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<h1 class="menu-title">A/B Street</h1>
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<h1><a class="header" href="#travel-demand" id="travel-demand">Travel demand</a></h1>
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<p>A/B Street simulates people following a schedule of trips over a day. A single
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<em>trip</em> has a start and endpoint, a departure time, and a mode. Most trips go
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between buildings, but the start or endpoint may also be a border intersection
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to represent something outside the map boundaries. The mode specifies whether
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the person will walk, bike, drive, or use transit. Without a good set of people
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and trips, evaluating some changes to a map is hard -- what if the traffic
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patterns near the change aren't realistic to begin with? This chapter describes
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where the travel demand data comes from.</p>
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<h2><a class="header" href="#scenarios" id="scenarios">Scenarios</a></h2>
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<p>A <em>scenario</em> encodes the people and trips taken over a day. See the
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<a href="https://github.com/dabreegster/abstreet/blob/master/sim/src/make/scenario.rs">code</a>.</p>
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<p>TODO:</p>
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<ul>
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<li>talk about vehicle assignment / parked car seeding</li>
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</ul>
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<h2><a class="header" href="#data-sources" id="data-sources">Data sources</a></h2>
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<h3><a class="header" href="#seattle-soundcast" id="seattle-soundcast">Seattle: Soundcast</a></h3>
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<p>Seattle luckily has the Puget Sound Regional Council, which has produced the
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<a href="https://www.psrc.org/activity-based-travel-model-soundcast">Soundcast model</a>.
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They use census stats, land parcel records, observed vehicle counts, travel
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diaries, and lots of other things I don't understand to produce a detailed model
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of the region. We're currently using their 2014 model; the 2018 one should be
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available sometime in 2020. See the
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<a href="https://github.com/dabreegster/abstreet/tree/master/importer/src/soundcast">code</a>
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for importing their data.</p>
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<p>TODO:</p>
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<ul>
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<li>talk about how trips beginning/ending off-map are handled</li>
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</ul>
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<h3><a class="header" href="#berlin" id="berlin">Berlin</a></h3>
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<p>This work is <a href="https://github.com/dabreegster/abstreet/issues/119">ongoing</a>. See
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the
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<a href="https://github.com/dabreegster/abstreet/blob/master/importer/src/berlin.rs">code</a>.
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So far, we've found a population count per planning area and are randomly
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distributing the number of residents to all residential buildings in each area.</p>
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<h3><a class="header" href="#proletariat-robot" id="proletariat-robot">Proletariat robot</a></h3>
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<p>What if we just want to generate a reasonable model without any city-specific
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data? One of the simplest approaches is just to spawn people beginning at
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residential buildings, make them go to some workplace in the morning, then
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return in the evening. OpenStreetMap building tags can be used to roughly
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classify building types and distinguish small houses from large apartments. See
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the <code>proletariat_robot</code>
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<a href="https://github.com/dabreegster/abstreet/blob/master/sim/src/make/activity_model.rs">code</a>
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for an implementation of this.</p>
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<p>This is <a href="https://github.com/dabreegster/abstreet/issues/154">ongoing</a> work
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spearheaded by Mateusz. Some of the ideas for next steps are to generate
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different types of people (students, workers), give them a set of activities
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with durations (go to school for 7 hours, 1 hour lunch break), and then further
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pick specfic buildings to travel to using more OSM tags.</p>
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<h3><a class="header" href="#custom-import" id="custom-import">Custom import</a></h3>
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<p>If you have your own data, you can import it. The input format is JSON -- an
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example:</p>
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<pre><code>{
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"scenario_name": "monday",
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"people": [
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{
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"origin": {
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"longitude": -122.303723,
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"latitude": 47.6372834
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},
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"trips": [
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{
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"departure": 10800.0,
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"position": {
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"longitude": -122.3075948,
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"latitude": 47.6394773
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},
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"mode": "Drive"
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}
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]
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}
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]
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}
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</code></pre>
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<p>Run the tool:</p>
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<pre><code>cargo run --bin iotool -- import_traffic --map=data/system/maps/montlake.bin --input=/path/to/input.json
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</code></pre>
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<p>The tool matches input positions to the nearest building or border intersection,
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within 100 meters. The <code>departure</code> time is seconds since midnight. The tool will
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fail if any point doesn't match to a building, or if any of the specified trips
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can't be created (due to graph connectivity problems, for example). If your
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requirements are different or you have any trouble using this format/tool,
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please file a Github issue -- just consider this tool and format a prototype.</p>
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<h2><a class="header" href="#modifying-demand" id="modifying-demand">Modifying demand</a></h2>
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<p>The travel demand model is extremely fixed; the main effect of a different
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random number seed is currently to initially place parked cars in specific
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spots. When the player makes changes to the map, exactly the same people and
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trips are simulated, and we just measure how trip time changes. This is a very
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short-term prediction. If it becomes much more convenient to bike or bus
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somewhere, then more people will do it over time. How can we transform the
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original demand model to respond to these changes?</p>
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<p>Right now, there's very preliminary work in sandbox mode for Seattle weekday
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scenarios. You can cancel all trips for some people (simulating lockdown) or
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modify the mode for some people (change 50% of all driving trips between 7 and
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9am to use transit).</p>
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<h2><a class="header" href="#research" id="research">Research</a></h2>
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<ul>
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<li><a href="https://github.com/replicahq/doppelganger">https://github.com/replicahq/doppelganger</a></li>
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<li><a href="https://github.com/stasmix/popsynth">https://github.com/stasmix/popsynth</a></li>
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<li><a href="https://zephyrtransport.github.io/zephyr-directory/projects/">https://zephyrtransport.github.io/zephyr-directory/projects/</a></li>
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<li><a href="https://activitysim.github.io">https://activitysim.github.io</a></li>
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<li><a href="https://github.com/BayAreaMetro/travel-model-one">https://github.com/BayAreaMetro/travel-model-one</a></li>
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<li><a href="https://github.com/RSGInc/DaySim">https://github.com/RSGInc/DaySim</a></li>
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<li><a href="https://github.com/arup-group/pam">https://github.com/arup-group/pam</a></li>
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</ul>
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