004.

REFERENCES

Abdulaal, M. and L. J. Leblanc (1979). Continuous equilibrium network design models, Transp. Res. Part B, 13 (1), 19-32. Antunes, A., A. Seco and N. Pinto (2003). An accessibility-maximization approach to road network planning, Comput.-Aided Civ. Infrastruct. Eng., 18 (3), 224-240. Ben-Ayed, O., C. Blair, D. Boyce and L. Leblanc (1992). Construction of a real-world bilevel linear programming model of the highway network design problem, Ann. Oper. Res., 34 (1), 219-254. Boyce, D. E. and B. N. Janson (1980). A discrete transportation network design problem with combined trip distribution and assignment, Transp. Res. Part B, 14 (1-2), 147-154. Cantarella, G. E. and A. Vitetta (2006). The multi-criteria road network design problem in an urban area, Transportation, 33 (6), 567-588. Chen, A. and K. Subprasom (2007). Analysis of regulation and policy of private toll roads in a build-operate-transfer scheme under demand uncertainty, Transp. Res. Part A, 41 (6), 537-558. Chen, M. and A. S. Alfa (1991). A network design algorithm using a stochastic incremental traffic assignment approach, Transp. Sci., 25 (3), 215-225. Chiou, S. W. (2005). Bilevel programming for the continuous transport network design problem, Transp. Res. Part B, 39 (4), 361-383. European Commission (1999). Meet-methodology for calculating transport emissions and energy consumption. European Commission, COST 319 - Estimation of Pollutant Emissions from Transport. EUROSTAT (2006). Demographic statistics: Population. Statistical Office of the European Communities. Feng, C. M. and J. Y. J. Wu (2003). Highway investment planning model for equity issues, J. Urban Plann. Dev., 129 (3), 161-176. Friesz, T. L., G. Anandalingam, N. J. Mehta, K. Nam, S. J. Shah and R. L. Tobin (1993). The multiobjective equilibrium network design problem revisited - A simulated annealing approach, Eur. J. Oper. Res., 65 (1), 44-57. Friesz, T. L., C. Hsun-Jung, N. J. Mehta, R. L. Tobin and G. Anandalingam (1992). A simulated annealing approach to the network design problem with variational inequality constraints, Transp. Sci., 26 (1), 18-26. Hansen, W. G. (1959). How accessibility shapes land-use, J. Am. Inst. of Planners, 25 (2), 73-76.

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Holl, A. (2007). Twenty years of accessibility improvements. The case of the Spanish motorway building programme, J. Transp. Geogr., 15 (4), 286-297. INE-P (2004) Projecções de população residente – Portugal e NUTS II – 2000-2050. Instituto Nacional de Estatística, Lisboa, Portugal. INE-S (2005). Population projections. Base census 2001. Instituto Nacional de Estadística,Madrid, Spain. Janson, B. N., L. S. Buckels and B. E. Peterson (1991). Network design programming of United-states highway improvements, J. Transp. Eng. 117 (4), 457-478. Jara-Diaz, S. and M. Farah (1988). Valuation of users benefits in transport-systems, Transp. Rev., 8 (3), 197-218. Jara-Diaz, S. and T. L. Friesz (1982). Measuring the benefits derived from a transportation investment, Transp. Res. Part B, 16 (1), 57-77. Keeble, D., P. L. Owens and C. Thompson (1982). Regional accessibility and economic- potential in the European-community, Regional Stud., 16 (6), 419-431. Kim, B. and W. Kim (2006). An equilibrium network design model with a social cost function for multimodal networks, Ann. Regional Sci., 40 (3), 473-491. Kim, B., W. Kim and B. Song (2008). Sequencing and scheduling highway network expansion using a discrete network design model, Ann. Regional Sci., 42 (3), 621- 642. Kocur, G. and C. Hendrickson (1982). Design of local bus service with demand equilibration, Transp. Sci., 16 (2), 149-170. Leblanc, L. J. (1975). An algorithm for the discrete network design problem, Transp. Sci., 9 (3), 183-199. Levinson, D. (2003). Perspectives on efficiency in transportation, Int. J. Transp. Manage., 1 (3), 145-155. Meng, Q., H. Yang and M. G. H. Bell (2001). An equivalent continuously differentiable model and a locally convergent algorithm for the continuous network design problem, Transp. Res. Part B, 35 (1), 83-105. Morris, J. M., P. L. Dumble and M. R. Wigan (1979). Accessibility indicators for transport planning, Transp. Res. Part A, 13 (2), 91-109. Poorzahedy, H. and F. Abulghasemi (2005). Application of ant system to network design problem, Transportation, 32 (3), 251-273. Santos, B., A. Antunes and E. Miller (2009). A multi-objective approach to long-term interurban multi-level road network planning, J. Transp. Eng., 135 (9), 640-649. Santos, B., A. Antunes and E. J. Miller (2005) Solving an accessibility-maximization road network design model: A comparison of heuristics. In: Advanced OR and AI Methods in Transportation, A. Jaszkiewicz, M. Kaczmarek, J. Zak and M. Kubiak (eds), Poznan, Poland, 692-697. Scaparra, M. P. and R. L. Church (2005). A GRASP and path relinking heuristic for rural road network development, J. Heuristics, 11 (1), 89-108. Solanki, R. S., J. K. Gorti and F. Southworth (1998). Using decomposition in large-scale highway network design with a quasi-optimization heuristic, Transp. Res. Part B, 32 (2), 127-140.

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Szeto, W. Y. and H. K. Lo (2006). Transportation network improvement and tolling strategies: The issue of intergeneration equity, Transp. Res. Part A, 40 (3), 227-243. Szeto, W. Y. and H. K. Lo (2008). Time-dependent transport network improvement and tolling strategies, Transp. Res. Part A, 42 (2), 376-391. TRB (2000). Highway capacity manual. Transportation Research Board, National Research Council, Washington, D.C., USA. Tzeng, G. H. and S. H. Tsaur (1997). Application of multiple criteria decision making for network improvement, J. Adv. Transp., 31 (1), 49-74. Ukkusuri, S. V., T. V. Mathew and S. T. Waller (2007). Robust transportation network design under demand uncertainty, Comput.-Aided Civ. Infrastruct. Eng., 22 (1), 6-18. Vickerman, R., K. Spiekermann and M. Wegener (1999). Accessibility and economic development in Europe, Regional Stud., 33 (1), 1-15. Williams, H. and W. M. Lam (1991). Transport policy appraisal with equilibrium-models .1. Generated traffic and highway investment benefits, Transp. Res. Part B, 25 (5), 253- 279. Xu, K. (2004). How has the literature on Gini's index evolved in the past 80 years? , Dalhousie University, Department of Economics, Halifax, Nova Scotia, Canada. Yang, H. and M. G. H. Bell (1997). Traffic restraint, road pricing and network equilibrium, Transp. Res. Part B, 31 (4), 303-314. Yang, H. and M. G. H. Bell (1998). Models and algorithms for road network design: A review and some new developments, Transp. Rev., 18 (3), 257-278.

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APPENDIX 1 – NODE DATA

Coordinates Population Center Label X Y Municipality (x1000) 1 Coimbra -25.25 59.87 Coimbra 149.3 2 Aveiro & Ilhavo -43.92 108.58 Aveiro 117.3 3 Leiria -59.44 6.9 Leiria 136.4 4 Viseu 18.83 110.32 Viseu 100.4 5 Castelo Branco 54.21 19.59 Castelo Branco 53.0 6 Guarda 77.29 100.1 Guarda 48.2 7 & Barquinha -28.35 -23.06 Santarem 31.5 8 Ovar -41.26 132.36 Aveiro 58.7 9 Murtosa -42.93 120.25 Aveiro 8.5 10 -68.37 9.37 Leiria 35.4 11 Nazare -79.31 -6.56 Leiria 13.9 12 Figueiro dos Vinhos -10.03 29.18 Leiria 6.0 13 Lourinha -102.15 -46.85 Lisboa 24.5 14 Peniche -103.59 -37.52 Leiria 27.9 15 -11.25 68.15 Coimbra 15.3 16 -1.31 -14.95 Santarem 3.5 17 Ferreira do Zezere -14.95 3.57 Santarem 8.3 18 -16.49 46.15 Coimbra 14.0 19 Constancia -17.34 -20.79 Santarem 3.2 20 Pedrogao Grande -1.93 28.18 Leiria 3.7 21 -19.74 -5.71 Santarem 40.6 22 Sever do Vouga -19.91 117.98 Aveiro 11.3 23 -20.44 37.32 Coimbra 5.7 24 Alvaiazere -21.09 18.36 Leiria 6.8 25 Ansiao -25.92 27.7 Leiria 12.2 26 Mealhada -26.88 79.16 Aveiro 22.8 27 Anadia -27.35 88.74 Aveiro 32.7 28 Albergaria-a-Velha -28.67 111.67 Aveiro 26.5 29 Agueda -28.83 98.42 Aveiro 52.3 30 Oliveira do Bairro -30.54 94.42 Aveiro 23.2 31 Condeixa-a-Nova -31.55 49.02 Coimbra 17.7 32 -34.21 -22.81 Santarem 34.0 33 Estarreja -34.89 123.73 Aveiro 27.8 34 Oliveira de Frades -3.7 118.08 Viseu 9.7 35 Ourem -37.43 -4.69 Santarem 53.3 36 Cantanhede -38.41 75.6 Coimbra 35.8 37 -3.98 -20.02 Santarem 35.8 38 Soure -42.16 43.46 Coimbra 18.3 39 Pombal -42.42 29.71 Leiria 58.8 40 -43.22 -21.43 Santarem 14.1 41 Montemor-o-Velho -43.47 54.2 Coimbra 22.3 42 Vagos -46.34 98.55 Aveiro 24.7 43 Mira -49.45 83.84 Coimbra 11.3 44 -60.36 53.63 Coimbra 58.8

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Coordinates Population Center Label X Y Municipality (x1000) 45 Batalha & Porto de Mos -62.36 -5.15 Leiria 39.8 46 -6.31 37.45 Leiria 2.7 47 Alcobaca -70.91 -11.92 Leiria 55.7 48 Alenquer -72.24 -74.11 Lisboa 45.1 49 Vila de Rei -0.8 0.78 Castelo Branco 2.7 50 Mortagua -7.98 80.99 Viseu 9.2 51 Obidos -86.4 -32.96 Leiria 9.9 52 Sobral M Agraco & Arruda -87.39 -70.71 Lisboa 22.6 53 -88.02 -28.93 Leiria 55.0 54 & Cadaval -89.16 -44.57 Leiria 27.3 55 -9.19 58.87 Coimbra 7.8 56 Torres Vedras -97.12 -60.96 Lisboa 75.8 57 Lousa -9.93 51.12 Coimbra 18.0 58 Macao 10.3 -17.13 Santarem 6.1 59 Almeida 103.41 117.97 Guarda 6.0 60 Tabua 10.88 77.08 Coimbra 11.0 61 Santa Comba Dao 1.21 79.47 Viseu 11.8 62 15.92 42.05 Coimbra 4.1 63 Gois 1.84 55.07 Coimbra 3.9 64 Oleiros 18.93 27.61 Castelo Branco 5.0 65 Castro Daire 19.11 137.6 Viseu 14.2 66 Proenca-a-Nova 20.57 7.28 Castelo Branco 7.2 67 24.65 75.1 Coimbra 19.7 68 Nelas 25.2 95.96 Viseu 12.7 69 Mangualde 31.63 105.27 Viseu 18.3 70 Satao 33.13 120.68 Viseu 11.8 71 Vila Nova de Paiva 34.43 130.71 Viseu 5.7 72 Vila Velha de Rodao 34.88 1.21 Castelo Branco 2.9 73 Seia 35.08 84.81 Guarda 23.2 74 Penalva do Castelo 36.57 112.18 Viseu 8.1 75 Serta 3.7 16.16 Castelo Branco 13.6 76 Gouveia 42.98 94.04 Guarda 13.3 77 Tondela 4.61 94.34 Viseu 27.5 78 Manteigas 50.3 81.65 Guarda 3.3 79 Aguiar da 50.67 128.84 Guarda 5.2 80 Fornos de Algodres 52.16 105.56 Guarda 4.4 81 Sao Pedro do Sul 5.46 121.46 Viseu 16.4 82 Covilha 54.63 61.48 Castelo Branco 50.7 83 Fundao 55.7 52.28 Castelo Branco 28.6 84 Celorico da Beira 62.22 106.54 Guarda 8.1 85 Vouzela 0.65 117.69 Viseu 10.3 86 6.7 61.25 Coimbra 12.1 87 Trancoso 67.91 121.4 Guarda 9.3 88 Meda 73.26 144.27 Guarda 4.5

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Coordinates Population Center Label X Y Municipality (x1000) 89 Idanha-a-Nova 77.88 27.55 Castelo Branco 8.6 90 Penamacor 82.05 55.95 Castelo Branco 4.6 91 Sabugal 87.43 77.7 Guarda 11.4 92 Pinhel 90.27 122.56 Guarda 8.2 93 Carregal do Sal 9.64 84.58 Viseu 8.9 94 Figueira de Castelo Rodrigo 97.54 134.17 Guarda 5.5 95 Penafiel -12.24 170.91 (Penafiel) 571.1 96 Guimaraes -13.08 197.31 (Guimaraes) 536.1 97 Braga -24.19 209.55 (Braga) 423.0 98 Sao Joao da Madeira -30.33 137.11 (S.J. Madeira) 292.0 99 Porto -46.35 168.28 (Porto) 1300.6 100 Santiago do Cacem -49.19 -182.8 (S. Cacem) 101.1 101 Santarem -50.95 -44.38 (Santarem) 250.0 102 Setubal -54.93 -120.07 (Setubal) 807.2 103 Viana do Castelo -57.57 225.51 (V. Castelo) 232.2 104 Lisboa -88.44 -101.56 (Lisboa) 1982.0 105 Braganca 114.44 238.23 (Braganca) 192.5 106 Faro 17.92 -293.45 (Faro) 419.0 107 Evora 20.06 -123.19 (Evora) 172.7 108 Beja 23.99 -184.23 (Beja) 123.6 109 Vila Real 33.01 181.59 (V. Real) 185.6 110 Portalegre 59.99 -41.13 (Portalegre) 116.1 111 Toledo 345.93 46.57 (Spain) 304.3 112 Santiago -35.28 347.2 (Spain) 397.9 113 Merida 135.61 -101.76 (Spain) 160.5 114 Oviedo 159.98 393.72 (Spain) 152.3 115 Sevilla 199.84 -248.99 (Spain) 1223.2 116 Valladolid 212.97 210.03 (Spain) 360.5 117 Santander 360.07 393.01 (Spain) 86.4 118 Madrid 374.21 91.82 (Spain) 937.8 119 Murcia 528.34 -142.21 (Spain) 217.3 120 Vitoria-Gasteiz 551.67 362.61 (Spain) 454.3 121 Zaragoza 640.05 190.1 (Spain) 189.4 122 Valencia 686.01 -22.72 (Spain) 754.7 123 Barcelona 859.94 221.21 (Spain) 1095.9 124 Paris 866.28 769.97 (Europe) 22420.6 125 Ourique -13.49 -215.28 --- 0 126 Vale de Cambra -21.65 137.35 --- 0 127 Valenca -41.28 261.27 --- 0 128 Placenca 165.64 32.43 --- 0 129 Chaves 46.11 237.83 --- 0 130 Macedo de Cavaleiros 95.56 214.89 --- 0 131 Benavente 147.73 275.66 --- 0 132 Salamanca 170.77 136.89 --- 0

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Coordinates Population Center Label X Y Municipality (x1000) 133 Burgos 366.43 298.27 --- 0 134 El Canavate 475.32 -3.63 --- 0 135 Intersection 135 -11.5 56.5 --- 0 136 Intersection 136 -15.04 56.09 --- 0 137 Intersection 137 -17.36 111.45 --- 0 138 Intersection 138 -19.82 0.44 --- 0 139 Intersection 139 -20.63 27.68 --- 0 140 Intersection 140 -26.36 -20.21 --- 0 141 Intersection 141 -26.4 67.1 --- 0 142 Intersection 142 -26.73 19.94 --- 0 143 Intersection 143 -26.78 45.19 --- 0 144 Intersection 144 -27.21 34.69 --- 0 145 Intersection 145 -29.62 67.27 --- 0 146 Intersection 146 -30.08 59.26 --- 0 147 Intersection 147 -30.55 77.45 --- 0 148 Intersection 148 -30.94 51.73 --- 0 149 Intersection 149 -32.64 87.55 --- 0 150 Intersection 150 -32.93 113.41 --- 0 151 Intersection 151 -33.38 68.6 --- 0 152 Intersection 152 -35.79 111.32 --- 0 153 Intersection 153 -36.09 134.69 --- 0 154 Intersection 154 -36.18 96.78 --- 0 155 Intersection 155 -36.72 40.63 --- 0 156 Intersection 156 -4.16 103.72 --- 0 157 Intersection 157 -42.98 96.51 --- 0 158 Intersection 158 -46.22 59.55 --- 0 159 Intersection 159 -46.27 30.57 --- 0 160 Intersection 160 -48.87 -2.27 --- 0 161 Intersection 161 -51.98 8.21 --- 0 162 Intersection 162 -5.39 76.12 --- 0 163 Intersection 163 -5.4 113.18 --- 0 164 Intersection 164 -56.27 35.79 --- 0 165 Intersection 165 -63.81 8.9 --- 0 166 Intersection 166 -65.35 -11.9 --- 0 167 Intersection 167 -70.37 -39.49 --- 0 168 Intersection 168 -0.7 113.55 --- 0 169 Intersection 169 -75.38 -8.55 --- 0 170 Intersection 170 -82.29 -17.74 --- 0 171 Intersection 171 -8.68 71.26 --- 0 172 Intersection 172 0.1 98.43 --- 0 173 Intersection 173 12.99 37.94 --- 0 174 Intersection 174 13.88 87.75 --- 0 175 Intersection 175 14.55 73.23 --- 0 176 Intersection 176 19.06 83.53 --- 0

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12th WCTR, July 11-15, 2010 – Lisbon, Portugal

22 A MULTI-OBJECTIVE STUDY ON THE IMPROVEMENT OF THE SECONDARY ROAD NETWORK OF THE CENTRO REGION OF PORTUGAL Santos, Bruno F.; Antunes, António P.; Miller, Eric J.

Coordinates Population Center Label X Y Municipality (x1000) 177 Intersection 177 19.69 71.41 --- 0 178 Intersection 178 2.14 66.74 --- 0 179 Intersection 179 2.84 37.99 --- 0 180 Intersection 180 29.01 39.35 --- 0 181 Intersection 181 35.34 37.12 --- 0 182 Intersection 182 47.83 72.33 --- 0 183 Intersection 183 5.11 45.7 --- 0 184 Intersection 184 53.16 131.84 --- 0 185 Intersection 185 5.5 109.29 --- 0 186 Intersection 186 56.08 28.02 --- 0 187 Intersection 187 64.42 72.65 --- 0 188 Intersection 188 68.89 33.86 --- 0 189 Intersection 189 69.47 107.86 --- 0 190 Intersection 190 76.68 136.42 --- 0 191 Intersection 191 80.46 69.57 --- 0 192 Intersection 192 81.61 42.17 --- 0 193 Intersection 193 8.76 -2.97 --- 0 194 Intersection 194 0.88 19.36 --- 0 195 Intersection 195 97.18 105.28 --- 0 196 Intersection 196 98.32 111.36 --- 0

(Continued)

12th WCTR, July 11-15, 2010 – Lisbon, Portugal

23 A MULTI-OBJECTIVE STUDY ON THE IMPROVEMENT OF THE SECONDARY ROAD NETWORK OF THE CENTRO REGION OF PORTUGAL Santos, Bruno F.; Antunes, António P.; Miller, Eric J.

APPENDIX 2 – LINK DATA

Road Start End Length Land Current Solution Link Lable Center Center (km) Factor Network 1 2 3 4 1 A29 153 152 18.0 2.0 Projected road 4-lane FW 4-lane FW 4-lane FW 4-lane FW 2 IC6 177 82 45.0 2.0 Projected road Projected road Fast 2-lane Projected road Projected road 3 IP3 77 50 19.8 2.0 Projected road Projected road Possible road Projected road Projected road 4 A14-IP3 145 151 4.5 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 5 A14-IP3 151 158 17.7 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 6 A14-IP3 158 44 17.5 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 7 A15-IP6 51 167 18.4 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 8 A17-IC1 157 43 14.8 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 9 A17-IC1 43 44 33.4 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 10 A17-IC1 44 164 22.1 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 11 A17-IC1 164 165 30.2 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 12 A17-IC1 152 157 16.2 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 13 A19 3 161 9.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 14 A19 10 165 4.6 1.0 Fast 2-lane Fast 2-lane 4-lane FW Fast 2-lane Fast 2-lane 15 A1-IP1 149 147 10.7 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 16 A1-IP1 150 154 17.5 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 17 A1-IP1 145 146 8.1 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 18 A1-IP1 146 148 7.7 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 19 A1-IP1 160 40 23.1 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 20 A1-IP1 147 145 10.5 1.0 4-lane FW 6-lane FW 6-lane FW 4-lane FW 6-lane FW 21 A1-IP1 33 150 10.8 1.0 4-lane FW 6-lane FW 6-lane FW 6-lane FW 6-lane FW 22 A1-IP1 161 160 12.4 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 23 A1-IP1 148 159 27.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 24 A1-IP1 159 161 24.1 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 25 A1-IP1 154 149 10.2 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 26 A1-IP1 33 153 11.1 1.0 4-lane FW 6-lane FW 6-lane FW 6-lane FW 6-lane FW 27 A23 58 72 34.9 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 28 A23-IP2 82 187 16.1 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 29 A23-IP2 186 83 25.5 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 30 A23-IP2 83 82 9.7 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 31 A23-IP2 72 5 30.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 32 A23-IP2 5 186 8.8 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 33 A23-IP2 187 6 32.1 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 34 A23-IP6 40 32 9.5 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 35 A23-IP6 32 140 8.5 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 36 A23-IP6 37 58 15.9 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 37 A23-IP6 19 37 14.4 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 38 A23-IP6 140 19 9.2 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 39 A24-IP3 4 65 33.5 2.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 40 A25-IP5 185 168 12.3 2.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 41 A25-IP5 163 137 12.9 2.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 42 A25-IP5 137 28 13.7 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 43 A25-IP5 69 4 14.6 2.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 44 A25-IP5 4 185 14.1 2.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW Note: Solution 1 – weighted travel costs, Gini Index, and fuel consumption; Solution 2 – weighted accessibility, Gini Index, and fuel consumption; Solution 3 – average speed, Gini Index, and fuel consumption; Solution 4 – consumer surplus, Gini Index, and fuel consumption. FW = “Freeway”.

12th WCTR, July 11-15, 2010 – Lisbon, Portugal

24 A MULTI-OBJECTIVE STUDY ON THE IMPROVEMENT OF THE SECONDARY ROAD NETWORK OF THE CENTRO REGION OF PORTUGAL Santos, Bruno F.; Antunes, António P.; Miller, Eric J.

Road Start End Length Land Current Solution Link Lable Center Center (km) Factor Network 1 2 3 4 45 A25-IP5 168 163 5.0 2.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 46 A25-IP5 84 80 10.7 2.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 47 A25-IP5 189 84 7.9 2.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 48 A25-IP5 6 189 13.4 2.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 49 A25-IP5 195 6 21.4 2.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 50 A25-IP5 80 69 21.6 2.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 51 A25-IP5 28 150 4.6 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 52 A25-IP5 152 2 13.1 2.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 53 A25-IP5 152 150 7.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 54 A8-IC1 169 170 11.8 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 55 A8-IC1 165 169 21.7 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 56 A8-IC1 170 53 13.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 57 A8-IC1 53 51 4.6 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 58 A8-IC1 56 54 21.2 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 59 A8-IC1 51 54 14.2 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 60 A8-IC36 165 3 5.3 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 61 IC12 174 93 5.6 2.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 62 IC12 93 61 10.1 2.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 63 IC13 21 140 16.5 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 64 IC13 7 140 3.2 1.0 Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 65 IC13 139 23 10.3 1.0 Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 66 IC2 141 1 7.7 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 67 IC2 26 141 12.7 1.0 Fast 2-lane Fast 2-lane Fast 2-lane 4-lane FW Fast 2-lane 68 IC2 155 39 12.9 1.0 Fast 2-lane 4-lane FW Fast 2-lane Fast 2-lane Fast 2-lane 69 IC2 31 155 10.0 1.0 Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 70 IC2 27 29 10.7 1.0 Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 71 IC2 29 28 14.0 1.0 Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 72 IC2 167 166 30.8 1.0 Fast 2-lane 4-lane FW 4-lane FW 4-lane FW 4-lane FW 73 IC2 1 31 13.1 1.0 Fast 2-lane Fast 2-lane Fast 2-lane 4-lane FW 4-lane FW 74 IC2 26 27 9.8 1.0 Fast 2-lane Fast 2-lane Fast 2-lane 4-lane FW 4-lane FW 75 IC2 39 3 30.7 1.0 Fast 2-lane 4-lane FW 4-lane FW 4-lane FW 4-lane FW 76 IC2 166 45 7.6 1.0 Fast 2-lane 4-lane FW 4-lane FW 4-lane FW 4-lane FW 77 IC2 45 3 12.9 1.0 Fast 2-lane 4-lane FW 4-lane FW 4-lane FW 4-lane FW 78 IC2 167 48 38.3 1.0 Fast 2-lane 6-lane FW 6-lane FW 6-lane FW 6-lane FW 79 IC6 171 178 13.7 2.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 80 IC8 159 39 4.1 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 81 IC8 12 20 8.5 2.0 Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 82 IC8 194 75 4.4 2.0 Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 83 IC8 25 139 6.7 1.0 Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 84 IC8 164 159 12.8 1.0 Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 85 IC8 20 194 10.9 2.0 Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 86 IC8 66 72 17.5 2.0 Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 87 IC8 75 66 20.5 2.0 Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 88 IC8 139 12 12.2 2.0 Fast 2-lane Fast 2-lane 4-lane FW Fast 2-lane Fast 2-lane Note: Solution 1 – weighted travel costs, Gini Index, and fuel consumption; Solution 2 – weighted accessibility, Gini Index, and fuel consumption; Solution 3 – average speed, Gini Index, and fuel consumption; Solution 4 – consumer surplus, Gini Index, and fuel consumption. FW = “Freeway”.

(Continued)

12th WCTR, July 11-15, 2010 – Lisbon, Portugal

25 A MULTI-OBJECTIVE STUDY ON THE IMPROVEMENT OF THE SECONDARY ROAD NETWORK OF THE CENTRO REGION OF PORTUGAL Santos, Bruno F.; Antunes, António P.; Miller, Eric J.

Road Start End Length Land Current Solution Link Lable Center Center (km) Factor Network 1 2 3 4 89 IC8 39 25 20.6 1.0 Fast 2-lane Fast 2-lane 4-lane FW 4-lane FW Fast 2-lane 90 IC9 169 47 5.9 1.0 Slow 2-lane Slow 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 91 IC9 11 169 5.0 1.0 Slow 2-lane Slow 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 92 IC9 47 166 5.8 1.0 Slow 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 93 IP3 141 145 3.3 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 94 IP3 162 171 6.4 2.0 Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 95 IP3 171 15 5.2 2.0 Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 96 IP3 61 162 8.9 2.0 Fast 2-lane 4-lane FW Fast 2-lane Fast 2-lane Fast 2-lane 97 IP3 77 61 16.2 2.0 Fast 2-lane Fast 2-lane Fast 2-lane 4-lane FW Fast 2-lane 98 IP3 4 77 22.8 2.0 Fast 2-lane 4-lane FW Fast 2-lane Fast 2-lane Fast 2-lane 99 IP3 15 141 19.7 2.0 Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 100 IP6 14 51 14.7 1.0 Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 101 N1 148 31 2.8 1.0 Slow 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 102 N102-IP2 87 189 14.9 2.0 Slow 2-lane 4-lane FW Fast 2-lane Slow 2-lane Fast 2-lane 103 N102-IP2 190 87 18.1 1.0 Slow 2-lane Fast 2-lane Fast 2-lane Slow 2-lane Fast 2-lane 104 N109 2 42 11.0 2.0 Slow 2-lane Slow 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 105 N109-5 33 9 10.9 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Fast 2-lane Fast 2-lane 106 N110 15 1 24.2 2.0 Slow 2-lane 4-lane FW Fast 2-lane Slow 2-lane Fast 2-lane 107 N110-IC13 138 21 6.8 2.0 Slow 2-lane Fast 2-lane 4-lane FW Fast 2-lane 4-lane FW 108 N110-IC13 23 143 12.2 1.0 Slow 2-lane Fast 2-lane Fast 2-lane 4-lane FW Fast 2-lane 109 N110-IC13 24 138 21.0 2.0 Slow 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 4-lane FW 110 N110-IC3 143 31 6.2 1.0 Slow 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 111 N112 180 181 8.0 2.0 Slow 2-lane Fast 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 112 N112 183 62 18.1 2.0 Slow 2-lane Fast 2-lane Slow 2-lane Fast 2-lane Slow 2-lane 113 N112 181 5 27.5 2.0 Slow 2-lane Fast 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 114 N112 62 180 17.2 2.0 Slow 2-lane Fast 2-lane Slow 2-lane Fast 2-lane Slow 2-lane 115 N16 34 85 4.9 2.0 Slow 2-lane Slow 2-lane Fast 2-lane Slow 2-lane Slow 2-lane 116 N16 81 85 8.4 2.0 Slow 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 117 N17 135 55 3.3 2.0 Slow 2-lane 4-lane FW Slow 2-lane Fast 2-lane Fast 2-lane 118 N17 55 178 17.7 2.0 Slow 2-lane 4-lane FW Slow 2-lane Slow 2-lane Slow 2-lane 119 N17 1 136 14.1 2.0 Slow 2-lane Fast 2-lane 4-lane FW 4-lane FW Fast 2-lane 120 N17 136 135 4.0 2.0 Slow 2-lane 4-lane FW Fast 2-lane Fast 2-lane Fast 2-lane 121 N17-1 23 18 10.3 2.0 Slow 2-lane Slow 2-lane Fast 2-lane Slow 2-lane Fast 2-lane 122 N17-1 18 136 11.9 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Fast 2-lane Slow 2-lane 123 N17-IC6 175 177 6.0 2.0 Slow 2-lane Fast 2-lane Fast 2-lane Slow 2-lane Fast 2-lane 124 N17-IC6 178 175 15.7 2.0 Slow 2-lane 4-lane FW Slow 2-lane Slow 2-lane Slow 2-lane 125 N17-IC7 76 73 12.4 2.0 Slow 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 126 N17-IC7 73 67 15.0 2.0 Slow 2-lane Fast 2-lane Fast 2-lane Slow 2-lane Fast 2-lane 127 N17-IC7 177 67 6.3 2.0 Slow 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 128 N17-IC7 84 76 23.7 2.0 Slow 2-lane Fast 2-lane Fast 2-lane Slow 2-lane Fast 2-lane 129 N18-3 187 191 18.1 1.0 Slow 2-lane Fast 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 130 N2 183 179 8.8 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 131 N2 55 63 12.9 2.0 Slow 2-lane Fast 2-lane Slow 2-lane 4-lane FW Slow 2-lane 132 N2 75 49 17.8 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Note: Solution 1 – weighted travel costs, Gini Index, and fuel consumption; Solution 2 – weighted accessibility, Gini Index, and fuel consumption; Solution 3 – average speed, Gini Index, and fuel consumption; Solution 4 – consumer surplus, Gini Index, and fuel consumption. FW = “Freeway”.

(Continued)

12th WCTR, July 11-15, 2010 – Lisbon, Portugal

26 A MULTI-OBJECTIVE STUDY ON THE IMPROVEMENT OF THE SECONDARY ROAD NETWORK OF THE CENTRO REGION OF PORTUGAL Santos, Bruno F.; Antunes, António P.; Miller, Eric J.

Road Start End Length Land Current Solution Link Lable Center Center (km) Factor Network 1 2 3 4 133 N2 16 37 5.8 1.0 Slow 2-lane Slow 2-lane 4-lane FW Slow 2-lane Slow 2-lane 134 N2 15 55 13.8 2.0 Slow 2-lane Slow 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 135 N2 49 16 18.3 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 136 N2 183 63 12.3 2.0 Slow 2-lane Fast 2-lane Slow 2-lane Fast 2-lane Slow 2-lane 137 N221 94 92 18.0 1.0 Slow 2-lane 4-lane FW Fast 2-lane Slow 2-lane Slow 2-lane 138 N221 6 92 28.1 2.0 Slow 2-lane 4-lane FW Fast 2-lane Fast 2-lane Slow 2-lane 139 N224-2 8 153 5.7 2.0 Slow 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 140 N225 65 71 19.8 1.0 Slow 2-lane Slow 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 141 N226 87 92 25.5 1.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 142 N226-IC26 184 87 25.3 1.0 Slow 2-lane Fast 2-lane Fast 2-lane Slow 2-lane Slow 2-lane 143 N228 50 162 6.2 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 144 N228 185 172 13.0 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 145 N228 172 50 17.5 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 146 N228 81 65 25.3 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Fast 2-lane 147 N229 4 70 18.8 2.0 Slow 2-lane 4-lane FW 4-lane FW 4-lane FW Fast 2-lane 148 N229 79 184 4.3 1.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 149 N229 70 79 20.8 2.0 Slow 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Slow 2-lane 150 N230 77 93 13.5 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 151 N230 172 77 6.5 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Fast 2-lane 152 N230 174 176 7.9 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 153 N230 156 172 11.0 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Fast 2-lane Fast 2-lane 154 N230 176 67 14.7 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 155 N230 29 156 28.3 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 156 N230-IC6 177 82 53.3 2.0 Slow 2-lane Fast 2-lane Slow 2-lane Slow 2-lane Fast 2-lane 157 N231-2 68 176 19.7 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 158 N231-IC37 68 73 17.4 2.0 Slow 2-lane Slow 2-lane Fast 2-lane 4-lane FW Fast 2-lane 159 N231-IC37 4 68 19.9 2.0 Slow 2-lane Slow 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 160 N232 76 78 25.8 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 161 N232 76 69 18.9 2.0 Slow 2-lane Slow 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 162 N233 91 191 12.6 1.0 Slow 2-lane Fast 2-lane Slow 2-lane Fast 2-lane Slow 2-lane 163 N233 90 191 19.3 1.0 Slow 2-lane Fast 2-lane Slow 2-lane Slow 2-lane Fast 2-lane 164 N233 6 91 26.6 1.0 Slow 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 165 N234 147 36 8.3 1.0 Slow 2-lane Slow 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 166 N234 26 147 4.3 1.0 Slow 2-lane 4-lane FW 4-lane FW Fast 2-lane Fast 2-lane 167 N234 26 50 19.7 2.0 Slow 2-lane Slow 2-lane Slow 2-lane 4-lane FW Fast 2-lane 168 N234 43 36 14.0 1.0 Slow 2-lane Slow 2-lane Slow 2-lane Fast 2-lane Fast 2-lane 169 N234-1 36 151 8.9 1.0 Slow 2-lane Slow 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 170 N234-6 61 60 11.6 2.0 Slow 2-lane Fast 2-lane Fast 2-lane 4-lane FW Fast 2-lane 171 N234-IC12 68 174 14.4 2.0 Slow 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 172 N234-IC12 69 68 13.0 2.0 Slow 2-lane Slow 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 173 N235 30 154 6.7 1.0 Slow 2-lane Slow 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 174 N235 154 2 14.7 1.0 Slow 2-lane Fast 2-lane 4-lane FW 4-lane FW 4-lane FW 175 N235 27 30 6.9 1.0 Slow 2-lane Slow 2-lane Slow 2-lane Fast 2-lane Fast 2-lane 176 N236 57 135 5.9 2.0 Slow 2-lane Slow 2-lane 4-lane FW Fast 2-lane Fast 2-lane Note: Solution 1 – weighted travel costs, Gini Index, and fuel consumption; Solution 2 – weighted accessibility, Gini Index, and fuel consumption; Solution 3 – average speed, Gini Index, and fuel consumption; Solution 4 – consumer surplus, Gini Index, and fuel consumption. FW = “Freeway”.

(Continued)

12th WCTR, July 11-15, 2010 – Lisbon, Portugal

27 A MULTI-OBJECTIVE STUDY ON THE IMPROVEMENT OF THE SECONDARY ROAD NETWORK OF THE CENTRO REGION OF PORTUGAL Santos, Bruno F.; Antunes, António P.; Miller, Eric J.

Road Start End Length Land Current Solution Link Lable Center Center (km) Factor Network 1 2 3 4 177 N236 46 179 16.8 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 178 N236-1 46 12 9.1 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 179 N238 17 138 7.7 2.0 Slow 2-lane Fast 2-lane 4-lane FW 4-lane FW Fast 2-lane 180 N238 83 180 36.4 2.0 Slow 2-lane 4-lane FW Slow 2-lane Fast 2-lane Slow 2-lane 181 N238 75 64 20.8 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Fast 2-lane Slow 2-lane 182 N238 17 194 24.6 2.0 Slow 2-lane Fast 2-lane Fast 2-lane 4-lane FW Fast 2-lane 183 N238 181 64 23.7 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 184 N238-IC35 137 22 9.2 2.0 Slow 2-lane Fast 2-lane Slow 2-lane Fast 2-lane Fast 2-lane 185 N239-IC31 188 192 16.4 1.0 Slow 2-lane Slow 2-lane Slow 2-lane Fast 2-lane Fast 2-lane 186 N239-IC31 186 188 14.8 1.0 Slow 2-lane Slow 2-lane Fast 2-lane 4-lane FW Fast 2-lane 187 N240 5 89 33.4 1.0 Slow 2-lane 4-lane FW Slow 2-lane Slow 2-lane Slow 2-lane 188 N241-1 66 193 20.3 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 189 N243 45 40 27.0 1.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 190 N244 58 193 19.8 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 191 N248-IC11 52 48 17.0 1.0 Slow 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 192 N248-IC11 56 52 12.4 1.0 Slow 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 193 N324 196 195 6.3 1.0 Slow 2-lane Fast 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 194 N324 190 92 24.0 1.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 195 N324 190 88 10.4 1.0 Slow 2-lane Fast 2-lane Fast 2-lane Slow 2-lane Fast 2-lane 196 N324 92 196 15.4 1.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 197 N324 91 195 32.1 1.0 Slow 2-lane Fast 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 198 N329 71 70 10.7 2.0 Slow 2-lane Fast 2-lane 4-lane FW Slow 2-lane Slow 2-lane 199 N329 74 69 8.7 2.0 Slow 2-lane Slow 2-lane 4-lane FW Slow 2-lane Fast 2-lane 200 N329 70 74 10.2 2.0 Slow 2-lane Slow 2-lane Fast 2-lane Slow 2-lane Slow 2-lane 201 N330 79 80 27.5 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 202 N330 80 76 15.3 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 203 N332 59 94 18.5 1.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 204 N332 192 90 14.8 1.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Fast 2-lane 205 N332 196 59 9.7 1.0 Slow 2-lane Fast 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 206 N333 154 29 7.6 1.0 Slow 2-lane Slow 2-lane Fast 2-lane Fast 2-lane 4-lane FW 207 N333 42 157 4.1 1.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Fast 2-lane 208 N333 157 154 7.4 1.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Fast 2-lane 209 N333 85 168 5.4 2.0 Slow 2-lane Fast 2-lane Fast 2-lane Slow 2-lane Fast 2-lane 210 N333-2 163 156 12.2 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Fast 2-lane Fast 2-lane 211 N333-3 163 34 6.0 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 212 N334-IC12 149 27 5.4 1.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 213 N334-IC12 43 149 17.7 1.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 214 N334-IC12 50 61 10.8 2.0 Slow 2-lane Slow 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 215 N334-IC12 27 50 23.0 2.0 Slow 2-lane Slow 2-lane Fast 2-lane Slow 2-lane 4-lane FW 216 N337 60 175 5.6 2.0 Slow 2-lane Slow 2-lane Fast 2-lane Slow 2-lane Fast 2-lane 217 N337 176 60 12.9 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 218 N337 4 174 26.4 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 219 N338 78 182 11.9 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 220 N339 182 82 18.0 2.0 Slow 2-lane Slow 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Note: Solution 1 – weighted travel costs, Gini Index, and fuel consumption; Solution 2 – weighted accessibility, Gini Index, and fuel consumption; Solution 3 – average speed, Gini Index, and fuel consumption; Solution 4 – consumer surplus, Gini Index, and fuel consumption. FW = “Freeway”.

(Continued)

12th WCTR, July 11-15, 2010 – Lisbon, Portugal

28 A MULTI-OBJECTIVE STUDY ON THE IMPROVEMENT OF THE SECONDARY ROAD NETWORK OF THE CENTRO REGION OF PORTUGAL Santos, Bruno F.; Antunes, António P.; Miller, Eric J.

Road Start End Length Land Current Solution Link Lable Center Center (km) Factor Network 1 2 3 4 221 N339 73 182 26.1 2.0 Slow 2-lane Slow 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 222 N341 146 1 5.4 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 223 N341 158 41 6.1 1.0 Slow 2-lane Fast 2-lane Fast 2-lane Slow 2-lane Fast 2-lane 224 N341 41 146 15.4 1.0 Slow 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 225 N342 38 31 12.2 1.0 Slow 2-lane Slow 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 226 N342 31 18 18.4 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Fast 2-lane 227 N342 18 57 9.0 2.0 Slow 2-lane Slow 2-lane Fast 2-lane Slow 2-lane Fast 2-lane 228 N342 63 57 11.3 2.0 Slow 2-lane Slow 2-lane Fast 2-lane Slow 2-lane Fast 2-lane 229 N342 86 177 19.5 2.0 Slow 2-lane Slow 2-lane 4-lane FW Slow 2-lane Fast 2-lane 230 N342 164 38 17.0 1.0 Slow 2-lane Slow 2-lane Slow 2-lane 4-lane FW Slow 2-lane 231 N342 86 63 10.8 2.0 Slow 2-lane Slow 2-lane Fast 2-lane Slow 2-lane Fast 2-lane 232 N342-1 38 41 12.6 1.0 Slow 2-lane Slow 2-lane Slow 2-lane Fast 2-lane Fast 2-lane 233 N342-4 178 86 10.2 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Fast 2-lane Slow 2-lane 234 N344 179 173 12.6 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 235 N344 173 62 6.0 1.0 Slow 2-lane Slow 2-lane Slow 2-lane Fast 2-lane Slow 2-lane 236 N345 78 187 25.0 1.0 Slow 2-lane Fast 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 237 N346 90 83 29.5 1.0 Slow 2-lane 4-lane FW Fast 2-lane Slow 2-lane Fast 2-lane 238 N347 23 46 20.8 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 239 N347 31 41 14.0 1.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 240 N347-1 143 144 10.8 1.0 Slow 2-lane Slow 2-lane Slow 2-lane Fast 2-lane Slow 2-lane 241 N348 17 49 15.4 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 242 N348 155 144 11.6 1.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 243 N348 144 25 7.3 1.0 Slow 2-lane Slow 2-lane Slow 2-lane Fast 2-lane Fast 2-lane 244 N348 38 155 6.2 1.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Fast 2-lane 245 N348 25 142 7.9 1.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 246 N348 49 193 11.1 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 247 N349 35 32 20.4 1.0 Slow 2-lane Fast 2-lane Fast 2-lane Slow 2-lane Fast 2-lane 248 N350 142 24 6.6 1.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 249 N350 161 142 33.1 1.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 250 N350 24 139 11.9 2.0 Slow 2-lane Fast 2-lane Fast 2-lane 4-lane FW Fast 2-lane 251 N351 64 66 26.9 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 252 N351 173 64 13.6 2.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 253 N353 89 188 12.1 1.0 Slow 2-lane Slow 2-lane Fast 2-lane Fast 2-lane Slow 2-lane 254 N356 24 35 35.1 1.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Fast 2-lane 255 N356-IC9 35 160 13.1 1.0 Slow 2-lane Fast 2-lane 4-lane FW 4-lane FW Fast 2-lane 256 N356-IC9 160 45 16.2 1.0 Slow 2-lane Slow 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 257 N356-IC9 21 35 20.5 1.0 Slow 2-lane Slow 2-lane 4-lane FW Fast 2-lane Fast 2-lane 258 N361 54 13 14.6 1.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 259 N8 170 47 13.9 1.0 Slow 2-lane Slow 2-lane Fast 2-lane Slow 2-lane Fast 2-lane 260 N8-IC11 14 13 10.2 1.0 Slow 2-lane Slow 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 261 N8-IC11 13 56 18.0 1.0 Slow 2-lane Fast 2-lane Fast 2-lane 4-lane FW Fast 2-lane 262 A1_E 118 133 235.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 263 A10 48 102 62.0 1.0 6-lane FW 6-lane FW 6-lane FW 6-lane FW 6-lane FW 264 A10-IC2 48 104 35.7 1.0 6-lane FW 6-lane FW 6-lane FW 6-lane FW 6-lane FW Note: Solution 1 – weighted travel costs, Gini Index, and fuel consumption; Solution 2 – weighted accessibility, Gini Index, and fuel consumption; Solution 3 – average speed, Gini Index, and fuel consumption; Solution 4 – consumer surplus, Gini Index, and fuel consumption. FW = “Freeway”.

(Continued)

12th WCTR, July 11-15, 2010 – Lisbon, Portugal

29 A MULTI-OBJECTIVE STUDY ON THE IMPROVEMENT OF THE SECONDARY ROAD NETWORK OF THE CENTRO REGION OF PORTUGAL Santos, Bruno F.; Antunes, António P.; Miller, Eric J.

Road Start End Length Land Current Solution Link Lable Center Center (km) Factor Network 1 2 3 4 265 A11 95 96 33.0 1.0 6-lane FW 6-lane FW 6-lane FW 6-lane FW 6-lane FW 266 A11 96 97 20.0 1.0 6-lane FW 6-lane FW 6-lane FW 6-lane FW 6-lane FW 267 A11_E 105 116 171.0 1.0 Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 268 A13 102 101 88.0 1.0 6-lane FW 6-lane FW 6-lane FW 6-lane FW 6-lane FW 269 A15-IP6 167 101 21.8 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 270 A1-IP1 40 101 26.6 1.0 6-lane FW 6-lane FW 6-lane FW 6-lane FW 6-lane FW 271 A1-IP1 48 101 39.2 1.0 6-lane FW 6-lane FW 6-lane FW 6-lane FW 6-lane FW 272 A1-IP1 153 99 50.0 1.0 6-lane FW 6-lane FW 6-lane FW 6-lane FW 6-lane FW 273 A2_E 118 121 304.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 274 A2_E 121 123 301.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 275 A24 65 109 53.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 276 A24 109 129 67.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 277 A28-A11 103 97 56.0 1.0 6-lane FW 6-lane FW 6-lane FW 6-lane FW 6-lane FW 278 A28-IC1 103 99 68.0 1.0 6-lane FW 6-lane FW 6-lane FW 6-lane FW 6-lane FW 279 A28-IC1 103 127 58.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 280 A2-IP1 104 102 53.0 1.0 6-lane FW 6-lane FW 6-lane FW 6-lane FW 6-lane FW 281 A2-IP1 102 100 57.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 282 A2-IP1 125 106 105.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 283 A2-IP1 125 100 41.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 284 A3 97 127 71.0 1.0 6-lane FW 6-lane FW 6-lane FW 6-lane FW 6-lane FW 285 A3_E 118 134 164.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 286 A3_E 134 122 181.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 287 A30_E 134 119 229.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 288 A31_E 119 122 208.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 289 A3-IP1 99 97 51.0 1.0 6-lane FW 6-lane FW 6-lane FW 6-lane FW 6-lane FW 290 A4 99 95 15.5 1.0 6-lane FW 6-lane FW 6-lane FW 6-lane FW 6-lane FW 291 A4 95 109 56.6 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 292 A4 109 130 119.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 293 A42_E 111 118 68.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 294 A49_E 106 115 186.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 295 A4-IP4 130 105 41.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 296 A5_E 107 113 156.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 297 A5_E 118 128 171.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 298 A51_E 118 132 203.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 299 A52_E 129 131 162.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 300 A55_E 127 112 91.8 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 301 A5-A66_E 128 113 144.0 1.0 6-lane FW 6-lane FW 6-lane FW 6-lane FW 6-lane FW 302 A6_E 131 116 80.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 303 A6_E 116 118 174.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 304 A60_E 116 132 79.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 305 A62_E 195 132 143.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 306 A62_E 116 133 145.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 307 A66_E 132 128 119.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 308 A66_E 113 115 184.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW Note: Solution 1 – weighted travel costs, Gini Index, and fuel consumption; Solution 2 – weighted accessibility, Gini Index, and fuel consumption; Solution 3 – average speed, Gini Index, and fuel consumption; Solution 4 – consumer surplus, Gini Index, and fuel consumption. FW = “Freeway”.

(Continued)

12th WCTR, July 11-15, 2010 – Lisbon, Portugal

30 A MULTI-OBJECTIVE STUDY ON THE IMPROVEMENT OF THE SECONDARY ROAD NETWORK OF THE CENTRO REGION OF PORTUGAL Santos, Bruno F.; Antunes, António P.; Miller, Eric J.

Road Start End Length Land Current Solution Link Lable Center Center (km) Factor Network 1 2 3 4 309 A66_E 131 114 178.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 310 A67_E 117 133 181.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 311 A68_E 121 120 241.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 312 A6-IP7 102 107 60.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 313 A8_E 112 114 328.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 314 A8_E 114 117 193.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 315 A8_E 117 120 154.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 316 A8-IC1 56 104 59.0 1.0 6-lane FW 6-lane FW 6-lane FW 6-lane FW 6-lane FW 317 A92_E 115 119 527.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 318 AP1_E 120 124 910.0 1.0 6-lane FW 6-lane FW 6-lane FW 6-lane FW 6-lane FW 319 AP1_E 133 120 101.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 320 AP53_E 112 129 147.7 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 321 AP7_E 122 123 342.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 322 EX108 192 128 145.0 1.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane 323 IC2 98 28 23.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 324 IC2-A1 98 153 21.0 1.0 4-lane FW 4-lane FW 4-lane FW 4-lane FW 4-lane FW 325 IC35 126 22 30.0 1.0 Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 326 IC35 126 95 66.0 1.0 Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 327 IP2 88 130 93.0 1.0 Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 328 IP2 110 72 78.0 1.0 Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 329 IP2 107 110 84.0 1.0 Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 330 IP2 108 107 80.0 1.0 Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 331 IP2 125 108 60.0 1.0 Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 332 IP8 100 108 69.0 1.0 Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 333 N227 126 98 8.0 1.0 Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane Fast 2-lane 334 N925_E 131 105 129.0 1.0 Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Slow 2-lane Note: Solution 1 – weighted travel costs, Gini Index, and fuel consumption; Solution 2 – weighted accessibility, Gini Index, and fuel consumption; Solution 3 – average speed, Gini Index, and fuel consumption; Solution 4 – consumer surplus, Gini Index, and fuel consumption. FW = “Freeway”.

(Continued)

12th WCTR, July 11-15, 2010 – Lisbon, Portugal

31