The project concerns the feasibility study of a new traffic policy in the central area of Turin. The new traffic policy is in line with those adopted in many European cities. The goal of the project is to conduct an impact analysis of the policy. In order to do that, social, economic and transportation studies were performed. The social and economic aspects were evaluated analysing the positive and negative externalities and using a cost/revenue model. Impacts on transportation level were inspected through a traffic simulator programmed specifically for this case study.
2018
Marco Rapelli, Valerio Palma, Maurizio Veronese, Giacomo Rosso, Giandomenico Gagliardi, Marco Bono
Research project
In the first part of the project, a report was drawn up on the principles on which the design of road pricing tariff tools is based, essentially focused on the concept of externalities caused by the phenomenon of urban mobility and on the mechanisms to ‘internalise’ these externalities. Subsequently, a review of the empirical evidence is proposed on the effects, effectiveness and quantitative evaluation of the application of government instruments of externalities caused by mobility, focusing in particular on pollution and congestion, following both the lenses of literature and the ‘motivations’ that drive the adoption of these policies. The report ends with a particular focus on the description of the main road pricing experiences implemented on a European scale (London, Stockholm and Milan) and on the discussion of how much these experiences may suggest to those wishing to design new hypotheses of intervention of this type.
The second part of the work is dedicated to the analysis of the current behaviour of those who, for various reasons, travel in motor vehicles that involve crossing the cordon of the Limited Traffic Zone (ZTL) in Turin. These behaviours are very varied throughout the day, including in relation to the type of people travelling.
The ‘estimate of the request for access and transit in the ZTL’ for groups of individuals and during the various hours of the day will be more or less reliable depending on the quality of the information concerning the behaviour of those who access, stop or transit in the area. In this regard, the available information largely refers to precise data relating to a few more or less significant days. It was therefore not possible to measure the reliability of the estimates that follow, with respect to which we have only a few qualitative confirmations available.
Once the volumes of journeys by purpose, distribution over time and type of individual were best estimated, it was possible to identify a series of scenarios in which different reactions are associated to the introduction of the access tariff, and consequently to attribute an effect to each scenario in terms of congestion, emissions and revenues. The information available did not allow a ‘forecast of future demand’ in the strict sense, an exercise that, however, proved to be limited in terms of effectiveness even in the previous experiences narrated in the scientific literature. For this reason, it was considered appropriate to compose plausible scenarios in which different elasticities of demand emerge. The result is an assessment of reasonable upper and lower limits for changes in demand. Which scenario will then be realised depends on many variables, in many cases completely unpredictable and in part not yet determined, such as tariffs, methods of communicating the policy and characteristics of the offer of alternative transport solutions.
In the present study, the urban traffic simulator SUMO (Simulation of Urban Mobility) was used to analyse in detail the behaviour of the urban network and traffic in Turin after the application of new policies for access to the central area.
The map of the Municipality of Turin and its surroundings was derived from OpenStreetMap and imported into the simulator via the NETCONVERT library. Since the objective of this study is to analyse the impacts of new central area regulation policies, the following changes have been made to the map to simplify the model:
All the pedestrian paths, cycle paths and tracks used for trains or trams and roads closed to traffic were removed from the map.
The next step involved analysing average speeds with zero flow (the speed that on average a vehicle has on the given road in zero traffic conditions) on the roads included in the model. The data is cross-matched with the average data calculated by the Supervisor system (SV) and those made available by the Google API Distance Matrix service. From these speeds, with random logics, a maximum speed on the road was attributed, between the average speed with zero flow increased by 20% and the same speed decreased by 20%. This process made it possible to obtain a no-load speed map capable of taking into account realistic travelling speeds, which could deviate from the speed limits dictated by the Highway Code due to excess or dearth.
Once the offer model (graph) was defined, the question model was constructed. For this purpose, the OD Matrix of the SV was used for the School Day type.
In the SUMO simulation model, each vehicle randomly chooses a starting road and an arrival road within the limits, respectively, of the origin zone and the destination zone.
Once a road of origin and a destination has been defined for each vehicle, the route is chosen according to the criterion of the minimum path according to Dijkstra’s algorithm, using the road travel times as weights. However, since the routes are calculated a priori, all vehicles that have to move from one area to another will choose the same roads with minimum travel time. In this way, choices that may be excellent a priori, are no longer good in the course of the simulation. Adaptive routing has therefore been added to the model that allows vehicles stopped in the queue for a time above a set threshold to calculate an alternative route with less travel time.
In order to predict possible impacts of the policy on transportation, two scenarios have been created using the model described above. A first scenario in which the central area is accessible at all hours for everyone and a second scenario in which the area is closed all day and nobody can enter. For both scenarios, measures of travel times, average speeds and emissions were extracted and compared.