AN EVALUATION OF ENVIRONMENTAL ISSUES IN URBAN DISTRIBUTION

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1 AN EVALUATION OF ENVIRONMENTAL ISSUES IN URBAN DISTRIBUTION Nathalia de Castro Zambuzi Cláudio Barbieri da Cunha Departamento de Engenharia de Transportes Escola Politécnica da Universidade de São Paulo Edgar Blanco Center for Transportation and Logistics (CTL) Massachusetts Institute of Technology - MIT Hugo T. Y. Yoshizaki Carla D. Carvalho Programa de Mestrado em Engenharia de Sistemas Logísticos Escola Politécnica da Universidade de São Paulo ABSTRACT In this paper we evaluate the impacts of freight policies on CO 2 emissions of delivery vehicles in the city of São Paulo. CO 2 emissions are estimated for two different types of freight vehicles under different operational scenarios. Based on current regulations in the city of São Paulo, the paper includes a comparison between the use of smaller freight vehicles, the VUCs ("Veículos Urbanos de Carga") and larger trucks, under two freight policies: (i) restriction of circulation for large/heavy vehicles and (ii) imposition of an area-wide delivery time window. A discussion about the main operational challenges of implementing these policies is also included. RESUMO Este artigo tem como objetivo avaliar os possíveis impactos, resultantes da implementação de políticas públicas de transporte, nas emissões de CO 2 provenientes dos veículos de entrega na cidade de São Paulo. As emissões de CO 2 foram estimadas para diferentes tipos de veículos de entrega, em diferentes cenários operacionais. Baseada nas atuais regulamentações vigentes na cidade de São Paulo, a análise contemplou a comparação da utilização do VUC (Veículo Urbano de Carga) com a de caminhões maiores, em cenários representativos da implementação de duas políticas de transporte: (i) restrição à circulação de veículos grandes/pesados e (ii) imposição de janelas de entrega. 1. INTRODUCTION São Paulo's population is the largest of any city in Brazil, in the Americas, and in the Southern Hemisphere; there are more people living in São Paulo than in New York City, Mexico City or Lagos (UN, 2011). As several big cities in the world, São Paulo faces a variety of problems that arise from its size. A prominent one is the heavy and congested traffic, which continues to get worse. With more than 4.5 million cars, trucks and buses in circulation (CET-SP, 2013), traffic jams have become a routine for drivers of both passenger car and buses as well as for freight vehicles. As the number of vehicles continues to increase (in the last five years about 1.3 million new vehicles, or 21% of the fleet, were new registrations in the municipality of São Paulo), other negative consequences are also in the rise, such as noise, vibration, traffic accidents and air pollution. According to the last published data from the Environment Company of the State of São Paulo (CETESB, 2012), in 2011, motor vehicles were responsible for the emission of about 227,000 tons of air pollutants, almost 87% of total emissions in the metropolitan region of São Paulo. From this total, delivery vehicles generated approximately 52,000 tons, i.e. 23% of the total emissions came from 14% of the total fleet in that year. Concerning specifically CO 2 emissions, delivery vehicles produced more than 9,000 tons in 2011 (about 35% of the total vehicular CO 2 emissions). Moreover, high levels of

2 pollution found in the city of São Paulo are responsible for reducing life expectation in about one and a half years, and about 4,000 people die every year from diseases related to high levels of air pollution (Saldiva, 2012). Even though freight vehicles have a significant contribution in road congestion and CO 2 emissions, they also supply goods to more than 200,000 commercial establishments across the municipality of São Paulo (Department of Finance, 2012). These goods are in charge to satisfy, every day, the consumption and service need of its 11 million citizens. Planning and implementing actions and policies that seek to balance the need for goods and environmental concerns has increasingly been identified as a fundamental role in a modern city, specially taken into consideration the continued growth in the number of delivery vehicles. In order to reduce the congestion and mitigate environmental impacts of traffic, mainly greenhouse gas (GHG) emissions, local authorities in São Paulo have implemented several initiatives, such as license plate restrictions. They also created the Zona de Máxima Restrição de Circulação (ZMRC). The ZMRC comprises the inner and denser area of the city with approximately 100 km 2 and a higher concentration of stores and service activities. Regular trucks are restricted to circulate within the ZMRC during specific periods of the day: Monday to Friday between 5 a.m. and 9 p.m. and Saturday between 10 a.m. and 2 p.m. Since 2008, freight vehicles less than 6.3 meters long and 2.2 meters wide, known as Veículos Urbanos de Carga, or VUC, are allowed to circulate throughout the city with no time restrictions, except the license plate restrictions that all vehicles in the city of São Paulo are subject to. This has resulted, over the past few years, in a significant growth of the VUC fleet: more than 40% over the last ten years (DETRAN-SP, 2013). This paper aims to analyze and estimate the environmental impact, from a CO 2 emissions point of view, of the increased number of VUCs in place of regular delivery trucks. The remainder of the paper is organized as follows. Section 2 briefly describes some of the most common freight policies implemented around the world, as well as their likely impacts on delivery operations. Section 3 presents the literature review of environmental metrics relevant to estimate freight operations. Section 4 details the proposed approach for the estimations of CO 2 emissions, which results, obtained for a set of scenarios, are presented and discussed in Section 5. Finally, Section 6 summarizes the main findings of the analyses and concludes the paper. 2. FREIGHT POLICIES AND IMPACTS OF CO 2 EMISSIONS São Paulo and other cities around the world have been implementing different policies to regulate the transport of goods within their urban areas, aiming for reductions in congestion, as well as better quality of air and less GHG emissions. The Dutch city of Nijmegen, for example, had obtained good results in terms of improvements in air quality since the implementation of an urban consolidation center, in 2008 (Rooijen and Quak, 2010). Other examples can be highlighted, like the night deliveries experiments in New York City, US (Holguín-Veras et al., 2011), the multiple uses of traffic lanes by trucks, buses or cars, in different hours of the day, in Barcelona, Spain ( 1 Dablanc, 2007) and the road pricing applied at some congestion zones of London, United Kingdom ( 2 Ibid.). Table 1 contains a set of the most common freight policies in place around the world and the

3 likely changes on delivery operations that might impact CO 2 emissions. Table 1: Most common urban freight policies Policy Description Expected changes in operation Vehicle's size/weight restriction Area-wide delivery time window Night delivery "Green" vehicles Load consolidation Urban consolidation center Road pricing Emission certificates Parking initiatives Exclusive or multi-use roads/lanes Vehicles above a certain limit of size/weight are not allowed to circulate in some areas and roads Vehicles can only circulate during specific periods of the day (usually out of peak-hours) Deliveries are only allowed in specifics periods during the night and/or dawn Access to a certain area is forbidden to regular fuel truck. Only clean fuel vehicles are allowed Different loads with close destination are transported by the same vehicle (loads from different companies or optimization of loads from the same company) Platforms and specialized depots are implemented at strategic-logistic locations inside the city. Loads are transferred from big trucks or trains to smaller vehicles that deliver in the urban area. In order to use a specific road, companies have to pay for each single trip or for a subscription Vehicles' emission must be within a specified range, so they can circulate in the urban area More loading/unloading zones or shared parking spaces Creation of exclusive roads/lanes for delivery vehicles or shared use of specific roads/lanes with other types of vehicles (like buses) Use of smaller vehicles Easier to park Number of vehicles required increases Increases the number of vehicles required Decreases time to park Night drivers (labor contracts) New vehicle fleet (possible smaller vehicle's capacity) May change fleet to bigger vehicles Less half-empty vehicles in circulation Less vehicles in circulation Add intermediaries in the logistic chain Changes in delivery routes Smaller vehicles deliveries in urban area Changes in delivery routes Changes in fleet (if only a specific group of vehicles is affected) Changes in delivery hours (if possible) Change or update actual vehicle fleet Easier and faster parking Easier and faster load and unload operations Changes in delivery routes More customers attended (if available space in truck) Changes in delivery routes Higher speeds (no car traffic influence) More customers attended (if available space in truck) Most of the policies impact the commercial freight vehicle fleet. Thus, investigating the circumstances in which these changes are beneficial to the city is of great relevance. Specifically, two of the presented policies, "vehicles size/weight restriction" and "area-wide delivery time window", were chosen to conduct a quantitative analysis of CO 2 emissions impacts. Two different types of delivery vehicles fleet, VUC and TOCO, were selected, distinguished primarily by their sizes, weights and transport capacity. A VUC is a small vehicle specially designed to meet regulations, including a smaller payload; while a TOCO is a regular delivery vehicle commonly used in freight distribution. This choice was consistent with the policy currently in practice in the city of São Paulo that both restricts vehicle sizes and limits the circulation of big trucks during a certain period of the day. 3. LITERATURE REVIEW Several works present the estimative of environmental impacts, especially on air quality, as an important measure for the evaluation of freight policies.

4 The estimative of CO 2 emissions is a common indicator to evaluate air quality. Taniguchi and Van Der Heijden (2000) estimate the quantitative effects of logistics initiatives (such as advanced routing systems and control of vehicle's loading factor) in terms of their total cost and CO 2 emissions (expressed in kg/day). A model that combines vehicle routing and dynamic traffic simulation determines the optimal delivery routes and CO 2 emissions for both freight and passenger vehicles within the urban area. Quak and Koster (2006) discuss the impacts of time access restrictions and vehicle weight restriction on the environment, estimating CO 2 emissions (expressed in g/km) for different vehicles types and running speeds of trucks. The authors developed five scenarios adopting or not the policy measures and assessed the possible behavior of retailers when facing the implementation of each of them. CO 2 emissions were then estimated based on the travelled distances of delivery vehicles on their optimized routes. Dablanc and Rakotonarivo (2010) analyze CO 2 emissions generated by the deconcentration of terminal locations in the metropolitan region of Paris over the period between 1974 and CO 2 emissions (expressed in g/t.km) are estimated for different freight vehicles, sorted by capacity, considering an average value of 10 kilometers as being the additional distance traveled by trucks due to the new location of these terminals. Other pollutants are also considered when analyzing air quality. Roijen and Quak (2010) analyze the effects on air quality and noise nuisance, as well as other impacts related to urban distribution activities, one year after the start up of an urban consolidation center located in Nijmegen, Germany. Air quality is evaluated considering the concentration of PM10 and NO 2 (both expressed microgram/m 3 ) result of the current and expected reductions in travelled distance of delivery vehicles (the latter obtained through traffic simulation). The work of Gebresenbet et al. (2011) uses several different pollutants to analyze air quality when evaluating the impacts of a coordinated and optimized food distribution system implemented in and around Uppsala city, Sweden. In this case CO 2, CO, HC, NO x and SO 2 emissions (expressed as g/t.km) are considered and also estimated thought simulation. Holguin Veras et al. (2010) presents a different approach to estimate the benefits due to the shift of truck traffic to the off-hours. In this case, all the results, including air pollution and GHG emissions, are expressed in monetary terms, where air pollution and green house gas are considered to cost $0.06/mile and $0.016/mile, respectively. A traffic simulation thus gives the total distance travelled by the vehicles, which is used to calculate the total cost of the network. 4. ESTIMATING CO 2 EMISSIONS 4.1. Proposed Approach The CO 2 emissions estimations in this study are based on "Network for Transport and Environment" methodologies (NTM, 2010). For a vehicle type v, travelling on a road of type r, CO 2 emissions (E " (,)) depend on three main parameters, which are related to each other according to Equation 1: E " (,) = EF () FC, D (,) (1) where, EF () : emission factor of CO 2 [g/l], for fuel type f; FC, : fuel consumption rate [l/km] of vehicle type v, driving on a road type r; D (,) : distance traveled [km] by vehicle type v on a road type r.

5 Considering a complete route of a delivery vehicle v, i.e., (i) the vehicle driving from the depot (also known as distribution center - DC) to the inner city area of delivery, (ii) the stops and deliveries at different customers within this area, and (iii) the vehicle returning to the depot/dc), and also taking into account that this route can contain R different types of roads, total CO 2 emissions (TE " ()) could be estimated by the expression given in Equation 2: TE " () = EF () FC, D (,) (2) Besides the vehicle moving, there is a parcel of CO 2 emissions for the idling vehicle, i.e., when the vehicle is parked but its engine is still running. This kind of situation is common, for example, for some types of refrigerated vehicles, which must remain turned on when parked, so that its refrigeration system keeps working. NTM methodology provides the formulations to estimate CO 2 emissions from idling but they will not be considered in our model Traveled Distances and Customer's Demand Assumptions A delivery vehicle must attend a certain amount of customers in its delivery route, according to the demand of customers and to its load capacity. For the same company, it is assumed that all customers' orders have the same characteristics, i.e., the items delivered belong to the same category and the drop size is comparable during the entire route. We assume that all the deliveries are made by driving the vehicle to the customer and no delivery by foot is considered. The vehicle starts its route from the company s depot/dc and travels to the delivery area. After servicing all the customers, the vehicle must then return to the depot/dc. These two legs of the round trip are assumed to be of same length, name here as line haul distance (d l ), as shown on Figure 1. Figure 1: Traveled distances for a delivery vehicle route It is considered that the vehicle starts its deliveries from the center point of the delivery area, so the distance between two consecutives customers (d c ) is assumed to be the same across the route. Then, the total traveled distance by a delivery vehicle is given by (Equation 3): d = 2 d + n d (3) where, d: total traveled distance of the delivery vehicle; d : line haul distance; d : average distance between two consecutive customers; n: number of customers attended by the vehicle in its the delivery route. The distance between two consecutive customers is actually considered as function of a density parameter, assumed here as customers per block and considering a block a surface of 100 m x 100 m. In this sense, (Equation 4): d = 1 (10 δ) (4) where, δ: density of customers [customers/block];

6 d : average distance between two consecutive customers. Any other distances, such as extra driving to find a parking spot, are not added to the route. For the purpose of the analysis an entire delivery operation is evaluated, as opposed to the vehicle's emissions on a single route. We also assume that the total number of customers that must be serviced is known. Thus, it is necessary to determine the total number of vehicles required to service all customers, which depends on customer's drop size and vehicle's load capacity. In addition, when evaluating area-wide delivery time window restriction policies, it is necessary to take into account both capacity and time to define the number of vehicles required, as explained on the following sections Vehicle's Size/Weight Restriction Policy - Capacity Constrained Formulation In the capacity constrained problem, the number of customers (n) attended by one vehicle route, must meet the constraint expressed by Equation 5: where, s W (5) s : drop size of customer c, in tons or cubic meters; W: maximum load capacity of the vehicle, also in tons or cubic meters, whichever is more binding. Let N be the total numbers of customers to be serviced in a daily delivery operation. Thus, the number of required vehicles (V) is: V = (6) Equation 6 calculates the number of vehicles required for a daily delivery operation. In practice, however, the number of vehicles cannot assume fractional values and must be rounded up. In other words (Equation 7): V [V ] = min {n Z n V} (7) 4.4. Area-Wide Delivery Time Window Policy - Capacity and Time Constrained Formulation In the capacity and time-constrained problem, beyond the vehicle's capacity, the number of customers (n) attended by a single vehicle's route must also meet a time constraint, as expressed by Equation 8: s W and t + t,) T (8) where, s : drop size of customer c, in tons or cubic meters; W: maximum load capacity of the vehicle, also in tons or cubic meters, whichever is more binding; t : delivery time at customer c; t, : travel time from a customer c to the next customer c+1; T: maximum available delivery time. The delivery time at a customer (t ) is the time a vehicle is allocated to that customer. It may include parking the vehicle, unloading products and any other administrative procedure, like to get customer's signature. For the analysis purpose, t is calculated according to Equation 9: t = a + b s (9) where a, b are constant terms, denoting a fixed time that does not depend on the drop size.

7 Travel time between two consecutives customers (t, ) depends on the distance traveled between them and the vehicle's speed. For the analysis purpose, vehicle speed (v) is considered constant during the entire delivery route and the same average distance between customers (d ) is adopted, as explained. Equation 10 then calculates travel time: t( c, c+ 1) = dc v (10) For the capacity and time constrained problem, being N the total numbers of customers to be attended in a daily delivery operation, the number of required vehicles (V) is (Equation 11): V [V ] = min {n Z n V}; V = Max,, (11) 4.5. Input Parameters Tables 2 and 3 present, respectively, the assumed values for the parameters used in the analysis. Delivery time constants (a and b) are based on average values from data collected. We consider the same type of road for the entire delivery route as São Paulo is mainly composed of urban streets and the traveled distances on not-urban roads observed are irrelevant compared with the urban-streets portion. No adjustments on travel speeds are made due to changes in congestion during the day in the urban area. The adopted value for vehicle's speed is of 50 km/h, considering the historical average speeds for the morning period (Map data 2013 Google, MapLink) and the speed limit of 60 km/h for most of São Paulo s urban roads. Table 2: Vehicles' Operational Parameters Table 3: Delivery Time Parameters VUC TOCO ParaeParameters Delivery time constant f(timestime [min] 4 Average inner dimensions (length x width x height) [m] 4.3 x 2.2 x x 2.4 x 2.7 Delivery time constant [min/kg] 0.1 Daily number of customers 100 Average load capacity [kg] Average vehicle's speed [km/h] 50 Average load capacity [m 3 ] Type of fuel Diesel Diesel Fuel consumption [l/km] Diesel emission factor [kg/l] Source: Abril Group, SCENARIOS ANALYSIS AND RESULTS For each scenario analyzed, CO 2 emissions of the two vehicles (VUC and TOCO) have been computed, using the parameters shown in Table 2. The total number of customers is also constant and equal to 100. In all charts below, the lowest CO 2 emission type of vehicle for the whole fleet is chosen as a function of two parameters: line haul distance and customer density. The curve in the chart is called "indifference curve and represents the points where CO 2 emissions are the same for VUC and TOCO, i.e., below or above the indifference curve, one of them is the less pollutant choice to deliver to all the customers "Vehicle Size/Weight Restriction" Policy "Vehicle's size/weight restriction" policy is strict related to vehicle's load capacity. This is the only constraint that determines the total number of vehicles required to attend all customers in a daily delivery operation. i.e., considering the same number of customers and drop size, and no other restriction (such as driver working hours or customer receiving hours). Thus, more small vehicles will always be needed compared with ones with more capacity.

8 The next charts represent different scenarios of analysis where the effects on vehicle's choice are examined considering changes in the following parameters: customer's drop size (Figure 2), VUC's load capacity (Figure 3) and VUC's fuel consumption (Figure 4). The last two scenarios assume a fixed customer's drop size equal to100 kg. 1" 1" Density(([customers/block]( 0.9" 0.8" 0.7" 0.6" 0.5" 0.4" 0.3" 0.2" DROP SIZE 50 kg 100 kg 300 kg 500 kg 700 kg Density(([customers/block]( 0.9" 0.8" 0.7" 0.6" 0.5" 0.4" 0.3" 0.2" VUC'S CAPACITY 1000 kg 2000 kg 3000 kg 4000 kg 0.1" 0.1" 0" 0" 5" 10" 15" 20" 25" 30" Linehaul(distance([km]( Figure 2: Indifference curves: drop sizes 0" 0" 5" 10" 15" 20" 25" 30" Linehaul(distance([km]( Figure 3: Indifference curves: VUC's capacity 1" 0.9" Density(([customers/block]( 0.8" 0.7" 0.6" 0.5" 0.4" 0.3" 0.2" VUC'S FUEL CONSUMPTION " 0" 0" 5" 10" 15" 20" 25" 30" Linehaul(distance([km]( Figure 4: Indifference curves: VUC's fuel consumption rate Figure 2 shows that TOCO outperforms VUC when the number of customers decreases, due to large drop sizes; on the other hand, a VUC is more suitable for areas with smaller density of customers and shorter line haul distances as well. As drop size increases, TOCO would be the best option, if the policy that restricts the traffic of larger vehicles was not in place. From Figure 3, it can be noticed that a small capacity vehicle would be preferred for smaller density of customers, and also for shorter line haul distances. As these parameters increase, the advantages to use a vehicle with a higher capacity also increase. For even larger VUC's load capacities (limited to TOCO's capacity of 6,000 kg), VUC is always the best choice, since the number of VUCs and TOCOs required results the same. Thus, allowing the traffic of vehicles slightly larger than VUC, but not as slow or noisy as big trucks, could be a good alternative in terms of reducing air pollution and the total number of delivery vehicles on streets, without significantly affecting traffic speed and noise levels. Figure 4 shows that as VUC becomes greener (due to decreased fuel consumption rate), it

9 tends to become a better choice than TOCO for most combinations of density and line haul distance. When VUC's fuel consumption rate is closer to TOCO's (0.256 l/km), the latter is the best choice, showing that, if a cleaner vehicle policy was adopted (so that TOCO's CO 2 emission was equivalent to VUC's) TOCO could be adequate in more situations, since not only result emissions would be lower but also there would be a smaller number of delivery vehicles on streets, thus leading to a better traffic. In general, TOCO is the superior choice for a wide range of density-line haul distance pairs, which is expected in the cases where only capacity is determinant in the number of vehicles required, and the alternative vehicle use the same type of fuel "Area-Wide Delivery Time Window" Policy An "area-wide delivery time window" policy allows freight vehicles to circulate and perform their deliveries within city boundaries only in specific time periods, generally off-peak traffic hours. In this case, not only vehicles' load capacity, but also the length of this time window can be decisive to the total number of vehicles required to service all customers. The total load in a route only influences the number of vehicles if route duration does not exceed the time window. Otherwise, a greater number of vehicles is needed, though their capacities are not fully utilized. In these cases, the scenarios of analysis, represented by the next charts, examine the following situations: different customer's drop size, considering a delivery time window of 7 hours (Figure 5), different time window lengths (Figure 6) and different customer's drop size and time to park, considering a delivery time window of 7 hours and a line haul distance equal to 15 kilometers (Figure 7). 1" 1" 0.9" 0.8" TIME WINDOW 10 hours 12 hours 0.9" 0.8" TIME WINDOW 10 hours 12 hours Density([customers/block]( 0.7" 0.6" 0.5" 0.4" 0.3" 16 hours 20 hours Density([customers/block]( 0.7" 0.6" 0.5" 0.4" 0.3" 16 hours 20 hours 0.2" 0.2" 0.1" 0.1" 0" 5" 10" 15" 20" 25" 30" Linehaul(distance([km]( Figure 5: Indifference curves: drop size 0" 5" 10" 15" 20" 25" 30" Linehaul(distance([km]( Figure 6: Indifference curves: time window As shown in Figure 5, an increase in drop size leads to a wider range of line haul distance and customers density combinations for which TOCO is the top choice. Considering the same drop size, TOCO is more adequate for higher customers density and line haul distances. However, as line haul distances increase, it is no longer feasible to use TOCO even for smaller drop sizes. This is due to the imposed area-wide time window, which prevents total TOCO's capacity to be used; in other words, although TOCO s load capacity would allow delivering to more customers with smaller drop sizes, the total required time would be longer than the 7 hours-time window. This yields to more TOCO vehicles circulating not fully occupied, and more CO 2 emissions than VUC. Thus, even for smaller drop sizes (not showed

10 in the charts) TOCO could never be superior. Then, even if there may be a positive outcome of this time window policy in terms of traffic and CO 2 emissions, it can increase the number of half-empty vehicles on streets when they are allowed to circulate. In these cases, a more appropriate decision should consider all the possible impacts, since it is possible that during the time window period the number of delivery vehicles, especially those delivering larger drop sizes, is so elevated, that this policy may become not effective. It is interesting to notice in Figure 6 that, for higher values of time window, the indifference curves do coincide. This makes sense since, in these cases, the vehicle load capacity is the binding constraint that determines the required number of vehicles (either VUC or TOCO), which are constant for every delivery time window. Figure 6 also shows that line haul distances are no longer relevant, and VUC tends to be the best choice, as customers become more distant from each other, and the length of delivery time window decreases. Also in this case, for a time window of about 9 hours and line haul distances greater than 25 km the use of TOCO is no longer feasible, due to the time constraint. 4" Time%to%Park%[min]% 3.5" 3" 2.5" 2" 1.5" DROP SIZE 100"kg" 300"kg" 500"kg" 700"kg" 900"kg" 1" 0.5" 0" 0" 0.02" 0.04" 0.06" 0.08" 0.1" 0.3" 0.5" 0.7" 0.9" 2" 4" 6" 8" 10" Density%[customers/block]% Figure 7: Indifference curves: drop size Finally, Figure 7 shows that the time a vehicle spends looking for an available parking space is of major relevance when a time window policy is in force, once this extra time wasted could be used to deliver to more customers (in case there is enough vehicle's load capacity). In this sense, a shorter time to park makes TOCO feasible for more values of drop sizes, even for longer distances between customers. As drop sizes increases, customer density becomes less important to determine the best type of vehicle, once the travelled time/distance between customers becomes not significant when compared to the additional time/distance due to the need to find a parking space. As time to park increases, customers need to be closer to each other so that TOCO becomes the top choice, especially for smaller drop sizes; in these cases VUC becomes more appropriate in most alternatives. Therefore, to be more effective in reducing the number of delivery vehicles on streets, an area-wide delivery time window policy should be implemented as more parking spaces were available, whether in the form of exclusive loading/unloading zones or shared parking. In a large city, this concern related to dedicated parking spots becomes more relevant, once commercial areas with high densities of stores are typical, there is an expressive number of small stores (small drop sizes) and, still, general parking spaces may become scarce.

11 6. FINAL REMARKS In order to evaluate the current and extensive use of small trucks (VUCs) in the city of São Paulo, the expected effects of some adopted policies were analyzed for different scenarios, considering when VUC becomes advantageous, in terms of CO 2 emissions, when compared to a larger urban delivery vehicle (TOCO). If no restricted delivery time window is imposed, TOCO has showed to be the best choice in terms of CO 2 emissions in most of the cases. Given its larger capacity, a smaller number of vehicles can service more customers in a single route and, once VUC and TOCO use the same type of fuel, lower emissions of CO 2 are expected from TOCO. Also, by considering different VUC capacities and fuel consumption rates it is possible to conclude that, in order to allow the traffic of slightly larger vehicles than VUC, or to incentive the use of greener large vehicles could be good alternatives in terms of reducing the air pollution and the total number of vehicles on streets as well. For the scenarios where a delivery time window was imposed, the use of TOCO has showed to be more attractive in cases where this period is longer. This is because, even if TOCO could deliver to a greater number of customers, due to its larger load capacity, there is not enough available time to complete all the possible deliveries for shorter time windows, thus leading to a greater number of half-loaded TOCOs in circulation. Additionally, in some cases the use of TOCO is no longer feasible for any combination of line haul distance and customer density. As for the time window analysis, the relevance of the time to park on vehicle's choice was also stated. It was clear that the additional times spent by the delivery vehicle due to the lack of available parking spaces are crucial to efficiency of the delivery operation. This is easy to realize, once this time is subtracted from the total available delivery period, what in practice, represents a shorter area delivery time window. Still, to be more effective in reducing the number of delivery vehicles on streets, an "area-wide delivery time window" policy should be implemented as more parking spaces were available. Finally, we believe that this analysis could be significantly improved if the effects of the interactions of these freight deliveries on the general traffic, in terms of speeds and fuel consumption, could be also taken into consideration. Other aspect that might be considered is the influence of larger (and wider) delivery vehicles on the narrower traffic lanes that have been created in order to accommodate small passenger cars in low speeds due to congestion. Acknowledgments The authors gratefully acknowledge Itau Foundation, MISTI-Brazil and CAPES for the financial aid that allowed this collaborative research to be completed. REFERENCES ABRIL GROUP. Relatório do Inventário Piloto de emissões de GEE. Gases de Efeito Estufa CET-SP (2013). Companhia de Engenharia de Tráfego. Available in: < CETESB (2012). Qualidade do ar no estado de São Paulo Companhia Ambiental do Estado de São Paulo. Departamento de Meio Ambiente, Governo do Estado de São Paulo. Available in: < publicacoes-e-relatorios>. DABLANC, L. Goods transport in large european cities: difficult to organize, difficult to modernize. Transportation Research Part A, (41), p , DAGANZO, C. (1984). The length of tours in zones of different shapes. Transportation Research B, v. 18B, p

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