Types of traffic models

Modelling in Transportation Planning General Aspects UNIVERSITÄT STUTTGART INSTITUT FÜR STRASSEN- UND VERKEHRSWESEN (ISV) LEHRSTUHL VERKEHRSPLANUNG UND VERKEHRSLEITTECHNIK (VuV) „ A traffic forecast is the numerical calculation or evaluation of future traffic within the borders of a planned traffic system based upon the present volume of traffic”[1]. [1]: Steierwald, Lecture on Traffic Engineering, University of Stuttgart 1980 [2]: Hensel: Dictionary and compendium on models for the algorithm of traffic forecasts, Aachen, 1976

Modelling in Transportation Planning General Aspects UNIVERSITÄT STUTTGART INSTITUT FÜR STRASSEN- UND VERKEHRSWESEN (ISV) LEHRSTUHL VERKEHRSPLANUNG UND VERKEHRSLEITTECHNIK (VuV) “Traffic forecast is a probability prediction about future states of the traffic system based upon the condition of the infrastructure, of settlement, and transportation. Predictions of (1) traffic flow at links and nodes, (2) modal split, (3) space-time relation of the settlement (4) and other assessment values are of major importance“ [2]. [1]: Steierwald, Lecture on Traffic Engineering, University of Stuttgart 1980 [2]: Hensel: Dictionary and compendium on models for the algorithm of traffic forecasts, Aachen, 1976

Modelling in Transportation Planning Terminology UNIVERSITÄT STUTTGART INSTITUT FÜR STRASSEN- UND VERKEHRSWESEN (ISV) LEHRSTUHL VERKEHRSPLANUNG UND VERKEHRSLEITTECHNIK (VuV) Forecast for public transportation As for the forecast of public transportation both the development of the entire traffic as well as the urban and rural planning structural developments and the planning intentions are of importance. Forecast period The time span of traffic forecasts should cover 5 to 10 years, 15 years at most, whereas the development has to be checked up at least every 5 years. Forecast models Present forecast models are a result of the observation and adjustment of former trends. Fundamental research on the relation between traffic and structural quantities and last but not least the development of data processing were of major importance for this development.

Examples of impact models to determine transport volumes, noise emissions and opertation costs in public transportaion

Model terms System Object Activity System state System environment

Representation of objects in macroscopic and microscopic modelling approaches

Descriptive Models

Data Analysis Models

Impact Models

Optimization Models

Assessment Models

Classification of transport models Location choice Vehicle purchase choice Landuse models Choice of activities Destination choice Mode choice Departure time choice Route choice Travel demand models Choice of travel speed Choice of lane Choice of vehicle headway Traffic flow models

Four-stage Model Origin zone i: Oi Destination zone j: Dj Trip generation models determine for traffic zone i the number of activities and categorized by purpose and time. Movement from zone i to zone j: Tij Trip distribution models determine the destinations j of the movements. Movement from zone i to zone j with mode m: Tijm Modal-split models describe the selection of the mode of transport m Movement from zone i to zone j with mode m on route r: Tijmr Assignment models determine the routes r within the transport network.

Zoning and basic transportation network Source: Cascetta Transportation system analysis Springer, 2009

Transport modelling- trip generation UNIVERSITÄT STUTTGART INSTITUT FÜR STRASSEN- UND VERKEHRSWESEN (ISV) LEHRSTUHL VERKEHRSPLANUNG UND VERKEHRSLEITTECHNIK (VuV) Socio-economic aspects

Time Budget 1990 UNIVERSITÄT STUTTGART INSTITUT FÜR STRASSEN- UND VERKEHRSWESEN (ISV) LEHRSTUHL VERKEHRSPLANUNG UND VERKEHRSLEITTECHNIK (VuV) Home-Outside during workdays Source:

Transport modelling- trip generation UNIVERSITÄT STUTTGART INSTITUT FÜR STRASSEN- UND VERKEHRSWESEN (ISV) LEHRSTUHL VERKEHRSPLANUNG UND VERKEHRSLEITTECHNIK (VuV) Number of intercity trips per month per person (all modes)

The output of trip generation UNIVERSITÄT STUTTGART INSTITUT FÜR STRASSEN- UND VERKEHRSWESEN (ISV) LEHRSTUHL VERKEHRSPLANUNG UND VERKEHRSLEITTECHNIK (VuV) In elementary models the travel pattern reflects just the number of trips by trip purpose during a given time period

The output of trip generation UNIVERSITÄT STUTTGART INSTITUT FÜR STRASSEN- UND VERKEHRSWESEN (ISV) LEHRSTUHL VERKEHRSPLANUNG UND VERKEHRSLEITTECHNIK (VuV) In modern models a restriction is introduced for trip generation by first focusing on the total travel distance per time period The number of trips is then de- rived via the total travel distance and the average trip length Reason:Travel distance per time interval is directly related to travel cost and travel time. Travel cost and travel time per individual or per household can be employed as constraints thus eliminating unrealistic results already at the beginning

Trip Distribution UNIVERSITÄT STUTTGART INSTITUT FÜR STRASSEN- UND VERKEHRSWESEN (ISV) LEHRSTUHL VERKEHRSPLANUNG UND VERKEHRSLEITTECHNIK (VuV)

Origin - Destination Table UNIVERSITÄT STUTTGART INSTITUT FÜR STRASSEN- UND VERKEHRSWESEN (ISV) LEHRSTUHL VERKEHRSPLANUNG UND VERKEHRSLEITTECHNIK (VuV)

Trip Distribution UNIVERSITÄT STUTTGART INSTITUT FÜR STRASSEN- UND VERKEHRSWESEN (ISV) LEHRSTUHL VERKEHRSPLANUNG UND VERKEHRSLEITTECHNIK (VuV) Intervening opportunity method In a behavioral model assumptions are made about the behaviour of an individual in choosing one out of several possible destinations. Opportunities (attractions) are randomly distributed around the travellers origin, and the highest probability is to select the closest opportunity. But other opportunities are occasionally considered as well (they have a lower level of acceptance for the traveller because of the distance from the origin). Thus the acceptance function must be declined from the maximum, which is closest to the origin, and must reach 0 for opportunities, which are never considered as a result of their distance from the origin.

UNIVERSITÄT STUTTGART INSTITUT FÜR STRASSEN- UND VERKEHRSWESEN (ISV) LEHRSTUHL VERKEHRSPLANUNG UND VERKEHRSLEITTECHNIK (VuV) Example for defining ‘opportunities‘ in trip distribution

Factors for modal choice UNIVERSITÄT STUTTGART INSTITUT FÜR STRASSEN- UND VERKEHRSWESEN (ISV) LEHRSTUHL VERKEHRSPLANUNG UND VERKEHRSLEITTECHNIK (VuV)

UNIVERSITÄT STUTTGART INSTITUT FÜR STRASSEN- UND VERKEHRSWESEN (ISV) LEHRSTUHL VERKEHRSPLANUNG UND VERKEHRSLEITTECHNIK (VuV) Modelling in Transportation Planning The Concept of Modal Split

UNIVERSITÄT STUTTGART INSTITUT FÜR STRASSEN- UND VERKEHRSWESEN (ISV) LEHRSTUHL VERKEHRSPLANUNG UND VERKEHRSLEITTECHNIK (VuV) Modelling in Transportation Planning Modal Split calculated after Trip Generation (trip end modal split, no feed back)

UNIVERSITÄT STUTTGART INSTITUT FÜR STRASSEN- UND VERKEHRSWESEN (ISV) LEHRSTUHL VERKEHRSPLANUNG UND VERKEHRSLEITTECHNIK (VuV) Modelling in Transportation Planning Traffic Relation Oriented Mode of Transportation Selection

Selection of specific travel modes UNIVERSITÄT STUTTGART INSTITUT FÜR STRASSEN- UND VERKEHRSWESEN (ISV) LEHRSTUHL VERKEHRSPLANUNG UND VERKEHRSLEITTECHNIK (VuV) Components which determine the selection: Socio-economic characteristics of the traveller Level of service of the transport system supply Trip characteristics (purpose, length, etc.) The basic information about travel behaviour and its relationship with above groups of components is drawn from the survey information

Modal Split between air and road in intercity travel UNIVERSITÄT STUTTGART INSTITUT FÜR STRASSEN- UND VERKEHRSWESEN (ISV) LEHRSTUHL VERKEHRSPLANUNG UND VERKEHRSLEITTECHNIK (VuV)

Modal Split between public transport and private car in urban travel UNIVERSITÄT STUTTGART INSTITUT FÜR STRASSEN- UND VERKEHRSWESEN (ISV) LEHRSTUHL VERKEHRSPLANUNG UND VERKEHRSLEITTECHNIK (VuV)

Intercity modal split - passenger trips UNIVERSITÄT STUTTGART INSTITUT FÜR STRASSEN- UND VERKEHRSWESEN (ISV) LEHRSTUHL VERKEHRSPLANUNG UND VERKEHRSLEITTECHNIK (VuV)

Trip Assignment UNIVERSITÄT STUTTGART INSTITUT FÜR STRASSEN- UND VERKEHRSWESEN (ISV) LEHRSTUHL VERKEHRSPLANUNG UND VERKEHRSLEITTECHNIK (VuV) Apart from behavioural aspects, ‘internal‘ interrelations have to be observed. Concentration in most assignment approaches on travel time (as indicator) travel distance and travel cost. Two methods: 1. All-or-nothing assignment: based on minimum path method (measured in travel time) 2. Capacity restraint techniques: developed from all-or-nothing method by splitting assignment of trips into several steps

UNIVERSITÄT STUTTGART INSTITUT FÜR STRASSEN- UND VERKEHRSWESEN (ISV) LEHRSTUHL VERKEHRSPLANUNG UND VERKEHRSLEITTECHNIK (VuV) Relation between density and speed Verkehrsanalyse und -prognose Umlegung Speed- density relation Speed v Density k in veh/km

UNIVERSITÄT STUTTGART INSTITUT FÜR STRASSEN- UND VERKEHRSWESEN (ISV) LEHRSTUHL VERKEHRSPLANUNG UND VERKEHRSLEITTECHNIK (VuV) Relation between speed and traffic volume Verkehrsanalyse und -prognose Umlegung speed-traffic volume relation Speed v Traffic volume in veh/h

UNIVERSITÄT STUTTGART INSTITUT FÜR STRASSEN- UND VERKEHRSWESEN (ISV) LEHRSTUHL VERKEHRSPLANUNG UND VERKEHRSLEITTECHNIK (VuV) Relation between travel time and traffic volume Verkehrsanalyse und -prognose Umlegung Travel time- traffic volume diagram Travel time T Traffic volume in veh/h

Relation between travel time and traffic volume for free flow and with capacity restraint UNIVERSITÄT STUTTGART INSTITUT FÜR STRASSEN- UND VERKEHRSWESEN (ISV) LEHRSTUHL VERKEHRSPLANUNG UND VERKEHRSLEITTECHNIK (VuV) Verkehrsanalyse und - prognose Umlegung Traffic volume in veh/h Travel time T in [s/km] course for stable traffic course beyond capacity

Analytic expression for capacity restraint function:

UNIVERSITÄT STUTTGART INSTITUT FÜR STRASSEN- UND VERKEHRSWESEN (ISV) LEHRSTUHL VERKEHRSPLANUNG UND VERKEHRSLEITTECHNIK (VuV) Different types of capacity restraint function Verkehrsanalyse und -prognose Umlegung Travel time T Saturation g (g = volume/ capacity)

10% 20% 30% 40% 20% 30% 50% 4 or 3 slices for successive assignment

Successive assignment T ijm = numbers of trips per mode Slice select n of trip portion Network loading Path travel time Link flows, densities, speeds Convergence? Final assignment T ijmr Next slice n=n+1 YesNo

Example for network coding UNIVERSITÄT STUTTGART INSTITUT FÜR STRASSEN- UND VERKEHRSWESEN (ISV) LEHRSTUHL VERKEHRSPLANUNG UND VERKEHRSLEITTECHNIK (VuV)

Demand line plots UNIVERSITÄT STUTTGART INSTITUT FÜR STRASSEN- UND VERKEHRSWESEN (ISV) LEHRSTUHL VERKEHRSPLANUNG UND VERKEHRSLEITTECHNIK (VuV) Plotting stratified OD-links Trips aggregated on a cell basis to flows. Small volume flows between cells not shown.

Exercise for transportation forecast with the 4-stage-model a) For trip assignment a capacity restraint function T=T free (1+α(q/Cap) b ) for the travel time T as a function of the number q of trips per hour to be assigned is used (T free is the travel time at free flow conditions, Cap is the capacity, α and b are parameters). Where does this capacity restraint function deviate from the real travel time behaviour? How is the contradiction solved?

b) Origin A-town and destination B-town are connected by (1) a federal freeway with free flow travel time of 1 h and travel time at capacity (= 5400 veh/h) 120% higher and (2) an elevated expressway with free flow travel time of 50 min and travel time at capacity (= 5100 veh/h) 135% higher. In both cases the exponent b is 4. An expected number of trips of 5000veh/h should be assigned to the highways. What is the solution, if you follow the all-or-nothing principle?

c) In a successive assignment, the number of 5000 trips in 1 hour is divided into slices of (1) 50%, (2) 30%, and (3) finally 20% size. If you assign the slices consecutively, what is the result for the assignment now? Which result is more realistic, solution b) or solution c)?