Trans-Sense：使用智能手机的实时的运输计划估计
Trans-Sense: Real Time Transportation Schedule Estimation Using Smart Phones
摘要：
发展中国家面临交通拥堵、公路/铁路网规划不善以及缺乏公共交通设施等问题。这种情况导致燃料消耗增加，污染程度，金钱损失，大规模延误和生产力下降。另一方面，它对通勤者的感受和情绪产生了负面影响。实时交通信息的可用性-通过使用GPS设备提供公共交通车辆位置-有助于估计乘客的等待时间并解决上述问题。然而，这种解决方案对发展中国家来说是昂贵的。本文旨在设计和实现一种基于众包手机的解决方案，以估计乘客在公共交通系统中的期望等待时间，预测乘客上下车的剩余时间，并构建一个实时的公共交通时刻表。Trans-Sense使用不同Android手机每天收集超过800小时的真实数据进行评估，并在不同时间段使用不同的轻轨交通线路。实验结果表明，该方法在识别光迹站点时，平均召回率和准确率分别达到95.35%和90.1%。此外，基于不同延误时间对乘客出行时间影响的经验分布，预测乘客到达目的地的正确时间的准确率为91.81%，同时，系统对车站规模的估计准确率为95.71%。
英文摘要：
Developing countries suffer from traffic congestion, poorly planned road/rail networks, and lack of access to public transportation facilities. This context results in an increase in fuel consumption, pollution level, monetary losses, massive delays, and less productivity. On the other hand, it has a negative impact on the commuters feelings and moods. Availability of real-time transit information - by providing public transportation vehicles locations using GPS devices - helps in estimating a passenger's waiting time and addressing the above issues. However, such solution is expensive for developing countries. This paper aims at designing and implementing a crowd-sourced mobile phones-based solution to estimate the expected waiting time of a passenger in public transit systems, the prediction of the remaining time to get on/off a vehicle, and to construct a real time public transit schedule. Trans-Sense has been evaluated using real data collected for over 800 hours, on a daily basis, by different Android phones, and using different light rail transit lines at different time spans. The results show that Trans-Sense can achieve an average recall and precision of 95.35% and 90.1%, respectively, in discriminating lightrail stations. Moreover, the empirical distributions governing the different time delays affecting a passenger's total trip time enable predicting the right time of arrival of a passenger to her destination with an accuracy of 91.81%.In addition, the system estimates the stations dimensions with an accuracy of 95.71%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
