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Manuscript submitted to ASCE Journal of Urban Planning and Development – Final version

A simulation-based analysis of road pricing prospects for Athens, Greece Georgios Sarlas1a, Vasileia Papathanasopoulou2, and Constantinos Antoniou3*

1 Rural and Surveying Engineer, M.Sc. in Transport Systems, KTH Royal Institute of Technology, Stockholm, Sweden, email: [email protected] a Work done while at National Technical University of Athens, Greece

2 Rural and Surveying Engineer, Laboratory of Transportation Engineering, School of Rural and Surveying Engineering, National Technical University of Athens, Zografou 15780, Athens, Greece. email: [email protected] 3 Assistant Professor, Laboratory of Transportation Engineering, School of Rural and Surveying Engineering, National Technical University of Athens, Zografou 15780, Athens, Greece. email:[email protected] * corresponding author

Journal of Urban Planning and Development. Submitted June 19, 2012; accepted January 23, 2013; posted ahead of print January 25, 2013. doi:10.1061/(ASCE)UP.1943-5444.0000145

Copyright 2013 by the American Society of Civil Engineers

J. Urban Plann. Dev.

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A simulation-based analysis of road pricing prospects for Athens, Greece

ABSTRACT The objective of this paper is to explore the potential future use of road pricing for Athens, Greece. Road pricing schemes are surveyed, focusing primarily on the European experience, and are used to contribute to the discussion regarding the possible future adoption of a road pricing scheme in Athens, Greece. The main features and problems of the transportation landscape in Athens are outlined and results from a SWOT analysis are presented. The key issues, required conditions and options associated with a possible future implementation of an urban road pricing scheme in the Athens metropolitan area are presented. The analysis is validated through a series of face-to-face interviews that were undertaken with a panel of key experts. The selected parameters of possible future road pricing schemes in Athens are simulated and various measures of effectiveness are collected and analysed. Sensitivity analysis of the demand levels that would result from the deployment of the system is also performed, while the elasticities of the demand in response to the system are also calculated. The results indicate that while currently there are more direct instruments to address traffic congestion, in the future urban tolls may provide a useful complementary tool towards a sustainable transportation system for Athens. Keywords: road pricing; transport management; simulation-based analysis;

Athens, Greece




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INTRODUCTION Countries and cities around the world are struggling with ever increasing road congestion. Demand is exceeding available supply while additional infrastructure without certain traffic restrictions is controversial because it may meet the needs for unrestricted demand. The expansion of road network is expensive and raises environmental concerns, too (Ueckermann and Venter, 2008). Consequently it is vital that attention is paid to policies to promote more efficient use of the existing infrastructure. This recognition combined with technological advances has raised the interest in road pricing. Furthermore, the successful implementation of this policy in many countries has indicated that direct user charging can lead to the reduction of congestion and the desired mode shift, while overcoming political and social-economic concerns (Ueckermann and Venter, 2008). Naturally, it is recognised that the starting point of the investigation of whether road pricing would be a suitable solution for a given area is the analysis of the specific problems that need to be solved. Priority should be given to conventional tools and solutions and only when these cannot provide the desired outcome, should road pricing be attempted.

The term “road pricing” is used for the direct charge, which is imposed on motorists for driving on a particular roadway or in a particular area (Victoria Transport Policy Institute, 2010). The main social costs, which are caused by vehicles, consist of accident externalities, environmental pollution, road damage, and congestion (Newbery, 1990). Guo and Hsu (2010) explore how external cost pricing (in a rail transit system) can impact household travel decisions and provide environmental benefits in urban areas. Road pricing aims to internalise these costs, forcing road users to pay for them. The average cost of a trip includes only the vehicle operating costs (fuel, vehicle wear and tear, possible parking fees) and the time spent by the driver/ user. The marginal cost exceeds the average cost by the marginal external cost of congestion, which equals the increase in travel time of other drivers/ users attributable to extra congestion (Parry, 2008). When road users do not pay for the social costs, they do not take them into consideration when making a decision for a trip (Newbery, 1990). The main objectives for implementing a road pricing scheme, as recognised among others by Lindberg (1995), Tsolakis and Naude (2006) and Eliasson (2010), may include the need for raising revenues, reducing traffic emissions and addressing traffic congestion or a combination of these. Levinson (2010) provides a synthesis of equity aspects related to congestion pricing, while Mahendra (2007) studies the feasibility of congestion pricing for four Latin American cities, with an emphasis on equity aspects.




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Various types of road pricing can be implemented, which generally include single road or single lane pricing, cordon pricing, area pricing, distance based pricing, time based pricing, externality based pricing (mainly referred to congestion and pollution costs) and occupancy based pricing (Victoria Transport Policy Institute, 2010; Jones, 2000; Harsman et al., 2000). The most important and common technologies used in road pricing schemes are Automatic Number Plate Recognition (ANPR), Dedicated Short Range Communications (DSRC), Global Navigation Satellite Systems (GNSS) (Blythe, 2005). These technologies are constantly developing and their cost is reduced, contributing to the implementation of road pricing schemes.

Large-scale road pricing projects have been implemented in several countries, including the U.K., France, Norway, Sweden, Germany, Switzerland, Singapore and Australia over the past three decades. Additionally, road pricing has been analysed and evaluated through numerous studies in nearly all EU member countries, in Southeast Asia, Canada, Australia and New Zealand (Bhatt et al., 2008). The CURACAO project (www.curacaoproject.eu, CURACAO, 2009) provided relevant results, concluding that the cities that have implemented urban road user charging have all achieved reductions in traffic entering the charging zone in the range of 14% to 23%, which is unlikely to be approached by other available transport policy instruments. Road pricing is one element of a larger program of initiatives working collectively to address traffic congestion and its impacts in these cities. The policy, innovative and drastic, was one of a wider package of demand management measures such as the development of a reliable and convenient public transport system, construction of cycle routes, public information campaign, parking fee, gas taxes (Bhatt et al., 2008; Transport for London, 2004; Rotaris et al., 2010; Santos, 2005). One of the most representative projects is the original Central London Congestion Charging Scheme that has been in operation since February 2003, was extended westwards in February 2007 and continues to work well. During hours of operation, drivers are required to pay a standard daily charge of £8 (increased from £5 in July 2005) to travel within the Congestion Charging zone, subject to a number of discounts and exemptions. The scheme produces net revenues which are mainly allocated to improvements in public transportation (80%) and at a lower percentage to borough plans, roads and bridges, road safety, environment, walking and cycling (Transport for London, 2008). Following its introduction in 2003, congestion was substantially reduced within the zone and traffic entering the zone was reduced by around 20 per cent after the first year of implementation and cars entering the zone during charging hours reduced by 33%. In the inner road the traffic increased lightly by 4% (Transport




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for London, 2004). The case study of London provides a hopeful view for application of such a scheme in Athens. The existence of many access points to both city centers makes the implementation of the scheme difficult, but evidently not impossible. However, the aforementioned cities, including London, facilitate a higher level of public transport (e.g. more extended metro network) and feature better use of alternative means of transport (e.g. walking and cycling) than Greece. Secondly, these schemes are constantly monitored and adapt to the circumstances of each period. Therefore, changes such as the extension of congestion zone (London), replacement of technology (Stockholm, Singapore) or adaptations in toll rate and operating days, hours (Singapore) could constantly be examined. Both London and Stockholm had years of public debate about congestion charging before the political decision to implement was made (Small and Gomez-Ibanez, 1998; Smeed, 1964). This means that a road pricing scheme needs the required time of preparation and maturation. Moreover, the use of exemptions as well as using revenues to fund transit helped to address issues of equity. Although the charging areas in London and Stockholm included the city centre and its commercial area, negative impacts in economy were limited (Transport for London, 2004; City of Stockholm, 2009; CURACAO, 2009). Finally, these examples from the international experience indicate that obstacles such as political and public acceptance could be overcome and the targeted outcomes be achieved, if there are clearly defined and well-understood policy goals (Taylor and Kalauskas, 2010).

This paper conducts a research into the applicability of an urban road pricing scheme in Athens, the design and impacts of its implementation. The city of Athens in Greece has been taken as a case study on the grounds that it confronts heavy congestion problems. The 2001 White Paper for transport by the European Commission (EU, 2001) proposed the limitation of driving time to 48 hours for commercial vehicle drivers on average per week and promoted intelligent charging systems to contribute into rational use of road infrastructure. In that sense, Greece should examine the opportunity to comply with these principles leading to a more sustainable transportation system. The paper draws conclusions on the international experience of road pricing schemes, assesses prospects for Athens and describes aspects of experts about the subject. Furthermore, a simulation-based assessment of the key selected parameters of possible future road pricing schemes in Athens is performed and various measures of effectiveness are collected and analysed. Sensitivity analysis of the demand levels that would result from the deployment of the system is also




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performed, while the elasticities of the demand in response to the system are also calculated.

PROSPECTS OF CONGESTION PRICING APPLICATION IN ATHENS

Athens is the capital city of Greece with a population of over 4 million people in a metropolitan area of 427 km2 and constitutes the cultural, historic and financial centre of the country. The population represents about a third of the population of Greece with a very high density, especially in the city centre, where 25% of the city population lives and 30% works (Milakis et al., 2008). The city has a typical monocentric form and there is a strong mixture of land uses. The region is located in a basin and surrounded by mountains and sea that constitute a natural barrier to growth and consequently impose constraints on the transport system.

The transport situation in Athens is characterised by dramatic growth in the number of vehicles since the 1990s. The car ownership index reached a conservative figure for the Athens area close to 0.6 vehicles per capita in 2007, considering that sources bring the 2010 estimate to 0.65 (Akritidis, 2007). Nowadays, traffic congestion is severe and traffic generates a lot of air pollution. During the 1980s Athens introduced a restriction in auto use aimed at reducing automotive emissions; vehicles were allowed to the CBD (the inner ring) only on odd or even days based on the last digit of the license plate. The outcome was an increase in car ownership, where the second car was commonly older, polluting more than the new cars (Giaoutzi and Damianides, 1990). Such a response was not anticipated and ultimately rendered the policy counter-productive.

Transport planning has lagged behind actual development in Athens and as a result the city’s road network is inadequate in serving the traffic demand. The limited room for further development (due to the surrounding sea and mountains) makes the development of further infrastructure challenging. The Attica Tollway built before the Athens Olympic Games provides a partial ring offering some temporary relief, as it is currently distributing more than 300.000 vehicles daily (Halkias and Tyrogianni, 2008). Loudon et al. (2010) discuss the importance of incorporating congestion pricing and related measures into transportation planning. Urban pricing remains an unpopular measure in Greece and therefore the public acceptance would be difficult. Rentziou et al. (2011) used data from a questionnaire survey to specify and estimate a multivariate probit model aiming to provide insight on the factors that shape public perception with respect to road pricing and other traffic management measures. Regarding the attitude towards tolls, Greeks are willing to pay tolls if they enjoy a comfortable and fast trip, as it




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is proved by the Attica Tollway, the first urban tollway in Greece. Particularly, reactions are likely to arise from owners of shops due to their fear of exclusion and reduction in their profits. Social acceptability depends on the use of the revenues (Santos and Rojey, 2002; Buxbaum, 2009) and the availability of alternative means of transport (Bonsall and Kelly, 2005).

An analysis using the SWOT (strengths-weaknesses-opportunities-threats) approach (presented in Papathanasopoulou and Antoniou, 2011) suggests that initially there are many problems to be overcome, but after the appropriate solutions are identified, the implementation of congestion charging may provide significant benefits and opportunities. Overall, it appears that the prevailing scenario for a possible future road pricing scheme in Athens could include the following parameters:

Type of road pricing: cordon pricing, charging a fixed amount per entry, or charging according to mileage, location and congestion of the travel time

Charging area: inner ring and possible future extension to a larger area Days of operation: weekdays and special occasions Hours of operation: same as current downtown access restriction based

on license plate 07.00-20.00 (Monday-Thursday) and 07.00-15.00 (Friday) Toll rate: 2-6€ per entry, either fixed or dynamic Technology: DSRC and complementary ANPR, with the possibility of

future graduation to GNSS-based technologies Exemptions: emergency vehicles, public transport, two-wheelers Discounts: low-emission vehicles (e.g. emitting less than 120grCO2/km),

vehicles registered to residents of pricing zone

An important aspect of the acceptance of road pricing schemes is the allocation of the collected revenue (DeCorla-Souza and Luskin, 2009). The first step towards this analysis is an estimation of the expected revenues, which is a very difficult and uncertain exercise, considering that we are talking about a possible future implementation with unknown parameters. Having said that, a rough calculation has been attempted, considering that currently about 340.000 vehicles enter the inner ring in a typical day (OASA, 2006). In order to better cope with the uncertainty, a sensitivity analysis has been performed (Papathanasopoulou, 2010), estimating the total revenue using toll rates between 1 and 10Euro per entry and resulting decrease in incoming vehicles in the charging zone up to 50%. Analysis of the international experience and the Athens transportation network led to the selection of 10 to 25% (based also in the range of 14-23% for other European cities according to CURACAO, 2009) as the




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more reasonable reduction in traffic and this range was combined with the toll rate range of 2-6Euro that was previously determined to estimate possible expected revenues between 80€ million and 280€ million per year. To provide an indication of how these funds could be invested in Athens, it is indicated that this could be used for the construction of (i) 0.7-2.5km of metro lines per year or (ii) the construction of 7-25km of tram lines per year or (iii) the construction of 75-260km of bicycle lanes and 100K-360K m2 of pavement renewal per year. Based on this assessment, one can also expect that the initial implementation cost of the system could be offset by the revenues in a few years. Clearly, a more detailed assessment of the cost is needed before any practical decisions can be made.

INPUT FROM EXPERT PANEL

The parameters of the simulation have been refined through a series of thirteen structured interviews with transport experts. This content analysis of qualitative data is a recognised method of primary research and analysis (Patton, 1994). The disadvantages associated with the small sample size and the open-ended nature of the questions are in practice compensated for by the richness of the resulting data (Rye, 1997). The origin of this method lies in the Delphi procedure (Dalkey and Helmer, 1963), based on the knowledge and experience of several experts, providing a group judgment for subject matters where there is a lack of precision. The method has proved to be an efficient way to achieve a ‘‘best estimate’’ in uncertain contexts (Prins and Wind, 1993). A model based analysis and an expert based analysis could be employed, so as experts could assess critically the reliability of model outcomes and suggest modifications to model use, inputs, or results (Langen et al., 2012). In this study, local experts’ opinions were taken into account in order to support, dismiss or modify authors’ suggestions and to highlight the important issues to be considered in designing a simulation process and its inputs. Expert judgment can be useful for forecasting, decision-making, and assessing risks. Although, some studies have practically implemented this approach (e.g. Verhagen et al., 2011), experts’ ideas could be uncertain or conflicting and sometimes not as objective as they should. Typically, some uncertainty is expected in prediction of future events and informed decision-making should take this into consideration (Skinner, 2001).

The objective of these interviews is to depict opinion of the experts about the potential implementation of congestion pricing in Athens, validate the conclusions and extract new ideas. In particular, the experts were asked about the purpose




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and the preconditions of such a scheme, the way that a road pricing scheme could be implemented in Athens and its possible effects on the city. The choice of respondents has been guided by the fact that this is an exploratory research, and that a small, carefully selected number of experts in the field would provide better information for an in-depth analysis of the reality than a representative sample of general responders (Gar, 2007).

In order to select the experts, interviewees were sought from four main categories: experience with tolling (two managers of an urban tollway in Athens), transport planning (consultant with over 40 years of transport planning for the Athens metropolitan area), public transport (two managers of the Athens Metro planning authority and two managers of the Athens Urban Transport Organisation, OASA), public service (one manager of the traffic and parking regulations section of the Municipality of Athens, two managers from the Traffic Management Centre of the Hellenic Ministry of Public Works) and academics specialised on transport planning (three professors from two Schools of the National Technical University of Athens).

Generally, the respondents who are familiar with tolling applications are optimistic about the benefits that can be obtained and they do not put emphasis on the need for the improvement of the public transport, while experts from the public transit sector argue that important improvement and extension of public transport should precede such a development. Experts from the two latter categories (administration and academics) are hesitant about the expected effectiveness of the measure. According to the majority of experts (10/13), a road pricing scheme could be implemented in Athens in the future, providing that other measures be enforced first. The rest of the interviewees (2 experts dealing with public transport, 1 academic) do not exclude the potential implementation of this measure, but they set many preconditions for it and they would suggest it if all other measures fail to address congestion. Generally, the respondents dealing with tolling applications put emphasis on the public consultation about the role of road pricing and scheme planning rather than the need for the improvement of the public transport that could be achieved gradually using the revenues of the scheme. On the other hand, experts dealing with public transport set as precondition for the scheme the significant improvement of available public transport. All the other experts agree that the improvement (including expansion) of public transport is a high-priority requirement, while public consultation about the need for constraint in car use is also important, a finding stressed also by Pulichino and Coughlin (2005). In an analysis of 11 case studies, Pulichino and Coughlin (2005) found that opposition to such initiatives does not come from the general public, but from specific interest groups. A rigorous discussion on the




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topic of road space reallocation to public transport is provided by González-Guzmán and Robusté (2011). Experts from the other categories (administration and academics) think that other measures such as parking regulations or alternative means of transport may be more effective and easily applicable.

<PLEASE INSERT TABLE 1 ABOUT HERE>

In terms of implementation timeframe, the majority of experts indicate that approximately 4-6 years are required for the necessary conditions to be available. The experts clarify that the implementation timeline will be influenced by the political and social will, as well as the future technological, political and economical conditions. The main suggestions of the experts regarding the possible implementation of a congestion pricing scheme in Athens are outlined in Table 1.

SIMULATION-BASED IMPACT ASSESSMENT The objective of this section is to assess the impacts of a hypothetical future application of the congestion charging policies discussed in the previous sections for access to some parts of the Athens road network. The output of this study may be used to support a decision making process and support the debate on the specific topic with some concrete evidence. Travel demand forecasting software that can effectively evaluate the impacts of pricing on individuals’ travel patterns is lacking (Lee et al., 2010) and there are not many such studies in the literature. A macroscopic analysis of Athens’s road network is conducted by means of a dynamic simulation system, and the analysis of suitable quantitative indices facilitates an overall assessment of the impacts. The main results of the simulation-based application under two different charging schemes are presented, and are accompanied by a sensitivity analysis. Elasticities of the traffic volumes entering the charging zone –with respect to the considered congestion pricing rates– are calculated as well, in order to enable a comparison with other currently implemented congestion charging schemes around the world. Case study setup The study is conducted using the VISUM software by PTV AG (version 9.5). VISUM is a program for computer-aided transport planning that serves as a planning and analysis tool for transportation systems. The transport model of




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Athens was created for the needs of the study and the investigation of the impacts of the examined road charging scenarios. The model was created from scratch, since there is no previous, available model considered to be sufficient for the purposes of the present study, and comprises a network model (the supply model), a demand model, and a traffic assignment model. Only the trips conducted by private cars are taken into consideration, as only these are directly affected by the congestion charging schemes. The following information may be helpful to put this into perspective. 50% of the daily trips use motorised private modes, the modal share for public transport is 40% and less than 10% are on foot (OASA, 2006). A large fleet of rather inexpensive taxis (15.000-20.000 according to estimates by HITE, 2008) provides a popular way of transport moving constantly and loading the road network (HITE, 2008). The trip purpose distribution is dominated by work trips (40%) supplemented with approximately 12% shopping, 9% leisure, 6% education, 15% personal and 7% social (OASA, 2006). The network model integrates the relevant supply data of the transport system and mainly consists of the road network and the corresponding traffic zones. It is constructed through the application of digitizing techniques over satellite pictures in Google Earth, and the use of GIS software in order to integrate the required data. For the scope of this work the network model accommodates the arterial roads of the entire network of the Attica Region (Figure 1), with a particular focus in additional detail (i.e. including collectors) in the network around and inside the city center, as it is the main area affected by the imposed charging scheme. The created network consists of 2,500 links. The criterion based on which the selection of the included links in the model is made, is to create a representative overall network model of Athens, in terms of simulating adequately the actual route choice decisions that drivers are facing. In addition, traffic zones are created so that they correspond to the Athens metro area municipalities (with the exception of the Municipality of Athens, each of the 7 districts of which is coded into an individual zone).

<PLEASE INSERT FIGURE 1 ABOUT HERE> The demand model contains the data related to the travel demand and it is described through time-dependent origin-destination (O-D) matrices. The demand model is developed based on the O-D matrix for car trips, as estimated by the Athens Metro Development Study (ATTIKO METRO SA, 1998). The O-D matrix is adjusted to reflect recent (year 2010) demand levels, as well as




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structural changes in the demand patterns. This is achieved by taking into consideration residential development as well as land-use changes, economic growth, and development of major infrastructure projects (e.g. the construction of Attica Toll-way and the new Athens International Airport). More information on these patterns can be found in Antoniou et al. (2011). Assumptions based on empirical data (including traffic flow profiles from selected sensors in the network) have been made regarding the temporal distribution of the demand, in order to determine the time-dependent OD matrices. This is achieved by assigning different proportions of the total daily demand, in the one-hour time intervals of the specified analysis period. Naturally, simplifications have been made in the present study, mostly to overcome the lack of some types of data. More specifically, it is assumed that there is a conservation of demand for car trips before and after the implementation; the mode choice and the probability of cancelling a trip are not taken into account. Consequently, only the route choice is affected by the new direct cost of congestion charging, while the demand for car trips remains the same. However, in order to account for the impact of this, a sensitivity analysis has been performed, considering different levels of reduction in traffic demand, in response to the introduction of the congestion charging scheme. Given the fact that the model is simplified in some aspects, the results are considered appropriate to be taken into consideration primarily as qualitative indicators, capturing the general trends, suitable for the purposes of a preliminary assessment analysis. A detailed, large-scale study would be required prior to the planning decisions of a congestion charging scheme in Athens. A Dynamic User Equilibrium traffic assignment model is used in order to simulate the traffic conditions that result from the interactions between the demand and supply models. This model is aimed at solving the Within-Day Dynamic Traffic Assignment (WDDTA) on road networks, addressing explicitly the simulation of queue spillovers. It is based on a macroscopic and continuous-time formulation Dynamic User Equilibrium. (More information regarding the model can be found in the manual of VISUM.) A critical component of the traffic assignment model is the route choice model. The route choice model is used in order to assign the demand into the different routes of the network, based on the generalized cost of each alternative path. The generalized cost is a term denoting the cost that each traveler has to bear by choosing a certain route in order to reach his/ her destination. The generalized cost comprehends the effect and the cost of various attributes of the trips. In




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order to use it in the analysis of the demand, all its terms should be expressed in monetary units. The main attributes of the trips contributing to the generalized cost are: a) the cost of the duration of the trip (travel time), b) the costs of using the chosen mode (fuel, ticket, wear and tear, etc.), c) the cost of gaining access to a certain part of the road network (tolls), and d) the cost of schedule delay by arriving either in earlier or later moments than the desired ones, in order to decrease travel time or avoid paying tolls. In the present study, the cost of the travel time and the cost of using the chosen mode are merged into one term, denoted as journey time. The formula used for the calculation of the generalized cost is shown below. gc = α ∗ toll + β ∗ journeytime[h] + γ ∗ SDE[h] + δ ∗ SDL[h] (1) The values of the parameters in the formula are set based on the findings of other available studies. More specifically, parameter α corresponds to the effect of the direct charge for using a certain part of the road network. Since the direct charge is already expressed in monetary units, the parameter α takes a value equal to 1. The parameter of the travel time β denotes the monetary cost of the time that a traveler spends inside the car (in-vehicle time) and represents the value of time and is set to 6 € per hour (Antoniou et al, 2007). According to Lam (2004), the values of the other two parameters of the formula, depend on the value of time. Particularly, the cost of arriving earlier is equal to 0.6 times the value of time, while in the case of arriving later, the corresponding parameter δ is equal to 2.4 times the value of time. The significant difference between these two parameters can be justified by the fact that arriving earlier than the desirable time to the destination has a smaller cost compared to arriving later. Additionally to the above, the maximum possible scheduled delay, is set equal to 30 minutes, allowing subsequently the travelers to shift the beginning of their trip in order to experience less cost (due to congestion or to avoid the charge). However, the anticipated generalized cost of each trip might be perceived differently by each traveler. This differentiation in the perception is strongly related to the value of time that each traveler assigns to his trip, based on his socio-economic characteristics and also the purpose of the trip. Nevertheless, for practical reasons, in the present study the route choice model adopted a deterministic view. The analysis time period is defined to accommodate the desired outcomes of the study. A morning period of a typical weekday, 6:00 to 11:30, is considered sufficient for the purposes of investigating the impacts of a congestion charging scheme in Athens, both during the peak hours period (7:30 to 9:30) but also




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earlier and later. Additionally, a warm-up period of 2 hours –prior to the analysis period– is used to allow the network to reach a fully-loaded state that would be representative of the expected traffic conditions at the beginning of the analysis period. The convergence criterion for the termination of the simulation procedure is defined based on the assignment results. In particular, a 1% difference between the results of consecutive iterations is assumed sufficient for convergence. The calibration of the model is conducted by taking into account available traffic counts at various points dispersed around the study network. More precisely, these traffic counts concern average traffic flows and average speed measurement on the main roads of the city during the morning peak hour. The calibration followed a “trial-and-error” approach.

Experimental design Different scenarios are developed for the simulation-based impact assessment of a hypothetical congestion charging scheme in Athens. All scenarios correspond to the base year 2010. The base scenario, against which the congestion charging scenarios are compared, corresponds to an initial demand level for transport with private cars, representative of the current situation. It pertains to a “do-nothing” scenario, i.e. without congestion charging. For the congestion charging scenarios in the network of Athens, the cordon ring approach is chosen as the type of road pricing, where a fixed amount per entry is charged. The hours of operation are set between 08:00 and 20:00 during weekdays, with the exception of Friday where they are set between 08:00 and 15:00. The boundaries of the charging zone coincide with the existing boundaries of the inner ring. The technology that could be used for the charging (DSRC and complementary ANPR) would not create any additional impedance at the entrance points. The scenarios consider two different charging levels, keeping the remaining characteristics of the scenarios constant. The considered toll rates are 3 and 5 Euros. As mentioned before, a conservation of demand for car trips before and after the implementation is assumed. However, it is expected that the additional direct cost of congestion pricing is going to lead gradually to decreased demand for car trips, at least towards and from the charging area. Since the attraction of car trips is expected to decrease, it is safe to draw the conclusion that production of trips is going to be affected in a similar way. As a consequence, a number of travelers will choose either to shift to an alternative mode (for instance public transport), or to cancel/ postpone their trip. This is expected especially for the case of trips related to activities such as shopping, leisure and recreation. A sensitivity




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analysis is conducted to assess the impact of decreased demand for car trips after the implementation of the congestion scheme, in order to capture such effects in a more realistic way. A reduction of the trips conducted by car with destination (or origin) within the charging zone area, by 10 and 20 per cent respectively, has been considered for each chosen toll rate to account for trips that would switch to public transport or other modes or be cancelled/rescheduled. These reduction levels are applied accordingly to update the corresponding demand of all the affected O-D pairs proportionally. The results presented below are calculated in comparison to the do-nothing scenario results. Finally, an estimation of the elasticity of demand for entering the charging zone, with respect to the charge, is given.

Results Table 2 summarizes the results in a tabular form. The results of the simulation-based application are given per time interval and link, and are aggregated in order to extract macroscopic results for the corresponding areas of interest – i.e. the total network, as well as the portions of the network outside and inside the charging zone. The main focus is on the evaluation of the effectiveness of the congestion charging and the achievement of the goal for traffic reduction in the CBD area. For this reason, the percent change in the number of vehicles entering the charging area, based on the simulation-based application results, is observed. Further results are presented regarding the implications of the measure in the areas of interest. The total number of vehicle kilometers driven and the total number of vehicle hours spent in the different areas of the network are chosen as representative indicators. These indicators are considered sufficient to exhibit the general trends for a preliminary assessment of the impacts of the hypothetical congestion charging scheme in Athens.

Impact of congestion charge

The base scenario is representative of the current situation. A rather interesting finding that reveals the current congestion problem occurring in the CBD area, is the fact that even though the vehicle-kilometers driven inside the charging area correspond to only 0.01% of the total vehicle kilometers driven in the whole network, the vehicle hours spent correspond to almost 15% of the total vehicle hours. This finding can provide a quantitative view of the current situation and the existing congestion problems. Based on the above, it becomes evident that the total vehicle kilometers driven outside this area consist the main and critical component of the total vehicle kilometers driven in the whole network, and thus it is expected that those two will have the same variations through the different




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developed scenarios. The number of the vehicles that enter the CBD area, according to the simulation results, is found to be equal to around 65,000 vehicles during the analysis period (i.e. morning period only). This result is considered reasonable with respect to the estimation of 340,000 vehicles with four or more wheels crossing into the inner ring area in a typical day (OASA, 2006), since the simulation results refer to the morning period solely (and not the entire day), where only private cars are taken into consideration, not including taxis, buses, vans, lorries etc. The impact of the congestion charge is evaluated next. At first, the effectiveness of the congestion charging scheme is evaluated by calculating the percent change in the number of vehicles entering the charging zone. According to the simulation results, when a congestion charging level of 3 Euros is introduced, there is a reduction of almost 38% to the number of entering vehicles. This reduction leads to a significant improvement of the traffic conditions inside the CBD area. The number of vehicle kilometers driven inside the CBD decreases by more than 24%, while the vehicle hours spent decrease by almost 59%, reflecting the improvement in the traffic conditions which results in higher level of service as well. However, as expected, a number of drivers choose not to bear the new, additional direct cost of congestion charging, and thus choose to follow alternatives routes, further loading the network outside the CBD area. The results indicate an increase of the driven vehicle-kilometers both outside the charging zone, and also in total, by almost 4%, while the number of hours spent is increased by more than 14% outside the charging zone, and 3.5% in the entire network. In the case of 5 Euros’ toll rate, the number of vehicles entering the charging area decreases further, resulting to a total decrease of more than 44%. As expected, the traffic conditions inside the area are improved even more compared to the previous scenario, since fewer vehicles are choosing to enter the charging area. This improvement is reflected in the aggregated results for the charging area where vehicle kilometers decrease by more than 30%, while the vehicle hours decrease by more than 66%. However, the traffic conditions outside the charging area are becoming more severe, resulting to an increase of more than 4% of vehicle kilometers and almost 20% of the corresponding vehicle hours. The results for the whole network show a more than 4% increase in the travelled vehicle kilometers and a more than 7% increase in the vehicle hours spent. These findings indicate that the traffic conditions in the whole network are becoming worse, compared to the previous charging scenario, as a result of the much smaller number of vehicles using the road network enclosed by the




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charging area. The roads outside the CBD area have to serve additional demand while their capacity, based on the simulation results, appears not to be sufficient, creating congestion problems and inefficiency in new locations around the network.

Sensitivity analysis scenarios

The sensitivity analysis scenarios are developed in order to assess the impact of the demand decrease in the results of both charging schemes investigated earlier. As expected, the number of entering vehicles in the charging area decreases as the demand decreases while the vehicle hours spent and the vehicle kilometers driven decrease too, both inside and outside the charging area. The results of the 3 Euros toll rate scenario show that the vehicle hours spent in total decrease by 0.85% and 3.3% respectively, for the investigated reduction levels. The vehicle kilometers remain increased compared to the base scenario. However this increase is significantly lower than before resulting to an increase of slightly more than 2% and 1% respectively. These results indicate that the overall performance of the network has been improved, resulting in less congestion even that the driven distance has been increased, compared to the base scenario. This can be justified by the fact that part of the demand has been diverted onto links that had residual capacity, increasing the efficiency of the transport system in general. In the case of the 5 Euros charging scenarios, the number of entering vehicles decreases further, reaching almost a reduction level of 50%. However, the results of the simulation present that both the vehicle kilometers and the vehicle hours still remain increased, in comparison with the base case scenario. The corresponding results for all scenarios mentioned above are summarized in Table 2.

<PLEASE INSERT TABLE 2 ABOUT HERE> A rough evaluation of the sensitivity analysis process and its findings is accomplished by a comparison with the findings from existing congestion charging schemes. One of the main assumptions of this study is that there is a conservation of demand for car trips before and after the implementation. This conservation forces all drivers, who choose not to enter the charging area, to follow alternatives routes around this area. However, this is not representative of the reality. The key findings of London’s scheme (TfL, 2008) suggest that 50% to 60% of the drivers that no longer cross into the charging area, have shifted to public transport, while only 30% of them is actually diverted around. In the present study, given the fact that only private car trips are taken into account, this change in the mode choice is partially achieved by proportionally reducing the




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demand to and from the charging area. However, the imposed levels of demand reductions correspond to only 4% to 12% of the drivers that shift to public transport, percentages rather low compared to London’s case. Based on the above, along with the exhibited trends due to the demand reduction (Table 2), it can be expected that the vehicle kilometers driven and the vehicle hours spent outside the charging area, are going to be significantly further decreased in reality, probably reaching the levels of the base scenario, or even lower. This can contribute to the evaluation of congestion charging policy in Athens as an overall beneficial policy, where its benefits can be distributed among the society and not at the expense of certain groups.

Elasticity analysis

Based on the results presented above, a calculation of the elasticity of traffic volume entering the charging zone -with respect to the charge level- is also performed. Since the demand function of the traffic volume is not available in closed form but derives from the simulation process, available information from the simulation procedure at a small number of toll rate (cost) - traffic volume (demand) pairs is utilized to compute the midpoint arc-elasticity of traffic volume. The midpoint arc-elasticity formula uses the arithmetic averages of the values of traffic demand (Q1,Q2) and charge level (C1,C2), before and after the change as the basis for percentage change calculation. E = ( )/( )/ (2)

Consequently, three different categories of elasticity have been calculated for each demand level (current demand, decreased by 10%, and decreased by 20%). The first two categories (i.e. from 0 to 3€ and 0 to 5€, respectively) reveal the effect that the implementation of congestion charging with flat rate is estimated to have on the traffic volume entering the charging area, while the third category is chosen to illustrate the marginal effect that a time-varying rate (changing from 3€ to 5€) could have. The calculated values of elasticity are presented in Table 3.


In the case of the first two categories, the elasticity of the traffic volume is found to be equal to -0.23 and -0.28 respectively, reflecting how sensitive the traffic volume is with respect to the implementation of a fixed toll rate. A further increase in the elasticity is observed as a consequence of the decrease of the demand level. The last category of calculated elasticity is used to demonstrate




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the effect of a time-varying toll, changing from 3 to 5 Euros. As it can be observed, the elasticity is close to zero, lying between -0.05 to -0.07, indicating that the demand is not very responsive to a change in toll rate after a certain point. The similar elasticity of the cases of moving from 0€ to 3€ and moving from 0€ to 5€ supports the argument that the elasticity for a hypothetical move from 3€ to 5€ is expected to be small (in absolute value). This can be interpreted as follows: drivers have a certain (small) aversion towards the imposition of the toll, but moving from 3€ to 5€ does not lead to a considerable reduction in traffic. This finding can be useful in the case where a further decrease of the traffic volume is desired (i.e. during peak hour). A rough comparison is attempted between the estimated elasticity of traffic volume with existing similar congestion charging schemes around the world where data is available. More specifically, the cases of Singapore, Norway, and London are taken into account for the comparison. In Table 4, the results mentioned above are presented.

As it can be observed in Table 4, the estimated elasticity in the case of Athens (fixed toll rate) is consistent with the findings from Norway’s, London’s, and Singapore’s schemes. Especially in the case of Oslo’s and Trondheim’s toll ring, the calculated elasticity lies in the same range. In the case of London, and more specifically for the Central Charging Zone (CGZ) and the introduction of the £5 charge, the estimated elasticity is found to be higher than the calculated one for the Athens scheme. Nevertheless, it should be mentioned that in London’s case, the elasticity is calculated in response to the cost of fuel and the charge, taking into account only the chargeable trips, excluding those that are not eligible to the full charges (i.e. residents, alternative fuel vehicles etc). Estimates of the corresponding elasticity for all trips (Evans, 2008) lie between -0.29 and -0.39 and can be regarded as more compatible with the estimates of this research. In the case of Singapore, the comparison reflects that there is a slight difference. However, it should be mentioned that in this case the elasticity is calculated with respect to the Electronic Road Pricing (ERP) rates which vary significantly during the day, depending on the congestion level (Olszewski and Xie, 2002). Hence, it is considered more appropriate to make a comparison with the elasticity calculated for studying the impacts of the time-varying rates on the traffic volume in Athens. In this case, the estimated elasticity is found to lie between -0.05 and -0.07, a range which is much more consistent with the result from Singapore scheme (-0.106). A comparison of the aforementioned elasticity with London’s estimated elasticity in response to the increase of the charge from £5 to £8 reveals also a consistency. According to Evans (2008), in the case of the chargeable car trips only, the elasticity is estimated to be -0.16. However, when all the car trips are accounted, the elasticity lies in the range between -0.08 and -




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0.10 which is more consistent compared to this research estimations.


DISCUSSION

Summarising the main findings from the international experience, the analysis of the transportation landscape of Athens and the experts’ opinions, one concludes that a congestion pricing scheme could not be implemented in Athens in the near future (i.e. in the next 5 years) and before the fulfillment of certain pre-requisite conditions. The main purpose of a congestion pricing application in Athens should be the increase in use of public transport. The opportunity for possible redistribution of urban space and implementation of urban regeneration projects would also appear, meaning that many inertial and other barriers might appear. The main obstacles are the absence of necessary legislation and political will, the implementation costs of any scheme (especially considering the current fiscal difficulties Greece is facing), the still developing public transport system, the lack of enforcement of illegal parking and the issues of political and public acceptance. Depending on the scheme that will be considered for implementation, the implications of the traffic conditions surrounding the toll zone area should be further investigated, perhaps with the use of simulation. Social inequalities and unpredictable effects in the economy should be also investigated ex-ante, so that the possible implementation can be accompanied by appropriate supporting measures to mitigate these impacts. In the present study, a transport model of Athens is created and used to assess the impacts of a hypothetic congestion charging scheme in Athens’s network through simulation. The study is simplified in some aspects to overcome limitations associated with data availability. Therefore, the results are considered appropriate to be taken into consideration only as qualitative indicators, being able to exhibit the general trends. The simulation results reveal a reduction between 38-50% of the car traffic entering the charging zone, caused by the implementation of congestion charging policy. In addition, if this percentage is adjusted for the number of vehicles that are exempt to the charge, then perhaps this reduction will be slightly decreased but still remain significant. The importance of congestion charging policy being part of a wider plan that involves more policies, capable to ease up the side-effects, becomes evident through the conducted sensitivity analysis. Attempting an investigation of the winners and losers of this policy, the corresponding results indicate that if the congestion




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pricing policy is implemented, with the addition of other supporting policies like improvement of the public transport network, it can lead to a more efficient transport system where the traffic in the center is reduced significantly and the living conditions consequently improve due to fewer emissions, but not in expense of other areas or social groups. In the short term, it is expected that drivers with lower income and/or value of time might experience increase of their travel time and subsequently of their cost, either by choosing longer routes or by changing modes. However, in the long term and given the stated reinvestment of the generated revenue into the improvement of the public transport network, gradually a higher number of users can be expected to benefit. The reinvestment of the generated revenues is vital since it can spread the benefits to all social groups and overcome issues regarding equity matters that arise, and also contribute to the increase of the acceptability. The calculated elasticities and the comparison with elasticity calculations from existing schemes, indicate that similarities with already implemented schemes can be found. Although each scheme has its particularities, the useful obtained knowledge related to each one might be able to be applied in the case of Athens, contributing to the development of guidelines for the most efficient implementation. Some of the inputs to this analysis (such as the value of time) is taken from relatively older studies and therefore may be somewhat outdated. It would be advisable to perform a sensitivity analysis of the impact of changes in these inputs. The desired outcome of this study is not to decide whether or not congestion pricing should be implemented, but to study the impacts of a hypothetic congestion charging scheme and provide some concrete evidence to the discussion of improving the transport system of Athens. Other alternatives, less costly in both monetary and social terms, which can be used to maximize the capacity of the existing network, are certainly preferable over the congestion pricing policy. In addition to the above, the explicit economic crisis that Greece undergoes should not be overseen. At the present time, is highly likely that if congestion charging policy is considered for implementation, it will be perceived as another austerity measure, resulting in low acceptability levels. However, the results advocate to the fact that congestion pricing is an alternative that could be taken into consideration if other -less drastic- alternatives fail to meet the reduction goals set and result in a more efficient transport system. A key question, however, resulting from this work is whether restrictive measures such as road pricing should be applied, or whether there are other ways to make




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citizens aware of the actual cost of their travel options and thus limit voluntarily the use of private vehicles, switching to alternative means of transport. The latter seems to be the fairest solution, though perhaps utopian. Therefore, it is suggested to focus on making citizens aware of why congestion pricing is a realistic solution and laying the basis for adoption of congestion pricing in combination with adequately improved transit systems and reliable alternative means of transport, when the conditions are appropriate.

ACKNOWLEDGEMENT

The authors would like to acknowledge the significant contribution of late Prof. Petros Vythoulkas, who was sadly taken far too early.

REFERENCES

Akritidis, Ch. (2007), Application of the controlled parking system in the Municipality of Athens. Presentation in the Workshop: Use-based charging for road infrastructure, organised by the Hellenic Institute of Transportation Engineers, Athens, March 15, 2007 (in Greek).

Antoniou C., Matsoukis E. and Roussi P. (2007). A Methodology for the Estimation of Value-of–Time Using State–of–the–Art Econometric Models, Journal of Public Transportation, Vol. 10, No. 3.

Antoniou, C., B. Psarianos and W. Brilon (2011). Induced traffic prediction inaccuracies as a source of traffic forecasting failure. Transportation Letters: The International Journal of Transportation Research, Vol. 3, Iss. 4, pp. 253-264.

Attiko Metro (1998). Athens Metro Development Study, Attiko Metro SA, Department of Research and Planning, Athens (in Greek)

Bhatt, K., T. Higgins, J. T. Berg (2008), Lessons Learned From International Experience in Congestion Pricing, Final Report, prepared for the U.S. Department of Transportation, Federal Highway Administration.

Blythe, P.T. (2005), Congestion charging: Technical options for the delivery of future UK policy, Transportation Research Part A 39, pp. 571–587.

Bonsall, P. and C. Kelly (2005), Road User Charging and Social Exclusion: The Impact of Congestion Charges on At-risk Groups, Transport Policy, Vol. 12, No. 5, pp. 406-418.

Buxbaum, J. N. (2009), Congestion Pricing Basics, TR News, Number 263.




Dow

nloa

ded

from

asc

elib

rary

.org

by

Uni

vers

ity O

f Sy

dney

on

05/0

5/13

. Cop

yrig

ht A

SCE

. For

per

sona

l use

onl

y; a

ll ri

ghts

res

erve

d.

Accep

ted M

anus

cript

Not Cop

yedit

ed

23

City of Stockholm (2009), Traffic Administration, Analysis of traffic in Stockholm– with special focus on the effects of the congestion tax 2005–2008.

CURACAO (2009), Coordination of Urban Road User Charging Organisational Issues, Deliverable D2: State of the Art Review (FINAL), Work Package 2 / Version 8.4 / 22 May 2009.

Dalkey, N., & Helmer, O. (1963). An experimenta lapplication of the Delphi method to the use of experts. Management Science, 9, 458–467.

DeCorla-Souza, P. And D. Luskin (2009), Revenues from a Statewide Congestion Pricing Program Estimation on a Shoestring Budget. Transportation Research Record: Journal of the Transportation Research Board, No 2115, pp. 37-40.

Ecola, L. and T. Light (2011), Making Congestion Pricing Equitable. Transportation Research Record: Journal of the Transportation Research Board, Νο. 2187, pp. 53-59.

Eliasson, J. (2010), So you’re considering introducing congestion charging? Here’s what you need to know, Discussion Paper No. 2010-4, Prepared for the ITF/OECD Round Table of 4-5 February 2010 on Implementing Congestion Charging.

EU (2001), European transport policy for 2010: time to decide. White Paper COM(2001) 370 final, European Union, Brussels.

Eurostat (2012): EUROSTAT Statistics Database, http://epp.eurostat.ec.europa.eu/portal/page/portal/eurostat/home/ (Accessed, October 8, 2012).

Evans, G. (2008), Demand Elasticities for Car Trips to Central London as revealed by the Central London Congestion Charge, Prepared for the Modeling and Evaluation Team, Transport for London, Policy Analysis Division, September 2008.

Gar, Y. Ng. (2007), Quality of Judicial Organisation and checks and balances. Doctoral thesis, Utrecht University, The Netherlands.

Giaoutzi, M. and Damianides, L. (1990), The Greek transport system and environment. In Transport Policy and the Environment: Six Case Studies, eds J. Brade and K. Button. Earthscan, London.

González-Guzmán, C.A. and F. Robusté (2011). Road Space Reallocation According to Car Congestion Externality. Journal of Urban Planning and Development, Vol. 137, No. 3, pp. 281-290.

Guo, S.-P. and C.-I. Hsu (2010). Impacts of Transportation External Cost Pricing and Transit Fare Reductions on Household Mode/Route Choices and Environmental Improvements. Journal of Urban Planning and Development, Vol. 136, No. 4, pp. 339-348.

Halkias B. and H. Tyrogianni (2008), PPP Projects in Greece: The case of Attica tollway, Route-Roads No. 342.




Dow

nloa

ded

from

asc

elib

rary

.org

by

Uni

vers

ity O

f Sy

dney

on

05/0

5/13

. Cop

yrig

ht A

SCE

. For

per

sona

l use

onl

y; a

ll ri

ghts

res

erve

d.

Accep

ted M

anus

cript

Not Cop

yedit

ed

24

Harsman, B., S. Pädam, and B. Wijkmark (2000), Ways and Means to Increase the Acceptance of Urban Road Pricing. Pricing Measures Acceptance (PRIMA), EC Transport RTD Program.

Jones, P. and Hervik, A. (1992). Restraining car traffic in European cities: An emerging role for road pricing. Transportation Research Part A 26, pp. 133–145.

Jones, P. (2000), Urban Road Pricing: Public Acceptability and Barriers to implementation. Road Pricing, Traffic Congestion and the Environment. In: K. Button and E. Verhoef (Eds.), Edward Elgar, UK, pp. 263-284.

Lam T. (2004). Evaluating Value – Pricing Projects with Both Scheduling and Route Choices, Regional Science and Urban Economics 34, 225 – 240.

Langen P., Meijeren J., Tavasszy L.(2012), Combining Models and Commodity Chain Research for Making Long-Term Projections of Port Throughput: an Application to the Hamburg- Le Havre Range, EJTIR, Issue 12(3), 2012, pp. 310-331.

Lee, K.-S., A.G. Hobeika, H. B. Zhang and H. Jung (2010). Travelers’ Response to Value Pricing: Application of Departure Time Choices to TRANSIMS. Journal of Transportation Engineering, Vol. 136, No. 9, pp. 811-817.

Levinson, D. (2010). Equity Effects of Road Pricing: A Review. Transport Reviews, Vol. 30, No. 1, pp. 33-57, January 2010.

Lindberg, G. (1995), Road pricing: policy and options for the future. In: Johansson, B., Mattson, L.-G. (Eds.). Road Pricing: Theory, Empirical Assessment and Policy, Kluwer, Boston, pp. 205–221.

Loudon, W. R, J. Synn and H. Miller (2010), Consideration of Congestion Pricing and Managed Lanes in Metropolitan Transportation Planning. Transportation Research Record: Journal of the Transportation Research Board, Vol. 2187, pp. 60-67.

Mahendra, A. (2008). Vehicle Restrictions in Four Latin American Cities: Is Congestion Pricing Possible?, Transport Reviews, Vol. 28, No. 1, pp. 105–133, January 2008.

Milakis, D., Vlastos Th. and Barbopoulos N. (2008), Relationships between Urban Form and Travel Behaviour in Athens, Greece. A Comparison with Western European and North American Results, EJTIR, Issue 8(3), September 2008, pp. 201-215, ISSN: 1567-7141.

Newbery, D.M. (1990), Pricing and congestion: economic principles relevant to pricing roads, Oxford Review of Economic Policy, Vol. 6, pp. 22-38.

OASA (2006) Transport Studies for the development of a Transport Master Plan for the Athens Metropolitan Area, Athens Urban Transport Organisation (OASA) (in Greek).

Olszewski, P. and Xie, L. (2002). Traffic demand elasticity with respect to road pricing —some evidence from Singapore. In: Proceedings of International




Dow

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asc

elib

rary

.org

by

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f Sy

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on

05/0

5/13

. Cop

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ht A

SCE

. For

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sona

l use

onl

y; a

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anus

cript

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yedit

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25

Conference on Seamless and Sustainable Transport, Nanyang Technological University, Singapore.

Papathanasopoulou, V. (2010). Assessment of road pricing prospects in Athens, Diploma thesis, School of Rural and Surveying Engineering, National Technical University of Athens, Greece (in Greek).

Papathanasopoulou, V. and C. Antoniou (2011). Assessment of congestion pricing prospects for Athens, Greece. Proceedings of the 90th Annual Meeting of the Transportation Research Board, January 2011, Washington, D.C.

Parry, I. W. H. (2008), “Pricing Urban Congestion”, Discussion paper, Resources for the Future.

Patton, J. (1994), Doing Qualitative Research (Berkeley, CA: Sage). Polak, J. and Meland, S. (1994). An assessment of the effects of the Trondheim

toll ring on travel behaviour and the environment. In: The First World Congress on Applications of Transport Telematics and Intelligent Vehicle Highway Systems, Paris.

Prins, H. H. T., and Wind, J. (1993). Research for nature conservation in Southeast Asia. Biological Conservation, 63(1), 43–46.

PTV (2007). VISUM 9.5 manual, Planung Transport Verkehr AG. Pulichino, M., and Coughlin, J. F. (2005). Introducing transit preferential

treatment: Is a political maverick necessary for public transportation to innovate? Journal of Urban Planning and Development, Vol. 131, No. 2, pp. 79-86.

Rentziou, A., C. Milioti, K. Gkritza and M.G. Karlaftis (2011). Urban Road Pricing: Modeling Public Acceptance. Journal of Urban Planning and Development, Vol. 137, No. 1, pp. 56-64.

Rotaris L., R. Danielis, E. Marcucci, J. Massiani (2010), The urban road pricing scheme to curb pollution in Milan: a preliminary assessment, Transportation Research Part A 44(5) (2010), pp. 359–375.

Rye, T. (1997), The implementation of workplace transport demand management in large organisations. Unpublished PhD Thesis, Nottingham Trent University, Nottingham.

Santos G. (2005), Urban Congestion Charging: A Comparison between London and Singapore, Transport Reviews, 25: 5, pp. 511- 534.

Santos G. and L. Rojey (2002), Distributional impacts of road pricing: the truth behind the myth. Proceedings of the 81st Annual Meeting of the Transportation Research Board, Washington, D.C.

Skinner, D C 2001. Introduction to Decision Analysis. A Practitioner’s Guide to Improving Decision Quality, 2nd edition. Gainesville: Probabilistic Publishing.

Small K.A. and J.A. Gomez-Ibanez (1998), Road Pricing for Congestion




Dow

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on

05/0

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Management: The Transition from Theory to Policy, The University of California Transportation Center University of California at Berkeley, Reprinted from Road Pricing, Traffic Congestion and the Environment Issues of Efficiency and Social Feasibility, K. Button and E. Verhoef (Eds), pp 213-246.

Smeed (1964), Ministry of Transport, Road Pricing: The Economic and Technical Possibilities (London: HMSO).

Taylor, B.D. and R. Kalauskas (2010), Addressing equity in political debates over road pricing. Transportation Research Record: The Journal of the Transportation Research Board, Vol. 2187, pp. 44-52.

Transport for London (TfL) (2004), Central London Congestion Charging, Second annual impacts monitoring report.

Transport for London (TfL) (2008), Central London Congestion Charging, Sixth annual impacts monitoring report 2008-07.

Tsolakis D. and C. Naude (2006), Road Pricing Options for addressing Congestion: Lessons and Possibilities for Australia, ARRB Group, 22nd ARRB Conference – Research into Practice, Canberra Australia, 2006.

Ueckermann T.M. and C. Venter (2008) International experience with road and congestion pricing and options for Johannesburg, Proceedings of the 27th Southern African Transport Conference (SATC 2008).

Verhagen, P., M. van Leusen, B. Ducke, A. Millard & H. Kamermans, 2011. The Bumpy Road to Incorporating Uncertainty in Predictive Modelling. In: Jerem, E., Redö, F. & Szeverényi, V. (eds.): On the Road to Reconstructing the Past. Computer Applications and Quantitative Methods in Archaeology. Proceedings of the 36th International Conference, Budapest, April 2-6, 2008, pp. 301-308.

Victoria Transport Policy Institute (2010), Road Pricing: Congestion Pricing, Value Pricing, Toll Roads and HOT Lanes. Online TDM Encyclopedia, Canada, http://www.vtpi.org/tdm/tdm35.htm.




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List of Tables

TABLE 1 Suggestions of the majority of experts for a potential road pricing scheme in Athens

TABLE 2 Summary of the results of all scenarios

TABLE 3 Calculated elasticities for each demand level

TABLE 4 Comparison of elasticities of traffic volume with respect to toll rate in different cities




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TABLE 1 Suggestions of the majority of experts for a potential road pricing scheme in Athens

Implementation timeline At least after 4-6 years

Main objective Increase in use of public transport

Specific objectives Reduction in traffic congestion and pollution, financing future infrastructure

Toll zone The inner ring or an area slightly larger than the inner ring

Type of road pricing Cordon pricing, variable charging per entry in the restricted area depending on the traffic volume, possibly in combination with other factors such as vehicle occupancy and emitted pollutants

Toll rate Variable, maximum charging per entry during peak hours: approximately 3-5 € (at current prices). It could be influenced by economical factors, such as the price of gas.

Operation days Weekdays and possibly Saturdays

Operation hours Operation hours of inner ring road (possibly with slight differentiations) or dynamic operation around the clock

Technology DSRC and complementary use of ANPR, developments in GPS technology are expected (determinative factors: implementation and operating cost)

Exemptions and discounts

Limited exemptions such as bicycles, public transport, emergency vehicles, vehicles used by disabled people or patients who require frequent treatment, vehicles registered to residents of the toll zone (special discount or free pass for limited entries)

Use of revenues Investment in public transport largely, improvement of alternative means of transport and environmental issues to a small percentage

Accepted Manuscript Not Copyedited




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TABLE 2 Summary of the results of all scenarios

Congestion charging scenario (compared to the base case)

Result Area 3 € 5 € 3€ 10% 3€ 20% 5€ 10% 5€ 20%

Number of

vehicles

entering CBD Inside charging zone -37.91% -44.27% -39.40% -42.52% -47.52% -49.70%

Vehicle

kilometers

driven

Total 3.78% 4.37% 2.07% 1.05% 2.47% 2.29%

Inside charging zone -24.27% -30.80% -26.58% -30.16% -35.92% -37.60%

Outside charging zone 3.78% 4.37% 2.07% 1.05% 2.48% 2.30%

Vehicle hours

spent

Total 3.51% 7.20% -0.85% -3.26% 6.09% 2.20%

Inside charging zone -58.97% -66.20% -64.11% -70.14% -68.44% -72.56%

Outside charging zone 14.23% 19.79% 10.00% 8.21% 18.88% 15.03%





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TABLE 3 Calculated elasticities for each demand level

Demand level Category

0€ - 3€ 0€ - 5€ 3€ - 5€

Initial demand level -0.23 -0.28 -0.05

10% reduction -0.25 -0.31 -0.07

20% reduction -0.27 -0.33 -0.07





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TABLE 4 Comparison of elasticities of traffic volume with respect to toll rate in different cities

Location / scenario Elasticity range Remarks Source

Oslo Toll Ring, Norway -0.22 Cars, substitution effects

considered Jones and Hervik

(1992)

Trondheim Toll Ring, Norway -0.3 Cars, AM peak Polak and Meland

(1994)

Singapore Restricted Zone (ERP, cordon) -0.106 Cars, AM peak

Olszewski and Xie

(2002)

London, U.K., Central Charging Zone (CCZ),

Introduction of £5 charge -0.47

Chargeable car trips only, in response to fuel cost and

charge Evans (2008)

London, U.K., CCZ, increase in the charge from £5 to £8 -0.16

Chargeable car trips only, in response to fuel cost and

charge

Evans (2008) Athens (fixed toll rate) -0.23 to -0.33 Cars, AM peak This research

Athens (marginal effect due to time-varying rate) -0.05 to -0.07 Cars, AM peak This research





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List of figure captions

FIGURE 1 Overview of the digitized road network; main road network (top)

and detailed road network of the city center along with the charging area

(bottom).

Figure Caption List




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