Travel time estimation from GPS trips is of great importance to order
duration, ridesharing, taxi dispatching, etc. However, the dense trajectory is
not always available due to the limitation of data privacy and acquisition,
while the origin destination (OD) type of data, such as NYC taxi data, NYC bike
data, and Capital Bikeshare data, is more accessible. To address this issue,
this paper starts to estimate the OD trips travel time combined with the road
network. Subsequently, a Multitask Weakly Supervised Learning Framework for
Travel Time Estimation (MWSL TTE) has been proposed to infer transition
probability between roads segments, and the travel time on road segments and
intersection simultaneously. Technically, given an OD pair, the transition
probability intends to recover the most possible route. And then, the output of
travel time is equal to the summation of all segments' and intersections'
travel time in this route. A novel route recovery function has been proposed to
iteratively maximize the current route's co occurrence probability, and
minimize the discrepancy between routes' probability distribution and the
inverse distribution of routes' estimation loss. Moreover, the expected log
likelihood function based on a weakly supervised framework has been deployed in
optimizing the travel time from road segments and intersections concurrently.
We conduct experiments on a wide range of real world taxi datasets in Xi'an and
Chengdu and demonstrate our method's effectiveness on route recovery and travel
time estimation.

本文提出一种基于道路网络的 OD 行程旅行时间估计方法，结合迁移概率、路段和交叉口的旅行时间和路线恢复等技术，提出了多任务弱监督学习框架，该框架同时针对路段和交叉口优化期望对数似然函数，从而解决了 GPS 轨迹缺失的问题，实验验证了该方法对旅行时间估计和路线恢复的有效性。

基于多任务弱监督学习的起点终点旅行时间估计

Multitask Weakly Supervised Learning for Origin Destination Travel Time Estimation

Due to the rapid development of Internet of Things (IoT) technologies, many
online web apps (e.g., Google Map and Uber) estimate the travel time of
trajectory data collected by mobile devices. However, in reality, complex
factors, such as network communication and energy constraints, make multiple
trajectories collected at a low sampling rate. In this case, this paper aims to
resolve the problem of travel time estimation (TTE) and route recovery in
sparse scenarios, which often leads to the uncertain label of travel time and
route between continuously sampled GPS points. We formulate this problem as an
inexact supervision problem in which the training data has coarsely grained
labels and jointly solve the tasks of TTE and route recovery. And we argue that
both two tasks are complementary to each other in the model-learning procedure
and hold such a relation: more precise travel time can lead to better inference
for routes, in turn, resulting in a more accurate time estimation). Based on
this assumption, we propose an EM algorithm to alternatively estimate the
travel time of inferred route through weak supervision in E step and retrieve
the route based on estimated travel time in M step for sparse trajectories. We
conducted experiments on three real-world trajectory datasets and demonstrated
the effectiveness of the proposed method.

该论文针对 IoT 技术下数据采样不足的情况，提出了一种基于 EM 算法的不完全监督模型，通过粗略标记数据，同时解决旅行时间估计和路径恢复问题，实现更高精度的路径推断和更准确的时间预测。