Generated Trips and their Implications forGenerated Trips and their Implications forTransport Modelling using EMME/2Transport Modelling using EMME/2

Marwan AL-AzzawiMarwan AL-AzzawiSenior Transport PlannerSenior Transport Planner

PDC Consultants, UKPDC Consultants, UKAlso at Napier University, UKAlso at Napier University, UK

IntroductionIntroduction

Models assume traffic flows through network like water through pipes

But in many urban situations, more like a gas Improvements to road system affect travel ‘costs’

which generate additional flows (travellers change route, destination, time, mode etc)

Known as ‘generated’ trips This paper will describe:

How ‘Generated’ Trips occur; Define different types of ‘Generated’ Trips; Discuss their impacts; and Outline 3 modelling techniques tested with EMME/2

Definition of Generated Traffic

Additional travel from new transport infrastructure. Congestion tends to maintain a self-limiting equilibrium.

(Reduced congestion causes more trips until congestion once again constrains further growth.)

Short run and long run effects. Short Run

– trips diverted from other routes, times and modes.– reduced congestion reduces travel costs, but overall travel demand

does not change. Long Run

– consists of induced travel (new to the area).– outward shift to become more car-orientated.

Traffic Modelling Techniquesusing EMME/2

Three methods tested to simulate driver’s decisions to suppress, change or divert their trips.

The methods described are: Shadow networks; Matrix capping; and Elastic assignment.

Shadow Networks

Method duplicates the real highway network by its shadow which is connected at origins and destinations.

Number of links in the network is approximately doubled.

Shadow links assumed have fixed speeds and infinite capacity.

When travel “cost” between O-D pair less than minimum path cost in real network, trips between O-D pair divert to the shadow network.

Trips diverted to the shadow network are suppressed trips.

Shadow network has more links than necessary, so possible to construct a skeleton of alternative network.

Matrix Capping

Matrix capping is:– Identify links in network where

demand exceeds capacity;– Use select-link analysis to

determine the O-D matrix loading these links;

– Reduce the movements proportionally to level required on overload links to capacity;

– Re-converge assignment using the revised matrix

– Repeat above stages as necessary.

In effect, matrix capping treats the starting matrix as a demand matrix or an initial estimate of the actual matrix and adapts the starting matrix to retain only those movements which can be made in the modelled period on the network.

Elastic Assignment

Concept is demand for travel between any O-D pair is not fixed but rather a decreasing function of the cost of making a trip.

As the highway network becomes more congested, cost increases and more trips are diverted to pseudo links which represents trip suppression, change of mode or change of departure time.

Could be used to simulate:– peak spreading as the

result of increasing levels of congestion

– assessing the changes in travel patterns from car to another non-road path (e.g. public transport).

– identify what levels of traffic congestion could potentially result in a change from private to public transport.

Advantages & Disadvantages of Techniques

Method Advantages Disadvantages

ShadowNetworks

Preliminary applicationshave been successful.

When suppression speed on real network isreached, all further growth in demand will bediverted to shadow network and there must besome doubt as to realism of this.

Matrix Capping

No increases in networksize.

No problems with choiceof demand functions or aspeed diversion criteria.

Reduces long & shorttrips the same.

Whole process can take a considerable amountof computing time.

ElasticAssignment

Allows suppressionfunctions to be definedon a zone to zone basis.

The main problem is the choice ofmathematical function and values ofparameters.

Logit forms tend towards greater suppression oflong distance trips. Power functions tend todivert on basis of cost ratios and hence greatersuppression of short distance trips.

ConclusionsConclusions The three methods all produce reduced demand

matrices on the basic concept that increasing personal travel costs will reduce the use of the private vehicle in heavily congested networks.

Because the shadow network approach is based on average speeds it tends to suppress a greater number of short distance trips than matrix capping which suppresses short and long distance trips equally.

In elastic assignment, the choice of suppression curve determines whether short or long distance trips will be most suppressed, and this may be allowed to vary with individual O-D movements.