Fundamentals of Transportation/Route Choice
Route assignment, route choice, or traffic assignment concerns the selection of routes (alternative called paths) between origins and destinations in transportation networks. It is the fourth step in the conventional transportation forecasting model, following Trip Generation, Destination Choice, and Mode Choice. The zonal interchange analysis of trip distribution provides origin-destination trip tables. Mode choice analysis tells which travelers will use which mode. To determine facility needs and costs and benefits, we need to know the number of travelers on each route and link of the network (a route is simply a chain of links between an origin and destination). We need to undertake traffic (or trip) assignment. Suppose there is a network of highways and transit systems and a proposed addition. We first want to know the present pattern of travel times and flows and then what would happen if the addition were made.
Link Performance FunctionEdit
The cost that a driver imposes on others is called the marginal cost. However, when making decisions, a driver only faces his own cost (the average cost) and ignores any costs imposed on others (the marginal cost).
where is the total cost, and is the flow.
BPR Link Performance FunctionEdit
Suppose we are considering a highway network. For each link there is a function stating the relationship between resistance and volume of traffic. The Bureau of Public Roads (BPR) developed a link (arc) congestion (or volume-delay, or link performance) function, which we will term Sa(Qa)
ta = free-flow travel time on link a per unit of time
Qa = flow (or volume) of traffic on link a per unit of time (somewhat more accurately: flow attempting to use link a)
ca = capacity of link a per unit of time
Sa(Qa) is the average travel time for a vehicle on link a
There are other congestion functions. The CATS has long used a function different from that used by the BPR, but there seems to be little difference between results when the CATS and BPR functions are compared.
Can Flow Exceed Capacity?Edit
On a link, the capacity is thought of as “outflow.” Demand is inflow.
If inflow > outflow for a period of time, there is queueing (and delay).
For Example, for a 1 hour period, if 2100 cars arrive and 2000 depart, 100 are still there. The link performance function tries to represent that phenomenon in a simple way.
Wardrop's Principles of EquilibriumEdit
Each user acts to minimize his/her own cost, subject to every other user doing the same. Travel times are equal on all used routes and lower than on any unused route.
Each user acts to minimize the total travel time on the system.
Price of AnarchyEdit
The reason we have congestion is that people are selfish. The cost of that selfishness (when people behave according to their own interest rather than society's) is the price of anarchy.
The ratio of system-wide travel time under User Equilibrium and System Optimal conditions.
Price of Anarchy =
For a two-link network with linear link performance functions (latency functions), Price of Anarchy is < 4/3.
Is this too much? Should something be done, or is 33% waste acceptable? [The loss may be larger/smaller in other cases, under different assumptions, etc.]
Conservation of FlowEdit
An important factor in road assignment is the conservation of flow. This means that the number of vehicles entering the intersection (link segment) equals the number of vehicles exiting the intersection for a given period of time (except for sources and sinks).
Similarly, the number of vehicles entering the back of the link equals the number exiting the front (over a long period of time).
The above examples are adequate for a problem of two links, however real networks are much more complicated. The problem of estimating how many users are on each route is long standing. Planners started looking hard at it as freeways and expressways (motorways) began to be developed. The freeway offered a superior level of service over the local street system and diverted traffic from the local system. At first, diversion was the technique. Ratios of travel time were used, tempered by considerations of costs, comfort, and level of service.
The Chicago Area Transportation Study (CATS) researchers developed diversion curves for freeways versus local streets. There was much work in California also, for California had early experiences with freeway planning. In addition to work of a diversion sort, the CATS attacked some technical problems that arise when one works with complex networks. One result was the Moore algorithm for finding shortest paths on networks.
The issue the diversion approach didn’t handle was the feedback from the quantity of traffic on links and routes. If a lot of vehicles try to use a facility, the facility becomes congested and travel time increases. Absent some way to consider feedback, early planning studies (actually, most in the period 1960-1975) ignored feedback. They used the Moore algorithm to determine shortest paths and assigned all traffic to shortest paths. That’s called all or nothing assignment because either all of the traffic from i to j moves along a route or it does not.
The all-or-nothing or shortest path assignment is not trivial from a technical-computational view. Each traffic zone is connected to n - 1 zones, so there are numerous paths to be considered. In addition, we are ultimately interested in traffic on links. A link may be a part of several paths, and traffic along paths has to be summed link by link.
An argument can be made favoring the all-or-nothing approach. It goes this way: The planning study is to support investments so that a good level of service is available on all links. Using the travel times associated with the planned level of service, calculations indicate how traffic will flow once improvements are in place. Knowing the quantities of traffic on links, the capacity to be supplied to meet the desired level of service can be calculated.
To take account of the affect of traffic loading on travel times and traffic equilibria, several heuristic calculation procedures were developed. One heuristic proceeds incrementally. The traffic to be assigned is divided into parts (usually 4). Assign the first part of the traffic. Compute new travel times and assign the next part of the traffic. The last step is repeated until all the traffic is assigned. The CATS used a variation on this; it assigned row by row in the O-D table.
The heuristic included in the FHWA collection of computer programs proceeds another way.
- Step 0: Start by loading all traffic using an all or nothing procedure.
- Step 1: Compute the resulting travel times and reassign traffic.
- Step 2: Now, begin to reassign using weights. Compute the weighted travel times in the previous two loadings and use those for the next assignment. The latest iteration gets a weight of 0.25 and the previous gets a weight of 0.75.
- Step 3. Continue.
These procedures seem to work “pretty well,” but they are not exact.
Dafermos (1968) applied the Frank-Wolfe algorithm (1956, Florian 1976), which can be used to deal with the traffic equilibrium problem.
To assign traffic to paths and links we have to have rules, and there are the well-known Wardrop equilibrium (1952) conditions. The essence of these is that travelers will strive to find the shortest (least resistance) path from origin to destination, and network equilibrium occurs when no traveler can decrease travel effort by shifting to a new path. These are termed user optimal conditions, for no user will gain from changing travel paths once the system is in equilibrium.
The user optimum equilibrium can be found by solving the following nonlinear programming problem
where is the number of vehicles on path r from origin i to destination j. So constraint (2) says that all travel must take place: i = 1 ... n; j = 1 ... n
= 1 if link a is on path r from i to j ; zero otherwise.
So constraint (1) sums traffic on each link. There is a constraint for each link on the network. Constraint (3) assures no negative traffic.
There are also methods that have been developed to assign passengers to transit vehicles. In an effort to increase the accuracy of transit assignment estimates, a number of assumptions are generally made. Examples of these include the following:
- All transit trips are run on a set and predefined schedule that is known or readily available to the users.
- There is a fixed capacity associated with the transit service (car/trolley/bus capacity).
- How can we get drivers to consider their marginal cost?
- Alternatively: How can we get drivers to behave in a “System Optimal” way?
- - total cost
- - travel cost on link
- - flow (volume) on link
- VDF - Volume Delay Function
- LPF - Link Performance Function
- BPR - Bureau of Public Roads
- UE - User Equilbrium
- SO - System Optimal
- DTA - Dynamic Traffic Assignment
- DUE - Deterministic User Equilibrium
- SUE - Stochastic User Equilibrium
- AC - Average Cost
- MC - Marginal Cost
- Route assignment, route choice, auto assignment
- Volume-delay function, link performance function
- User equilibrium
- System optimal
- Conservation of flow
- Average cost
- Marginal cost
Use the ADAM software at the STREET website and try Assignment #3 to learn how changes in network characteristics impact route choice.
- Dafermos, Stella. C. and F.T. Sparrow The Traffic Assignment Problem for a General Network.” J. of Res. of the National Bureau of Standards, 73B, pp. 91-118. 1969.
- Florian, Michael ed., Traffic Equilibrium Methods, Springer-Verlag, 1976.
- Wardrop, J. C. Some Theoretical Aspects of Road Traffic Research,” Proceedings, Institution of Civil Engineers Part 2, 9, pp. 325–378. 1952
- Eash, Ronald, Bruce N. Janson, and David Boyce Equilibrium Trip Assignment: Advantages and Implications for Practice, Transportation Research Record 728, pp. 1–8, 1979.
- Evans, Suzanne P. . "Derivation and Analysis of Some Models for Combining Trip Distribution and Assignment." Transportation Research, Vol 10, pp 37–57 1976
- Hendrickson, C.T. and B.N. Janson, “A Common Network Flow Formulation to Several Civil Engineering Problems” Civil Engineering Systems 1(4), pp. 195–203, 1984