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A Transit graph for static macro assignment
This page is a description of a graph structure for a transit network, used for static, link-based, frequency-based assignment. For the sake of clarity and conciseness, we won't go over all the details of the transit graph features.
Let's start by giving a few short definitions:
- transit definition from Wikipedia:
system of transport for passengers by group travel systems available for use by the general public unlike private transport, typically managed on a schedule, operated on established routes, and that charge a posted fee for each trip.
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transit network: a set of transit lines and stops, where passengers can board, alight or change vehicles.
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assignment: distribution of the passengers (demand) on the network (supply), knowing that transit users attempt to minimize total travel time, time or distance walking, time waiting, number of transfers, fares, etc...
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static assignment : assignment without time evolution. Dynamic properties of the flows, such as congestion, are not well described, unlike with dynamic assignment models.
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frequency-based (or headway-based) as opposed to schedule-based : schedules are averaged in order to get line frequencies. In the schedule-based approach, distinct vehicle trips are represented by distinct links. We can see the associated network as a time-expanded network, where the third dimension would be time.
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link-based: the assignment algorithm is not evaluating paths, or any aggregated information besides attributes stored by nodes and links. In the present case, each link has an associated cost (travel time)
c
[s] and frequencyf
[1/s].
We are going at first to describe the input transit network, which is mostly composed of stops, lines and zones.
Transit stops are points where passenger can board, alight or change vehicles. Also, they can be part of larger stations, where stops are connected by transfer links.
In this figure, we have two stops : A and B, which belong to the same station (in red).
A transit line is a set of services that may use different routes.
A routes is described by a sequence of stop nodes. We assume here the routes to be directed. For example, we can take a simple case with 3 stops:
In this case, the L1
line is made of two distinct routes:
- ABC
- CBA.
But we can have many different configurations:
- a partial route at a given moment of the day: AB,
- a route with an additional stop : ABDC
- a route that does not stop at a given stop: AC
So lines can be decomposed into multiple "sub-lines" depending on the distinct routes, with distinct elements being part of the same commercial line. In the above case, we would have for example:
line id | commercial name | stop sequence | headway (s) |
---|---|---|---|
L1_a1 | L1 | ABC | 600 |
L1_a2 | L1 | ABDC | 3600 |
L1_a3 | L1 | AB | 3600 |
L1_a4 | L1 | AC | 3600 |
L1_b1 | L1 | CBA | 600 |
The headway is associated to each sub-line and corresponds to the mean time range between consecutive vehicles. It is related to the inverse of the line frequency. The frequency is what is used as link attribute in the assignment algorithm.
A line segment is a portion of a transit line between two consecutive stops. With the previous example line L1_a1
, we would get two distinct line segments:
line id | segment index | origin stop | destination stop | travel_time (s) |
---|---|---|---|---|
L1_a1 | 1 | A | B | 300 |
L1_a1 | 2 | B | C | 600 |
Note that we included a travel time for each line segment. This is another link attribute used by the assignment algorithm.
In order to assign the passengers on the network, we also need to express the demand in the different regions of the network. This is why the network area is decomposed into a partition of transit assignment zones, for example into 4 non-overlapping zones:
Then the demand is express as a number of trips from each zone to each zone: a 4 by 4 Origin/Destination (OD) matrix in this case.
Also, each zone centroid is connected to some network nodes, in order to connect the supply and demand. These are the connectors.
We now have all the elements required to describe the assignment graph.
The transit network is used to generate a graph with specific nodes and links used to model the transit process. Links can be of different types:
- on-board
- boarding
- alighting
- dwell
- transfer
- connector
- walking
Nodes can be of the following types:
- stop
- boarding
- alighting
- od
- walking
Here is a figure showing how a simple stop is described:
The waiting links are the boarding and transfer links. Basically, each line segment is associated with a boarding, an on-board and an alighting link.
Transfer links appear between distinct lines at the same stop:
They can also be added between all the lines of a station if increasing the number of links is not an issue.
walking links connect stop nodes, while connector links connect the zone centroids (od nodes) to stop nodes:
Connectors that connect od to stop nodes allow passengers to access the network, while connectors in the opposite direction allow them to egress. Walking nodes/links may be used to connect stops from distant stations.
Here is a table that summarize the link characteristics/attributes depending on the link types:
link type | from node type | to node type | cost | frequency |
---|---|---|---|---|
on-board | boarding | alighting | trav. time | |
boarding | stop | boarding | const. | line freq. |
alighting | alighting | stop | const. | |
dwell | alighting | boarding | const. | |
transfer | alighting | boarding | const. + trav. time | dest. line freq. |
connector | od or stop | od or stop | trav. time | |
walking | stop or walking | stop or walking | trav. time |
The travel time is specific to each line segment or walking time. For example, there can be 10 minutes connection between stops in a large transit station. A constant boarding and alighting time is used all over the network. The dwell links have constant cost equal to the sum of the alighting and boarding constants.
We can use more attributes for specific link types, e.g.:
- line_id: for on-board, boarding, alighting and dwell links.
- line_seg_idx: the line segment index for boarding, on-board and alighting links.
- stop_id: for alighting, dwell and boarding links. This can also apply to transfer links for inner stop transfers.
- o_line_id: origin line id for transfer links
- d_line_id: destination line id for transfer links
Next, we are going see a classic transit network example with only four stops and four lines.
This example is taken from Spiess and Florian [1]:
Travel time is indicated on the figure. We have the following four line characteristics:
line id | route | headway (min) | frequency (1/s) |
---|---|---|---|
L1 | AB | 12 | 0.001388889 |
L2 | AXY | 12 | 0.001388889 |
L3 | XYB | 30 | 0.000555556 |
L4 | YB | 6 | 0.002777778 |
Passengers want to go from A to B, so we can divide the network area into two distinct zones: TAZ 1 and TAZ 2. The assignment graph associated to this network has 26 links:
Here is a table listing all links :
link id | link type | line id | cost | frequency |
---|---|---|---|---|
1 | connector | 0 | ||
2 | boading | L1 | 0 | 0.001388889 |
3 | boading | L2 | 0 | 0.001388889 |
4 | on-board | L1 | 1500 | |
5 | on-board | L2 | 420 | |
6 | alighting | L2 | 0 | |
7 | dwell | L2 | 0 | |
8 | transfer | 0 | 0.000555556 | |
9 | boarding | L2 | 0 | 0.001388889 |
10 | boarding | L3 | 0 | 0.000555556 |
11 | on-board | L2 | 360 | |
12 | on-board | L3 | 240 | |
13 | alighting | L3 | 0 | |
14 | alighting | L2 | 0 | |
15 | transfer | L3 | 0 | 0.000555556 |
16 | transfer | 0 | 0.002777778 | |
17 | dwell | L3 | 0 | |
18 | transfer | 0 | 0.002777778 | |
19 | boarding | L3 | 0 | 0.000555556 |
20 | boarding | L4 | 0 | 0.002777778 |
21 | on-board | L3 | 240 | |
22 | on-board | L4 | 600 | |
23 | alighting | L4 | 0 | |
24 | alighting | L3 | 0 | |
25 | alighting | L1 | 0 | |
26 | connector | 0 |
[1] Heinz Spiess, Michael Florian, Optimal strategies: A new assignment model for transit networks, Transportation Research Part B: Methodological, Volume 23, Issue 2, 1989, Pages 83-102, ISSN 0191-2615, https://doi.org/10.1016/0191-2615(89)90034-9.