Details of the Baseline HMM

[sxwee]

How do you evaluate on HMM? According to the data preprocessing flow you provided, your ground truth is obtained by interpolation followed by HMM matching, is the evaluation also conducted by first interpolating and then using HMM matching?

[chenyuqi990215]

Linear + HMM first performs linear interpolation without road segment information, i.e., on Euclidean space, and then performs map matching.

[chenyuqi990215]

We provide a simple code of linear interpolation of GPS trajectory below:

def process_trajectory(record, epsilon, interpolate_func, roadmap=None):
    if len(record) == 0:
        return []
    timestamp = {}
    count = [0 for _ in range(epsilon)]
    for item in record:
        timestamp[int(item[0])] = [int(item[0]), float(item[1]), float(item[2]), int(item[3])]
        count[int(item[0]) % epsilon] += 1
    m = np.argmax(count)
    st = int(record[0][0])
    while st % epsilon != m:
        st += 1
    en = int(record[-1][0])
    processed_traj = []
    while st <= en:
        if st in timestamp:
            processed_traj.append(timestamp[st])
        else:
            pre = st
            while pre not in timestamp:
                pre -= 1
            post = st
            while post not in timestamp:
                post += 1
            if roadmap is not None:
                gps = interpolate_func(timestamp[pre], timestamp[post], st, roadmap)
            else:
                gps = interpolate_func(timestamp[pre], timestamp[post], st)
            if gps[0] == -1:
                processed_traj.append([st, timestamp[pre][1], timestamp[pre][2], -997])
            else:
                processed_traj.append(gps)
        st += epsilon
    return processed_traj

def noiseInterpolate(pre, post, time):
    gps_pre = [pre[1], pre[2]]
    gps_post = [post[1], post[2]]

    time_pre = time - pre[0]
    time_post = post[0] - time

    cluster = [gps_pre for _ in range(time_post)] + [gps_post for _ in range(time_pre)]
    return [time, *center_geolocation(cluster), pre[-1]]

process_trajectory(record, epsilon, noiseInterpolate)