CESSE-ULB
Centre for Economic and Social Studies on the Environment
UniversitÚ Libre de Bruxelles

Summary of the study :
 

Overview and Analysis of the links between "Models of Mobility" and "Models of Pollutant Emissions from Transport"
 
 
 

Benoit GILSON and Vincent FAVREL under the direction of Dr Walter HECQ.
Study funded by the European Commission under COST319 Action.

Pdf version of complete report will be soon available.


Overview of mobility models | Emission models | Linking emission models and mobility models

    I. Overview of Mobility Models
Transport demand is induced by economic and socio-demographic factors, political conditions as well as socio-cultural factors. These factors influence transport demand with different orders of magnitude following complex mechanisms.

To modelise mobility demand, two main types of models are available. On the one hand, econometric models which explain a variable related to mobility analysis as a function of the most significant socio-economic variables. On the other hand, mobility models (often called network flow models) which modelise traffic flow on a transport network. These last models are more dis-aggregated and provide more detailed output than the econometric models.

      I.1. Econometric models
Econometric models are stochastic. They explain a variable of interest (dependant variable) as a parametric relationship with socio-economic variables (explanatory variable) which are time series. Times series can be either yearly, monthly or quarterly. The goal of these models is the understanding of the main determinants of the dependant variable studied on the considered time period.

Three econometric models are pointed. They differ following the dependant variable studied : either an indicator of mobility, a fuel consumption, or a variable related to car fleet. In the case of modelling indicators of mobility, the dependent variable is a measure of the volume of transport, e.g. number of passenger-kilometres, vehicle miles travelled, miles travelled per car (or light trucks or lorries) etc, see Greene (1992). For fuel consumption, the dependant variable is aggregated and can be expressed in gasoline, diesel or LPG consumption per capita, per household or per vehicle, see Epsey (1996). Finally, in the case of car fleet analysis, the dependant variable can be the motorization rate (mean number of car per adult). The methodology for the latter case is somewhat different than the previous models as a demographic approach (longitudinal analysis) is used. This last approach is based on household expenditure survey and panel data estimation techniques, see Madre (1995).

All of these models consider income and price/cost as main explanatory variables, introduced as exogenous inputs. Only a few other socio-economic variables are taken into account in these models but they are weakly significant (e.g. driven licence in the case of passenger-kilometre analysis). Data (explanatory and dependant variables) are, in general, easily available and predictable, but only on a broad aggregated (national) scale.

      I.2. Network flow models
"Network flow models" are, unlike econometric models, based on more dis-aggregated data. These models modelise traffic demand taking into account a defined transport network structure and by means of the estimated "Origin-Destination" (O-D) matrices. The main goal is to simulate traffic on a geographic network per time period. These models consider a defined area which is split into several zones. Trips between zones are modelised. Areas covered can be local (e.g. urban level) and on a wider scale (e.g. regional or national level). The zoning system is, in general, at NUTS (Nomenclature of Territorial units of Statistics) 0,1,2 or 3 level.

The network is represented by links and nodes. Usually links are physical and logical (e.g. transfer between transport modes) connections. Two types of nodes are used : nodes representing a junction where three or more links meet or when a route changes its characteristics and the centroid nodes which represent origin zones and destination zones.

Each link of the network includes a start node, an end node and a link type. Different parameters can be coded on the link : distance, capacity, travel time/speed, even delays for custom formalities, etc. Representations of links for road, rail and air networks are depending on the level of detail covered by the model. O/D matrices represent the number of trips between each centroid node.

Classical transport models are made up of 4 main steps (sub-models): generation, distribution, modal choice and assignment steps. For a more detailed description, see Transport Research-APAS 22 studies.

The first step, which is called the generation/attraction model, estimates the number of trips leaving a zone (generated trips) and entering a zone (attracted trips) on the basis of socio-economic variables. Passenger and freight transportation are handled separately.

The second step is the distribution model where O/D matrices are built. These models estimate where the produced trips will go to and where the attracted trip comes from.

The modal choice model constitutes the third step of transport models where the share of trips following the transport mode used is estimated. This results in the subdivision of the O/D matrices built in the previous step into several sub-matrices, one for each transport mode.

The final step of the 4-step model is the trip assignment model where route choices are modelled. Trips, calculated in the previous steps, are assigned to a network. This results in a loaded network. The outputs are calculated for each O/D pair as path flows, junction delays, O-D travel costs. The assignment procedures can be either deterministic or stochastic. Travellers choose paths which minimise their generalised cost (or utility) functions (mainly the time parameter). In the stochastic case, a random term is added to the assignment algorithm.

Finally, a validation procedure is often added. Note that some models include exclusively the assignment phase taking O/D matrices as exogenous input.

The four-step transport model scheme is used by the most well-known modelling tools. APAS 22 give an overview of the strategic or multi-national models available in the European Economic Area. The APAS database describes 62 passenger models and 43 freight models, collected with several criteria (e.g. for passenger models) : scale (part of country, one country, part of Europe, European Union, Europe); area (in square km2) ; scope ((part) of the home country, international); number of O/D matrices (for cars, public transport, air, sea, bicycle, pedestrian, others); number of trip purposes (0, 1, 2, 2); number of zones (0-200, 22-500, 55-1000, >1000); number of links in a road network (no network, 0-5000, 5000-10000, 10000-50000, >50000); time basis (year/month, day, morning peak, evening peak, day + peak, parts of the day average weekday); etc..

One important aspect for emission assessment is that these models can infer average speed on the links in relation to the traffic flow (number of passenger cars per hour and number of lorries per hour). The speed-flow function depends on link characteristics : capacity, number of lanes, terrain characteristics (slopes, bends).

    II. Emission models
As a remainder, numerous emission models have been developed to assess emissions from transportation in function of explaining variables. The section 3.3 presents an extensive overview of the bottom-up emission models describing their main characteristics (e.g. time scale, traffic input, fleet description, pollutant involved, kind of output, etc.). Emission modelling focuses mainly on hot emissions but specific methodologies are proposed to take into account cold start emissions (see section 3.1.4), evaporative emissions and the influence of road gradient or load factor (see sections 3.1.5 and 3.1.6). Most of these elements are found in the COPERT II methodology [Ahlvik et al., 1997] and the German-Swiss model [Hassel et al. (1993), Keller et al. (1995)] which can be considered as references. Therefore, we have chosen these two models to consider linking between emission models and mobility models.

Concerning non road transportation, models for the calculation of non-road transport emissions have not been specifically considered in a linking perspective in this study.

    III. Linking emission models and mobility models
Mobility models and emission models represent two components of one modelling process. These components have been largely developed independently from one to another and few studies have been lead on linking emission and mobility models (Hammarstr÷m, 1995). This is one of the reasons why, currently, mobility models cannot directly provide usable data to emission models.
      III.1. Data required for the road emission calculation
From a linking perspective, the main incomplete data for hot emission calculation are the following  : the number of vehicles per category, the kilometres driven per vehicle category on different road section types and the average speed per road type taken into account (COPERT) or allocation of typical traffic situations to the road network with respect to different road section types (GERMAN/SWISS model).

Considering cold start emissions, apart from meteorological parameters and fuel properties, the data required that could possibly be supplied by mobility models concern : the average trip length per vehicle trip and the total annual kilometres driven by the vehicle of each category (referring to COPERT) or the distance travelled by the vehicle, the number of starts per day and per vehicle and parking duration before the trip (referring to the German/Swiss model).

COPERT II suggests a methodology for evaporative emission calculation. It requires many parameters that are, most of the time unavailable and have to be estimated. These parameters are the following : the fraction of trips finished with hot engines, the fraction of trips finished with cold engines or with the catalyst below its light-off temperature, the yearly average number of trips per vehicle per day and the total annual mileage of each vehicle category. Referring to the German/Swiss model and for the same purpose, other parameters have to be estimated : the number of times the engine is turned off, the frequency distribution of the travelled distance before the engine is turned off and the frequency distribution of the parking duration after the engine is turned off.

      III.2. Linking considerations with econometric models for road transport
The ability of econometric models to predict future changes of fuel consumption, vehicle kilometre or vehicle fleet composition can be considered for the assessment of future air pollution reduction measures. However, some major disadvantage remains on a linking point of view : the aggregated character of data (mobility is modelled as a whole) and these models intrinsically do not deal with any measure of mean speed which is completely exogenous.

The aggregated character implies that we do not have modelisation on mobility per transport mode, per vehicle category, etc. Other econometric models could be built to split, for example, urban from non urban vehicles travelling, provided statistics are available. Further investigation would be requested to assess this possibility. The existing econometric models, which have been developed with other goals than emission assessment, partly satisfy the requirements of emission models, provided simplifying assumptions are made.

When based on fuel consumption, models do not differentiate the different fuel types. Once again, if data on total annual fuel consumption for each type of fuel can be available, models could be built on a time series basis. These models can be linked with emission models, such as COPERT II, which calculates the total annual fuel consumption as a calibration parameter for estimating uncertain parameters (e.g. average annual mileage driven on each road class and for each vehicle category).

Furthermore, econometric models could provide information on car fleet composition or motorisation rate, which is of great interest for all types of emissions, using age cohort models. But these models provide once again only aggregated information on the car fleet as a whole. In fact, emission models require not only the total number of cars per country/region but also the structure of the car fleet, i.e. : the share of diesel, gasoline and LPG cars, or the share of different vehicle cubic capacities and the age categories of each vehicle.

      III.3. Linking considerations with complex "Road network flow" models
From mobility models, it is possible to infer for each O/D trip : the number of vehicles travelling per mode and the average speed from the origin to the destination (knowing the average speed on each link type travelled). Trip distance, number of kilometres travelled per time period, number of starts can also be deduced from the input and output of the mobility models. The main matching problems between emission calculation and mobility models remain in the calculation of kilometres driven per vehicle category and of kilometres driven per road type.

In particular, concerning cold start emissions, the number of starting operations is unknown. To find it, we can make the restrictive assumption that each trip leaving a zone is considered as a start. Concerning parking duration distribution before the trip, further information has been requested from mobility model developers in order to establish if this parameter can be provided one way or another. Cold start and evaporative emissions also depend on the outside temperature which can be different following the parking location of the vehicles (indoor, outdoor). This aspect is not considered in the models and requires additional data concerning the share of vehicles parked in an indoor heated parking. Up to now, neither mobility models nor emission models consider this aspect. New developments in cold start emission modelling [SÚriÚ et Joumard, 1997] consider the driving pattern at the beginning of the trip using the average speed as additional data. This last parameter is available from the mobility models.

Concerning evaporative emission, the fraction of trips finished with hot engines and the fraction of trips finished with cold engines or with the catalyst below its light-off temperature, can be determined knowing the trip length distribution and the ambient temperature. The number of trips per vehicle and per day and the total annual mileage of the vehicle category can also be determined processing the output data of mobility models. The number of times the engine is turned off can be roughly estimated making the assumption that it is equal to the number of trips arriving at a zone.

As mentioned, matching problems arise while considering the linking emission and mobility models. Firstly, mobility models can only distinguish the share of kilometres driven by car, bus/coach and by truck. In order to reconcile them with emission models, two solutions are envisaged :

Secondly, differences are observed in the road typologies used for mobility and emission models. A homogenisation and a standardisation will make the link easier between both models. For instance : Attention must be paid to the fact that mobility models are a modelisation of the reality and the output data still remains an estimation associated with uncertainty. It is perhaps negligible for the objectives for which mobility models have been initially built (analysis of congestion, economic inefficiencies, alternative development patterns, etc.) but for linking with emission models, we have to assess the degree of certitude needed for input data (average speed, trip distances, etc.) to get acceptable results. Finally, the transportation network area studied with mobility models is still partly covering the actual transport network. Therefore no validation with fuel consumption statistics is possible at a national level.

Html created by Benoit GILSON , last modified 27/08/98
Pdf version of complete report will be soon available.