Evaluation of travel demand management policies in the urban area of Naples

Evaluation of travel demand management policies in the urban area of Naples S. de ~uca' & A. papola2 i Department of Civil Engineer, University of Salerno, Italy. 2 Department of Transportation Engineer, University of Naples, Italy Abstract In this paper we focus on some applications of TDM policies in the metropolitan area of Naples. The policies tested include "push" and "pull" measures, designed to increase respectively the car generalized cost and the attractiveness of alternative modes, using instruments such as parking pricing, restricted access areas, introduction of Park and Ride nodes and of new railway transport services. Seven different scenarios were generated and several impacts were estimated by means of a system of models previously specified, calibrated and validated. The main impacts were on travel demand and on performance of the supply system such as impacts on system effectiveness, on system efficiency and the environment. The results may be summarized as follows: push policies depress travel demand and have negative impacts on the local economic; pull policies need substantial investments but do not produce results as to justifj, such investments; by combining the different policies the best results may be achieved in terms of effectiveness and efficiency and at the same time guarantee a global increase in accessibility and user surplus. 1 Introduction In recent years there has been a considerable increase in non-systematic trips and exchange mobility between the center and the suburbs in most urban areas. This has led to an increase in car use with severe consequences involving noise and air pollution, energy consumption, road safety and, not least of all, user travel times. Hence it is necessary to adopt new strategies for the control of travel demand including the joint use of new transport infrastructures and traffic demand management (TDM) policies. Infiastructural projects generally entail conspicuous economic, social and environmental costs; therefore it is very

important to simulate the impacts mentioned above, in order to optimize the total benefits of the investments. However, the effects of infrastructural projects depend on combined TDM policies, and especially on those policies intended to modify modal split. This paper focuses on some applications of TDM policies in the metropolitan area of Naples. The policies tested include "push" and "pull" measures, i.e. measures intended to increase respectively the car generalized cost and the attractiveness of alternative modes such as parking pricing (PP), restricted access areas (R.A.A.s), introduction of Park and Ride nodes (P&R) and of new railway transport services. Different scenarios were generated including the realization of one or more of the previous TDM measures. For each scenario, the demand variation in the short (modal split) and in the long term (trip distribution, active and passive accessibility) was assessed with respect to a reference scenario. Moreover, the impacts caused by such variation on the performance of the supply system were evaluated. In particular, trip demand was estimated by means of a system of models previously specified, calibrated and validated. The impacts on the performance of the supply system are impacts on system effectiveness (average saturation degree, passengers*km, passengersrh, etc.), on system efficiency (trip generalized cost, active and passive accessibility, etc.), and on the environment (pollutant emissions and concentrations, sound pressure levels, etc.). 2 The study area The aim of this paper is to evaluate the impacts of different scenarios on congestion, accessibility and on the environment. All the impacts were evaluated on Naples city. Since the effects depend on travel demand variations, it is important to define a large enough study area to simulate both internal demand variations as well as incoming and leaving demand. Consequently, the study area is the metropolitan area of Naples consisting of 130 comuni and 3.76 million inhabitants (de Luca, Papola [l]). This area was divided into 219 traffic zones; the comune of Naples was divided into 145 traffic zones while the remaining cornuni were aggregated in 74 traffic zones. For a more disaggregated analysis, Naples city was divided into 6 catchment areas representing different but homogeneous economic and residential situations. 3 Policies and scenarios The measures tested consist both of TDM policies and infra-structural projects and can be subdivided into: - Push measures: Park Pricing (PP) and Restricted Area Access (R.A.A.); - Pull measures: Park and Ride (P&R) and railway infra-structural interventions included in the Naples Transportation Masterplan [2]; These measures were applied only to the Naples area. In particular PP involves most of the municipal area of Naples with fares progressively increasing from the outskirts to the city center. R.A.A. involves a considerable part of the historic city center. The P&R nodes are generally located close to the boundary of the

municipal area of Naples and to railway transport services. The infra-structural interventions are those provided by the Naples Transportation Masterplan and consist in the building of new railway infrastructures (underground, trams, funiculars) and of I8 transit interchange nodes in order to obtain a closely integrated transit network (86.3 km of underground infrastructures, 8 underground lines, 96 stations, 112 km of tram infrastructures, 6 funicular railways). These measures were combined differently in order to implement the 7 scenarios summarized in Table 1. Table 1. The 7 scenarios simulated Scenario I Policy 1 PP 2 Infra-structural intervention 3 P&R + infra-structural intervention 4 PP + P&R + infra-structural intervention 5 PP+ZTL 6 ZTL + PP + infra-structural intervention 7 ZTL + PP + P&R + infra-structural intervention 4 The impacts Scenario's name Push Pull l Pull2 Push&Pull RAP.+ Push (RA.. I) W + Pull2 (RAA-2) RAA+ Push&Pull (RAA-4) For each scenario, all the following impacts were evaluated with respect to the reference (status quo) scenario, considering all land use variables constant and given: Impacts on transport demand: - short term impacts: modal split (evaluated for the rush hour, for the whole day and for each trip purpose); - long terms impacts: modal split and trip distribution (evaluated for the rush hour, for the whole day and for each non systematic trip purpose), active and passive accessibility; Impacts on transport supply performance, evaluated by using the following indicators: - effectiveness indicators (average degree of traffic congestion, total length of congested links, vehicles*km, vehicles*h); - quality indicators (average trip duration, average speed, average trip distance). Impacts on the environment: - CO, NO, and HC emissions (CORINAIR [3]); - CO, NO, and HC concentration (CORINAIR [3]); - Noise emissions (Corriere, Lo Bosco [4]); - Fuel consumption. 1

5 Mathematical models All impacts mentioned above were assessed by using a system of mathematical models including supply models, demand models, assignment models, pollutant emission models, pollutant dispersion models and noise emission models. A private transport supply model and a transit transport supply model were implemented for the study area. The private transportation system was modeled by a graph consisting of 219 centroids, 1400 nodes and 6700 road links and their related performance functions. The transit network was modeled by a synchronic graph with 328 transit lines and 5800 transit line links. The demand models were implemented in a previous work by de Luca, Papola [l]. They are partial share Multinomial Logit models (Ben Akiva, Lerman 151, Cascetta [6]) with inclusive variables taking into account the influence of "lower" choice dimensions on "upper" levels. These models estimate the average number d,,d[s,h,m] of round trips undertaken by the generic individual i between the zone of residence o and the destination d, for purpose S in the reference period h, with mode m: Six travel purposes (work, professional business, study, recreational and sport, shopping, and other purposes) and five mode alternatives (car, walking, motorbike, bus and integrated bus-train services) were considered. The morning peak hour (8 a.m.- 9 a.m.) was chosen as simulation interval (period) and intraperiod stationarity was assumed. The assignment model is an equilibrium model with stochastic route choice model (Probit) for the private transport system and a network loading model with deterministic hyperpath choice model for the transit transport system. Finally, the Corinair models were used as pollutant emission models, the pollutant dispersion models are derived from the Gaussian dispersion theory, and the noise emission model is by Corriere Lo Bosco 141. 6 Simulation results Some of the main simulation results obtained are summarized in the following Tables (2-5) and Figures (1-5). The results of each category of impacts are compared with the base scenario: Impacts on transport demand In Figure 1 the variations in active and passive accessibility are shown. Active accessibility measures how easily users can reach the other zones of the study area; passive accessibility measures how easily a generic economic activity can be reached by users. To understand the impacts on trip distribution, two different analyses of the accessibilities are proposed: aggregate analysis relative to Naples city area and a more disaggregate analysis where the same area is divided in six Catchment areas. All the impacts on active and passive accessibilities should be read together with the consequences on trip distribution (Figure 2 and Table 2).

As expected, in the push scenario both accessibilities decrease. At the same time, Naples loses 4% of its incoming demand, while the internal demand is not affected by any impacts. By contrast, the consequences on incoming travel demand in each catchment area are not so negligible (Table 2). Due to the variations in accessibilities, two of the most important catchment areas (A and D) suffer a substantial reduction in demand. Although the reduction might have an interesting impact on network performance, it has negative consequences on the economic activities located in such areas. In others words, many users prefer to remain in their origin zones, or they decide to reduce their trip length, remaining in their catchment areas. Similarly, the users coming towards Naples choose peripheral destination zones instead of central ones. In the pull scenarios a growth in accessibility can be noticed. Internal demand increases in both scenarios 2 and 3 while, only in scenario 3, the externallinternal demand increases substantially. In scenario 2 the two accessibilities increase in the same way and the slight effects in terms of trip distribution are equally spread between the catchment areas (Table 2). In scenario 3, due to Park and Ride, passive accessibility increases much more than active. Consequently, Naples city attracts 27% more users than the base scenario and in each catchment area the demand has a generalized increment. Hence the incoming demand in each catchment area is no longer well balanced. Although the increments mean an increase in user surplus and might lead to significant impacts on the economy, it is worth noting that such benefits involve catchment areas that are already very congested. In the push and pull scenario (4) a good compromise is reached since the park pricing policy mitigates the positive impacts of the pull policies. Internal demand increases, external\intemal demand rises less than in scenario 3 and the consequent distribution among the catchment areas is more sustainable. Given the above results, Naples city might enjoy interesting economic growth due to the gain in accessibility. Respect the analogous scenarios, all the scenarios with R.A.A. show a generalized decrease of accessibility but little impact on trip distribution. Scenario 5 shows that catchment area A is much more penalized by R.A.A.; by contrast, scenario 7 gives similar results to those of corresponding scenarios without R.A.A.. Since there are no negative impacts on trip distribution towards the zones included in the area, the infrastructural interventions succeed in mitigating the accessibility decrease due to R.A.A.. Scenarios Active Passive Base 8.13 7.80 Push -1.6% -2.1% Pulll +2.6% +2.8% Pull2 +2.6% +6.3% Push&Pull +1.0% +3.1% RAA-I -2.1% -2.3% RAA-2 +0.8% +1.8% RAA-4 +0.8% +2.9% I Active 0 Passive Figure 1: Average active and passive accessibility in Naples

Base scenario I 99,207 39,100 I 66,178 Figure 2: Aggregate analysis of trip distribution variation Table 2 - Desegregate analysis of trip destination variation The impacts on the modal split As regards the impacts on the modal split we will distinguish two kinds of problems: user's modal split from Metropolitan area to Naples and from Naples to Naples (Figure 3 and Figure 4) The Push policy affects car use but not many users switch to transit transport modes. Although the number of cars decreases down to 17,000 units, the transit users increase only up to 8,000. As seen above, thousands of people choose nearer destinations and alternative transport modes such as walking or motorbike. The Push policy cannot be a winning strategy on its own: it reduces car use by depressing the travel demand and reducing trip length. The Pull policies show two different impacts. The modal split from the metropolitan area to Naples (Figure 4) demonstrates that many users decide to abandon individual modes and public modes, preferring to enter Naples by choosing the Park and Ride alternative. This effect is undesirable, because the demand on public modes should be maintained. In other words, a Park and Ride policy should capture just the demand on individual modes. Furthermore, car use

increases in the metropolitan area of Naples with obvious congestion and environmental consequences, in particular near the Park and Ride nodes. The same problem occurs in the modal split for trips within Naples (Figure 3). Although the combined mode increases its percentage use, the demand gain comes from all the other modes and not only from car users. As a result, the number of cars in Naples is 10,000 less than the base scenario, but 7,000 more than the push scenario. Although the travel demand increases (Figure 2), it might be hard to justify such important and expensive infrastructural interventions, without having considerable effects on modal split. The push and pull policies allow us to solve the problems encountered in the previous scenarios. As shown in Figure 3 and Figure 4, both the modal splits present a significant reduction in car use and a growth in combined transit and Park and Ride. As a consequence, the number of cars decreases, there are far more transit users and, at the same time, the total demand increases due to better accessibility. Scenarios 5 and 7 present similar results to those of the corresponding scenarios without R.A.A.. The analysis is too aggregated to appreciate possible impacts on the modal split. The results obtained highlight several critical points: if push policies let to reach interesting results to the detriment of user's satisfaction, on the other hand pull policies give desired modal splits at an economic sacrifice of whole community. All pull measures must satisfy public transportation demand by ensuring good quality service, otherwise the simulation results might be not realistic; it is important to pay attention to the Park and Ride nodes owing to pollution and congestion problem that could well occur. Base scenario Car I Motorbike I Walking 43% 1 5% 1 16% Bus l combined Transit 15% I 2 1 % Figure 3: Modal split of trips inside Naples

192 Urban Traruport a d the Ernironmerlt in the 21sr Century Base scenario Car I Motorbike I Walking I Bus I Combined Transit I 63%1 5% 1 I% 16%1 25% I 0% Park and Ride 4% 7% 6% S% 5% 8% 18% 20% 30% El Park and Ride 25% 29% Figure 4: Modal split of trips into Naples Impact on network performance By analyzing network performance (Figure 5), it may be noted that the push scenario and pull scenarios give promising but rather similar results. Indeed, any scenario might be adopted to produce a good impact on network performance. Despite the interesting results, it must be remembered that the push scenario negatively affects user surplus, and the pull scenarios require very high investment levels. With the combination of push and pull measures better results are obtained, but the improvements are modest. Since any scenario might be suitable to solve network performance problems, road network indicators cannot be used as an overall criterion to assess the best policy. -80% J 1 mflowzapac~ly ratio OAverage travel time (h).average speed (kmh) CIPassengers*Km BPassengers*h Figure 5: Road network indicators Impacts on the environment As regards environmental impacts (Table 3 and Table S), the best results can be obtained with the Push and Push&Pull policies. While the first policy achieves good results by reducing the number of cars on the network, the second succeeds

in increasing the global demand and in reducing car use. Furthermore, the results of Push&Pull policies are much more convincing, as can be seen in Table 3. The results regarding fuel consumption can be interpreted in the same way (Table 4). The pull scenarios always give worse results; for the first time they are not competitive with other policies. In all the scenarios worse impacts can be observed for the Sound Pressure level. Since the average speed increases on all the network, the sound pressure level increases in the same way. Scenarios 5 and 7, show better aggregate results to the corresponding scenarios without R.A.A.. The R.A.A. includes Naples historical center, which is one of the most congested and polluted area in the city. The closure of this area has considerable impacts on pollutant concentrations; the aggregate impacts can be analyzed in Table 3, the impacts in particular zones of the historical center of Naples can be read in Table 5. Table 3 - Environmental indicators: "C" pollutant concentrations (ppm) and "E" emissions (Kgh) and "Sp" Sound Press. Level (db) Scenarios CO C NOX Base 6.27 519.45 Push 1-24.0% -16.0% Pull1 2-13,8% -9.5% Pull2 3-15.60% -10.90% Push&Pull 4-33.60% -21.50% RAA-I 5-28.7% -20.2% RAA-2 6-33.9% -23.3% RAA-4 7-38.4% -26.7% SPL 94.05 1.5% -0.55% 0.01% 1.84% -3.89% -3.59% -3.54% Table 4 - Fuel consumption (literh) Table 5 - Pollutant concentrations in particular zones of Naples

194 U~harr Trarrsport arrd rlre Erz~irotrnznlr it1 the 21st Cetrtrirv 7 Conclusion The results obtained show that the different policies must be closely integrated in order to achieve the best results in terms of effectiveness and efficiency and at the same time guarantee a global increase in accessibility and user surplus. The desired modal split from private to transit modes (and the consequent reduction in car traffic, congestion, he1 consumption, environmental pollution, etc.) can be satisfactorily obtained by using only push measures, such as PP or R.A.A. (compare scenarios 1 and 5 with the others in figures 3, 4, 5 and tables 3 and 5), but at the cost of an undesired and considerable decrease in both active and passive accessibility (see scenarios 1 and 5 in Figure 1). By contrast, the positive effects that can be obtained by using only pull measures (such as railway infra-structural interventions) in terms of car traffic reduction and impacts on the system effectiveness are too limited to justify the large economic investments (compare scenario 2 with the others in figures 2,3,4, 5 and tables 3 and 5). The integration of push and pull measures enhances the positive effects of push measures while eliminating most of their negative effects (see scenarios 4, 6 and 7 in figures 1, 2, 3, 4, 5 and tables 3, 5). The reduction in car accessibility due to the push measures is generally more than compensated by the increase in transit accessibility due to the pull measures. Furthermore, residents in the metropolitan area of Naples are almost indifferent to the infra-structural interventions provided for the city by the Naples Transportation Masterplan (see scenario 2 in figures 2 and 4), while they are very sensitive to the introduction of Park&Ride nodes close to railway transport services. The main result of the latter measure is a considerable increase in the number of trips coming fkom the metropolitan area towards Naples and a severe decrease in the percentage of such trips made by car (see scenarios 3, 4 and 7 in figures 2 and 4). Finally the R.A.A. measure, applied in the central zones of Naples, has visible effects only on modal split towards Naples (compare scenarios 1 and 5 and scenarios 4 and 7 in figure) and on path choices in the city center, with consequent benefits for the environment (compare scenarios 1 and 5 and scenarios 4 and 7 in table 3 and see Table 5). References [l] de Luca, S., Papola, A., Un sistema di modelliper la stima della domanda di mobilitir nell'area metropolitana di Napoli, Metodi e tecnologie dell'ingegneria dei trasporti, Franco Angeli ed., 2001. [2] Comune di Napoli, Assessorato alle infrastrutture di trasporto (1997), Piano Comunale dei Trasporti. [3] CORINAIR, Working group on emission factors for calculating emissions from road traffic, Final Report, 199 1. [4] Corriere, Lo Bosco, Valutazione previsionale dell'inquinamento acustico nella viabilit& urbana, Autostrade, 199 1. [5] Ben Akiva, M., Lerman, S., Discrete Choice Analysis: Theoty and Application to Travel Demand, MIT Press, Cambridge, Mass, 1985. [6] Cascetta, E., Transportation systems engineering theory and methods, Kluwer Academic Publisher, (200 1).