Scalable Framework for Enhancing Raw GPS Trajectory Data: Application to Trip Analytics for Transportation Planning

Laan, Zachary Vander; Franz, Mark; Marković, Nikola

doi:10.1007/s42421-021-00040-5

Cited by 8 publications

(3 citation statements)

References 12 publications

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“…The deployment of crash avoidance, real-time traffic routing, and network security related to 5G connected mobility was successful because of a collaboration between NTU and M1 Limited (M1). This collaboration succeeded in integrating 5G technology with vehicle-to-everything (C-V2X), which is being tested on real-life applications in public transport [49,50].…”

Section: Singaporementioning

confidence: 99%

Development of Smart Mobility Infrastructure in Saudi Arabia: A Benchmarking Approach

Alanazi

2023

Sustainability

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Smart mobility systems offers solutions for traffic congestion, transport management, emergency, and road safety. However, the success of smart mobility lies in the availability of intelligent transportation infrastructure. This paper studied smart mobility systems in three Asia-Pacific countries (South Korea, Singapore, and Japan) to highlight the major strategies leading their successful journey to become smart cities for aspiring countries, such as the Kingdom of Saudi Arabia (KSA), to emulate. A robust framework for evaluating smart mobility systems in the three countries and Saudi Arabia was developed based on the indicators derived from the smart mobility ecosystem and three major types of transport services (private, public, and emergency). Sixty indicators of smart mobility systems were identified through a rigorous search of the literature and other secondary sources. Robots, drones, IoT, 5G, hyperloop tunnels, and self-driving technologies formed part of the indicators in those countries. The study reveals that the three Asia-Pacific countries are moving head-to-head in terms of smart mobility development. Saudi Arabia can join these smarter countries through inclusive development, standardization, and policy-driven strategies with clear commitments to public, private, and research collaborations in the development of its smart mobility ecosystem. Moreover, cybersecurity must be taken seriously because most of the smart mobility systems use wireless and IoT technologies, which may be vulnerable to hacking, and thus impact system safety. In addition, the smart mobility system should include data analytics, machine learning, and artificial intelligence in developing and monitoring the evaluation in terms of user experience and future adaptability.

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Section: Singaporementioning

confidence: 99%

Development of Smart Mobility Infrastructure in Saudi Arabia: A Benchmarking Approach

Alanazi

2023

Sustainability

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“…Here, we chose to build a Valhalla routing engine, since it needs significantly lower memory. A comprehensive study on map-matching using OSRM is performed by Vander Laan et al [43]. They propose a scalable well-constructed enhancement framework for GPS data that could map-match millions of trajectories.…”

Section: Available Map-matching Servicesmentioning

confidence: 99%

A Practical Guide to an Open-Source Map-Matching Approach for Big GPS Data

Saki

Hagen

2022

SN COMPUT. SCI.

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This work shows how map-matching helps to minimize errors in GPS data by finding the most probable corresponding points of the recorded waypoints of a trajectory on a road network. We investigate an open-source alternative for map-matching trajectories called Valhalla, which could replace limited and costly commercial map-matching services. Valhalla is an open-source routing engine, which provides different services, such as path-finding, map-matching, and generating maneuvers based on a path. We build a cloud-based big data analytics framework on Amazon Web Services (AWS) platform for map-matching. This well-established framework is scalable and could process millions of trajectories. Using an example GPS dataset, it is demonstrated how Valhalla can be used for map-matching at scale. The dataset consists of about 18 million trips in the year 2019 that have at least one recorded point in a bounding box surrounding Frankfurt am Main. The map-matching results confirm an adequate performance of Valhalla map-matching, show a reduction of errors by distance calculation, and allow for further street-segment-based analysis.

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“…The research interest on human mobility analysis has extensively expanded over the last few years, driven by the increasing availability of trajectory data acquired by pervasive motion tracking technologies. These data represent a primary source of information on human travel behaviors [1,2], giving rise to a multitude of data mining investigations on motion analysis and trajectory-related applications [3][4][5][6], ranging from personalized recommendation systems [7,8], to transportation planning [9,10], to resource management plans [11,12]. In today's digital world of location-based services and positioning devices, the collection of mobility data covers a variety of acquisition modalities, including mobile phone networks, GPS signals, and social media platforms.…”

Section: Introductionmentioning

confidence: 99%

TraceBERT—A Feasibility Study on Reconstructing Spatial–Temporal Gaps from Incomplete Motion Trajectories via BERT Training Process on Discrete Location Sequences

Crivellari

Resch

Shi

2022

Sensors

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Trajectory data represent an essential source of information on travel behaviors and human mobility patterns, assuming a central role in a wide range of services related to transportation planning, personalized recommendation strategies, and resource management plans. The main issue when dealing with trajectory recordings, however, is characterized by temporary losses in the data collection, causing possible spatial–temporal gaps and missing trajectory segments. This is especially critical in those use cases based on non-repetitive individual motion traces, when the user’s missing information cannot be directly reconstructed due to the absence of historical individual repetitive routes. Inserted in the context of location-based trajectory modeling, we tackle the problem by proposing a technical parallelism with the natural language processing domain. Specifically, we introduce the use of the Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art language representation model, into the trajectory processing research field. By training deep bidirectional representations from unlabeled location sequences, jointly conditioned on both left and right context, we derive an explicit predicted estimation of the missing locations along the trace. The proposed framework, named TraceBERT, was tested on a real-world large-scale trajectory dataset of short-term tourists, exploring an effective attempt of adapting advanced language modeling approaches into mobility-based applications and demonstrating a prominent potential on trajectory reconstruction over traditional statistical approaches.

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Scalable Framework for Enhancing Raw GPS Trajectory Data: Application to Trip Analytics for Transportation Planning

Cited by 8 publications

References 12 publications

Development of Smart Mobility Infrastructure in Saudi Arabia: A Benchmarking Approach

Development of Smart Mobility Infrastructure in Saudi Arabia: A Benchmarking Approach

A Practical Guide to an Open-Source Map-Matching Approach for Big GPS Data

TraceBERT—A Feasibility Study on Reconstructing Spatial–Temporal Gaps from Incomplete Motion Trajectories via BERT Training Process on Discrete Location Sequences

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