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Many times when we read stories about a new public transit project, one of the things we read about is how a certain mode will not provide enough capacity for the expected ridership, while another mode may provide too much capacity for the expected ridership.
The capacity of a transit mode refers to how many passengers per hour a mode can be expected to carry. Since when we discuss capacity we are usually discussing it in terms of a rapid transit project, the capacity should be defined as the maximum number of passengers per hour a given mode could carry at its maximum average operating speed. We can visualize this in terms of an expressway: a gridlocked expressway will have more cars per unit area than one at free flow, but this fact does not mean that the gridlock represents the capacity of the freeway, because the freeway is not designed to operate ata state of gridlock
Overall, the capacity of a given transit mode expressed in passengers per hour can be represented as the result of multiplying the number of vehicle sets (trains) that could pass by a particular stop in one hour (the frequency) by the number of vehicles per train and the number of passengers that could be carried by each vehicle.
The maximum frequency of trains operating in a rapid-transit like setting depends on if they are operating at grade or they are grade-separated. Since in order to maximize average speed vehicles operating at grade need to have traffic signal priority, the maximum frequency of trains operating at grade depends on the signal priority. For signal priority to work effectively, trains can pass by the signal no more than once every four minutes so that the other traffic has a chance to proceed as well. While, of course, trains operating at grade can operate more than every four minutes, doing so will result in some of the trains being forced to stop at red lights, causing delay. Readers who are familiar with streetcar routes in Torontooperating along streets with traffic signal priority and operating more frequently than every four minutes such as Spadina will no doubt recall times when their vehicle has been forced to stop for red lights.
In a grade-separated setting, the maximum frequency of transit vehicles is determined mainly be signalization, turn-around time at the route termini, and dwell time at the busiest stations. In general, the above factors mean that afully maxed out grade-separated transit vehicle could operate every two minutes, although fully-automated trains, such as Vancouver's SkyTrain, can operate as frequently as every ninety seconds. Attempting to operate more frequently than this, even if allowed to be the block signals, will likely result in bottlenecks at very busy and terminal stations.
In an at-grade system, the maximum number of vehicles per train is usually three, due to the requirement that the train not block intersections when stopped at a red light or at a station. In a grade-separated setting, the maximum number of vehicles per train is determined by how long the station platforms are. Most subway systems allow for a maximum of six sixty-feet cars per train, although some especially BART, which can have up to ten-car trains have longer consists, while others, especially Vancouver's new Canada Line which has only four car trains, have shorter consists.
The other factor that affects how many passengers can be carried by transit is the number of passengers that can fit on each vehicle a number that is represented in transit by theload factor. While in buses load factor is usually limited to a maximum of 1.5 meaning that all of the seats are filled and there are standees equal in number to half of the seats rail vehicles, which are often designed to have additional standee spaces, can have a higher load factor of 2.0 or even higher. For the sake of this article, we will assume that a high-floor subway car could carry 100 passengers per vehicle while a low-floor articulated bus or light rail car could carry 90 passengers per vehicle.
90 passengers per vehicle * 15 vehicles per hour = 1,350 passengers per hour per direction. This number suggests a maximum daily ridership of around 20,000, which is what the Los Angeles Metro Orange Line is averaging.
90 passengers per vehicle * 30 vehicles per hour = 2,700 passengers per hour per direction. Note that by lengthening the platforms at bus rapid transit stations to provide more than one space where a bus can stop, you can add more vehicles and thus more capacity.
100 passengers per vehicle * 10 vehicles per train * 30 vehicle sets per hour = 30,000 passengers per hour. This number suggests a maximum daily ridership of around 450,000. The Bloor line in Toronto has a daily ridership of almost 500,000, while the Yonge line, which is really two lines, Yonge and University-Spading, has a ridership of over 700,000.
The above numbers assume lines with only one peak load point; i.e., with no turnover of passengers. In addition, the numbers are meant as a general guide only, so you can see the magnitude of the difference in capacities amongst the different modes. With the exception of the biggest cities in the United States and Canada, no city will have enough demand to justify the cost of construction of grade-separated rapid transit. In the case of the biggest cities, care must be taken not to construct a line that does not have enough capacity to meet long-term demand. Los Angeles is perhaps the most guilty of this problem, with both the Orange Line and Blue Line at capacity.
When you configure an Auto Scaling group to launch multiple instance types, you have the option of defining the number of capacity units that each instance contributes to the desired capacity of the group, using instance weighting. This allows you to specify the relative weight of each instance type in a way that directly maps to the performance of your application. You can weight your instances to suit your specific application needs, for example, by the cores (vCPUs) or by memory (GiBs).
For example, let's say that you run a compute-intensive application that performs best with at least 8 vCPUs and 15 GiB of RAM. If you use c5.2xlarge as your base unit, any of the following EC2 instance types would meet your application needs.
By default, all instance types are treated as the same weight. In other words, whether Amazon EC2 Auto Scaling launches a large or small instance type, each instance counts toward the group's desired capacity.
With instance weighting, however, you assign a number value that specifies how many capacity units to associate with each instance type. For example, if the instances are of different sizes, a c5.2xlarge instance could have the weight of 2, and a c5.4xlarge (which is two times bigger) could have the weight of 4, and so on. Then, when Amazon EC2 Auto Scaling launches instances, their weights count toward your desired capacity.
The following table compares the hourly price for Spot Instances in different Availability Zones in US East (N. Virginia, Ohio) with the price for On-Demand Instances in the same Region. The prices shown are example pricing and not current pricing. These are your costs per instance hour.
With instance weighting, you can evaluate your costs based on what you use per unit hour. You can determine the price per unit hour by dividing your price for an instance type by the number of units that it represents. For On-Demand Instances, the price per unit hour is the same when deploying one instance type as it is when deploying a different size of the same instance type. In contrast, however, the Spot price per unit hour varies by Spot pool.
The easiest way to understand how the price per unit hour calculation works with weighted instances is with an example. For example, for ease of calculation, let's say you want to launch Spot Instances only in us-east-1a. The per unit hour price is captured below.
Start by choosing a few instance types that reflect the actual performance requirements of your application. Then, decide how much each instance type should count toward the desired capacity of your Auto Scaling group by specifying their weights. The weights apply to current and future instances in the group.
Be cautious about choosing very large ranges for your weights. For example, we don't recommend specifying a weight of 1 for an instance type when the next larger instance type has a weight of 200. The difference between the smallest and largest weights should also not be extreme. If any of the instance types have too large of a weight difference, this can have a negative effect on ongoing cost-performance optimization.
The size of the Auto Scaling group is measured in capacity units, and not in instances. For example, if your weights are based on vCPUs, you must specify the desired, minimum, and maximum number of cores you want.
Set your weights and desired capacity so that the desired capacity is at least two to three times larger than your largest weight.
If you choose to set your own maximum price for Spot, you must specify a price per instance hour that is high enough for your most expensive instance type. Amazon EC2 Auto Scaling provisions Spot Instances if the current Spot price in an Availability Zone is below your maximum price and capacity is available. If the request for Spot Instances cannot be fulfilled in one Spot Instance pool, it keeps trying in other Spot pools to leverage the cost savings of Spot Instances.
Current capacity will either be at the desired capacity or above it. Because Amazon EC2 Auto Scaling wants to provision instances until the desired capacity is totally fulfilled, an overage can happen. For example, suppose that you specify two instance types, c5.2xlarge and c5.12xlarge, and you assign instance weights of 2 for c5.2xlarge and 12 for c5.12xlarge. If there are 5 units remaining to fulfill the desired capacity, and Amazon EC2 Auto Scaling provisions a c5.12xlarge, the desired capacity is exceeded by 7 units.
When Amazon EC2 Auto Scaling provisions instances to reach the desired capacity, distributing instances across Availability Zones and respecting the allocation strategies for On-Demand and Spot Instances both take precedence over avoiding overages.
Amazon EC2 Auto Scaling can overstep the maximum capacity limit to maintain balance across Availability Zones, using your preferred allocation strategies. The hard limit enforced by Amazon EC2 Auto Scaling is a value that is equal to your desired capacity plus your largest weight.
When adding instance weights to an existing Auto Scaling group, you must include any instance types that are already running in the group.
When modifying existing instance weights, Amazon EC2 Auto Scaling will launch or terminate instances to reach your desired capacity based on the new weights.
If you remove an instance type, any running instances of that instance type will continue to have their last updated weight values, even though the instance type has been removed.
You can add weights to an existing Auto Scaling group, or to a new Auto Scaling group as you create it. You can also update an existing Auto Scaling group to define new configuration options (Spot/On-Demand usage, Spot allocation strategy, instance types). If you change how many Spot or On-Demand Instances you want, Amazon EC2 Auto Scaling gradually replaces existing instances to match the new purchase options.
Before creating Auto Scaling groups using instance weighting, we recommend that you become familiar with launching groups with multiple instance types. For more information and additional examples, see Auto Scaling groups with multiple instance types and purchase options.
The following examples show how to use the AWS CLI to add weights when you create Auto Scaling groups, and to add or modify weights for existing Auto Scaling groups. You can configure a variety of parameters in a JSON file, and then reference the JSON file as the sole parameter for your Auto Scaling group.
Use the create-auto-scaling-group command to create a new Auto Scaling group. For example, the following command creates a new Auto Scaling group and adds instance weighting by specifying the following:
The instance weights that correspond to the relative size difference (vCPUs) between instance types (16, 24)
The subnets in which to launch the instances (subnet-5ea0c127, subnet-6194ea3b, subnet-c934b782), each corresponding to a different Availability Zone
Use the update-auto-scaling-group command to add or modify weights. For example, the following command adds weights to instance types in an existing Auto Scaling group by specifying the following:
The instance types to launch in priority order (c5.18xlarge, c5.24xlarge, c5.2xlarge, c5.4xlarge)
The instance weights that correspond to the relative size difference (vCPUs) between instance types (18, 24, 2, 4)
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