Evaluation of travel demand management policies in the

Transactions on the Built Environment vol 52, © 2001 WIT Press, www.witpress.com, ISSN 1743-3509
Evaluation of travel demand management
policies in the urban area of Naples
S. de ~ u c a &
' A. papola2
i
2
Department of Civil Engineer, University of Salerno, Italy.
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
Transactions on the Built Environment vol 52, © 2001 WIT Press, www.witpress.com, ISSN 1743-3509
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
Transactions on the Built Environment vol 52, © 2001 WIT Press, www.witpress.com, ISSN 1743-3509
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
Scenario's name
1
PP
2
Infra-structural intervention
Pull l
3
P&R + infra-structural intervention
Pull2
4
PP + P&R + infra-structural intervention
Push&Pull
5
PP+ZTL
RAP.+ Push
(RA.. I)
6
ZTL + PP + infra-structural intervention
W
(RAA-2)
7
ZTL + PP + P&R + infra-structural intervention
RAA+ Push&Pull (RAA-4)
Push
+ Pull2
1
4 The impacts
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.
Transactions on the Built Environment vol 52, © 2001 WIT Press, www.witpress.com, ISSN 1743-3509
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).
Transactions on the Built Environment vol 52, © 2001 WIT Press, www.witpress.com, ISSN 1743-3509
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
I
Base
8.13
7.80
Push
-1.6%
-2.1%
Active
Pulll
+2.6%
+2.8%
Pull2
+2.6%
+6.3%
Push&Pull
+1.0%
+3.1%
RAA-I RAA-2 RAA-4
-2.1% +0.8% +0.8%
-2.3% +1.8% +2.9%
0Passive
Figure 1: Average active and passive accessibility in Naples
Transactions on the Built Environment vol 52, © 2001 WIT Press, www.witpress.com, ISSN 1743-3509
Base scenario
I
99,207
I
39,100
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
Transactions on the Built Environment vol 52, © 2001 WIT Press, www.witpress.com, ISSN 1743-3509
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
43%
I
1
Motorbike
5%
I
1
Walking
16%
Bus
15%
Figure 3: Modal split of trips inside Naples
lcombined Transit
I
2 1%
Transactions on the Built Environment vol 52, © 2001 WIT Press, www.witpress.com, ISSN 1743-3509
192 Urban Traruport a d the Ernironmerlt in the 21sr Century
Base scenario
Car
I Motorbike I Walking I Bus I Combined Transit I Park and Ride
5%
1 I % 1 6 % 1 25%
I 0%
63%1
4%
7%
6%
El Park and Ride
S%
5%
8%
18%
20%
30%
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.
1
-80% J
m f l o w z a p a c ~ l yratio
OAverage travel time (h)
.Average
speed ( k m h )
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
Transactions on the Built Environment vol 52, © 2001 WIT Press, www.witpress.com, ISSN 1743-3509
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)
Base
Push
Pull1
Pull2
Push&Pull
RAA-I
RAA-2
RAA-4
6.27
1
-24.0%
2
-13,8%
3
-15.60%
4
-33.60%
5
-28.7%
6
CO
-33.9%
7
-38.4%
NOX
519.45
-16.0%
-9.5%
-10.90%
-21.50%
-20.2% -23.3%
-26.7%
SPL
94.05
1.5%
-0.55%
0.01%
1.84%
Scenarios
C
-3.89%
-3.59% -3.54%
Table 4 - Fuel consumption (literh)
Table 5 - Pollutant concentrations in particular zones of Naples
Transactions on the Built Environment vol 52, © 2001 WIT Press, www.witpress.com, ISSN 1743-3509
194 U~harrTrarrsport arrd rlre Erz~irotrnznlrit1 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).

Download Report