Mobil换道模型python代码

Mobil换道模型是一种常用的交通流模型，主要用于研究车辆在高速公路等道路上的换道行为。它的核心思想是将车辆视为具有一定决策能力的个体，根据周围环境和自身特性来决定是否进行换道操作。

关于Mobil换道模型的Python代码实现，可以参考以下步骤：

1. 安装Python模拟器，例如SUMO或者Vissim。
2. 导入相关Python库，例如traci库，用于与模拟器进行交互。
3. 创建车辆类，包括车辆的属性和方法。
4. 实现Mobil换道模型的主要函数，包括计算加速度、计算换道意愿、计算换道目标等。
5. 在模拟器中调用相关函数，模拟车辆的行驶和换道行为。

如果您需要更具体的代码实现细节或者更深入的讨论，欢迎您提出更具体的问题。

相关问题

Python实现车辆换道模型

车辆换道模型主要是用于模拟车辆在多车道道路上的换道行为。下面是一个简单的Python实现，基于IDM和MOBIL模型：

import numpy as np

class Vehicle:
    def __init__(self, x, v, a, d, lane):
        self.x = x # 车辆位置
        self.v = v # 车辆速度
        self.a = a # 车辆加速度
        self.d = d # 车辆长度
        self.lane = lane # 车辆所在车道

class Road:
    def __init__(self, length, lanes):
        self.length = length # 道路长度
        self.lanes = lanes # 车道数
        self.vehicles = [[] for _ in range(lanes)] # 每条车道上的车辆列表

    def add_vehicle(self, vehicle):
        lane = vehicle.lane
        self.vehicles[lane].append(vehicle)

    def remove_vehicle(self, vehicle):
        lane = vehicle.lane
        self.vehicles[lane].remove(vehicle)

    def get_front_vehicle(self, vehicle):
        lane = vehicle.lane
        pos = vehicle.x
        front_pos = self.length
        front_vehicle = None
        for v in self.vehicles[lane]:
            if v != vehicle and v.x > pos and v.x < front_pos:
                front_pos = v.x
                front_vehicle = v
        return front_vehicle, front_pos

def idm_acc(vehicle, front_vehicle, front_pos, delta_t, a_max, b, v_des):
    s_star = vehicle.d + vehicle.v * delta_t + (vehicle.v * (vehicle.v - front_vehicle.v)) / (2 * np.sqrt(a_max * b))
    acc = a_max * (1 - (vehicle.v / v_des)**4 - (s_star / (front_pos - vehicle.d - vehicle.x))**2)
    return acc

def mobil_lane_change(vehicle, front_vehicle, back_vehicle, delta_t, a_max, b, v_des, p, s0):
    if front_vehicle is None:
        return False
    if back_vehicle is None:
        back_pos = -np.inf
    else:
        back_pos = back_vehicle.x
    delta_v = front_vehicle.v - vehicle.v
    s_star = vehicle.d + vehicle.v * delta_t + (vehicle.v * delta_v) / (2 * np.sqrt(a_max * b))
    acc = idm_acc(vehicle, front_vehicle, front_vehicle.x, delta_t, a_max, b, v_des)
    if acc < -b:
        acc = -b
    if vehicle.v + acc * delta_t > v_des:
        acc = (v_des - vehicle.v) / delta_t
    if front_vehicle.x - vehicle.x < s_star:
        return False
    if vehicle.x - back_pos < s0:
        return False
    if np.random.rand() > p:
        return False
    return True

def simulate(road, delta_t, a_max, b, v_des, p, s0):
    for lane in range(road.lanes):
        for vehicle in road.vehicles[lane]:
            front_vehicle, front_pos = road.get_front_vehicle(vehicle)
            back_vehicle, back_pos = None, None
            if len(road.vehicles[lane]) > 1:
                idx = road.vehicles[lane].index(vehicle)
                if idx > 0:
                    back_vehicle = road.vehicles[lane][idx-1]
                    back_pos = back_vehicle.x
            acc = idm_acc(vehicle, front_vehicle, front_pos, delta_t, a_max, b, v_des)
            if mobil_lane_change(vehicle, front_vehicle, back_vehicle, delta_t, a_max, b, v_des, p, s0):
                vehicle.lane = lane - 1
                road.vehicles[lane].remove(vehicle)
                road.vehicles[lane-1].append(vehicle)
                continue
            vehicle.a = acc
            vehicle.v += acc * delta_t
            vehicle.x += vehicle.v * delta_t

假设有一条双车道的道路，长度为1000米，车道宽度为3.5米，最大速度为120 km/h。我们可以按照以下方式模拟车辆行驶：

delta_t = 0.1 # 时间步长
a_max = 3 # 最大加速度 m/s^2
b = 3 # 最大减速度 m/s^2
v_des = 33.33 # 最大速度 m/s
p = 0.5 # 换道概率
s0 = 2 # 最小车距 m

road = Road(length=1000, lanes=2)
for i in range(20):
    x = np.random.rand() * 800 + 100
    v = np.random.rand() * v_des
    a = 0
    d = 5
    lane = 0 if i % 2 == 0 else 1
    vehicle = Vehicle(x, v, a, d, lane)
    road.add_vehicle(vehicle)

for _ in range(1000):
    simulate(road, delta_t, a_max, b, v_des, p, s0)

这里我们先生成20辆随机车辆，然后模拟1000秒的行驶过程。每秒钟调用一次simulate函数，更新每辆车的位置和速度。在simulate函数中，我们先计算车辆的加速度，然后判断是否需要换道，最后更新车辆的位置和速度。

SUMO-idm跟驰模型代码

SUMO-idm跟驰模型是一种基于间距-速度控制的车辆跟驰模型，其代码实现如下：

class IDMVehicle(AVLaneVehicle):
    """
    A vehicle using the IDM acceleration model.

    This vehicle implements the IDM car-following model. Additionally, it may
    use the MOBIL lane-changing model to change lanes.

    Parameters
    ----------
    * see parents *

    idm_params : dict, optional
        Dictionary with parameters for the IDM model. The default value is
        `{'a': 1.3, 'b': 2.0, 'v0': 30, 'T': 1.5, 's0': 2, 'delta': 4}`.
        For a description of the parameters, please refer to the `IDM model
        wikipedia page <https://en.wikipedia.org/wiki/Intelligent_driver_model>`_.

    Notes
    -----
    The MOBIL model is only used if the `lane_change_controller` is set to a
    `MobilLaneChanger` instance.

    Examples
    --------
    >>> from flow.controllers import IDMController, ContinuousRouter
    >>> from flow.core.params import VehicleParams, SumoParams
    >>>
    >>> sumo_params = SumoParams(sim_step=0.1, render=False)
    >>> vehicle_params = VehicleParams()
    >>> vehicle_params.add(
    ...     "human",
    ...     acceleration_controller=(IDMController, {}),
    ...     routing_controller=(ContinuousRouter, {}),
    ...     num_vehicles=20)
    >>>
    >>> # create a network and a scenario
    >>> from flow.networks import HighwayNetwork
    >>> from flow.scenarios import Scenario
    >>> from flow.core.params import NetParams
    >>> from flow.core.params import InitialConfig
    >>> from flow.scenarios import HighwayScenario
    >>> from flow.envs.ring.accel import IDMVehicle
    >>>
    >>> network = HighwayNetwork(
    ...     name='highway',
    ...     vehicles=vehicle_params,
    ...     net_params=NetParams(),
    ...     initial_config=InitialConfig(
    ...         spacing="uniform",
    ...         lanes_distribution=float("inf"),
    ...         lanes_count=2
    ...     )
    ... )
    >>> scenario = HighwayScenario(
    ...     name='highway',
    ...     generator_class=HighwayGenerator,
    ...     vehicles=vehicle_params,
    ...     net_params=NetParams(),
    ...     initial_config=InitialConfig(
    ...         spacing="uniform",
    ...         lanes_distribution=float("inf"),
    ...         lanes_count=2
    ...     )
    ... )
    >>>
    >>> # create the environment
    >>> from flow.envs import HighwayPOEnv
    >>> env = HighwayPOEnv(
    ...     env_params=EnvParams(),
    ...     sim_params=sumo_params,
    ...     scenario=scenario,
    ...     simulator='traci'
    ... )
    >>>
    >>> # run the simulation
    >>> obs = env.reset()
    >>> for i in range(100):
    ...     action = [1, 0]
    ...     obs, rewards, dones, info = env.step(action)
    ...     if i % 10 == 0:
    ...         env.render()
    >>> env.close()
    """

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        self.idm_params = kwargs.get(
            "idm_params", {'a': 1.3, 'b': 2.0, 'v0': 30, 'T': 1.5, 's0': 2,
                           'delta': 4})

    def get_accel(self, env):
        """See parent class."""
        leader = self.get_leader(env)

        if leader is None:
            return self.idm_params["a"]

        v = env.k.vehicle.get_speed(self.id)
        v_leader = env.k.vehicle.get_speed(leader)

        dv = max(0, v_leader - v)

        s = env.k.vehicle.get_headway(self.id)
        s_star = self.idm_params["s0"] + max(
            0, v * self.idm_params["T"] + v * dv /
            (2 * np.sqrt(self.idm_params["a"] * self.idm_params["b"])))

        return self.idm_params["a"] * (
            1 - np.power(v / self.idm_params["v0"], self.idm_params["delta"])
            - np.power(s_star / s, 2))

其中，`get_accel`方法是计算车辆加速度的主要函数。在该函数中，根据跟驰模型的公式计算出车辆的期望车头间距`s_star`，然后根据该期望车头间距计算车辆的加速度。具体来说，该模型主要包含以下几个参数：

- `a`：车辆加速度的最大值；
- `b`：车辆减速度的最大值；
- `v0`：车辆的期望速度；
- `T`：期望的时间间隔，即车辆跟前车保持的时间间隔；
- `s0`：车辆的最小车头间距；
- `delta`：车辆速度对加速度的影响因子。

通过调整这些参数，可以对车辆的行为进行调整。