Scholarly article on topic 'CLASS: A City Logistics Analysis and Simulation Support System'

CLASS: A City Logistics Analysis and Simulation Support System Academic research paper on "Civil engineering"

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Abstract of research paper on Civil engineering, author of scientific article — Antonio Comi, Luca Rosati

Abstract The paper presents a City Logistics Analysis and Simulation support System (CLASS) for the identification of critical stages and the simulation of city logistics scenarios. The analysis of current scenario focuses on logistics and freight transport in relation to land use, freight restocking demand and supply, logistic profile and road network performances and impacts. The simulation is able to point out the relations existing among city logistics measures, decision-maker choice dimensions by using a multi-stage demand model and a discrete choice approach for each decision level. Among the outputs, the freight vehicle flows on the road network links allow to compute the link performances in terms of congestion, pollution and road accidents involving freight vehicles. An application of the support system to the freight restocking for the inner area of Rome is presented.

Academic research paper on topic "CLASS: A City Logistics Analysis and Simulation Support System"

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Procedia - Social and Behavioral Sciences 87 (2013) 321 - 337

SIDT Scientific Seminar 2012

CLASS: a City Logistics Analysis and Simulation support System

Antonio Comia and Luca Rosatia*

a"Tor Vergata" University of Rome, 00133 Rome, Italy

Abstract

The paper presents a City Logistics Analysis and Simulation support System (CLASS) for the identification of critical stages and the simulation of city logistics scenarios. The analysis of current scenario focuses on logistics and freight transport in relation to land use, freight restocking demand and supply, logistic profile and road network performances and impacts. The simulation is able to point out the relations existing among city logistics measures, decision-maker choice dimensions by using a multi-stage demand model and a discrete choice approach for each decision level. Among the outputs, the freight vehicle flows on the road network links allow to compute the link performances in terms of congestion, pollution and road accidents involving freight vehicles. An application of the support system to the freight restocking for the inner area of Rome is presented.

© 2013TheAuthors.PublishedbyElsevierLtd.

Selectionandpeer-reviewunder responsibilityofSIDT2012ScientificCommittee. Keywords: support system; city logistics; analysis; transport simulation

1. Introduction

Today, there is a growing interest to support systems able to support decision-makers to understand the structure of freight urban system and to compute some indicators that compared with target and benchmarking values allow to identify its level of service. Some tools were developed in the last years and are based on different modeling approaches (Gonzales-Feliu et al., 2012; Anand et al., 2012; Taniguchi et al., 2012; Russo & Musolino, 2012; Nuzzolo et al., 2013). In Germany, Sonntag (1985) proposed the support system WIVER that allows to simulate vehicle trip Origin-Destination for restocking activities. WIVER starts from the estimation of O-D quantity matrices and provides information regarding total mileage, number of trips and tours, daily traffic distribution over time, subdivided into vehicle type and economic sectors (freight types). Furthermore, the relations between origins and destinations in terms of routes or single trips are modeled through some identified empirical findings. WIVER was also used in several German and European traffic planning processes (Ambrosini

* Corresponding author. Tel.: +39 - 06- 7259 7061; fax: +39 - 06- 7259 7053. E-mail address: rosati@lng.unlroma2.lt

1877-0428 © 2013 The Authors. Published by Elsevier Ltd.

Selection and peer-review under responsibility of SIDT2012 Scientific Committee.

doi: 10.1016/j.sbspro.2013.10.613

et al., 2008), like Hamburg, Berlin and within the framework of the COST 321 co-operative action (Urban Goods Transport) for the cities of Munich, Nuremberg, Augsburg, Hanover and Trier, as well as the European project REFORM (research on freight platforms) for the metropolitan areas of Madrid and Brussels. Following the approach of WIVER, Lohse (2004) developed the tool VISEVA-W that allows to consider in the simulation both the restocking and passenger flows. But, the two segments of mobility are managed independently without considering that restocking flows can be also generated by the requests of end consumers that move for shopping.

To improve the simulation of restocking, in France the delivery approach was proposed and implemented within the support system named FRETURB (Routhier & Toilier, 2007; Gonzalez-Feliu et al., 2012). The simulation unit is the movement/delivery (pick-up and delivery); in fact, the use of delivery as reference unit allows to build a direct link between producers/retailers and transport operators, through the use of the same reference unit. The models implemented within FRETURB consist of a sequence of statistic-descriptive models within three modules which interact with each other: a pick-up and delivery model including flows between all the economic activities of a town; a town management module, consisting of transport of goods and raw material for public and construction works, maintenance of urban networks (sewers, water, phone), and garbage; a purchasing trips model, modelling shopping trips by car, which represents the main last kilometer trips to end consumers. The pick-up and delivery (generation and attraction) model is a regression-based model. The weekly average number of goods movements (deliveries and pick-ups) is a function of 45 freight types, type of the activities (store, warehouse, office, headquarter) and number of employees at the establishment. These flows are characterized in terms of transport service, vehicle and journey types, and, finally, are allocated to some identified journeys in order to obtain the vehicle O-D matrices. The tool allows us to estimate traffic volumes in and between each zone, according three types of vehicles and the type of served activity. At moment, the model has been implemented in about 20 French towns (including Paris, Lyon and Lille).

Based on the same approach implemented in FRETURB and within the European Project (CityPorts, City Ports, 2005), CityGoods was developed by Gentile and Vigo (2006). This support system was tested on several cities of Emilia-Romagna Region (Italy). The objective was to build a demand generation model in order to estimate the yearly number of operations generated by each zone in terms of tours. On the basis of surveys among transporters, shippers, establishments, they proposed a specific approach for the generation of total number of movements as a function of the NACE (European Classification of Economic Activities) code and the number of employees at each establishment. The generation model uses a hierarchical classification of activities of the establishments in a zone. The distribution and network assignment models are in progress.

Focusing mainly on logistics chains and on the main assumptions that livability and accessibility of urban areas are influenced by freight traffic resulting from logistical choices in the supply chain, like warehouse location, delivery frequencies, vehicle type and routing, Boerkamps and van Binsbergen (2000) proposed a general framework implemented in GoodTrip. The developed models simulate these choices and their effects, in current and future situations, through some identified empirical relationships. GoodTrip allows the estimation of goods flows (in terms of quantity), urban freight traffic and its impacts, like vehicle mileage, network loads, emissions and, finally, energy use of urban freight distribution.

From the above literature analysis, it emerges that some support systems were developed but many of them are based on empirical relations that well describe the current state of the system but they fail when new city logistics scenarios (before implementation) are simulated and assessed. Based on these considerations, the paper proposes a new support system that implements some advanced models that allow to capture the effects of city logistics measures on actors' behavior (Nuzzolo & Comi, 2013a). This modeling system is a multi-stage model and considers a discrete choice approach for each decisional level. The freight flows (in terms of quantities, deliveries and vehicles) are simulated through three model sub-systems that estimate the quantity Origin-Destination matrices by transport service type (e.g. retailer on own account or wholesaler on own account or by carrier), the delivery O-D matrices by delivery time period, and the vehicle O-D matrices according to delivery tour departure time and vehicle type. Besides, to point out the complexity of urban restocking tours (where each restocker

jointly chooses the number and the location of deliveries for each tour and hence defines his/her tours, trying to reduce the related costs) for the translation of delivery O-D to vehicle O-D matrices, the DSS uses a two-step procedure developed by Nuzzolo and Comi (2013b) consisting of: definition of delivery tours from delivery O-D matrices through delivery tour models, and definition of the freight vehicle O-D matrices from the delivery tours. This allows the mechanisms driving tour generation to be captured more accurately, using also an aggregate approach.

More precisely, the contribution of the paper arises on the approach to analyze the current scenario in terms of land use, freight restocking demand and supply, logistic profile and road network performances and impacts, and hence to simulate restocking flows and to compute some indicators useful for identifying the critical stages of the system based on some performance and impact indicators.

The paper is structured as follows. Before the users' needs and the logical framework for meeting the identified needs of the decision-makers are analyzed (section 2), then the modeling system is described in section 3, while the results obtained for its implementation to the inner area of Rome are reported in section 4. Some conclusions and further developments in progress are given in section 5.

2. Users' needs and logical architecture of the proposed support system

2.1. Users' needs for city logistics analysis and assessment

The first step in the definition of the logical architecture is the identification of the users' needs, expressed as requirements and priorities that the support system has to satisfy for the analysis of critical stages and the simulation of the system. The users could be the City Planners that need to identify the main characteristics and the critical stages of the actual City Logistics System (CLS) and to assess and to verify the new ones. The CLS can be described by a set of indicators related to: land-use, freight demand and supply, and road network performance and impacts. This set of indicators can be obtained directly from surveys or from the simulation of current and future CLS scenarios. Hereinafter, a description of each above class of indicator and their metrics is reported below.

The land-use indicators allow to describe the commercial land-use characteristics of the study area. Several indicators can be used for characterizing each traffic zone. CLASS provides the evaluation of the following metrics:

• total number and ratio between employees and residents;

• total number and density of retailer outlets;

• total number and density of retail employees also disaggregated for freight type;

• average number of retail employees also disaggregated for freight type.

The freight demand indicators are:

• freight quantities produced and attracted by zones; for each zone of the study area, the total freight quantity (also disaggregated for freight type) required by retail and food-and-drink outlets, and service activities are computed;

• freight deliveries produced and attracted by zones; for each zone of the study area, the total number of deliveries (also disaggregated for freight type) required by retail and food-and-drink outlets, and service activities are computed;

• average delivered quantity; in relation to each types of transport service types (e.g. on own account and third party - 3P), freight types and type of retail outlets (e.g. small, medium and large), the average delivered quantity is computed;

• deliveries time slice distribution; according to the time the delivery freight flows are computed and hence split by time period (e.g. one in the morning between 8:00 and 10:00 am and one in the afternoon).

The transport freight supply indicators, calculated for each freight types, are:

• the services for transport quantity and deliveries offered by the different types of transport services (i.e. on own account and third party);

• the vehicles fleet used for freight restocking characterized in terms of vehicle types (e.g. light or medium or heavy), equipment (e.g. refrigerator), emission standards (e.g. Euro I, Euro II) or type of fuel (e.g. gasoline, diesel).

The logistic profile indicators allow to identify areas homogeneous respect to some specific logistics needs:

• commercial density and homogeneity through the total number of retail outlets and/or of the retail outlets types per block (i.e. small, medium or large);

• logistic accessibility through the congestion level on the streets serving the zone, the presence of traffic management measures for the zone (e.g. time windows, zone 30, Limited Traffic Zones, pedestrian streets), the number of loading/unloading zones and the average access distance among loading/unloading zones and retail outlets, the presence of logistic infrastructures as Urban Distribution Centre (UDC), Nearby Delivery Areas (NDA) or Transit Points (TP);

• product characteristics destined to retail outlets, like fragility, perishability or cooling needs;

• actor (e.g. retailers, wholesalers, carriers) needs according to particular requirements, like urgency of deliveries, frequency of deliveries, quantity to be delivered and time slice of deliveries (e.g. morning, afternoon or night).

According to Macario et al. (2008), the following logistics profiles can be defined:

• Profile A, cluster of shops specialized in one specific type of service/product characterized for high commercial density and homogeneity and low logistic accessibility;

• Profile B, hotels, restaurants, small grocery stores, small neighborhood markets mainly characterized by the perishability of the products;

• Profile C, business center characterized for high commercial density and low homogeneity with a low logistic accessibility;

• Profile D, large commercial stores mainly characterized by a good logistic accessibility and a big amount of freight to be delivered;

• Profile E, Residential areas with local trade characterized for low commercial density and homogeneity with a low logistic accessibility;

Therefore, a specific logistic profile is associated to every CLS zone in order to properly identify its logistic characteristics.

Finally, the considered road network performance indicators of CLS are the followings:

• vehicle-km on the network characterized for light (less than 1.5 ton), medium (between 1.5 and 3.5 tons) and heavy (more than 3.5 tons) vehicles and for transport service type (i.e. on own transport and 3P);

• vehicle-h on the network characterized for light, medium and heavy vehicles and for transport service type (i.e. on own transport and 3P);

• average speed characterized for light, medium and heavy vehicles and for transport service type (i.e. on own transport and 3P);

• traffic pollutant emissions characterized for pollutant type (e.g. CO, NOx), type of vehicle (i.e. light, medium, heavy), fuel (e.g. gasoline, diesel) and for transport service type (i.e. on own transport and 3P);

• road accidents characterized for type of vehicles and location.

2.2. Logical architecture

To meet users' needs described above, the logical architecture of CLASS consists of: input database, road network module, demand module, assignment module and output module (Fig. 1).

The input database contains information on exogenous variables of the scenario, as resident and employees of each traffic zones, traffic management measures, number of loading/unloading areas, presence and number of logistic infrastructures, like Urban Distribution/Consolidation Centre, Nearby Delivery Area or Transit Point, and information about freight vehicle road accidents.

The road network module includes the graph of the main road network with their relative link cost functions (see section 3.1).

The demand module simulates the relevant aspects of travel demand as function of the activity system and the road travel costs. It includes the modeling framework that gives the O-D matrices which are the input for the subsequent assignment module (see section 3.2).

The assignment module includes path choice models and network loading models for freight vehicles. The network loading model simulates how O-D vehicle flows load the paths and the links of the road network and estimates the link flows, i.e. the number of cars and freight vehicles loading each link (see section 3.3).

The output module allows to estimate, using data and results of above modules, the CLS indicators. In fact, the link flows (output of assignment module) are used for the estimation of several scenario indicators that, in turn, are used for computing the new scenario effects:

• network transportation costs, using time - flow functions, like BPR (BPR, 1964) or Davidson function (Davidson, 1966);

• traffic pollutant emissions, using average emission functions that allow to estimate pollutant emissions in relation to average link kinematic variables (e.g. vehicle speed; see section 3.4);

• road accidents connected to both passenger and freight vehicles, using Safety Performance Functions (see section 3.5).

Fig. 1. City logistics system modelling framework

3. CLASS models

The models used in CLASS are briefly analyzed in the following sections according to the above modules.

3.1. Road network model

The CLASS road network module uses a supply model in which the main road system of the city logistics system area is modeled. The supply model is a graph-based model where each link is described by several features, e.g. road class, length, number of lanes, width, number of secondary road intersections, pedestrian interference. Unlike cars, which can use all non-pedestrian links of the network, freight vehicles move on a subset of links that for geometric characteristics (e.g. width) and traffic rules are consistent with freight vehicle dimensions. Although CLASS allows to consider the generalized transportation costs of each road link made up by several performance attributes (i.e. travel time and cost), having verified that the main variable considered by transport and logistics operators is time, CLASS models the link generalized transportation cost only as function of travel time and then it is computed by BPR function.

3.2. Demand models

The freight vehicle O-D matrices modeling framework used in the Support System, derived from the current literature and is based on that proposed by Nuzzolo et al. (2012a and b) and Nuzzolo and Comi (2013a and b). It consists of three model subsystems (Fig. 2):

• quantity model sub-system; it allows us to estimate the quantity origin-destination (O-D) matrices characterized by freight types; this step highlights the effects due to implementation of strategic actions (e.g. urban land-use governance) on the locations of logistic facilities (e.g. warehouses and distribution centers) and retail activities (e.g. local shops or shopping centers);

• delivery model sub-system; it allows us to convert quantities into delivery O-D flows; the delivery flows are also split in terms of transport services used (e.g. retailer on own account, wholesaler on own account and carrier); this step serves specifically to study the definition of restocking journeys in terms of transport service and shipment size (i.e. tactical level);

• vehicle model sub-system; it allows us to obtain the vehicle O-D flows satisfying the given delivery O-D matrices, and investigate the tours undertaken to restock the study area; in particular, the tours are characterized by departure time, number of stops, vehicle used, and sequence of delivery locations.

Quantity

Fig. 2. Urban freight modeling framework (Nuzzolo & Comi, 2013a)

3.2.1. O-D flow quantities

Let Qod[k] be the average quantity of freight flows moved between zone o and zone d for restocking retail outlet type k that can be estimated as follows:

Qod [k ] =Qd [k ] • p[o/dk ] (1)

• Qd[k] is the average freight quantity attracted by zone d for restocking retail outlet type k; it is obtained by an attraction model;

• p[o/dk] is the probability that freight attracted by zone d for restocking retail outlet type k comes from zone o (e.g. warehouse location zone); it represents the acquisition share obtained by an acquisition model.

3.2.2. Delivery O-D flows

This step receives inputs from the previous model sub-system and provides as output the number of deliveries needed to transport the estimated freight quantity. Freight can be transported and hence each establishment can be restocked by different transport services according to which transport service is used (e.g. retailer or wholesaler on own account, carrier).

The average delivery O-D flow carried out by transport service type r on pair od, NDod[rk], can be determined as follows:

NDod [k] = Qod [rk] • p [r / odk]/q [rk] (2)

• p[r/odk] is the probability of restocking retail outlet type k by transport service type r obtained by a transport service type model;

• q[rk] is the average freight quantity delivered with transport service type r (shipment size) for restocking retail outlet type k.

3.2.3. Vehicle O-D flows

Having obtained the O-D flows in terms of deliveries, the next step is to convert them into tours and hence into O-D freight vehicles. The freight vehicle O-D matrices are obtained from the delivery O-D matrices using a two-step procedure: definition of delivery tours from delivery O-D matrices, definition of freight vehicle O-D matrices from delivery tours.

The total number of tours To[rk] departing from zone o to restock retail outlet k with transport service type r can be determined as follows:

To H = ldNDod\rk]/n0 [rk] (3)

where no [rk j is the average number of deliveries performed by tours departing from zone o to restock retail outlet type k.

Let p[n/or] be the probability that a tour departing from origin zone o has n stops/deliveries obtained by a trip chain order model. Therefore, no [rkj can be estimated as:

no [r&J = ^ n • p[n/orktj-p[t /rkoj (4)

• p[t/ro] is the probability that the delivery tours depart at a certain time t from an origin o (i.e. warehouse zone) obtained by a discrete choice delivery tour departure time model;

• p[n/tro] is the probability that deliveries are performed by tours departing from a given zone o with n stops obtained by a discrete choice trip chain order model.

Let p[v/nrko] be the probability of using a vehicle type v obtained by a vehicle type model. The number of tours with n stops/deliveries departing from origin zone o and operated by vehicle type v, To[vnkr], is obtained

To [vnkrj = r ||rk j • p \nv / rko j = To\rk j • p \n / rko j • p [v / nrkoj (5)

Let /dhvnrkoj be the probability of delivering in zone dj the delivery (h+1), conditional upon having

previously delivered in zone di delivery k, within a tour with n stops/deliveries departing from a given zone o and using a vehicle type v, obtained by a delivery location choice model.

Finally, the number of vehicles VCdd (freight vehicle O-D matrices) on pair (di dj) can be estimated as

follows:

VCdd [ vnrko j = dh+,dk [ vnrkoj = To [ vnkr j • p\^dh+1 / dhvnrkoj (6)

3.3. Assignment models

The truck-driver path choice within an urban network is constrained by the vehicle size but there are other factors that tend to influence the behaviors of truck drivers including driver preferences, vehicle and route performances (e.g. travel time, vehicle operating costs, gateway toll; Taniguchi et al., 2001; Russo et al., 2010). The implemented assignment model is a Deterministic User Equilibrium (DUE) model, obtained applying the equilibrium approach for congested networks under assumption of deterministic path choice behavior where the users choose always the minimum cost path for satisfy their travel needs. A pre-load of passenger vehicles is performed in order to update the link costs of the network.

3.4. Traffic pollutant emission models

According to COPERT (COmputer Programme to calculate Emissions from Road Transport; Eggleston et al., 2000; Ntziachristos & Kouridis, 2007) model that is the most extensive traffic emission modeling method used within Europe and was promoted by the European Environmental Agency within the CORINAIR programme. Even if COPERT was specified for estimation of national emissions of traffic related pollutants, following Filippi et al. (2010), in CLASS, the methodology was adapted for the urban and metropolitan contexts.

3.5. Road accident models

The implemented model for the evaluation of road accidents due to freight vehicles is based on the Safety Performance Functions (SPFs). The probability of accidents are evaluated in relation to vehicle flows and road characteristics, location and characteristics of infrastructures (e.g. junction), control system (e.g. traffic lights, crosswalks), and other standardization variables (e.g. reference period, environmental conditions); examples are in Poul Greibe (2003) and Dietze et al. (2005). At moment, this module is under development for the estimation both of aggregate and disaggregate indicators (Russo & Comi, 2013). In the following, some descriptive analyses based on the representation of input database are hence performed.

4. The CLASS application to the Freight Limited Traffic Zone of Rome

CLASS was used for the analysis and simulation of the current CLS of the inner area of Rome (Fig. 3). The study area is of about 9 km2, with about 50,000 inhabitants and 24,000 employees related to trade. The study was supported by some surveys carried out in 2008: traffic counts of commercial and other vehicles at the border of study area, with about 600 interviews of truck drivers in order to investigate the supply chain of freight

distribution within the study area, and about 500 interviews of retailers in order to investigate the retail trade in the study area for each freight type. At survey time, access was not permitted to pre-Euro vehicles and with a gross laden weight of less than 3.5 tons. Also vehicles with a gross laden weight under 8.5 tons were allowed access only at night-time and were restricted to some specific roads.

The study area is a mixed land-use area (CBD, residential, commercial, tourist) which is mainly affected by attraction freight flows (Comi et al., 2011), while the origins of freight flows take place mainly in the peripheral areas of Municipality. The analysis highlights freight movements in the study area amounting to about 15,000 tons per day and more than 66% is destined to retail or food-and-drink outlets. In terms of freight segmentation, 36% consists of foodstuffs (about 16% is dispatched to restaurants and cafe, and 20% to retailers), 61% consists of other end-consumer products (e.g. household and health products), and the remaining 3% are goods related to services. To analyze the system, the area of the municipality of Rome was divided into 99 traffic zones with a level of detail which increases as the inner area was approached. The inner area consisted of 16 traffic zones.

Piazza del Popolo

Fig. 3. Inner area zoning

4.1. Demand model implementation

The demand models implemented in the CLASS demand module were initially calibrated and validated by Nuzzolo at al. (2012a) and Nuzzolo and Comi (2013a and b). Then, an aggregate calibration of the demand models using the traffic counts was also performed. Fig. 4 reports a comparison between the revealed and estimated quantity O-D flows both for foodstuffs and remaining good. The Mean Square Error (MSE) and the ratio between the square root of the Mean Square Error and the average demand (RMSE%) are also given. The estimates for foodstuffs O-D quantity flows are slightly scattered. However, the model for remaining goods yields better results, particularly because the results are less fluctuating. Further analyses are in progress to verify the dispersion of foodstuff estimates, including other socio-economic data in the acquisition model.

Fig. 5 reports a comparison between the attracted revealed and estimated deliveries both for foodstuffs and remaining goods. For the 17 zones of inner area of Rome, the pictured results are encouraging: the model reproduces actual delivery movements quite well. Finally, the Figure 6 plots the comparison between the revealed and estimated freight vehicle O-D flows. The model quite well reproduces the current O-D flows as confirmed by comparison with the 45 degrees line, but further analyses are in progress for improving these results, in particular to improve the value of RMSE%.

MSE = 1.32E+03 RMSE% = 57.0%

100 200 300 400 500 600 700 800

estimated O-D flow [veh./day]

Fig. 6. Revealed vs Estimated freight vehicle O-D flows

4.2. CLASS output indicators

In the following, the result of CLASS application in terms of CLS indicator estimation for the inner city area of Rome is described. The output indicators are separately described for land-use, demand, transport supply, logistic profile and road network performances.

4.2.1. Land use indicators

Land use indicators are powerful instruments for understanding the main features of a CLS in terms of retail outlet and employee spatial localization and density. These indicators have a role-key in describing zones freight attraction in terms of freight quantity. Fig. 7 shows retailers spatial density in the inner area of Rome as thematic map and, for each traffic zone, the distribution of retailer types according to freight type (i.e. foodstuffs, home accessories, stationery, clothing, household and personal hygiene, building, other), as histograms. The northern zones have the highest density of retailers, mainly related to foodstuffs and clothing while the southern zones are characterized by a low retailer density. Fig. 7 also reports the ratio of retailers on residents for each zone. We note that the northern zones are more commercial than southern ones. These zones are those more interested by touristic attractions and then the density of ho.re.ca. (hotel, restaurant and catering) activities is higher.

4.2.2. Freight demand indicators

The freight demand indicators for the study area were evaluated only for attracted freight demand because, as discussed in section 4.1, the study area is mainly affected by attraction freight flows. Based on demand module results, an example of CLASS demand indicators are showed in Table 1 for each traffic zone. Overcoming on logistic profile information that will be detailed in the following, the Table shows that Castro Pretorio, Campo Marzio, Colonna, Parione and Pigna have the highest density of daily attracted quantity and deliveries; the average delivered quantity is about 0.42 tons per delivery and the 90% of daily deliveries are performed in the morning.

CLASS allows to characterize the freight flows in relation to the type of sender and receivers. The

Table 2 reports the current scenario revealed in the LTZ of Rome. We can see that more than 52% of quantities are destined to retailers, while the 31% are destined to end consumers and service activities.

Zone Logistic profile Surface [km2] Density of daily attracted quantity [tons/km2] Density of daily attracted deliveries [deliveries/km2] Average quantity delivered [tons/deliveries] Number of daily morning deliveries Daily afternoon deliveries

Campo Marzio B 0.86 2,753 6,280 0.44 4,876 522

Colonna B 0.44 4,678 11,447 0.41 4,530 481

Monti B 1.77 911 2,090 0.44 3,388 320

Trevi B 0.79 2,252 5,489 0.41 3,939 410

Parione B 0.29 5,137 13,086 0.39 3,422 362

Pigna B 0.32 2,821 6,053 0.47 1,687 235

Ponte E 0.33 682 1,402 0.49 430 38

S. Eustachio B 0.26 3,791 10,034 0.38 2,395 180

Castro Pretorio C 0.39 3,137 8,703 0.36 3,077 308

Ludovisi C 0.44 442 1,142 0.39 454 43

Sallustiano C 0.24 678 1,373 0.49 305 27

Campitelli E 0.89 98 194 0.50 157 15

Esquilino E 0.82 925 1,894 0.49 1,335 217

Regola E 0.36 665 1,278 0.52 403 54

Ripa E 0.86 56 155 0.36 118 15

S. Angelo E 0.17 1,326 2,413 0.55 355 65

Total 9.22 1,554 3,704 0.42 30,873 3,294

Table 1. Example of freight demand module indicators

Logistic Surface profile [km2]

Density of daily attracted quantity [tons/km2]

Density of daily attracted deliveries [deliveries/km2]

Average quantity

delivered

[tons/deliveries]

Number of daily morning deliveries

afternoon

deliveries

Campo Marzio

Colonna

Parione

S. Eustachio

Castro Pretorio

Ludovisi

Sallustiano

Campitelli

Esquilino

Regola

S. Angelo

0.86 0.44 1.77 0.79 0.29 0.32 0.33 0.26 0.39 0.44 0.24 0.89 0.82 0.36 0.86 0.17

2,753 4,678 911 2,252 5,137 2,821 682 3,791 3,137 442 678 98 925 665 56

11,447

13,086

10,034

0.44 0.41 0.44 0.41 0.39 0.47 0.49 0.38 0.36 0.39 0.49 0.50 0.49 0.52 0.36 0.55

4,876 4,530 3,388 3,939 3,422 1,687 430 2,395 3,077 454 305 157 1,335 403 118 355

522 481 320 410 362 235 38 180 308 43 27 15 217 54 15 65

30,873

Table 2. Example of freight demand module indicators: type of senders and receivers

Private End-Consumer (e.g. families) Business End-Consumer (e.g. service activities) Retailer (shop and ho.re.ca.) Wholesaler Distribution Centre Producer Not defined Total

Retailer 22% 12% 19% 0% 2% 0% 45% 100%

Wholesaler 6% 8% 66% 6% 0% 0% 13% 100%

Distribution Centre 12% 12% 67% 0% 2% 2% 6% 100%

Producer 16% 22% 47% 1% 3% 0% 11% 100%

Not defined 42% 9% 34% 0% 0% 0% 14% 100%

Average 16% 15% 52% 2% 1% 0% 14% 100%

4.2.3. Freight transport supply indicators

Fig. 8 shows a thematic map of the daily deliveries with graphs describing the distribution of deliveries between the two types of transport services (i.e. on own account and 3P) and the used vehicle type (i.e. light and medium). It emerges that decreasing the density of daily deliveries, the share of own account increases.

Daily deliveries

■ 3p merli jm

1 ■ owner own light

■ owner own medium

Daily deliveries

Fig. 8. An example of freight supply indicators map

4.2.4. Logistic profile indicators

An analytic study of land use, freight demand and supply indicators have allowed CLASS to define for each zone the logistic profile zones according to classification proposed by Macario et al. (2008, see section 2.1). CLASS identified 3 different logistic profiles for the study area (Fig. 9). The profile B, that includes areas with hotels, restaurants, small grocery stores, small neighborhood markets mainly characterized by the perishability of

the products, was applied to 7 traffic zones for a total extension of 4.7 km2. These areas contain the 50% of residents and the 65% of employees with a ratio of employees on residents equal to 0.80 and there are the most touristic zones of the city. The logistic profile C (i.e. business center characterized for high commercial density and low homogeneity with low logistic accessibility) was applied to 3 zones of study area for a total extension of 1.1 km2. In these areas, there are many office buildings, and therefore the ratio of employees on residents is equal to 1.26. Finally, the logistic profile E (i.e. area with local trade characterized for low commercial density and homogeneity with low logistic accessibility) was applied to 6 traffic zones with a total extension of 3.43 km2. These areas contain the 42% of residents and the 19% of employees with a ratio of employees on residents equal to 0.29.

4.2.5. Road network performance indicators

The road network performance indicators were evaluated considering the whole main road network of Rome municipality in order to evaluate the total amount of transport costs related to CLS of the inner zone. Table 3 shows the daily road network performance indicators of the actual city logistic system.

The road accident module provides some analysis based on location of road accidents involving freight vehicles, as pictured in Fig. 10. In 2012, there were 219 accidents in the study area that involved freight vehicle and mainly concentrated within the zone with the highest density of deliveries. Further analysis are in progress in order to implement models that allow to compute the analysis and forecasting indicators presented in the first part of paper.

Fig. 9. Logistic profiles

Table 3. Road network performance indicators

Indicator 3P Own account Total

light vehicles medium vehicles total light vehicles medium vehicles total light vehicles medium vehicles total

Veic-km/day 79,081 39,826 118,907 116,552 73,181 189,733 195,633 113,007 308,640

Veic-h/day 2,139 1,048 3,187 3,521 2,194 5,715 5,660 3,242 8,902

Average speed [km/h] 36.97 38 37.31 33.1 33.36 33.2 34.56 34.86 34.67

CO (kg/day) 75.68 23.00 98.68 111.60 42.28 153.88 187.28 65.28 252.56

NOx (kg/day) 82.36 95.84 178.20 121.36 176.08 297.44 203.72 271.92 475.64

NO2 (kg/day) 22.72 12.80 35.52 33.48 23.48 56.96 56.20 36.28 92.48

PM2.5 (kg/day) 6.28 3.80 10.08 9.24 6.92 16.16 15.52 10.72 26.24

PM10 (kg/day) 7.52 5.28 12.80 11.08 9.72 20.80 18.60 15.00 33.60

CO2 (t/day) 21.8 12.57 34.41 32.18 23.10 55.28 54.02 35.67 89.69

Fig. 10. Location of road accidents involving freight vehicle

4.3. CLASS application for new scenario

For testing how CLASS road network performance indicators can help City Planners in the assessment of city logistics measures, an hypothetical CLS scenario was implemented and road network performance indicators were calculate with CLASS. The new CLS scenario includes measures for banning the access to vehicles do not comply with Euro II emission standards. We suppose that these vehicles are replaced by Euro IV and Euro V vehicles. Table 4 reports the percentage difference between new and actual scenario road performance indicators. It shows a reduction of major pollutants with a decrease of PM10 and CO around 30% and reduction of PM2.5 around 38%.

Table 4. Road network performance indicators percentage composition

3P Own account Total

Indicator light medium total light medium total light medium total

vehicles vehicles vehicles vehicles vehicles vehicles

CO -37.6% -5.3% -30.1% -37.7% -5.2% -28.7% -37.7% -5.2% -29.3%

NOx -23.7% -18.4% -20.8% -23.6% -18.2% -20.4% -23.6% -18.3% -20.6%

NO2 3.5% -24.7% -6.7% 3.5% -24.5% -8.0% 3.5% -24.6% -7.5%

PM2.5 -42.1% -32.4% -38.5% -42.0% -32.0% -37.7% -42.1% -32.2% -38.0%

PM10 -35.2% -23.3% -30.2% -35.1% -23.1% -29.5% -35.1% -23.1% -29.8%

CO2 0.0% -2.1% -0.8% 0.0% -1.8% -0.8% 0.0% -1.9% -0.8%

5. Conclusions

In the paper, a support system for the analysis and the simulation of city logistics scenarios, with a focus on the forecasting of network performance indicators, was proposed. The analysis of current scenario allows to investigate the logistics and freight transport in relation to land use, freight restocking demand and supply, logistic profile and road network performances and impacts. In particular, CLASS computes different indicators for each of the above aspects of urban logistics and freight transport. Their comparison with target and benchmarking values allows to identify its level of services and the possible critical stages. The simulation is able to point out the relations existing among city logistics measures, decision-maker choice dimensions by using a multi-stage demand model and a discrete choice approach for each decision level.

The support system was applied to the city center of Rome, and three different logistic profiles were identified, highlighting that the study area is mainly characterized by high commercial density and low homogeneity. Although these first results confirm the goodness and the potentiality of the support system, further analysis and development are required.

This research is like to proceed in the following main directions: improvement of the network representation in relation to the different types of vehicle travelling within the urban area (i.e. light, medium and heavy goods vehicles, cars), development of disaggregate pollutant emission functions that allows to consider the driving cycle in the real traffic conditions in order to increase the accuracy of the estimates of the pollutant emissions; development of road accident models for the analysis and forecasting of described aggregate and disaggregate indicators.

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