Traffic congestion on urban roadways is a serious problem requiring novel ways to detect and mitigate it. Determining the routes that lead to the traffic congestion segment is also vital in devising mitigation strategies. Further, crowdsourcing this information allows for use of these strategies quickly and in places where infrastructure is not available. In this work, we present an unconventional method, using the barometer sensor of mobile phones to (a) detect road traffic congestion and (b) estimate the paths that lead to the congested road segment. We make the observation that roads are not completely flat and very often, altitude varies along the road. The barometer sensor chips are sensitive enough to measure these variations and consume very little energy of the mobile phone, compared to other sensors such as the GPS or accelerometer. We devise a feature set to map the rate of change of this altitude as the user moves into activities characterized as “still” and “motion,” which are further used by the traffic congestion detection algorithm (RoadSphygmo) to classify the group of users as being in “moving,” “congestion,” or “stuck” states. To estimate the paths that lead to the congested road segment, we compare the user’s barometer sensor readings with a pre-stored road signature of barometer values using Dynamic Time Warping (DTW). We show that by using correlation of barometer sensor values, we can determine if users are in the same vehicle. We crowdsource this information from multiple mobile phones and use majority voting technique to improve the accuracy of traffic congestion detection and path estimation. We find a significant increase in the accuracies using crowdsourced information as compared to individual mobile phones. Further, we show that we can use barometer sensor for other applications such as bus occupancy, boarding/deboarding of a vehicle, and so on. The validation of the state determined by RoadSphygmo is done by comparing it with average GPS speed calculated during the same time period. The path estimation is validated over different intersections and considering various cases of commuter travel. The results obtained are promising and show that the traffic state determination and the estimation of the path taken by the commuter can achieve high accuracy.

中文翻译：

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气压感应

城市道路上的交通拥堵是一个严重的问题，需要新的方法来检测和缓解它。确定导致交通拥堵路段的路线对于制定缓解策略也至关重要。此外，众包这些信息允许在基础设施不可用的地方快速使用这些策略。在这项工作中，我们提出了一种非常规的方法，使用手机的气压计传感器来 (a) 检测道路交通拥堵和 (b) 估计导致拥堵路段的路径。我们观察到道路并不完全平坦，而且高度经常沿着道路变化。与 GPS 或加速度计等其他传感器相比，气压计传感器芯片足够灵敏，可以测量这些变化并且消耗手机的极少能量。我们设计了一个特征集来映射当用户进入以“静止”和“运动”为特征的活动时该高度的变化率，交通拥堵检测算法 (RoadSphygmo) 进一步使用这些特征将用户组分类为处于“移动”、“拥堵”或“卡住”状态。为了估计导致拥堵路段的路径，我们使用动态时间规整 (DTW) 将用户的气压计传感器读数与气压计值的预存储道路特征进行比较。我们表明，通过使用气压计传感器值的相关性，我们可以确定用户是否在同一辆车中。我们从多部手机众包这些信息，并使用多数投票技术来提高交通拥堵检测和路径估计的准确性。与单个手机相比，我们发现使用众包信息的准确性显着提高。此外，我们展示了我们可以将气压计传感器用于其他应用，例如公共汽车占用、车辆上/下车等。由 RoadSphygmo 确定的状态的验证是通过将其与同一时间段内计算的平均 GPS 速度进行比较来完成的。路径估计在不同的交叉口上得到验证，并考虑了通勤旅行的各种情况。获得的结果是有希望的，并表明交通状态确定和通勤者所走路径的估计可以达到很高的准确性。等等。由 RoadSphygmo 确定的状态的验证是通过将其与同一时间段内计算的平均 GPS 速度进行比较来完成的。路径估计在不同的交叉口上得到验证，并考虑了通勤旅行的各种情况。获得的结果是有希望的，并表明交通状态确定和通勤者所走路径的估计可以达到很高的准确性。等等。由 RoadSphygmo 确定的状态的验证是通过将其与同一时间段内计算的平均 GPS 速度进行比较来完成的。路径估计在不同的交叉口上得到验证，并考虑了通勤旅行的各种情况。获得的结果是有希望的，并表明交通状态确定和通勤者所走路径的估计可以达到很高的准确性。