Significant Route Discovery: A Summary of Results

Oliver, Dev; Shekhar, Shashi; Zhou, Xun; Eftelioglu, Emre; Evans, Michael R.; Zhuang, Qiaodi; Kang, James M.; Laubscher, Renee; Farah, Christopher

doi:10.1007/978-3-319-11593-1_19

Cited by 18 publications

(14 citation statements)

References 12 publications

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“…Our technique is highly related to trajectory data mining. Based on the massive trajectory data, [27], [28] finds popular paths to help planning facilities along the road network. In addition, [29] finds top-k influential locations that cover as many trajectories.…”

Section: Related Workmentioning

confidence: 99%

Detecting Vehicle Illegal Parking Events using Sharing Bikes' Trajectories

Bao

et al. 2018

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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Illegal vehicle parking is a common urban problem faced by major cities in the world, as it incurs traffic jams, which lead to air pollution and traffic accidents. The government highly relies on active human efforts to detect illegal parking events. However, such an approach is extremely ineffective to cover a large city since the police have to patrol over the entire city roads. The massive and high-quality sharing bike trajectories from Mobike offer us with a unique opportunity to design a ubiquitous illegal parking detection approach, as most of the illegal parking events happen at curbsides and have significant impact on the bike users. The detection result can guide the patrol schedule, i.e. send the patrol policemen to the region with higher illegal parking risks, and further improves the patrol efficiency. Inspired by this idea, three main components are employed in the proposed framework: 1) trajectory pre-processing, which filters outlier GPS points, performs map-matching and builds trajectory indexes; 2) illegal parking detection, which models the normal trajectories, extracts features from the evaluation trajectories and utilizes a distribution test-based method to discover the illegal parking events; and 3) patrol scheduling, which leverages the detection result as reference context, and models the scheduling task as a multi-agent reinforcement learning problem to guide the patrol police. Finally, extensive experiments are presented to validate the effectiveness of illegal parking detection, as well as the improvement of patrol efficiency.

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Section: Related Workmentioning

confidence: 99%

Detecting Vehicle Illegal Parking Events using Sharing Bikes' Trajectories

Bao

et al. 2018

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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“…gentler) acceleration/braking pattern. Current related work has only focused on linear hotspots of aggregated counts such as hotspots of road fatalities or crime incidents [22]. Hence, it cannot identify driver or vehicle-dependent patterns.…”

Section: Lagrangian Pattern Mining On Road Networkmentioning

confidence: 99%

Future connected vehicles

Ali

Gunturi

Shekhar

et al. 2015

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems

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Modern vehicles are increasingly being equipped with rich instrumentation that enables them to collect location aware data on a wide variety of travel related phenomena such as the real-world performance of engines and powertrain, driver preferences, context of the vehicle with respect to others nearby, and-indirectly-traffic on the transportation network itself. Combined with their increased access to the Internet, these connected vehicles are opening up vast opportunities to improve the safety, environmental friendliness, and the overall experience of urban travel. However, significant spatial computing challenges need to be addressed before we can realize the full potential of connected vehicles. This paper presents some of the open research questions under this theme from the perspectives of query processing, data science and data engineering.

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“…Note that although likelihood ratio of a path described in this section is different that the log likelihood ratio function described in Circular and Ring-Shaped Hotspot Detection, both functions can be used to assess the test statistic of an enumerated path. (Oliver et al, 2014) The basic idea behind linear hotspot detection algorithm is to find all statistically significant shortest paths in the spatial network whose likelihood exceeds θ. Note that the shortest paths returned are constrained so that they are not sub-paths of any other path in the output.…”

Section: Figure 9 An Example Spatial Networkmentioning

confidence: 99%

“…Similar to the previous algorithms Naïve Linear Hotspot Detection algorithm (Oliver et al, 2014) consists of three steps.…”

Section: Naïve Linear Hotspot Detection Algorithmmentioning

confidence: 99%

“…Thus, monotonicity based pruning algorithms do not apply in LHD problem. Smart Significant Route Miner Algorithm (Oliver et al, 2014) A likelihood ratio pruning based algorithm is proposed to overcome the challenges of LHD problem. SmartSRM proposes an upper bound pruning approach to avoid computing all shortest paths of a graph.…”

Section: Naïve Linear Hotspot Detection Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Crime Hotspot Detection

Eftelioglu¹,

Shekhar²,

Tang³

Advances in Data Mining and Database Management

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Given a set of crime locations, a statistically significant crime hotspot is an area where the concentration of crimes inside is significantly higher than outside. The motivation of crime hotspot detection is twofold: detecting crime hotspots to focus the deployment of police enforcement and predicting the potential residence of a serial criminal. Crime hotspot detection is computationally challenging due to the difficulty of enumerating all potential hotspot areas, selecting an interest measure to compare these with the overall crime intensity, and testing for statistical significance to reduce chance patterns. This chapter focuses on statistical significant crime hotspots. First, the foundations of spatial scan statistics and its applications (i.e. SaTScan) to circular hotspot detection are reviewed. Next, ring-shaped hotspot detection is introduced. Third, linear hotspot detection is described since most crimes occur along a road network. The chapter concludes with future research directions in crime hotspot detection.

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Significant Route Discovery: A Summary of Results

Cited by 18 publications

References 12 publications

Detecting Vehicle Illegal Parking Events using Sharing Bikes' Trajectories

Detecting Vehicle Illegal Parking Events using Sharing Bikes' Trajectories

Future connected vehicles

Crime Hotspot Detection

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