Exploiting Multi-modal Contextual Sensing for City-bus’s Stay Location Characterization: Towards Sub-60 Seconds Accurate Arrival Time Prediction

IF 3.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Ratna Mandal, Prasenjit Karmakar, S. Chatterjee, Debaleen Das Spandan, S. Pradhan, Sujoy Saha, Sandip Chakraborty, S. Nandi
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引用次数: 3

Abstract

Intelligent city transportation systems are one of the core infrastructures of a smart city. The true ingenuity of such an infrastructure lies in providing the commuters with real-time information about citywide transport like public buses, allowing them to pre-plan their travel. However, providing prior information for transportation systems like public buses in real-time is inherently challenging because of the diverse nature of different stay-locations where a public bus stops. Although straightforward factors like stay duration extracted from unimodal sources like GPS at these locations look erratic, a thorough analysis of public bus GPS trails for 1,335.365 km at the city of Durgapur, a semi-urban city in India, reveals that several other fine-grained contextual features can characterize these locations accurately. Accordingly, we develop BuStop, a system for extracting and characterizing the stay-locations from multi-modal sensing using commuters’ smartphones. Using this multi-modal information BuStop extracts a set of granular contextual features that allows the system to differentiate among the different stay-location types. A thorough analysis of BuStop using the collected in-house dataset indicates that the system works with high accuracy in identifying different stay-locations such as regular bus stops, random ad hoc stops, stops due to traffic congestion, stops at traffic signals, and stops at sharp turns. Additionally, we develop a proof-of-concept setup on top of BuStop to analyze the potential of the framework in predicting expected arrival time, a critical piece of information required to pre-plan travel at any given bus stop. Subsequent analysis of the PoC framework, through simulation over the test dataset, shows that characterizing the stay-locations indeed helps make more accurate arrival time predictions with deviations less than 60 seconds from the ground-truth arrival time.
基于多模态上下文感知的城市公交停留位置表征:迈向60秒以下准确到达时间预测
智慧城市交通系统是智慧城市的核心基础设施之一。这种基础设施的真正独创性在于为通勤者提供全市交通(如公交车)的实时信息,使他们能够提前计划自己的出行。然而,为公交等交通系统提供实时的先验信息本身就具有挑战性,因为公交停站的不同停留位置具有多样性。尽管从这些地点的GPS等单一模式来源提取的停留时间等直接因素看起来不稳定,但对印度杜尔加普尔市1335.365公里公共汽车GPS轨迹的全面分析显示,其他几个细粒度的背景特征可以准确地描述这些地点。因此,我们开发了BuStop,这是一个使用通勤者智能手机从多模态传感中提取和表征停留位置的系统。利用这些多模态信息,BuStop提取了一组细粒度的上下文特征,使系统能够区分不同的停留位置类型。利用收集的内部数据对BuStop进行深入分析,结果表明,该系统在识别常规公交车站、随机临时车站、交通拥堵停车、交通信号停车、急转弯停车等不同停车地点方面具有很高的准确性。此外,我们在BuStop之上开发了一个概念验证设置,以分析该框架在预测预期到达时间方面的潜力,这是在任何给定的公共汽车站预先计划旅行所需的关键信息。通过对测试数据集的模拟,对PoC框架的后续分析表明,描述停留位置确实有助于更准确地预测到达时间,与地面真实到达时间的偏差小于60秒。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
5.20
自引率
3.70%
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