FullStop: Tracking unsafe stopping behaviour of buses
01 Jan 2018-pp 65-72
TL;DR: FullStop, a smartphone-based system to detect safety risks arising from bus stopping behaviour, is presented, which is based on the view obtained from looking out to the front of the vehicle using the camera of a smartphone that is mounted on the front windshield.
Abstract: Road safety is a critical issue the world-over, and the problem is particularly acute in developing countries, where the combination of crowding, inadequate roads, and driver indiscipline serves up a deadly cocktail. We believe that mobile devices can play a positive role in this context by detecting dangerous conditions and providing feedback to enable timely redressal of potential dangers. This paper focuses on a specific problem that is responsible for many accidents in developing countries: the stopping behaviour of buses especially in the vicinity of bus stops. For instance, buses could arrive at a bus stop but continue rolling forward instead of coming to a complete halt, or could stop some distance away from the bus stop, possibly even in the middle of a busy road. Each of these behaviours can result in injury or worse to people waiting at a bus stop as well as to passengers boarding or alighting from buses. We present FullStop, a smartphone-based system to detect safety risks arising from bus stopping behaviour, as described above. We show that the GPS and inertial sensors are unable to perform the fine-grained detection needed, by themselves. Therefore, FullStop is based on the view obtained from looking out to the front of the vehicle using the camera of a smartphone that is mounted on the front windshield. Using optical flow vectors, with several refinements, FullStop running on a smartphone is able to effectively detect safety-related situations such as a rolling stop or stopping at a location that is displaced laterally relative to the designated bus stop.
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TL;DR: A novel, cost-effective Internet of Things (IoT) architecture is introduced that facilitates the realization of a robust and dynamic computational core in assessing the safety of a road network and its elements and a new, meaningful, and scalable metric for assessing road safety.
Abstract: The Safe System (SS) approach to road safety emphasizes safety-by-design through ensuring safe vehicles, road networks, and road users. With a strong motivation from the World Health Organization (WHO), this approach is increasingly adopted worldwide. Considerations in SS, however, are made for the medium-to-long term. Our interest in this work is to complement the approach with a short-to-medium term dynamic assessment of road safety. Toward this end, we introduce a novel, cost-effective Internet of Things (IoT) architecture that facilitates the realization of a robust and dynamic computational core in assessing the safety of a road network and its elements. In doing so, we introduce a new, meaningful, and scalable metric for assessing road safety. We also showcase the use of machine learning in the design of the metric computation core through a novel application of Hidden Markov Models (HMMs). Finally, the impact of the proposed architecture is demonstrated through an application to safety-based route planning.
15 citations
Cites methods from "FullStop: Tracking unsafe stopping ..."
...Smartphone sensing is also utilized in [38] using both accelerometers and cameras to identify unsafe stopping behavior in busses....
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04 Jan 2022
TL;DR: In this article , a leader-based hierarchical clustering algorithm was proposed to reveal public bus-stops from the GPS traces with 92% and 95% accuracy, which can help transport policymakers make a timely decision for curbing stop irregularity to a large extent.
Abstract: Municipal authorities in the suburban cities of many developing countries need to deal with rapid unplanned urbanization, huge population bursts, and policy planning under severe budget shortfalls. We have studied the clear clutter and disorder in public bus-stop patterns that prevail upon the city transportation system in such cities. This paper develops an auto-tuned mechanism for identifying the bus-stops and characterizing their Spatiotemporal features from the GPS trails obtained through spatial crowdsensing. Existing systems and algorithms cannot be merely replicated in this context because of the kind of heterogeneity and chaotic situation prevalent in the transportation system. In this paper, we analyze GPS traces of more than 6000km (approx.) bus navigation, which has been collected for two years (2014–2016) via automated war-driving through customized hardware, for bus routes at a suburban city, Durgapur, in India. We develop a novel leader-based hierarchical clustering algorithm to reveal public bus-stops from the GPS trails with 92% and 95% accuracy. The proposed approach can help transport policymakers make a timely decision for curbing stop irregularity to a large extent.
2 citations
01 Dec 2018
TL;DR: A novel application of Hidden Markov Models (HMMs) at the core of road safety assessment is introduced and the use of the dynamic assessment in safety- based route planning is demonstrated.
Abstract: The Safe System approach to road safety emphasizes safety-by-design through ensuring safe vehicles, road networks, and road users. With a strong WHO/UN motivation, the approach is increasingly adopted worldwide. Considerations in Safe System, however, are largely made for the medium-to-long term. Our interest in this work is complement the approach with a short-to-medium term dynamic assessment of road safety. Towards this end, we introduce a novel application of Hidden Markov Models (HMMs) at the core of road safety assessment. We also demonstrate the use of the dynamic assessment in safety- based route planning.
Cites background from "FullStop: Tracking unsafe stopping ..."
...This includes, for instance, the work in [9] utilizing smartphones sensors (accelerometers and cameras) to identify unsafe stopping behavior in busses....
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References
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Proceedings Article•
09 Jul 2005TL;DR: This paper reports on the efforts to recognize user activity from accelerometer data and performance of base-level and meta-level classifiers, and Plurality Voting is found to perform consistently well across different settings.
Abstract: Activity recognition fits within the bigger framework of context awareness. In this paper, we report on our efforts to recognize user activity from accelerometer data. Activity recognition is formulated as a classification problem. Performance of base-level classifiers and meta-level classifiers is compared. Plurality Voting is found to perform consistently well across different settings.
1,561 citations
"FullStop: Tracking unsafe stopping ..." refers background in this paper
...But accelerometer usage has been explored for activity recognition in the past [22], [23]....
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TL;DR: An overview of the field of vehicular ad hoc networks is given, providing motivations, challenges, and a snapshot of proposed solutions.
Abstract: There has been significant interest and progress in the field of vehicular ad hoc networks over the last several years. VANETs comprise vehicle-to-vehicle and vehicle-to-infrastructure communications based on wireless local area network technologies. The distinctive set of candidate applications (e.g., collision warning and local traffic information for drivers), resources (licensed spectrum, rechargeable power source), and the environment (e.g., vehicular traffic flow patterns, privacy concerns) make the VANET a unique area of wireless communication. This article gives an overview of the field, providing motivations, challenges, and a snapshot of proposed solutions.
1,545 citations
"FullStop: Tracking unsafe stopping ..." refers methods in this paper
...High-end cars and self-driving cars [11] include an array of sensors (e.g., RADAR, LIDAR) and communication technologies such as DSRC and VANETs [12] to improve safety....
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...communication technologies such as DSRC and VANETs [12] to improve safety....
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05 Nov 2008
TL;DR: Nericell is presented, a system that performs rich sensing by piggybacking on smartphones that users carry with them in normal course, and addresses several challenges including virtually reorienting the accelerometer on a phone that is at an arbitrary orientation, and performing honk detection and localization in an energy efficient manner.
Abstract: We consider the problem of monitoring road and traffic conditions in a city. Prior work in this area has required the deployment of dedicated sensors on vehicles and/or on the roadside, or the tracking of mobile phones by service providers. Furthermore, prior work has largely focused on the developed world, with its relatively simple traffic flow patterns. In fact, traffic flow in cities of the developing regions, which comprise much of the world, tends to be much more complex owing to varied road conditions (e.g., potholed roads), chaotic traffic (e.g., a lot of braking and honking), and a heterogeneous mix of vehicles (2-wheelers, 3-wheelers, cars, buses, etc.).To monitor road and traffic conditions in such a setting, we present Nericell, a system that performs rich sensing by piggybacking on smartphones that users carry with them in normal course. In this paper, we focus specifically on the sensing component, which uses the accelerometer, microphone, GSM radio, and/or GPS sensors in these phones to detect potholes, bumps, braking, and honking. Nericell addresses several challenges including virtually reorienting the accelerometer on a phone that is at an arbitrary orientation, and performing honk detection and localization in an energy efficient manner. We also touch upon the idea of triggered sensing, where dissimilar sensors are used in tandem to conserve energy. We evaluate the effectiveness of the sensing functions in Nericell based on experiments conducted on the roads of Bangalore, with promising results.
1,407 citations
Additional excerpts
...Nericell [5] uses smartphones to detect various road and traffic events such as potholes, honking, etc....
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17 Jun 2008
TL;DR: This paper describes a system and associated algorithms to monitor this important civil infrastructure using a collection of sensor-equipped vehicles, which they call the Pothole Patrol (P2), which uses the inherent mobility of the participating vehicles, opportunistically gathering data from vibration and GPS sensors, and processing the data to assess road surface conditions.
Abstract: This paper investigates an application of mobile sensing: detecting and reporting the surface conditions of roads. We describe a system and associated algorithms to monitor this important civil infrastructure using a collection of sensor-equipped vehicles. This system, which we call the Pothole Patrol (P2), uses the inherent mobility of the participating vehicles, opportunistically gathering data from vibration and GPS sensors, and processing the data to assess road surface conditions. We have deployed P2 on 7 taxis running in the Boston area. Using a simple machine-learning approach, we show that we are able to identify potholes and other severe road surface anomalies from accelerometer data. Via careful selection of training data and signal features, we have been able to build a detector that misidentifies good road segments as having potholes less than 0.2% of the time. We evaluate our system on data from thousands of kilometers of taxi drives, and show that it can successfully detect a number of real potholes in and around the Boston area. After clustering to further reduce spurious detections, manual inspection of reported potholes shows that over 90% contain road anomalies in need of repair.
1,126 citations
"FullStop: Tracking unsafe stopping ..." refers methods in this paper
...Pothole patrol [6] too uses sensors such as the accelerometer to detect road anomalies, though using a dedicated measurement box....
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25 Jun 2012
TL;DR: A bus arrival time prediction system based on bus passengers' participatory sensing that achieves outstanding prediction accuracy compared with those bus operator initiated and GPS supported solutions and is more generally available and energy friendly.
Abstract: The bus arrival time is primary information to most city transport travelers. Excessively long waiting time at bus stops often discourages the travelers and makes them reluctant to take buses. In this paper, we present a bus arrival time prediction system based on bus passengers' participatory sensing. With commodity mobile phones, the bus passengers' surrounding environmental context is effectively collected and utilized to estimate the bus traveling routes and predict bus arrival time at various bus stops. The proposed system solely relies on the collaborative effort of the participating users and is independent from the bus operating companies, so it can be easily adopted to support universal bus service systems without requesting support from particular bus operating companies. Instead of referring to GPS enabled location information, we resolve to more generally available and energy efficient sensing resources, including cell tower signals, movement statuses, audio recordings, etc., which bring less burden to the participatory party and encourage their participation. We develop a prototype system with different types of Android based mobile phones and comprehensively experiment over a 7 week period. The evaluation results suggest that the proposed system achieves outstanding prediction accuracy compared with those bus company initiated and GPS supported solutions. At the same time, the proposed solution is more generally available and energy friendly.
465 citations
Additional excerpts
...The work in [7] uses smartphone-based crowd-sourcing for prediction of bus arrival time....
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