{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Taxi / Shared mobility" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The standard vehicles (added by default `addVehicle` or `adddemand`) in UXsim are a kind of privately owned vehicles: They travel from origin to destination and disappear. \n", "\n", "In this demonstration, we explain how to represent taxi-like (or *shared mobility* if you want a cool name) vehicles that repeatedly transport multiple passengers. Such taxi can be added by `addVehicle` with `mode=\"taxi\"` option. Furthermore, the passenger-to-taxi matching problem is handled by `uxsim.TaxiHandler` submodule.\n", "\n", "Let's define a grid shaped network scenario." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from pylab import *\n", "from uxsim import *\n", "from uxsim.TaxiHandler import *\n", "\n", "# World definition\n", "tmax = 7200\n", "deltan = 5\n", "W = World(\n", " name=\"\",\n", " deltan=deltan,\n", " tmax=tmax,\n", " print_mode=1, save_mode=1, show_mode=1,\n", " random_seed=0,\n", ")\n", "\n", "# Scenario definition: grid network\n", "#deploy nodes as an imax x jmax grid\n", "imax = 6\n", "jmax = 6\n", "nodes = {}\n", "for i in range(imax):\n", " for j in range(jmax):\n", " nodes[i,j] = W.addNode(f\"n{(i,j)}\", i, j, flow_capacity=1.6)\n", "\n", "#create links between neighborhood nodes\n", "links = {}\n", "for i in range(imax):\n", " for j in range(jmax):\n", " if i != imax-1:\n", " links[i,j,i+1,j] = W.addLink(f\"l{(i,j,i+1,j)}\", nodes[i,j], nodes[i+1,j], length=1000, free_flow_speed=20, number_of_lanes=1)\n", " if i != 0:\n", " links[i,j,i-1,j] = W.addLink(f\"l{(i,j,i-1,j)}\", nodes[i,j], nodes[i-1,j], length=1000, free_flow_speed=20, number_of_lanes=1)\n", " if j != jmax-1:\n", " links[i,j,i,j+1] = W.addLink(f\"l{(i,j,i,j+1)}\", nodes[i,j], nodes[i,j+1], length=1000, free_flow_speed=20, number_of_lanes=1)\n", " if j != 0:\n", " links[i,j,i,j-1] = W.addLink(f\"l{(i,j,i,j-1)}\", nodes[i,j], nodes[i,j-1], length=1000, free_flow_speed=20, number_of_lanes=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Taxis can be added by `W.addVehicle(origin_node, None, 0, mode=\"taxi\")` as follows. Their behavior is as follows:\n", "\n", "- When a taxi has no job, it circulates the network randomly.\n", "- When a taxi is ordered to serve a passenger, it travels to the origin node of the passenger to pickup, and then travels to the destination to drop-off.\n", "\n", "In addition, we need to define a taxi handler `Handler`. It is responsible to manage trip requests from passengers and give orders to taxis. This time, we use default `TaxiHandler_nearest` that matches a new passenger to their nearest vacant taxi." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "Handler = TaxiHandler_nearest(W)\n", "\n", "n_taxis = 3000\n", "for i in range(int(n_taxis/deltan)):\n", " node = random.choice(list(nodes.values()))\n", " W.addVehicle(node, None, 0, mode=\"taxi\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A passenger' trip request is represented by `TripRequest` class. It can be added by `Handler.add_trip_request(origin_node, destination_node, departure_time)`. In this scenario, they are randomly set as follows." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "n_passengers = 20000\n", "for i in range(int(n_passengers/deltan)):\n", " node1 = random.choice(list(nodes.values()))\n", " node2 = random.choice(list(nodes.values()))\n", " while node1 == node2:\n", " node2 = random.choice(list(nodes.values()))\n", " Handler.add_trip_request(node1, node2, i/n_passengers*deltan*tmax/2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can execute simulation. During the simulation, `Handler.assign_trip_request_to_taxi()` must be called repeatedly in order to let taxis do their jobs." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "simulation setting:\n", " scenario name: \n", " simulation duration:\t 7200 s\n", " number of vehicles:\t 3000 veh\n", " total road length:\t 120000 m\n", " time discret. width:\t 5 s\n", " platoon size:\t\t 5 veh\n", " number of timesteps:\t 1440\n", " number of platoons:\t 600\n", " number of links:\t 120\n", " number of nodes:\t 36\n", " setup time:\t\t 0.52 s\n", "simulating...\n", " time| # of vehicles| ave speed| computation time\n", " 0 s| 0 vehs| 0.0 m/s| 0.00 s\n", " 600 s| 3000 vehs| 13.4 m/s| 2.83 s\n", " 1200 s| 3000 vehs| 15.0 m/s| 4.63 s\n", " 1800 s| 3000 vehs| 14.1 m/s| 5.85 s\n", " 2400 s| 3000 vehs| 14.8 m/s| 6.88 s\n", " 3000 s| 3000 vehs| 14.7 m/s| 7.98 s\n", " 3600 s| 3000 vehs| 14.3 m/s| 9.03 s\n", " 4200 s| 3000 vehs| 15.5 m/s| 9.81 s\n", " 4800 s| 3000 vehs| 14.5 m/s| 10.81 s\n", " 5400 s| 3000 vehs| 14.0 m/s| 11.55 s\n", " 6000 s| 3000 vehs| 14.9 m/s| 12.34 s\n", " 6600 s| 3000 vehs| 14.5 m/s| 13.13 s\n", " 7200 s| 3000 vehs| 14.9 m/s| 13.87 s\n", " simulation finished\n", "results:\n", " average speed:\t 15.5 m/s\n", " number of completed trips:\t 0 / 0\n", " total distance traveled:\t 0.0 m\n" ] } ], "source": [ "# Run the simulation \n", "while W.check_simulation_ongoing():\n", " W.exec_simulation(duration_t = 60)\n", " Handler.assign_trip_request_to_taxi() # for every 60 seconds, the taxi is assgined\n", "\n", "# Results\n", "W.analyzer.print_simple_stats()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the trip-related stats shown by `W.analyzer.print_simple_stats()` are only for non-taxi trips, so the current one shows 0.\n", "\n", "The summary of taxi transportation can be printed as follows" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "results for taxi transportation:\n", " total trip rquests: 20000\n", " completed trip requests: 20000\n", " completed trip requests ratio: 1.00\n", " average number of completed requests per taxi: 6.67\n", " average waiting time: 86.0\n", " average in-vehicle time: 283.8\n", " average trip time: 369.8\n" ] } ], "source": [ "Handler.print_stats()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also export the trip logs as pandas.Dataframe." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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origdestdepart_timeget_taxi_timearrival_timetravel_timewaiting_timeused_taxi
0n(4, 2)n(3, 1)0.0165340340.0165.0418
1n(5, 5)n(4, 2)0.9160500499.1159.1180
2n(1, 3)n(4, 3)1.865220218.263.2230
3n(3, 1)n(3, 4)2.765245242.362.3236
4n(0, 5)n(2, 3)3.665325321.461.4131
...........................
3995n(4, 1)n(2, 2)3595.536453850254.549.5452
3996n(2, 0)n(3, 3)3596.437104095498.6113.6547
3997n(5, 0)n(3, 2)3597.336403870272.742.7355
3998n(1, 5)n(0, 1)3598.236403945346.841.80
3999n(2, 5)n(1, 3)3599.137253935335.9125.9340
\n", "

4000 rows × 8 columns

\n", "
" ], "text/plain": [ " orig dest depart_time get_taxi_time arrival_time travel_time \\\n", "0 n(4, 2) n(3, 1) 0.0 165 340 340.0 \n", "1 n(5, 5) n(4, 2) 0.9 160 500 499.1 \n", "2 n(1, 3) n(4, 3) 1.8 65 220 218.2 \n", "3 n(3, 1) n(3, 4) 2.7 65 245 242.3 \n", "4 n(0, 5) n(2, 3) 3.6 65 325 321.4 \n", "... ... ... ... ... ... ... \n", "3995 n(4, 1) n(2, 2) 3595.5 3645 3850 254.5 \n", "3996 n(2, 0) n(3, 3) 3596.4 3710 4095 498.6 \n", "3997 n(5, 0) n(3, 2) 3597.3 3640 3870 272.7 \n", "3998 n(1, 5) n(0, 1) 3598.2 3640 3945 346.8 \n", "3999 n(2, 5) n(1, 3) 3599.1 3725 3935 335.9 \n", "\n", " waiting_time used_taxi \n", "0 165.0 418 \n", "1 159.1 180 \n", "2 63.2 230 \n", "3 62.3 236 \n", "4 61.4 131 \n", "... ... ... \n", "3995 49.5 452 \n", "3996 113.6 547 \n", "3997 42.7 355 \n", "3998 41.8 0 \n", "3999 125.9 340 \n", "\n", "[4000 rows x 8 columns]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df = Handler.trips_to_pandas()\n", "display(df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## How many taxis do we need in a city?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is an important question (even [a Nature article](https://www.nature.com/articles/s41586-018-0095-1) addresses this issue). If the number of taxis is too small, it cannot provide sufficient services to the travelers. Contrary, if it is too large, the road get congested and the travel time will be increased.\n", "\n", "Let's answer this question using UXsim. First, we define simulation scenario as a function whose variable is the number of taxis and the number of passengers. The scenario setting is very similar to the previous example. This function returns some stats of the simulation results." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "from pylab import *\n", "from uxsim import *\n", "from uxsim.TaxiHandler import *\n", "\n", "def simulation_with_given_number_of_taxis(n_taxis, n_passengers):\n", " print(\"####\"*20)\n", " print(f\"CASE WTIH NUMBER OF TAXIS = {n_taxis}\")\n", "\n", " # World definition\n", " tmax = 3600*3\n", " tdemand = 3600\n", " deltan = 5\n", " W = World(\n", " name=\"\",\n", " deltan=deltan,\n", " tmax=tmax,\n", " print_mode=0, save_mode=1, show_mode=1,\n", " random_seed=0,\n", " )\n", "\n", " # Scenario definition: grid network\n", " #deploy nodes as an imax x jmax grid\n", " imax = 6\n", " jmax = 6\n", " nodes = {}\n", " for i in range(imax):\n", " for j in range(jmax):\n", " nodes[i,j] = W.addNode(f\"n{(i,j)}\", i, j, flow_capacity=1.6)\n", "\n", " #create links between neighborhood nodes\n", " links = {}\n", " for i in range(imax):\n", " for j in range(jmax):\n", " if i != imax-1:\n", " links[i,j,i+1,j] = W.addLink(f\"l{(i,j,i+1,j)}\", nodes[i,j], nodes[i+1,j], length=1000, free_flow_speed=20, number_of_lanes=1)\n", " if i != 0:\n", " links[i,j,i-1,j] = W.addLink(f\"l{(i,j,i-1,j)}\", nodes[i,j], nodes[i-1,j], length=1000, free_flow_speed=20, number_of_lanes=1)\n", " if j != jmax-1:\n", " links[i,j,i,j+1] = W.addLink(f\"l{(i,j,i,j+1)}\", nodes[i,j], nodes[i,j+1], length=1000, free_flow_speed=20, number_of_lanes=1)\n", " if j != 0:\n", " links[i,j,i,j-1] = W.addLink(f\"l{(i,j,i,j-1)}\", nodes[i,j], nodes[i,j-1], length=1000, free_flow_speed=20, number_of_lanes=1)\n", "\n", " # taxis and passengers\n", " for i in range(int(n_taxis/deltan)):\n", " node = random.choice(list(nodes.values()))\n", " W.addVehicle(node, None, 0, mode=\"taxi\")\n", "\n", " Handler = TaxiHandler_nearest(W)\n", " for i in range(int(n_passengers/deltan)):\n", " node1 = random.choice(list(nodes.values()))\n", " node2 = random.choice(list(nodes.values()))\n", " while node1 == node2:\n", " node2 = random.choice(list(nodes.values()))\n", " Handler.add_trip_request(node1, node2, i/n_passengers*deltan*tdemand)\n", "\n", " # Run the simulation \n", " while W.check_simulation_ongoing():\n", " W.exec_simulation(duration_t = 60)\n", " Handler.assign_trip_request_to_taxi() # for every 60 seconds, the taxi is assgined\n", "\n", " # Results\n", " W.analyzer.print_simple_stats()\n", "\n", " Handler.print_stats()\n", "\n", " df_veh = W.analyzer.vehicles_to_pandas()\n", "\n", " # Return the average travel time, waiting time, and vehicle speed\n", " return average(Handler.travel_times), average(Handler.waiting_times), average(df_veh[\"v\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we systematically execute simulation scenarios with different number of taxis. The total traveler demand is fixed." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "################################################################################\n", "CASE WTIH NUMBER OF TAXIS = 1000\n", "results for taxi transportation:\n", " total trip rquests: 20000\n", " completed trip requests: 20000\n", " completed trip requests ratio: 1.00\n", " average number of completed requests per taxi: 20.00\n", " average waiting time: 1559.7\n", " average in-vehicle time: 203.3\n", " average trip time: 1763.1\n", "################################################################################\n", "CASE WTIH NUMBER OF TAXIS = 1500\n", "results for taxi transportation:\n", " total trip rquests: 20000\n", " completed trip requests: 20000\n", " completed trip requests ratio: 1.00\n", " average number of completed requests per taxi: 13.33\n", " average waiting time: 473.8\n", " average in-vehicle time: 208.4\n", " average trip time: 682.2\n", "################################################################################\n", "CASE WTIH NUMBER OF TAXIS = 2000\n", "results for taxi transportation:\n", " total trip rquests: 20000\n", " completed trip requests: 20000\n", " completed trip requests ratio: 1.00\n", " average number of completed requests per taxi: 10.00\n", " average waiting time: 90.2\n", " average in-vehicle time: 223.5\n", " average trip time: 313.8\n", "################################################################################\n", "CASE WTIH NUMBER OF TAXIS = 2500\n", "results for taxi transportation:\n", " total trip rquests: 20000\n", " completed trip requests: 20000\n", " completed trip requests ratio: 1.00\n", " average number of completed requests per taxi: 8.00\n", " average waiting time: 83.1\n", " average in-vehicle time: 243.4\n", " average trip time: 326.5\n", "################################################################################\n", "CASE WTIH NUMBER OF TAXIS = 3000\n", "results for taxi transportation:\n", " total trip rquests: 20000\n", " completed trip requests: 20000\n", " completed trip requests ratio: 1.00\n", " average number of completed requests per taxi: 6.67\n", " average waiting time: 84.1\n", " average in-vehicle time: 285.2\n", " average trip time: 369.4\n", "################################################################################\n", "CASE WTIH NUMBER OF TAXIS = 3500\n", "results for taxi transportation:\n", " total trip rquests: 20000\n", " completed trip requests: 20000\n", " completed trip requests ratio: 1.00\n", " average number of completed requests per taxi: 5.71\n", " average waiting time: 92.0\n", " average in-vehicle time: 334.4\n", " average trip time: 426.4\n", "################################################################################\n", "CASE WTIH NUMBER OF TAXIS = 4000\n", "results for taxi transportation:\n", " total trip rquests: 20000\n", " completed trip requests: 20000\n", " completed trip requests ratio: 1.00\n", " average number of completed requests per taxi: 5.00\n", " average waiting time: 93.4\n", " average in-vehicle time: 359.5\n", " average trip time: 452.9\n", "################################################################################\n", "CASE WTIH NUMBER OF TAXIS = 4500\n", "results for taxi transportation:\n", " total trip rquests: 20000\n", " completed trip requests: 20000\n", " completed trip requests ratio: 1.00\n", " average number of completed requests per taxi: 4.44\n", " average waiting time: 101.5\n", " average in-vehicle time: 418.1\n", " average trip time: 519.6\n", "################################################################################\n", "CASE WTIH NUMBER OF TAXIS = 5000\n", "results for taxi transportation:\n", " total trip rquests: 20000\n", " completed trip requests: 20000\n", " completed trip requests ratio: 1.00\n", " average number of completed requests per taxi: 4.00\n", " average waiting time: 107.1\n", " average in-vehicle time: 466.5\n", " average trip time: 573.7\n", "################################################################################\n", "CASE WTIH NUMBER OF TAXIS = 5500\n", "results for taxi transportation:\n", " total trip rquests: 20000\n", " completed trip requests: 20000\n", " completed trip requests ratio: 1.00\n", " average number of completed requests per taxi: 3.64\n", " average waiting time: 115.2\n", " average in-vehicle time: 489.8\n", " average trip time: 604.9\n", "################################################################################\n", "CASE WTIH NUMBER OF TAXIS = 6000\n", "results for taxi transportation:\n", " total trip rquests: 20000\n", " completed trip requests: 20000\n", " completed trip requests ratio: 1.00\n", " average number of completed requests per taxi: 3.33\n", " average waiting time: 117.7\n", " average in-vehicle time: 548.5\n", " average trip time: 666.2\n" ] } ], "source": [ "res_passenger_travel_time = {}\n", "res_passenger_waiting_time = {}\n", "res_vehicle_speed = {}\n", "\n", "for n_taxis in range(1000, 6001, 500):\n", " ptt, pwt, vs = simulation_with_given_number_of_taxis(n_taxis, 20000)\n", " res_passenger_travel_time[n_taxis] = ptt\n", " res_passenger_waiting_time[n_taxis] = pwt\n", " res_vehicle_speed[n_taxis] = vs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now visualize the results." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "figure()\n", "ax1 = gca()\n", "ax1.plot(list(res_passenger_travel_time.keys()), list(res_passenger_travel_time.values()), \"bo-\", label=\"average travel time per trip\")\n", "ax1.plot(list(res_passenger_waiting_time.keys()), list(res_passenger_waiting_time.values()), \"go-\", label=\"average waiting time per trip\")\n", "ax1.set_xlabel(\"number of taxis\")\n", "ax1.set_ylabel(\"time (s)\")\n", "\n", "ax2 = ax1.twinx()\n", "ax2.plot(list(res_vehicle_speed.keys()), list(res_vehicle_speed.values()), \"rx--\", label=\"average vehicle speed\")\n", "ax2.set_ylabel(\"speed (m/s)\")\n", "\n", "ax1.legend(loc=\"upper left\")\n", "ax2.legend(loc=\"upper right\")\n", "\n", "show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can see that the waiting time almost monotonically decreases as the number of taxis increases. However, it converges to certain value after 2000 taxis (and slightly increases after that due to congestion).\n", "\n", "On the other hand, the average speed of vehicles monotonically decreases as the the number of taxis increases, resulting long travel time after some point. \n", "\n", "As a result, the optimal number of taxis (that balances both of these factors and minimizes the passenger travel time) is found to be around 2000. This means that 1 taxi serves to 10 passengers in 1-3 hours in this city. Sounds reasonable." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Defining custom TaxiHandler" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also define your own TaxiHandler that may have smarter passenger-to-vehicle matching or even ridesharing. To do so, it is recommended to define a custom class by inheriting `TaxiHandler` class. \n", "\n", "\n", "Below is an example of (slightly) advanced taxi handler.\n", "Important built-in variables and functions are as follows:\n", "\n", "- `TaxiHandler.trip_requests`: A list of all of the trip requests that is handled by `TaxiHandler`\n", "- `TaxiHandler.assign_taxi(taxi, trip_request)`: This assigns `taxi` to `trip_request`. The `taxi` immediately start traveling to pickup the `trip_request` once this function is called.\n", "\n", "For the details, please see the source code of `uxsim/TaxiHandler/TaxiHandler.py`" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "from uxsim.TaxiHandler import *\n", "\n", "class TaxiHandler_nearest_matching_radious(TaxiHandler):\n", " \"\"\"\n", " A taxi handler that assigns trip requests to nearest taxis that are within a certain radious of the origin node (based on Euclidean distance). \n", " \"\"\"\n", " def __init__(s, W, matching_radious):\n", " super().__init__(W)\n", " s.matching_radious = matching_radious\n", " \n", " def assign_trip_request_to_taxi(s):\n", " \"\"\"\n", " Assigns trip request to nearest available taxi that is within the radious of the origin node.\n", " \"\"\"\n", " vacant_taxis = [veh for veh in s.W.VEHICLES.values() if veh.mode == \"taxi\" and veh.state == \"run\" and veh.dest == None]\n", " random.shuffle(vacant_taxis)\n", " for trip_request in s.trip_requests[:]:\n", " if len(vacant_taxis) == 0:\n", " break\n", " if trip_request.depart_time <= s.W.TIME:\n", " dist_tmp = float(\"inf\")\n", " taxi_tmp = None\n", " for taxi in vacant_taxis[:]:\n", " x0, y0 = taxi.get_xy_coords()\n", " x1, y1 = trip_request.orig.x, trip_request.orig.y\n", " dist = math.sqrt((x0-x1)**2 + (y0-y1)**2)\n", " if dist <= s.matching_radious and dist <= dist_tmp:\n", " dist_tmp = dist\n", " taxi_tmp = taxi\n", " if taxi_tmp != None:\n", " vacant_taxis.remove(taxi_tmp)\n", " s.assign_taxi(taxi_tmp, trip_request)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function `assign_trip_request_to_taxi` is designed to assign trip requests to the nearest available taxi within a specified radius of the request's origin point. Here's a detailed explanation for its functionality:\n", "\n", "**Description**\n", "\n", "This function assigns each trip request to the nearest available taxi that is within the radius of the trip's origin node. It ensures that trip requests are matched efficiently to taxis that are not currently occupied and are in a state ready to run.\n", "\n", "**Workflow**\n", "\n", "1. **Identify Vacant Taxis**:\n", " - The function starts by filtering out all vehicles from `s.W.VEHICLES` that are marked as taxis (`veh.mode == \"taxi\"`), are in the running state (`veh.state == \"run\"`), and are not currently assigned to a destination to carry travelers (`veh.dest == None`).\n", "\n", "2. **Process Each Trip Request**:\n", " - It iterates over all current trip requests.\n", " - If there are no vacant taxis left, it breaks out of the loop as no more assignments can be made.\n", " - For each trip request, it checks if the request's departure time is less than or equal to the current world time (`s.W.TIME`).\n", "\n", "3. **Find the Nearest Available Taxi**:\n", " - For each trip request being processed, it initializes a variable to track the smallest distance found (`dist_tmp`) and a variable to hold the nearest taxi (`taxi_tmp`).\n", " - It then calculates the distance between the trip request's origin and each taxi's current coordinates using the Euclidean distance formula.\n", " - If a taxi is within the allowed matching radius (`s.matching_radious`) and closer than any previously checked taxi, it updates `taxi_tmp` to this taxi.\n", "\n", "4. **Assign the Taxi**:\n", " - If a nearest taxi (`taxi_tmp`) is found for a trip request, the taxi is removed from the list of vacant taxis to prevent it from being assigned to multiple requests.\n", " - The taxi is then officially assigned to the trip request using `s.assign_taxi(taxi_tmp, trip_request)`.\n", "\n", "**Notes**\n", "\n", "- The function assumes that there are methods and attributes within the object `s` that allow it to access current time (`s.W.TIME`), vehicle list (`s.W.VEHICLES`), and the function `assign_taxi` to handle the assignment.\n", "- The radius within which a taxi can be matched to a trip request is defined by `s.matching_radious`.\n", "- This function ensures that taxis are utilized efficiently by assigning them to the closest requests, thereby potentially reducing wait times for passengers and travel distances for taxis.\n" ] } ], "metadata": { "anaconda-cloud": {}, "colab": { "provenance": [] }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.6" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "02b3228f560f48128236b0924959a3a9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": 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