metadata: authors: 0: author: Anas,Alex
1: author: De Sarkar,Sayan
2: author: Abou Zeid,Maya
3: author: Timilsina,Govinda R.
4: author: El Nakat,Ziad Salim
count: Lebanon
docna: 0: docna: Reducing traffic congestion in Beirut : an
            empirical analysis of selected policy options
docty: Policy Research Working Paper
subtopic: Urban Housing and Land Settlements,Rural Roads & Transport,Transport in Urban Areas,Urban Governance and Management,Urban Housing,Urban Transport,Municipal Management and Reform,Transport Services,Intelligent Transport Systems
teratopic: Transport,Communities and Human Settlements,Urban Development
abstracts: cdata!: Beirut, the capital city of Lebanon,
            faces huge traffic congestion, the cost of which is
            estimated to be more than 2 percent of the city's gross
            regional product. Effective policies are needed, based on
            weighing their overall economic cost and benefit to society.
            This study developed an empirical model based on
            microeconomic theory, accounting for production and
            consumption behavior related to transportation in the
            Greater Beirut Area, to simulate various policy
            combinations. A key finding of the study is that individual
            supply-side policies, such as the expansion of roads or
            introduction of a bus rapid transit system, are quite
            effective at reducing traffic congestion while increasing
            economic output and welfare. They also account for most of
            the benefits from implementing policy packages with supply-
            and demand-side measures. The introduction of bus rapid
            transit with expansion of the road system to feed the bus
            rapid transit system reduces congestion by about 16 percent
            and congestion costs by more than 50 percent. This would
            increase Beirut's gross regional product by roughly 2
            percent, and the average social welfare of the residents of
            Beirut by 4 percent. In contrast, demand-side instruments,
            implemented alone, lower gross regional product and welfare
            with limited effects on congestion.
geo_reg_key: 517202,517191
repnme: repnme: Reducing traffic congestion in Beirut : an
            empirical analysis of selected policy options
display_title: Reducing traffic congestion in
            Beirut : an empirical analysis of selected policy options
listing_relative_url: /research/2017/08/27877071/reducing-traffic-congestion-beirut-empirical-analysis-selected-policy-options
topicv3: Transport m1326661 135,Mass Transit m1327372 1726,Land and Housing m1327648 817,Bus Rapid Transit m1582502 4232,Traffic Management m1583399 4231
publishtoextweb_dt: 2017-08-03T00:00:00Z
historic_topic: Transport,Communities and Human Settlements,Urban Development
no_of_pages: 100
author_keys: 0: author_key: 1437821
1: author_key: 940446
2: author_key: 890524
model_extractions: WPS8158 Policy Research Working Paper 8158 Reducing Traffic Congestion in Beirut An Empirical Analysis of Selected Policy Options _Alex Anas Sayan De Sarkar Maya Abou Zeid Govinda Timilsina Ziad Nakat_ Development Research Group Environment and Energy Team August 2017
Policy Research Working Paper 8158

# **Abstract**

Beirut, the capital city of Lebanon, faces huge traffic congestion, the cost of which is estimated to be more than 2 percent of the city’s gross regional product. Effective policies are needed, based on weighing their overall economic cost and benefit to society. This study developed an empirical model based on microeconomic theory, accounting for production and consumption behavior related to transportation in the Greater Beirut Area, to simulate various policy combinations. A key finding of the study is that individual supply-side policies, such as the expansion of roads or introduction of a bus rapid transit system, are quite effective at reducing traffic congestion while increasing economic output and welfare. They also account for most of the benefits from implementing policy packages with supply- and demand-side measures. The introduction of bus rapid transit with expansion of the road system to feed the bus rapid transit system reduces congestion by about 16 percent and congestion costs by more than 50 percent. This would increase Beirut’s gross regional product by roughly 2 percent, and the average social welfare of the residents of Beirut by 4 percent. In contrast, demandside instruments, implemented alone, lower gross regional product and welfare with limited effects on congestion.

This paper is a product of the Environment and Energy Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at gtimilsina@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Reducing Traffic Congestion in Beirut: An Empirical Analysis of Selected Policy Options**<sup>**1**</sup>

Alex Anas, Sayan De Sarkar, Maya Abou Zeid, Govinda Timilsina, Ziad Nakat<sup>2</sup>

**Keywords** : Urbanization, Traffic congestion, Urban transportation management, Beirut

**JEL Classification** : R13, R41

> 1 The author would like to thank Mike Toman and Harris Selod for their valuable comments and suggestions. We acknowledge the financial support from World Bank’s Strategic Research Program (SRP) Trust Fund. The views and interpretations are of author’s and should not be attributed to the World Bank Group.

> 2 Anas, De Sarkar: Department of Economics, State University of New York at Buffalo; Abou Zeid: American University of Beirut, Lebanon; Timilsina: The World Bank, Washington, D.C.; Nakat: World Bank, Lebanon.
# **1. Introduction**

Urban transportation in Beirut, the capital city of Lebanon, where more than 40% of the country’s total population lives, is facing several challenges, including inadequate infrastructure, traffic congestion, local air pollution and road accidents. The fifteen-year civil war between 1975 and 1990 caused significant destruction of the transportation infrastructure and contributed to the deterioration of the public transport system (Diab and Obeid, 2012). Expansion of urban transport capacity is not meeting the speed of population growth and urbanization, the centralization of activity around the capital and more recently the huge refugee influx from the Syrian Arab Republic. Traffic congestion is considered one of Beirut’s most serious urban development problems.

The urban transportation problem is such that up to 70% of travel time in the Greater Beirut Area (GBA) is lost in delays due to traffic congestion, and the average reported intermodal road speed is 11 kilometers per hour (calculated as the weighted average speed across all modes). Car speed is within 9.4-13.5 km/hour. Bus speed is 6.5-9.3 km/hour, minibus speed is 7.5-10.8 km/hour and taxi speed is in the range of 8.6-12.3 km/hour (all calculated from Abou Zeid and Hassan (2016)). The congestion problem is increasing due to rapid motorization along with increased household income and growth of middle income households. Almost half of the total vehicles in Lebanon circulate in the GBA and the traffic volume in the GBA reaches 7,000 vehicles per hour in the northern entrance of Beirut (World Bank, 2015).

Traditionally in developing as well as developed countries, supply-side measures are offered to address traffic congestion problems. These include expansion of road networks and improvement of public transportation systems through the introduction of new or the expansion of existing light rail transit, bus rapid transit and metro systems (see, for example Chalak et al. (2016)). In addition to these supply side responses, there is growing interest in using demand side measures, particularly fiscal or pricing reforms to address the broader societal costs (or negative externalities) of transportation systems.<sup>3</sup> A more novel approach is congestion tolls, which economists have long advocated as an effective way of allocating scarce roadway capacity to the highest valued users.

> 3 See Timilsina and Dulal (2008) for an in-depth discussion of fiscal policy instruments to reduce congestion and environmental pollution from urban transportation.

2
Several studies have evaluated demand side instruments for other cities in the developing world.<sup>4</sup> However, there exist very few studies of cities in the Mediterranean/North Africa (MENA) region. Parry and Timilsina (2012) evaluated demand side instruments to reduce urban transport externalities in the Greater Cairo Metropolitan Region (GCMR). However, demand side instruments alone may not provide the best solutions to reduce negative externalities from urban transportation if supply-side measures that complement the demand side instruments are limited. For example, increased taxation of private vehicles either through fuel, mileage driven or upfront capital costs would not cause sufficient substitution of private vehicles with mass transportation if adequate infrastructure for mass transportation does not exist. It is therefore important to examine the trade-offs between demand and supply-side instruments. The existing literature has not analyzed the demand and supply instruments together, focusing instead on demand side instruments only (see. e.g., Parry and Timilsina, 2012; Parry and Timilsina, 2015; Anas and Timilsina, 2015). This study compares both supply and demand side instruments.

The extent to which the supply side and demand side instruments would be effective in the GBA is an empirical question considering several characteristics specific to the GBA. For example, the GBA offers only a limited number of alternatives to private vehicles. Motorization has rapidly increased despite the fact that import duties on vehicles account for more than 50% of a vehicle’s total value, the gasoline tax is one of the highest in the region and parking space is severely limited. The situation is worsened by the high cost of housing which causes people to reside away from the city center whereas most jobs are concentrated there. The city also lacks a reliable public transportation system. The GBA’s transportation system is additionally strained due to the influx of Syrian refugees over the last few years. Affluent Syrian families, concentrated in the GBA, have brought their cars into Lebanon and intra-city trips in the GBA have significantly increased. It is estimated that the influx of Syrian refugees has resulted in sudden traffic increases in the GBA in the range of 15-25% (World Bank, 2015).

We develop an empirical model that can simulate both supply and demand side policy instruments to reduce the negative externalities from urban transportation in the GBA. On the supply side, the model considers expansion of urban roads, a bus rapid transit running on special

> 4 See, for example, Anas and Timilsina (2009b), Anas, Timilsina and Zheng (2009), and Parry and Timilsina (2009) for applications to São Paulo, Beijing, and Mexico City, respectively.

3
lanes and an increased number of regular buses. On the demand side, policy instruments included are higher fuel and parking pricing. The model represents the behavior of all relevant agents including households, producers (commercial enterprises) and the government. Travel cost includes various monetary costs to households including transit fares, expenditures on automobile fuel, possible congestion tolls levied on auto travel, and the costs of vehicle ownership as well as value of their time (e.g., wage rate). Households have a choice to live nearby their workplaces paying higher rents but avoiding costs of commuting, or they can live away from city centers with lower rental costs and real estate values but pay higher cost for commuting (including the value of time). Through a budget constraint, more spending on travel implies a trade-off as households have less money for other goods. Travel by each mode also involves a time cost, which again involves a trade-off as this reduces the amount of time people have available for other activities at home. Travel time per mile differs across modes, and reflects the inverse of the average travel speed for a transportation vehicle. The model is calibrated with data from Beirut and the economic implications of several urban transportation policies are simulated.

We study several policy packages. Policy package 1 introduces 120 Bus Rapid Transit (BRT) buses which will run on dedicated bus lanes in addition to 250 regular buses. It also includes a 25% increase in the parking tax. Policy package 2 is the same BRT but the parking tax increase is replaced by additional road lanes in suburban Greater Beirut. Policy package 3 includes building an international class ring road in suburban Greater Beirut accompanied by a doubling of the existing excise tax on gasoline. Policy simulation results show an improvement in traffic congestion and decreases in VMT and gasoline consumption across all three policy scenarios. Policy packages 1 and 2 show big gains in social welfare due to a significant increase in traffic speed under BRT. The cost-benefit ratio for each policy can be measured as a gain in social welfare in Lebanese pounds (LBP) per LBP of expenditure. While the cost-benefit ratio is 9.6 and 5.01 for Policy packages 1 and 2 respectively, the cost of implementing Policy package 3 outweighs its benefit.

There are a few existing studies for Beirut analyzing various transportation improvement scenarios such as the re-organization of the bus system and the implementation of bus rapid transit (e.g. DMJM & Harris and INI Group, 2003; IBI Group and TEAM, 2009). The existing studies are, however, limited to economics specific to the project activities, whereas the current study assesses the impacts to the entire city considering many factors, normally not included in a project 4
economic analysis, such as potential changes in the wage rate and real estate prices using a city level general equilibrium framework.

The paper is organized as follows. Section 2 describes the model equations and the equilibrium structure of the model. Section 3 describes how the model was calibrated from the data, followed by a description of the policy instruments and the scenarios simulated in the study. Section 4 presents and discusses the various policy instruments, and Section 5 presents the effects of the three policy packages. Appendix A presents supplemental tables that include the detailed output of the simulations. Section 6 draws conclusions. Detailed descriptions of data are presented in Appendix B (a summary of the more extensive report by Abou Zeid and Hassan (2016)).

# **2. Model structure**

The metropolitan area is divided into two zones as shown in Figure 1. The central area is zone 1 (Municipal Beirut or MB) and the outer area is zone 2 (Greater Beirut or GB). We will use the subscripts _i_ , _j_ , _z_  1,2 to denote these zones where _i_ will be used for the zone as a place of residence, _j_ as a place of work (job location) and the destination of a commute, and _z_ as the destination of a non-work or shopping trip from _i_ . Modes of travel are _m_  1,...,4 , where _m_  1 is private car, _m_  2 public bus, _m_  3 is minibus and _m_  4 is taxi. Bus rapid transit is introduced as a fifth mode as needed. All four modes share the roads.

The model consists of consumers, firms, real estate developers and the public sector and follows the economic methodology of Anas and Liu (2007). In the labor markets, consumers who are workers and firms that offer jobs are matched up and equilibrium wages are determined in each zone _j_  1,2. In production, output produced in each zone satisfies the demand for export and for consumption from local consumers coming to shop in that zone. In the residential ( _k_  1) and commercial ( _k_  2) building markets, consumers and firms are matched up to the stock of housing, and rents are determined for each type of building floor space in each zone _i_  1,2. The stock of buildings is adjusted by real estate developers who construct and demolish residential and commercial buildings. Demolishing buildings creates land that is added to the available developable land, and constructing buildings reduces the available developable land. The transport sector is controlled by the government that sets gasoline taxes, parking fees and can increase the 5
<!-- Start of picture text --> i<br>te<br><!-- End of picture text -->
 The parameters are as follows.<sup></sup> _i_<sup>is the share of disposable income spent on housing and 1</sup> _i_ is 1 the share spent on goods purchased from _z_  1,2. is the elasticity of substitution between 1  goods from _z_  1 and _z_  2;  0 controls the disutility of the commuting time, _Gijm_ , so that the 1 marginal disutility is  2 _G_ . _Eijm_ are fixed amenity effects of the choice ( , _i j_ , _m_ ), and _eijm_ are _ijm_ random utilities that for each ( , _i j_ , _m_ ) vary among the consumers. On the right side of the budget constraint we have the annual earned income _w Hdj_ where _H_ is work hours per day and _d_ is the number of work days per year and _w j_ is the hourly wage rate at workplace _j_ . _M i_ is unearned income. _gijm_ is the daily two-way monetary cost of commuting from residence zone _i_ to workplace _p_ zone _j_ using mode _m. g jm_ is the daily parking cost at _j._ Only private cars incur parking cost, and this is captured by  1 1 and  _m_ 0 for _m_  1. Hence, the right side of the budget constraint is the worker’s disposable income after commuting-related monetary costs are subtracted from annual earned plus unearned income. On the left side is the expenditure of the worker, consisting of housing floor area rented in zone _i_ at the unit housing rent _RHi_ , and expenditure on goods purchased in zones _z=_ 1 and z=2 at the travel-cost-inclusive unit prices _pz ijm_ |  _pz_   _gizm_  _m g zmp_  _qizm_ where<sup>_p_</sup> _z_<sup>is the unit mill price at the zone of sale</sup><sup>_z_, in the</sup> parenthesis is the two-way monetary cost of the trip and the parking and _qizm_ is the number of trips needed to purchase a unit quantity. Nonworking consumers solve the same problem, except that there is no workplace or commuting cost, hence unearned income is the only part of their disposable income. Dropping the workplace subscript _j_ :

subject to the budget constraint: Solving these inner stage maximization problems yields the following Marshallian demands for goods and for housing floor space. For workers: 7
For non-workers: Substituting the Marshallian demands into direct utility, we get the indirect utility functions for workers and non-workers: In the outer stage, the discrete choice utility maximization problem results in multinomial logit models by assuming that the random utilities are distributed accordingly among the consumers. So we have the following choice probabilities for workers and non-workers:

# **2.2 Firms**

8
Firms in a zone _j_ produce output _X j_ with a constant returns to scale Cobb-Douglas production function combining as inputs, annual hours of labor,<sup>_L_</sup> _j_ , at the unit wage rate _w j_ ; and  1  commercial building floor space, _SBi_ , at the unit business rent, _RBj_ : _X j_  _A Lj jSBj_ , where  is the cost-share of labor and _Aj_ , a constant reflecting exogenous zonal productivity effects. Firms are assumed to be competitive, hence making zero profits. This implies that the output price equals the marginal and average cost. Hence: The labor demand, _LD,_ and the demand for commercial floor, _SD_ , space in zones _j_ =1, 2 are:

# **2.3 Transportation**

As mentioned earlier, in the transportation sector there are trips by the four modes (private car, bus, minibus and taxi), and two trip purposes: commutes from residence to workplace location and non-work trips to buy goods. These trips are loaded to the road network to generate monetary costs of travel, _gijm_ , per person-trip under conditions of congestion. In addition, parking costs for private cars, _g jmp_ , are also part of the transportation sector. The congested travel times, _Gijm_ , consist of three additive components: waiting time, in-vehicle time and access/egress time: The monetary cost ex-parking, of a consumer’s person-trip by mode _m_ , from residence zone _i_ to workplace zone or non-work trip destination _j_ is given by: Recalling that  1 1 for the private car mode, while  _m_ 0 for _m_  2,3,4, the two additive terms measure gasoline expenditure and fares. _Dij_ is the one-way trip distance per kilometer and 9
> _Fijm_  _s_<sup></sup> _ijm_ ,e _m_  is liters per vehicle-kilometer for mode _m_ as a function of vehicle traffic speed _s_<sup></sup> _ijm_ (to be determined below from the congestion technology) and mode fuel efficiency e _m_ . _PFg_ is the price of gasoline per liter including any excise tax and  _VAT_ the ad-valorem tax rate on gasoline at the pump.  _ijm_ is the inverse vehicle occupancy of the mode, and<sup>_f_</sup> _m_<sup>is the two-way fare that</sup> applies for _m_  1. The liters per vehicle kilometer function is:

> _Fijm_  _s_  _ijm_ ,e _m_   e _m_ [  3.78541178\1.6093  (0.122619  0.0117211  _s_  _ijm_   0.0006413  ( _s_  _ijm_ )2  0.000018732  ( _s_  _ijm_ )3  0.0000003  ( _s_  _ijm_ )4  0.0000000024718  ( _s_  _ijm_ )5  0.000000000008233  ( _s_  _ijm_ )6,

Parking cost is positive only for the private car mode. It is assumed that commuters park off-street and non-work trips can park either off-street or on-street. The average parking cost per commuter per day (W) is: And the average parking cost per non-commuter per day (NW) is: To determine congested travel times, we need to add up trips by ( , _i j_ , _m_ ) and then calculate the private-car-equivalent traffic loads across the different modes. So the sum of work and non-work trips by the mode _m_ per day are:

> 5

> The percentage of private car commuters that pays for parking is 46% (in _j_  1 ) and 20% (in _j_  2 ). In the case of non-work

> trips, 25% pays for off-street parking in _j_  1 but no one pays for off-street parking in _j_  2 ; whereas the shares of on-street parking for non-work trips in _j_  1 and _j_  2 are 25% and 20% respectively.

10
and the number of non-workers is _N NW_ and the number of non-work trips is obtained by multiplying the number of workers and non-workers with their respective choice probabilities: To combine the trips by mode in order to derive a combined traffic load, we need  _m_ to convert vehicles of mode _m_ into car-equivalent units. Then, a car-equivalent traffic load is: We also calculate vehicle miles traveled (VMT) and total gasoline consumption (TGC): For congestion, we use the BPR-type flow congestion function with parameters _c_ 0, _c_ 1, _c_ 2 to get the in-vehicle travel times. The resulting travel times for the four ( , _i j_ ) zone pairs, adjusted for mode slowness by the parameters  _m_ are: _CAP_ 1, _CAP_ 2 are road capacities associated with the two zones. These are blended by using the coefficients  1, 2 to obtain the capacities of the road relevant to the inter-zonal trips.  1, 2 are coefficients we calibrate.

11
Finally, a refinement of the model is to take into account what happens when more buses or round-trips of existing buses are added to the public transportation system. Adding buses will increase congestion if the buses run empty or not very full. The inverse vehicle occupancy ratio for buses should increase, keeping total bus riders constant, but it can decrease as more people switch to bus, as the added buses reduce waiting times. To capture these two relationships, we use the functions: and where _Busij_ is the number of buses (fleet size) used in the system.

# **2.4 Labor market**

The labor market equilibrium in each zone is calculated by solving for the wages so that the supply of labor equals the demand for labor:

# **2.5 Output market**

The output produced in each zone satisfies the demand from the local population and the demand for export:

# **2.6 Real estate market**

In the residential real estate rental market, the floor space demanded by consumers (workers and non-workers) equals the available residential floor space stock, while in the 12
commercial real estate market the demand for floor space by firms equals the stock of commercial floor space: The values of floor space ( _VHi_ , _VBi_ ) and of developable land ( _V_ 0 _i_ ) are determined by the following three equations. These are derived by assuming the following competitive bidding process in stationary state by risk neutral and forward-looking investors, a framework adapted from Anas and Arnott (1991). Suppose that an investor buys land at the beginning of a time period. The bid per unit of such land reflects the rent on vacant land that is collected during the period and the expected value of the capital gains that can be realized by exercising the option to construct either residential or commercial floor space, or by keeping the land undeveloped. It is assumed that the investor would choose the most profitable of the three possible actions, but – in the beginning of the period – does not yet fully know the costs associated with each option.

In the above and the following equations, _mki_ is the structural density (floor space to land area ratio) of type _k_ building in zone _i_ . _Cki_ is the cost of constructing a type _k_ building in zone _i,_ and _Kki_ the non-financial cost.  0 _i_ is the dispersion parameter of the unobserved nonfinancial costs for land investors. _r_ is the interest rate.

An investor owning an existing residential or commercial building acts similarly with the land investor (and with similar parameters) but has two options: to either demolish the building or keep it as is. Hence, in the beginning of the period the building investor would bid the rent from the period plus the expected capital gains from the options to demolish or not: The construction probabilities are: 13
And the demolition probabilities are: We assume that at equilibrium, the flow of demolished floor space equals 40% of the flow of constructed floor space, an arbitrary assumption the plausibility of which was confirmed by simulations, and that the total amount of land in each zone remains unchanged: Given rents, the equilibrium values are calculated from (32a)-(32c), and given the values, the equilibrium stocks of available land and aggregate floor spaces ( _S_ 0 _i_<sup>,</sup> _SHi_ , _SBi_ ) are found by solving (34a)-(34c).

# **2.7 The public sector**

14
_ AW ~~r~~ POLICY 17POLICY BASE |,BASE = CV + ~~Texyue~~ (Dixqonp Si Vir — Sie EVES) 1 . . ~~—_~~ (APT Revenue + AGasolineTax Revenue + AParkingTax (Ne+Nue Revenue — Total Cost of New Bus — New Road Construction Cost)

BASE <u>1</u> e,BASE BASE <u>1</u> ue,BASE Wie ~~7~~ im ©Xp QUim e,POLICY _ , e,POLICY e,POLICY Vim = Uijm + INQVijm ue,POLICY _ | we,POLICY ue,POLICY Uim = Uim + In(y; BASE <u>1</u> e,POLICY e,POLICY e WwW. ~~7~~ ijm EXP A[Uiim + In(Yijm — CV yD WyeBASE ~~7~~ <u>1</u> im €xXp A[U;ue,POLICY + In(y;ue,POLICY — cv“)})we CV = ~~———CNe~~ V& ~~——~CVnue~~ "" (Ne+nue (Ne+Nue

PT Revenue = Yiijm=2,3 Tijm * fin . ParkingTax Revenue _= jG}p ilij,w + Uz GnPp ijl iztNW GasolineTax Revenue = Yiijme2 tvatrPrg! GCijm
The elasticity of location choice with respect to housing rent used in the model is -0.35. Anas and Chu (1984) reported a range for housing cost elasticity between -0.26 to -0.86 from previous studies and estimated it to be -0.36 for the Chicago MSA. Indra (2014) in a study of 275 metropolitan areas, found the residential choice elasticity with respect to housing cost in US to be -0.28. We believe any value around -0.36 is very reasonable. Based on this rent elasticity, we calibrated the dispersion parameter, �, in the consumer’s choice probability.

The elasticity of location choice with respect to commute time weighted across all modes is -1.0735. The data for this mode choice elasticity is taken from the study for Beirut by Danaf et al. (2014). Since no mode choice elasticity was present for minibus, we considered the mode choice elasticity for bus and minibus to be the same. Based on their weighted value of elasticity with respect to commute time, we calibrated the travel time disutility parameter,�, in the consumer’s choice probability.

There is no value in the literature related to housing construction from vacant land for Beirut. We assumed that the probability of housing construction from vacant land is 0.0035 in both MB and GB. These probabilities are derived from the supply of newly constructed housing floor space aggregated across MB and GB.<sup>6</sup> Assuming that the probability of construction is the same for MB and GB, the probability of vacant land constructed into housing is derived. As there are no data on the construction probability of commercial floor space from vacant land, we assumed that the share is based on the existing commercial floor space relative to residential floor space, adjusting the residential construction probability with this ratio.

Based on the above, the elasticity of housing/commercial construction with respect to the value of housing/commercial floor space was set at 0.5 (MB) and 2.1 (GB). The elasticity of housing/commercial demolition with respect to the value of vacant land was set at 0.05 (MB) and 0.21 (GB), that is at one-tenth the corresponding construction elasticity. Based on these construction and demolition elasticity values, the constants in the probabilities of construction and demolition are calibrated. We also confirmed that the assumed ratio of demolished to constructed floor space of 40% seems to yield plausible comparative statics results.

> 6 The source of these data is from the Order of Engineers.

16
The waiting time function for buses was derived from the relationship between average waiting time and number of buses as provided in Meignan et al. (2007). In the base case, the parameter constant, ����, is calibrated to match the base data on waiting time for bus by origin and destination, (�, �). Think of a scenario where there is an increase in bus supply. On the one hand, this will potentially encourage people to switch to bus from all other modes as the waiting time for bus improves. This will reduce aggregate traffic for all other modes and hence it will reduce congestion. On the other hand, additional buses running on roads will take more space and frequent stops will disrupt the traffic flow, which will increase congestion on the roads. The net effect depends on how many buses are running on roads, relative to the switch in ridership to bus from the other modes. Equation (26) implies that an additional bus can potentially create congestion on the road for the other modes. Equation in (25) is the most commonly used congestion function. The value of the exponent, ��, can typically range from 1.2 to 4. We are using the value of 3.5 suggested in Arnott (2013). Detailed discussions on the data and key assumptions are presented in Appendix A. The calibration results are summarized in Tables 1-4.

**TABLE 1:** Base data except transportation

||**Municipal**<br>**Beirut**|**Greater**<br>**Beirut**|
|---|---|---|
|_Residents_|445,184|997,422|
|_%Workers_|41.18|44.01|
|_Jobs_|198,839|423,489|
|**Production**|||
|_i_<sup>_A_</sup>|19,877.33|11,925.34|
|_i_<sup>_w_</sup>|4363.2|3452.035|
||0.30|0.30|
|/<br><br><sup>_X_</sup><br>|0.55|0.73|
|_i_<br>_i_<br>|||
|**Consumers**|||
|_i_<sup></sup>|0.29|0.27|
|_i_<br>_M_ (LBP)|3,636,000|2,876,696|
|1|2|2|
|1 <br>|||
|~~~~|0.7539|0.7539|
|**Real estate**|||
|��� (LBP/Sq.Meter)|503,640|73,350|
|_Bi_<br>_R_<br> (LBP)|426,750|227,400|
|_Hi_<br>_R_<br> (LBP)|175,800|83,550|

17
|_Hi_<br>_V_<br> (LBP)|5,860,000|2,785,000|
|---|---|---|
|_Bi_<br>_V_ (LBP)|8,535,000|4,548,000|
|0_i_<br>_V_ (LBP)|16,788,000|2,445,000|
|_Bi_<br>_S_<br>(Sq. Meters)|11,859,051|37,501,072|
|_Hi_<br>_S_<br>(Sq. Meters)|5,259,996|21,342,698|
|0_i_<br>_S_ (Sq. Meters)|4,650,000|52,610,000|

**TABLE 2:** Base data on transportation

|Origin,Destination<br>1,1|1,2|2,1|2,2|
|---|---|---|---|
|Mode splits (persontrips)<br> <br>||||
|Car<br>48388|99398|111876|241934|
|Bus<br>1051|2158|2429|5253|
|Minibus<br>6544|13442|15130|32718|
|Taxi<br>4052|8324|9369|20261|
|Distances (one-way)in kilometers||||
|Anymode<br>6.6|11.9|12.3|9.4|
|Traveltimes (one-way)in minutes||||
|Car<br>42.1|53|57|46.4|
|Bus<br>77.6|98.3|104.2|88.8|
|Minibus<br>63.1|81.7|86.8|73.5|
|Taxi<br>58.33|72.33|76.73|65.13|
|1<br>2<br>,<br><br>0.18||0.82||
|1<br>2<br>,<br>_CAP_<br>_CAP_<br>(square meters)<br>3,675,177<br>||17,295,559<br>||
|Car|Bus|Mini-<br>bus|Taxi|
|_m_<br><br>0.5882|0.0893|0.1686|0.8475|
|_m_<br><br>1|2|1.6|1.4|
|_ijm_<br><br>1|1.45|1.25|1.10|
|Wait time(in minutes) 0|6.5|0.5|6|
|Access/egress (in minutes)||||
|Car<br>0|0|0|0|
|Bus<br>10|15|15|15|
|Minibus<br>10|15|15|15|
|Taxi<br>6.03|8.03|8.03|8.03|
|Fare (one-way) in LBP||||
|Car<br>0|0|0|0|
|Bus<br>1000|1000|1000|1000|
|Minibus<br>1000|1000|1000|1000|
|Taxi<br>2000|4000|4000|4000|
|(in Bus)<br>(1,1)|(1,2)|(2,1)|(2,2)|
|0<br>1<br>,<br> <br>17.0865, 0.8|11.0164, 0.8|7.3064, 0.8|7.7022, 0.8|

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_a_ , <u></u> (in Bus) 42.6668, -0.335 41.0472,-0.335 41.0472,-0.335 41.0472,-0.335

> **TABLE 3:** Target values used in calibration

|**Name**|**Value**|
|---|---|
|Elasticity of location choice w.r.t housing rent|-0.35|
|Elasticity of location choice w.r.t commute time across all mode|-1.0735|
|Elasticity of housing/business construction w.r.t value of housing/business<br>stock by MB and GB respectively|0.5,2.1|
|Elasticity of housing/ business demolition w.r.t value of land by MB and GB<br>respectively|0.05,0.21|
|Demolition to construction ratio|0.40|
|New annual housing construction in housing units|12678|
|Labor share in production function|0.3|
|Free-flow speed (km/hour)|100|
|Congestion parameter constants (��, ��)|0.2,3.5|
|Adjustmentparameter inphiijm of bus(���)|0.8|
|Delivered price tomillprice|0.1|

**TABLE 4:** Calibrated values

|Variable Name|Symbol|Value|
|---|---|---|
|Dispersionparameter in utilityfunction|�|1.8353|
|Disutility parameter for commute|�|0.7317|
|Probabilityof housingconstruction from vacant land|����|0.0035;0.0035|
|Probabilityof housingdemolition into vacant land|����|0.0011;0.0041|
|Probabilityof commercial buildingconstruction from vacant land|����|0.0066;0.0066|
|Probabilityof commercial buildingdemolition from vacant land|����|0.0038;0.0141|

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# **4. Defining Policy Instruments and Simulation Scenarios**

The study considers both demand side and supply side scenarios. Demand side instruments aim to reduce the excessive part of the demand for transportation services that relies on private vehicles. Supply side instruments aim to adjust the infrastructure capacity to provide transportation services (buses or roads and parking spaces). While there could be a large number of policy instruments and scenarios, considering all of them is beyond the scope of the study. We considered the most plausible instruments based on discussions with various stakeholders in Beirut.

## **4.1. Demand Side Policy Instruments**

Demand side instruments increase the prices of transportation services provided by automobiles. Such instruments are the fuel tax, the parking fee, congestion charges, the tax on vehicles, etc. Since motorization has increased despite a very high vehicle tax (import duty), increasing that tax further may not be very effective. Congestion charges, which have been used in some cities in developed countries (Singapore, London, Stockholm, Milan), may be difficult to implement in Beirut. So we considered two pricing instruments: the fuel tax and the parking fee. An increase in transportation cost through an increase in the fuel tax or parking fee would work in two ways. First, it would reduce transportation service demand from private vehicles by cutting their unnecessary or wasteful use and second, it would encourage the substitution of private transportation with public transportation.

**_Increased fuel taxation:_** As in many cities around the world, gasoline and diesel are the main fuels used for transportation in Beirut. Since the excise tax on gasoline in Lebanon was halved from 33 US¢/liter (LBP 9,900/20 liters) to 16.5 US¢/liter (LBP 4,950/20 liters) in March 2011, one scenario could be to reinstate the previous tax level, doubling the current excise tax rate from that level. Since diesel is used mainly for public transportation and one strategy to reduce congestion is to encourage switching from the private transportation mode to the public transportation mode, we did not apply any tax on diesel.

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**_Increased parking fees_** _:_ The objective of this policy instrument is to make parking in GB more expensive so that it discourages the use of private vehicles. An increase in parking fee whether it is off-street parking or on-street parking is expected to contribute to this objective. At present, paid street parking allows a maximum of two hours of parking on the street. Most commuters use offstreet parking lots or garages. Recent statistics show that 46% of commuters pay for parking in Municipal Beirut and 20% of commuters pay for parking in Greater Beirut. In 2013, the average daily off-street parking rate is estimated to be 3,187 LBP/day in Municipal Beirut and 2,500 LBP/day in GB. Considering all commuters (those who pay and those who do not pay for offstreet parking), the average off-street parking cost across commuters is 1,466.1 LBP/day in MB and 500 LBP/day in GB.

On-street parking is usually used by non-commuters. Paid (or metered) street parking is installed only in Municipal Beirut (but not everywhere) and not in Greater Beirut. The current onstreet parking rate is 1,000 LBP/hour. We assumed that 50% of all non-commuting trips park for free, 25% use paid street parking, and 25% use paid off-street parking. For street parking, the average duration of parking is assumed to be around 45 minutes (TEAM International, 2009), resulting in an average street parking cost of 750 LBP. Since the off-street parking rate for commuters is 3,187 LBP/day, the average parking cost paid by non-commuters in Municipal Beirut is estimated to be LBP 984.25.<sup>7</sup> For GBA, the average parking cost paid by non-commuters is LBP 500.<sup>8</sup>

# **4.2. Supply Side Measures**

The objective of the supply side measures is to expand infrastructure capacity including construction of new roads, particularly in the periphery of Municipal Beirut where land is available for the expansion of road networks, construction of underground metro or over-ground light railway transit (LRT), bus rapid transit systems, etc. Considering the huge costs of building transportation infrastructure and the considerable time needed to complete the mega projects, we treat two relatively cheaper options: construction of a new ring road in the periphery of Municipal Beirut (i.e., in GB) and addition of lanes to existing roads.

> 7 _Off-street parking rate * percent of non-commuters that pay off-street parking + on-street parking rate * percent of non-commuters that pay on-street parking + 0 * percent of non-commuters that do not pay parking = 3187 * 0.25 + 750 * 0.25 = 984.25._

> 8 _Off-street parking rate * percent of non-commuters that pay off-street parking + 0 * percent of non-commuters that do not pay parking = 2500 * 0.2 = 500._

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**Road expansion in GB** : We considered a _peripherique_ (ring road) along the periphery of Municipal Beirut in GB. This 20-km road is estimated to cost US$2 billion including the cost of expropriation. It will have two levels with a total (over both levels) of 5 lanes per direction. Assuming a 3.6-m lane width, the total width is 36 m. The increase in road capacity in Greater Beirut (GB) due to the Peripherique is then 20,000 m × 36 m = 720,000 m<sup>2</sup> .

**Lane Addition** : We considered adding one lane in each direction to the coastal highway along a 10-km section in GB (in the part that falls north of MB). The expected cost is in the range of US$150 million to US$200 million. Assuming a 3.6-m lane width, the increase in road capacity in Greater Beirut (GB) due to the lane addition project is 2 × 3.6 × 10,000 = 72,000 m<sup>2</sup> .

**Network extension of buses** : Since the government has a plan to purchase 250 new buses to be deployed in Greater and Municipal Beirut, we considered a scenario of adding these buses which will be owned by the city. Increasing the number of buses, would reduce bus waiting times due to increased frequency of service, but the additional buses would also add to road congestion, if they did not draw enough riders from the other modes.

**Introducing Bus Rapid Transit (BRT):** BRT will primarily cover 22 km between Beirut-Tabarja and 20 km within Beirut. As the BRT will run on dedicated lanes, there will be a reduction in road capacity because one lane will be taken from the road in each direction. This will happen over a distance of 15 km. So we need to remove from GB a road capacity<sup>9</sup> of 108,000 m<sup>2</sup> .  Road capacity will not decrease in MB as dedicated lanes will be taken from the parking lanes in MB. The targeted speed that BRT will try to achieve is 30-35 km/hr in GB and 20-25 km/hr in MB. The expected speed in MB is lower because of traffic lights. The one-way fare of BRT bus is assumed as 60% higher than regular bus fare.

# **5. Results of the Policy Simulations**

We first present results from simulations in which we change the values of individual policy instruments or public investment activities. This is followed by results of three policy packages where these policy instruments and public investment activities described above, are combined at different levels. The policy simulation results discussed and presented in this section are driven by several important margins of adjustment in the model, such as:

> 9 2 For BRT scenario in GB, 2 lanes * 15 km (length) * 3.6 of width/lane = 108,000 m .

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- 1) Consumers’ choice of place of residence and place of workplace.

- 2) Switching of consumers from one mode to another for work and non-work trips.

- 3) Consumers substituting between composite good consumption and housing floor space consumption and between consumption acquired by making non-work trips to MB or GB.

- 4) Firms substituting between labor and building inputs in production.

- 5) Conversion of vacant land to residential /commercial floor space construction or demolition of residential /commercial floor space to vacant land.

# **5.1. Results from simulation of individual policy instruments or investment activities**

There are four simulations to measure the impacts of individual policy instruments or investment actions. These are: (i) Expanding road capacity; (ii) Adding bus capacity; (iii) Increasing the taxes on gasoline (increase in the excise tax); (iv) Parking cost increase (increasing the parking tax rate). Below we discuss the most important results from each of these simulations. Detailed results under each simulation are provided in the long tables of Appendix A.

# **5.1.1 Expanding road capacity**

The road capacity increase for GB is 720,000 square meters, i.e., an increase of 4.1% in total road area in GB. Detailed results of this simulation are shown in Table A1.

After increasing the road supply in GB, population and employment decentralizes to GB. As a result of that, wages in MB rise and fall in GB since the supply of labor to GB increases at the expense of the labor supply to MB. The fall in housing demand in MB leads to a fall in the residential rent in MB. Opposite results can be seen in GB where rent rises due to increase in housing demand. Price of output increases in MB and decreases in GB. Increase in nominal and real output increase the demand for commercial floor space resulting in an increase in the rent of commercial floor space.

With the increase in road supply, congestion decreases somewhat and because this favors private vehicles, people switch to private vehicle from the other modes. The aggregate mode share increases for private vehicles even when the private vehicle trips of MB-to-MB decrease as both population and employment shift to GB. Travel time decreases across all modes. The aggregate traffic load decreases for MB-MB and MB-GB but increased for GB-MB and GB-GB. Aggregate non-work trips along with VMT increase but gasoline consumption decreases due to the improved speed. The improved speed and the switch to private vehicles result in a decrease in revenues from the gas tax and public transit fares, but parking tax revenue increases.

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The increase in the rent for commercial floor space stimulates construction and stock increases in both MB and GB. For residential floor space, stock decreases in MB but increases in GB. The fall in residential stock in MB frees up land which is in part utilized for the construction of new commercial floor space. As demand increases for both residential and commercial floor space in GB, vacant land decreases.

Workers in MB benefit from falling residential rents and the rising wages along with decrease in travel cost and travel time, but are adversely affected by the rising output price. Non-workers in MB benefit from falling residential rents and decrease in travel cost but are affected adversely by the higher goods prices, whereas workers and non-workers in GB benefit from lower output prices and lower travel costs but are adversely affected by the increase in residential rents and the decrease in wages. An average worker seems to be better off by this policy while an average nonworker is worse-off by this policy. The overall social welfare improves.

Note that with a congestion function exponent of �� = 3.5, the change in social welfare is bigger than with �� = 2. The reason is that the effect of an increase in road capacity begets more congestion relief when the exponent is higher.

The main results of this policy on key transportation and economic indicators are summarized in Figures 2a and 2b, respectively. As illustrated in these figures, the expansion of roads in the GB would substitute bus (mini and large bus) trips with auto (car and taxi) trips. It would reduce travel times for all vehicles and also the total travel costs. While revenues from fuel taxation and parking fees decrease, total rents, total values of properties including existing buildings, new buildings and vacant lands and gross regional products of the city will increase. Although percentage change in rental and property values look small, in absolute terms they are large. For example, the expansion of roads will increase rental values in Beirut (both MB & GB) by 38 billion LBP to 62 billion LBP depending upon the value for congestion coefficients. Similarly, the expansion of roads will increase values of properties (residential, commercial and vacant lands in Beirut) by 575 billion LBP to 801 billion LBP. The gross regional product of Beirut would increase by 42 billion LBP to 67 billion LBP.

24
# **Figure 2a.** Impacts of road expansion policy on transportation activity (% change from the base case)

<!-- Start of picture text --> Bus travel time<br>Auto trips (car & Bus trips (mini & Auto travel time (mini & large<br>taxi) large bus) (car & taxi) bus) Total travel costs<br>1.00%<br>0.00%<br>-1.00%<br>-2.00%<br>-3.00%<br>-4.00%<br>-5.00%<br>-6.00%<br>-7.00%<br>C2=2 C2=3.5<br>Figure 2b. Impacts of road expansion policy on economic activity<br>(% change from the base case)<br>0.50%<br>0.00%<br>-0.50%<br>-1.00%<br>-1.50%<br>-2.00%<br>Government Total rent Total property value GRP-Nominal<br>revenues<br>C2=2 C2=3.5<br><!-- End of picture text -->

# **5.1.2 Adding bus capacity**

Bus capacity is increased by 91% across the study area. There are two cases possible based on whether the bus is completely owned (case 1) or partially owned/rented (case 2). We found that the results are mostly similar between these two cases. They only differ with respect to the 25
operational and maintenance costs of new buses which affect the change in the value of social welfare per person. The results are shown in Tables A2 and A3.

The increase in bus supply moves more population to MB and more jobs to GB. Wages in MB increase but decrease in GB. Residential rent decreases in both MB and GB. With an unchanged price of goods and a decrease in real output, the demand for commercial floor space decreases and this causes a fall in commercial rents in both MB and GB.

The increase in bus supply not only improves ridership of bus but also of all the other modes except private vehicle. However, the biggest gain in ridership is for bus. Increase in bus supply has two opposing effects: on the one hand, a decrease in bus wait times which improves the travel time by bus and encourages people to switch from private vehicles, reducing congestion; on the other hand, if the increase in bus supply does not adequately improve bus ridership then the additional buses will cause traffic congestion to increase. The increase in bus ridership not only shifts people from private vehicle to bus but to the remaining modes also. As a result of this spillover, though travel time by bus decreases due to lower waiting times, travel time for all the other modes increases due to higher congestion caused by the additional buses. The traffic load increases for all origin to destination pairs. There is an increase in both aggregate VMT and gasoline consumption. Gas tax and public transit revenues increase but parking tax revenue decreases. As the majority of the population uses private vehicles for work and non-work trips, the resulting increase in trip costs reduces the disposable income. In the short run, reduction in disposable income reduces housing demand (by the income effect) and hence residential rents. This reduction in rents causes substitution (the substitution effect) favoring more housing consumption. Also non-work trips decrease which means that the cost of non-work trips has increased. This leads to further substitution in favor of housing consumption.

There is an increase in the stock of residential floor space in both MB and GB. The increase in the demand for housing results in an increase in the construction of new housing floor space in MB and GB. But the fall in the nominal value of output affects the construction and the stock of commercial floor space. The fall in the stock of commercial floor space frees up some land which is used for the construction of new housing floor space. The stock of vacant land increases which results in lower rents and values of vacant land.

For an average worker, the benefit of falling residential rents and an increase in the wage in MB is less than the adverse effect of a fall in the wage in GB and an increase in travel time. As a 26
result, an average worker is worse-off. For an average non-worker, the increase in travel time increases the cost of trips thus reducing their non-work trips. This adverse impact is more than the benefit of a decrease in residential rent. As a result, an average non-worker is also worse-off. The social welfare decreases and decreases more with a more congestible road network, that is when �� = 3.5.

The key impacts on transport activities and city economy are presented in Figure 3a and 3b. As can be seen in Figure 3a, increased addition of buses without expanding road capacity and only adding buses will deteriorate the congestion situation by increasing travel times of all vehicles, and does not help reduce congestion in Beirut. Due to increased travel time, gasoline consumption by car increases. The higher gasoline tax revenue and the increased public transport revenue would increase total government revenues but it would certainly hurt the consumers. Consequently, total rental value, total property value and gross regional products of the city will all drop.

**Figure 3a.** Impacts of the bus addition on transportation activity (% change from the base case)

<!-- Start of picture text --> Bus travel time<br>Auto trips (car & Bus trips (mini & Auto travel time (mini & large<br>taxi) large bus) (car & taxi) bus) Total travel costs<br>2.00%<br>1.50%<br>1.00%<br>0.50%<br>0.00%<br>-0.50%<br>C2=2 C2=3.5<br><!-- End of picture text --> 27
**Figure 3b.** Impacts of bus addition on economic activity (% change from the base case)

<!-- Start of picture text --> 0.35%<br>0.30%<br>0.25%<br>0.20%<br>0.15%<br>0.10%<br>0.05%<br>0.00%<br>-0.05%<br>-0.10%<br>Government Total rent Total property value GRP-Nominal<br>revenues<br>C2=2 C2=3.5<br><!-- End of picture text -->

# **5.1.3 Gasoline tax**

This policy instrument considers doubling of excise tax on gasoline for the reason explained in scenario definition section above. The detailed results are shown in Table A4. The increase in the excise tax, shifts population to MB and jobs to GB. There is a decrease in wage and a decrease in the price of output and a lower nominal value of output in the region. This decreases the demand for commercial floor space and lowers commercial rents. Because floor space and labor are substitutes in production, wages are also lowered which reduces residential rent through the income effect.

The increase in the excise tax increases the operating cost for modes using gasoline (private vehicle, minibus and taxi service). For minibus and taxi service, we assumed that the increase in cost is not transferred to the rider. Under this situation, people switch from private vehicle to all other modes. Travel time for all modes decreases but the travel cost for private vehicles increases. As a result, non-work trips decrease along with VMT and gasoline consumption. Gas tax revenue and public transit revenue increase while parking tax revenue decreases.

As the majority of people use private vehicle for trips, the increase in the excise tax increases the cost of non-work trips. The increase in the excise tax dominates the decrease in output prices which makes the cost of non-work trips increase. The decrease in wage and disposable income 28
reduces the demand for housing floor space (income effect) which reduces the residential rent. This rent reduction causes substitution in favor of demand for housing floor space (the substitution effect). Also with an increase in non-work trip cost, people will shift their demands at the margin from the composite good to residential floor space.

From this result, we find that the substitution effect of an increase in the excise tax dominates its income effect which results in an increase in the stock and construction of residential floor space. But the decrease in the population of GB plays an important role in reducing the aggregate demand for housing floor space even in the presence of a strong substitution effect. Hence the stock and construction of new residential housing decreases in GB. But a falling commercial rent, reduces both the stock and construction of commercial floor space in MB and GB. As expected the reduction in stock and construction of both residential and commercial floor space in GB, increases its stock of vacant land. Whereas in MB, the increase in vacant land due to the fall in commercial floor space stock is not fully utilized for the construction of new residential floor space. As a result vacant land increases in MB.

For the average worker, the benefit from the decrease in the price of output and residential rent, and the travel time decrease is outweighed by the decrease in wage and the increase in the cost of commute and shopping trips as the majority of them use private vehicles. Thus an average worker is worse-off. Non-workers are better off because the benefits outweigh the higher travel cost of non-work trips and non-workers have no commute costs. The social welfare however is lower overall. An increase in the exponent of the congestion function, i.e. �� = 3.5 which makes the change in social welfare less negative. The average worker is now better off as the effect of a decrease in traffic load and hence trip time and trip cost outweighs the negative effect of the tax.

The key transportation sector and city economic impacts of the increase in the gasoline excise tax are reflected in Figures 4a and 4b. The discussion of the results above explains the direction of impacts. The magnitudes of these impacts are also significant. The doubling of gasoline excise tax would increase total travel costs by 14%. As expected it would increase government revenues through increases in gasoline tax revenues and increased public transportation revenues. Although the tax on diesel, the main fuel used for public transportation vehicles, has not changed, public transport revenue still increases due to increase in public transportation trips caused by switching of passengers from private transportation to public transportation. This policy would however have a significant negative impact on the city’s 29
economy as rents, property values and gross regional products will drop. The drops on rents amount to 173 billion to 181 billion LBP and the drops on property value would be more than 2 trillion LBP. The nominal gross domestic product of the city drops by almost 200 billion LBP.

**Figure 4a.** Impacts of the increase in the excise tax on gasoline on transportation activity (% change from the base case)

<!-- Start of picture text --> 4.00%<br>3.50%<br>3.00%<br>2.50%<br>2.00%<br>1.50%<br>1.00%<br>0.50%<br>0.00%<br>-0.50%<br>-1.00%<br>-1.50%<br>Government Total rent Total property value GRP-Nominal<br>revenues<br>C2=2 C2=3.5<br><!-- End of picture text -->

**Figure 4b.** Impacts of increase of excise tax on gasoline on urban economic activity (% change from the base case)

<!-- Start of picture text --> Bus travel time<br>Auto trips (car & Bus trips (mini & Auto travel time (mini & large<br>taxi) large bus) (car & taxi) bus) Total travel costs<br>16.00%<br>14.00%<br>12.00%<br>10.00%<br>8.00%<br>6.00%<br>4.00%<br>2.00%<br>0.00%<br>-2.00%<br>-4.00%<br>C2=2 C2=3.5<br><!-- End of picture text --> 30
# **5.1.4 Parking cost**

Under this policy, we increased the parking fees. As no specific number is given for parking fee increases, we have increased it by 10%, 15% and 25%. The detailed results are shown in Tables A5, A6 and A7. An increase in the parking tax, decentralizes both population and jobs to GB. Wages, rents and prices decrease. Due to the decrease in the value of nominal product, the demand for commercial floor space decreases which also decreases the commercial rent.

An increase in the parking tax, makes consumers switch from private vehicle to all other modes, improving the speed of all modes. The travel cost by private vehicle also decreases. Traffic load, VMT and gasoline consumption all decreased. Public transit and parking tax revenues increase at the expense of gas tax revenue.

As the majority of people use private vehicles for trips, the increase in parking tax increases the cost of non-work trips even when the gasoline cost of private vehicles decreases. As a result, there is a decrease in non-work trips. The increase in the parking tax dominates the decrease in output price which decreases the cost of non-work trips. The decrease in wage and disposable income reduces the demand for housing floor space (income effect) which reduces the residential rent. This rent reduction causes substitution in favor of demand for housing floor space (substitution effect). Also with an increase in the non-work trip cost, people shift their demand from the composite good to residential floor space.

The stock and construction of residential floor space increases in MB but decreases in GB. For MB, the substitution effect of the parking tax dominates its income effect and it is still strong enough to increase the stock and construction of residential floor space when the population moves out to GB. In GB, the residential stock and construction falls as the income effect of the parking tax dominates the substitution effect and an increase in population. Value and stock for commercial floor decrease in both MB and GB due to a fall in the nominal value of output. Decrease in commercial floor space stock frees up land for construction of new residential floor space in MB. But construction demand for new residential floor space is not enough to compensate for the land vacated due to the decrease in the stock of commercial floor space. As a result of that vacant land increases in MB. In GB, the stock of vacant land increases as the stock of both residential and commercial floor space decreases.

As most workers use private vehicle for trip purposes, an increase in the parking tax rate outweighs the benefit of a decrease in prices. Also the adverse impact on wages outweighs the 31
benefit from the decrease in travel cost, travel time and residential rent. The average worker is worse-off. Non-workers are better off as the benefit of a decrease in travel cost, travel time and residential rent is more than the loss in the demand for non-work trips. An increase in the exponent of the congestion parameter to �� = 3.5 makes the change in social welfare less negative. The average worker is now better off as the effect of a decrease in traffic load and hence trip time and trip cost outweighs the negative effect of tax.

The key transportation sector and city economic impacts of 25% increase on parking fee are reflected in Figure 5a and 5b. The discussion of the results above explains the direction of impacts. The magnitude of impacts are also significant. The doubling of gasoline excise tax would increase total travel costs by more than 12%. This policy would have a significant negative impact on the city’s economy as rents, property values and gross regional products will drop. Although the percentage drops on rents, property values and gross regional products are small, the absolute values are not. For example, 25% increase on parking fee would reduce the rents by more than 100 billion LBP and property value by more than 1 trillion LBP. The drop on nominal gross domestic products of the city would be more than 100 billion LBP.

**Figure 5a.** Impacts of 25% increase of parking fee on transportation activity (% change from the base case)

<!-- Start of picture text --> Bus travel time<br>Auto trips (car & Bus trips (mini & Auto travel time (mini & large<br>taxi) large bus) (car & taxi) bus) Total travel costs<br>1.50%<br>1.00%<br>0.50%<br>0.00%<br>-0.50%<br>-1.00%<br>-1.50%<br>C2=2 C2=3.5<br><!-- End of picture text --> 32
**Figure 5b.** Impacts of 25% increase of parking fee on urban economic activity (% change from the base case)

<!-- Start of picture text --> 14.00%<br>12.00%<br>10.00%<br>8.00%<br>6.00%<br>4.00%<br>2.00%<br>0.00%<br>-2.00%<br>Government Total rent Total property value GRP-Nominal<br>revenues<br>C2=2 C2=3.5<br><!-- End of picture text -->

# **5.1.5 Social welfare impacts of policy instruments or investment activities**

Figure 6 compares the effects of different policy instruments and investment actions on total social welfare. The impacts on social welfare calculated by the model include the effects of changes in total travel costs, total changes in rents, and total change in property values. Among the individual policies and investment activities, road expansion would increase the value of social welfare per person in the Greater Beirut Area by 200,000 to 300,000 LBP. All other policies and investment activities would reduce social welfare by varying amounts.

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**Figure 6.** Social welfare costs of policy instruments (‘000 LBP per person)

<!-- Start of picture text --> 350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>-50<br>-100<br>-150<br>Road Expansion Bus Addition Doubling of excise 25% increase in<br>tax of gasoline parking fee<br>C2=2 C2=3.5<br><!-- End of picture text -->

# **5.2. Results from the simulations of the policy packages**

We have simulated the effects of three policy packages that contain a mix of demand side policy instruments and supply side investment activities. The supply side activities include the Bus Rapid Transit (BRT) or ring road as described in section 4.2, as well as other investment activity such as lane additions. These are combined with demand side instruments such as higher parking fees or gasoline excise taxes, as described in section 4.1. The policy packages selected for study were recommended for study in this paper by Ziad Nakat of the World Bank in Lebanon and by Maya Abou Zeid. They seem to have realistic chances of consideration by the city administration and implementation, based on our understanding of discussions in Lebanon’s policy circles.

The first two policy packages involve the Bus Rapid Transit (BRT) system with 120 buses described in section 4.2. This proposed BRT is 90%-180% faster than all existing modes of transport. The targeted waiting time for the BRT bus will be 2-3 minutes. Additionally, bus network extension will include 250 regular buses that will improve the waiting and access/egress time by 10%-30%. Under Policy package 1, the parking tax increases by 25% in both MB and GB. Thus, the parking cost for work trips increased from 1,466.02 LBP to 1,832.5 LBP in MB and from 500 LBP to 625 LBP in GB. For non-work trips, parking cost increased from 984.25 LBP 34
to 1,230.3 LBP in MB and from 500 LBP to 625 LBP in GB. The purpose of the parking tax increase is to discourage driving and by doing so divert even more car trips to the BRT.

Policy package 2 accompanies the BRT with highway lane additions instead of the parking tax increase. Since under any policy involving the BRT, road capacity is reduced to accommodate the BRT, car congestion on the remaining roads would increase. While Policy package 1 attempts to alleviate the higher congestion by increasing the parking tax, Policy package 2 attempts to alleviate the congestion by adding more road capacity for cars.

Policy package 3 is the ring road in GB described in section 4.2, accompanied with a 100% increase in the excise tax on gasoline. This amounts to a 16.5% increase in the after-tax gasoline price per liter.

# **5.2.1 Policy package 1: BRT buses, bus extension and parking fee increase**

We simulated this policy package by introducing BRT buses as an alternative mode of transport which is 90%-180% faster than all existing modes of transport. The targeted waiting time for BRT bus will be 2-3 minutes. Bus network extension will improve the waiting and access/egress time by 10%-30%. The cost of implementing Policy 1 will be around 250 million USD which is 2% of the nominal gross product in the base year. Parking fee is increased by 25% in both MB and GB.

After the introduction of Policy package 1, there is an increase in social welfare per consumer. Welfare gains for workers and non-workers given in Table 5 are equivalent to 39% and 22% of their gross annual income. Both have greatly benefitted from reduction in travel time and travel cost for work and non-work trips. The speed of traffic improves by 139% and reduces congestion by 19%. The aggregate travel time in the Greater Beirut region is reduced by 43% which is equivalent to 24 minutes saved per trip.<sup>10</sup> There is an increase in public transit and parking tax revenue at the expense of gas tax revenue. Even when people switched away to public transit, especially BRT buses, there is a marginal increase in tax revenue as the tax rate is high enough to recover the loss due to revenue reduction from private vehicles. Public transit revenue increased by 149% due to higher share of public transit users. Gas tax revenue decreased by 74 LBP per consumer as people switched away from gasoline driven modes to BRT buses. Also the decrease in gas tax revenue can be attributed

> 10 Time saved per trip is total time saved divided by aggregate trips across all modes.

35
to parking tax being imposed on private vehicles which further reduces private vehicle use. All these factors improved traffic speed and reduced gasoline consumption and VMT. Improved travel time and reduced travel cost increased the disposable income for both workers and non-workers which resulted in a marginal increase in non-work trips by 0.03% and overall trips by 0.01% given in Table 5. Introduction of BRT created a mode share of 18% for BRT buses at the expense of all other modes.

From Table 6, we observe that some employment and population has moved into MB from GB. There is also an increase in labor supply relative to its demand which reduced the wage in MB but increased it in GB as labor supply decreases relative to its demand. In MB, rising rent encouraged the construction for new residential floor space to accommodate rising population. Whereas there is a negligible change in residential rent in GB and the stock of residential floor space decreased due to loss in population. There is an increase in nominal and real output which increased the demand for commercial floor space in both MB and GB. Increase in demand for commercial floor space also increased the rent of commercial floor space. Beirut, as a region, witnessed an increase in real estate value of 1.05% after the implementation of Policy package 1.

# **5.2.2. Policy package 2: BRT buses, bus extension and lane addition**

In Policy package 2, the parking tax in Policy package 1 is replaced by the construction of additional lanes. This would increase the road capacity in GB by less than 1%. Such lane additions will cost around 200 million USD i.e.1.5% of nominal gross product. So in total the cost of implementing Policy package 2 will be 3.5% of nominal gross product.

The welfare effects of both Policy package 1 and Policy package 2 are similar. The welfare of both workers and non-workers has increased as shown in Table 5. The welfare gains are equivalent to 40% and 22% of their gross annual income. The additional gains for workers under Policy package 2 relative to Policy package 1 are because traveling by private vehicle is less time consuming and less costly which is complementing the effect seen under the introduction of BRT and the regular bus network expansion. There is an increase in public transit revenue from the gas tax and the parking tax. Gas tax and parking tax revenue decreased by 74 LBP and 43 LBP as people switched to public transit especially BRT bus. Higher disposable income due to lower travel cost encouraged more non-work trips. Non-work trips under Policy package 2 are higher than under Policy package 1 as the higher parking tax under Policy package 1 reduced disposable 36
income. Under package 2, 18% of the total trips are made in BRT buses with an associated decrease in VMT and gasoline consumption and is similar to Policy package 1 outcome. Traffic speed improved, traffic congestion decreased and travel time saved per trip is equivalent to 23 minutes.

From Table 6, we observe that both employment and population has moved into MB from GB, similar to Policy package 1. There is also an increase in labor supply relative to its demand which reduced the wage in MB but wage increases in GB as labor supply decreases relative to its demand. In both MB and GB, residential rent has increased. The price effect of higher residential rent will lower individual floor space demand. Also population in MB has increased which should increase the demand for residential floor space. At the margin, the price effect of the rent increase is greater than the effect of the population increase in MB. Such an effect discourages construction of new residential floor space and hence residential stock decreased in MB. Residential stock decreased in GB too due to the adverse price effect and the decrease in population. There is an increase in nominal and real output which increased the demand for commercial floor space in both MB and GB. The increase in the demand for commercial floor space also raised the rent of commercial floor space. Beirut as a region witnessed an increase in real estate values of 1.33% after the implementation of Policy package 2.

# **5.2.3. Policy package 3: Road capacity increase (Ring road,** **_Peripherique_ ) and gasoline tax increase**

In Policy package 3, road capacity is increased in GB by building a ring road or ‘Peripherique’. It will increase the road supply by 4.1%. The construction cost of this new road including expropriation of land is around 2 billion USD i.e. 15% of the nominal gross product of the base year.

After the implementation of Policy package 3, welfare is decreased as the cost of implementation far outweighs its benefits. Welfare gained by workers is around 6% of annual income and welfare lost by non-worker is lower than 1% of their non-wage income. On the one hand, increasing road capacity in GB encouraged trips by private vehicles, but on the other hand, higher gasoline tax discouraged traveling by private vehicle. At the margin, the result in Table 5 show that congestion has decreased whereas the mode choice shares remained more or less unchanged. So the improvement in speed by 11% is not due to changes in mode choice in favor of public transit but because of a decrease in non-work trips by 0.91% which reduced the overall 37
traffic. Imposition of the higher gasoline tax outweighs the decrease in trip cost through improved traffic speed which reduces the disposable income and as such adversely affect the demand for non-work trips. There is a small decrease in public transit and parking tax revenue of 1 LBP and 3 LBP per person respectively. Gas tax increased marginally by 16 LBP only. Improved speed reduced VMT and gasoline consumption by 0.40% and 6% respectively. The travel time saved is around 5 minutes per trip.

In Table 6, we see that contrary to Policy packages 1 and 2, there is a decentralization of both population and employment to GB. Wage in MB increased as labor supply decreased relative to its demand, whereas wage decreases in GB. The higher gasoline tax lowered the disposable income which caused the residential rent to fall. In MB, the favorable price effect of the rent decrease and the higher wage outweighed the decrease in population. This resulted in an increase in the stock of residential floor space at the margin. In GB, higher population along with lower residential rent resulted in an increase in the stock of residential floor space. Fall in nominal gross product by 0.66% adversely affected the demand for commercial floor space and hence the commercial rent fell marginally by 0.46% and 0.19% in MB and GB respectively. Beirut as a region experienced a drop in real estate values after the implementation of Policy 3.

Figure 7 shows the percentage change in mode choice share under each of the three policies, while Figure 8 shows changes in traffic related variables and Figure 9 in economic variables. Public transit includes regular bus, minibus and BRT bus. Under Policy package 1 and Policy package 2 there is an increase in public transit ridership at the expense of private vehicle and taxi service. Whereas there is a marginal increase in the share of private vehicle at the expense of public transit and taxi service. In all the three policies, there is a decrease in gasoline consumption, VMT, total travel time and an increase in traffic speed. The magnitude of changes is higher under Policy package 1 and Policy package 2 in comparison to Policy package 3. Government revenue which includes public transit revenue, parking tax revenue and gasoline tax revenue increases under all the policy packages. Real estate values and gross product increased under Policy package 1 and Policy package 2 but both of them decreased under Policy package 3.

38
**<mark>TABLE 5:</mark>** <mark>Welfare and travel related results for all three policy packages</mark>

|**Welfare change**<br>**(LBP/year/consumer)**<br>|**Policy package 1:**<br>**BRT + bus ext. &**<br>**parking tax incr.**|**Policy package 2: BRT**<br>**+ bus ext. & land**<br>**additions**|**Policy package 3:**<br>**Ring road & gas.**<br>**tax**|
|---|---|---|---|
|Total welfare|2,496,441|2,345,318|-1,796,943|
|ConsumerCV|2,617,273|2,637,097|334,846|
|Worker|5,126,104|5,178,149|777,561|
|Non-worker|713,875|709,254|-1,033|
|Real Estate Value|138,968|176,043|-52,231|
|**Sources of tax revenue (LBP/**<br>|**year/consumer)**<br>|||
|Gas Tax|-74|-74|16|
|Public Transit|199|198|-1|
|ParkingTax|21|-43|-3|
|**Cost of policy implementation**<br>|**(LBP/year/consumer**<br>|**)**<br>||
|BRT|259,946|259,946|0|
|Road construction|0|207,957|2,079,570|
|**Consumer Utility**||**% change from base**||
|Overall|4.06|4.06|0.33|
|Worker|6.14|6.17|0.77|
|Non-worker|2.48|2.46|0|
|**TRAVEL COMPONENTS**<br>||**% change from base**<br>||
|Trips|0.01|0.28|-0.50|
|Non-Work Trips|0.03|0.56|-0.91|
|Average Speed|138.8|137.3|11|
|Gasoline|-43|-42.7|-6|
|VMT|-15.9|-15.5|-0.40|
|Total Travel Time|-42.6|-42.3|-10|

**<mark>TABLE 6:</mark>** <mark>Effects of the policy packages on employment, rent, wage and gross product</mark>

|**Policy package 1:**<br>**BRT+bus ext. &**<br>**parking tax incr.**|**Policy package 2:**<br>**BRT+bus ext. &**<br>**lane add.**|**Policy package 3:**<br>**Ring road & gas.**<br>**tax**|
|---|---|---|
|<br> <br>**Jobs**<br> <br>|**% changes from base**<br>||
|MB<br>6.8|6.8|-2.1|
|GB<br>-3.1|-3|1|
|**Hourly Wage**|||
|MB<br>-5.1|-4.5|1.5|
|GB<br>5.4|5.8|-1.7|
|**Annual Residential Rent**<br> <br>|||
|MB<br>6.9|7.4|-1.3|
|GB<br>0.01|0.51|-0.4|
|**Annual Commercial Rent**<br> <br>|||
|MB<br>1|1.5|-0.46|
|GB<br>0.53|0.64|-0.19|
|**Gross Product**<br>1.7|2.3|-0.66|

39
**FIGURE 7:** Percentage change in mode choice share under the three policy packages

<!-- Start of picture text -->
-34 -24 -14 -4 6 16 26 36 46 56 66 76<br>Policy 3 Policy 2 Policy 1<br><!-- End of picture text -->

> **FIGURE 8:** Percentage change in different traffic variables under the three policy packages

<!-- Start of picture text --> Change in Traffic Variables (%)<br>-45 -25 -5 15 35 55 75 95 115 135<br>Policy 3 Policy 2 Policy 1<br><!-- End of picture text --> 40
**FIGURE 9:** Percentage change in economic variables under the three policy packages

<!-- Start of picture text --> Change in Economic Variables (%)<br>Governemnt Revenue<br>Real Estate Value<br>Nominal GRP<br>-10 10 30 50 70 90 110<br>Policy 3 Policy 2 Policy 1<br><!-- End of picture text --> We did a further analysis in order to decompose the supply side from the demand side policy effects under each policy package. Recall that Policy package 1 includes the introduction of BRT buses together with a regular bus network extension on the supply side, and the demand side instrument of the parking fee increase. Policy package 2 includes the supply side policy of the introduction of BRT buses together with a regular bus network extension, and another supply side measure, highway lane addition in suburban GB. Policy package 3 includes a different supply side measure, the construction of a ring road in suburban GB, together with the increase in the gasoline tax as a demand side policy instrument. In Tables 7 and Table 8, which correspond to Tables 5 and 6, there are two runs (columns) reported for each policy. The first of these runs calculates the changes from the base situation when the supply side part of the policy is introduced, and the second run calculates the additional change when the other part of the policy is added (demand side for Policy package 1 and Policy package 3, and the other supply side part of Policy package 2).

The results show that the supply side instruments are responsible for a very large part of the improvements in total social welfare, and consumer welfare, while the demand side part of the package causes small changes in either direction depending on the policy. This is because the supply side policies and especially the BRT is very effective in directly speeding up public transportation, and indirectly travel by car, by getting car traffic off the roads. The 25% parking tax increase under Policy package 1 makes a small negative difference to consumer utility. This is 41
because the parking tax increase is small and a poor substitute to pricing congestion with a Pigouvian congestion toll.

**TABLE 7:** Welfare and travel related results for all three policy packages.

||**Policy p**<br>**BRT+bus ext.**<br>**in**<br>|**ackage 1:**<br>**& parking tax**<br>**cr.**<br>|**Policy pac**<br>**BRT+bus ex**<br>**add.**<br>|**kage 2:**<br>**t.& lane**<br> <br>|**Policy pa**<br>**Ring road**<br>|**ckage 3:**<br>**& gas. tax**<br>|
|---|---|---|---|---|---|---|
|**Welfare change**<br>**(LBP/year/consumer)**|**BRT+regular**<br>**bus extens.**|**Parking tax**<br>**incr.**|**BRT+regular**<br>**bus extens.**|**GB lane**<br>**additions**|**Ring road**|**Gasoline**<br>**tax incr.**|
|Total welfare|2,541,129|-44,688|2,541,129|-195,811|-1,738,447|-58,496|
|Consumerwelfare|2,626,094|-8,821|2,626,094|11,003|313,188|21,658|
|Worker|5,152,197|-26,093|5,152,197|25,952|733,142|44,419|
|Non-worker|709,591|4,284|709,591|-337|-5,423|4,390|
|Real Estate Value|174,899|-35,931|174,899|1,144|27,941|-80,172|
|**Sources of tax revenue**|**(LBP/year/con**|**sumer)**|||||
|GasTax|-73|-1|-73|-1|-4|20|
|Public Transit|198.7|0.3|198.7|-0.3|-2|1|
|ParkingTax|-43|64|-43|0|0.10|-3.1|
|**Cost of policy impleme**|**ntation (LBP/ye**|**ar/consumer)**|||||
|BRT|259,946|0|259,946|0|0|0|
|Road construction|0|0|0|207,957|2,079,570|0|
|**TRAVEL COMPONE**|**NTS**|**%**|**changes from b**|**ase**|||
|Trips|0.27|-0.26|0.27|0.01|0.12|-0.62|
|Non-Work Trips|0.55|-0.52|0.55|0.01|0.23|-1.1|
|<br>Average Speed|136.6|2.2|136.6|0.7|9|2|
|Gasoline|-42|-1|-42|-0.7|-4|-2|
|VMT|-15.6|-0.3|-15.6|0.11|0.39|-0.79|
|Total Travel Time|-42|-0.6|-42|-0.3|-7|-3|

Note: For each policy package, column 1 is change or % change from base and column 2 is change or % change from column 1 42
**TABLE 8:** Important results for all three policy packages

||**Policy pack**|**age 1**|**Policy pac**|**kage 2**|**Policy p**|**ackage 3**|
|---|---|---|---|---|---|---|
||**BRT+regular**<br>**bus extens.**|**Parking**<br>**tax incr.**|**BRT+regular**<br>**bus extens.**|**GB lane**<br>**Additions**|**Ring**<br>**road**|**Gasoline**<br>**tax incr.**|
|**Population**||**% chan**|**ge from base**||||
|MB|6.6|0|6.6|0|-0.32|0.04|
|GB|-3|0|-3|0|0.14|-0.01|
|**Jobs**|||||||
|MB|6.9|-0.1|6.9|-0.1|-1.9|-0.2|
|GB|-3.2|0.1|-3.2|0.2|0.9|0.1|
|**Hourly wage**|||||||
|MB|-4.6|-0.5|-4.6|0.1|2.5|-1|
|GB|5.8|-0.4|5.8|0|-0.7|-1|
|**Annual residen**|**tial rent**||||||
|MB|7.4|-0.5|7.4|0|-0.3|-1|
|GB|0.51|-0.5|0.5|0.01|0.6|-0.2|
|**Annual Comm**<br>|**ercial Rent**<br>||||||
|MB|1.4|-0.4|1.4|0.1|0.4|-0.86|
|GB|0.6|-0.07|0.6|0.04|0.1|-0.29|
|**Gross**<br>**Product**|2.2|-0.5|2.2|0.1|0.3|-0.96|

Note: For each policy package, column 1 is change or % change from base and column 2 is change or % change from column 1 Policy package 2 gives results very similar to policy package 1, except that in this case, the highway land additions have a small but net positive effect on consumer CV, but a negative effect on total welfare because of the cost of the additional lanes. The ring road under the third policy package increases consumer welfare but much less than the BRT because it is much less effective in alleviating congestion. Its total effect on welfare is negative because of its very high cost. The gasoline tax doubling under the same package has a notable additional effect on consumer welfare because it approximates well the effect of congestion pricing, much better than the parking tax does under the first policy package.

Table 9 presents the improvement in each of three different measures of congestion. The first two measures are utilized mostly by engineers and planners and measure congestion in physical terms. The first of these is the ratio of the composite traffic load per unit of road capacity or more commonly known as the flow-to-capacity ratio. Table 9 shows that this ratio is a very high 9-10 in 43
the base case. It falls by 15-17.5% under the first two policy packages involving the BRT but falls much less under the third package involving the ring road.

The second measure is that popularized by the Texas Transportation Institute commonly known as the TTI index. It measures congestion as the ratio of actual travel time to free-flow travel time. We take free-flow travel time to be 100 km/hr. (62 miles per hour). The base value of this measure is in the 7.4-10.6 range, but it improves by 44-50% under the BRT (packages 1 and 2) and by only 6-13% under the ring road (package 3).

The third measure is the aggregate congestion externality, favored by economists. It is the total monetary value of the congestion delays caused by all traffic in LBP per year per consumer in the model. In the base year this amounts to 283,399 LBP per consumer per year (approximately 189 $ US). The aggregate amount is equivalent to 2.11% of the Beirut region’s gross product and 3.96% of the per consumer income. Under the policy packages that include the BRT the aggregate congestion externality falls by about 53% but by only 5% under Policy package 3.

**TABLE 9:** Improvement in congestion measures relative to Base

||**Base**|**Policy package 1**|**Policy package 2**|**Policy package 3**|
|---|---|---|---|---|
||||**% changes from ba**|**se**|
|**FLOW TO CAPACITY R**|**ATIO**||||
|**MB-MB**|10.02|-17.78|-17.45|-1.72|
|**MB-GB**|9.04|-16.19|-16.14|-3.26|
|**GB-MB**|9.14|-17.15|-16.86|-2.11|
|**GB-GB**|9.31|-15.51|-15.47|-3.75|
|**TTI INDEX FOR ROAD T**<br>|**RAFFIC**<br>||||
|**MB-MB**|10.63|-49.51|-48.80|-5.87|
|**MB-GB**|7.42|-46.01|-45.89|-10.93|
|**GB-MB**|7.72|-48.13|-47.50|-7.19|
|**GB-GB**|8.23|-44.48|-44.38|-12.49|
|<br>**CONGESTION EXTERN**|**ALITY**||||
|**Aggregate congestion**<br>**externality (in billion**<br>**LBP/year)**|408.83|-53.82|-52.88|-5.70|
|**Congestion externality**<br>**per consumer**<br>**(LBP/year)**|283,399|-53.82|-52.88|-5.70|
|**Congestion externality as**<br>**a percent of Beirut gross**<br>**product**|2.11|-54.61|-53.91|-5.07|

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In summary, under policy packages 1 and 2, the BRT is very effective. It reduces congestion as measured by the flow to capacity ratio by about 16%. It increases the Beirut region’s gross product by 1.8% under package 1 and by 2.3% under package 2, implying an elasticity of gross product to congestion of 11-14% under these policies. Consumer welfare under both packages increases by about 4%, with almost all of this due to the BRT, implying an elasticity of consumer utility to congestion of about 25%. The congestion externality is reduced by about 54%, implying an elasticity of the congestion externality to congestion of 337.5%.

# **6. Conclusions**

Beirut, like most growing cities, faces the unwelcome effects of traffic congestion resulting from a number of causes. It is critical to alleviate the problem by implementing effective policies, based on weighing their overall economic cost and benefit to society. For this purpose, this study applied an empirical model based on microeconomic theory that accounts for consumption and production behavior related to transportation in the Greater Beirut Area (GBA). includes both Beirut Municipality (MB) and Greater Beirut (GB), the location of suburbs and exurbs. The model accounts for the origin and destination of trips and all their characteristics including trip type, trip frequency, vehicle fuel type, transportation mode, travel time and cost, real estate information, work and residential areas etc. We first simulated a number of supply and demand side measures aimed to reduce congestion individually, and compared these policies in terms of their impacts on the economy. This was followed by simulations of three plausible combination of these measures as well as with additional activities, based on our understanding of discussions in Lebanon’s policy circles and the options being evaluated by the City Administration.

One of the key findings of the study is that individual supply side policies such as the expansion of roads or the introduction of a Bus Rapid Transit (BRT) system would be more beneficial for Beirut compared to individual demand side policies such as an increased gasoline tax or higher parking fees. Similarly, in the policy packages considered, most of the benefits come from the supply-side components. This is because the supply side policies and especially the BRT are very effective in directly speeding up public transportation, and indirectly speeding up travel by car by getting car traffic off the roads. The introduction of the BRT with the expansion of the conventional bus systems to feed the BRT reduces congestion, as measured by the flow to capacity ratio, by about 16%. The reduction of costs caused by congestion would be more than 50%, while the gross 45
product of the Beirut region would increase by 1.8%. The introduction of the BRT and the expansion of associated road network would have similar effects on congestion and on congestion costs and would increase the Beirut region’s gross product by 2.3%. Social welfare, measured in terms of consumer utility, would increase by about 4%. On the other hand, demand side instruments such as increased gasoline taxes and parking fees, if implemented on their own, would have negative economic consequences including slight drops in gross national product and in welfare.

Considering that most past studies on transport congestion management focused mostly on demand side instruments, this study has brought additional insights comparing various policy instruments on both the demand and the supply sides. Some limitations should be kept in mind while interpreting the results and policy findings. Although we found that the BRT is a most promising option for addressing congestion in Beirut, it may, in the future, cause problems of overcrowding and other unintended negative social impacts.<sup>11</sup> In the future, congestion might occur again due to increased volume of vehicles and transport service demand as population of the city, income level and vehicle ownership would increase. Therefore, demand side options have an important role as complements to supply-sided measures in the longer-run. Another limitation is that we had to work on a high level of aggregation dividing Beirut into just two zones. The results would have been more precise had we divided the city into additional zones. However, detailed data needed to divide the city more regions for a more detailed study are not currently available.

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