Stop Detection in Smartphone-based Travel Surveys

Zhao, Fang; Ghorpade, Ajinkya; Pereira, Francisco C.; Zegras, Christopher; Ben‐Akiva, Moshe

doi:10.1016/j.trpro.2015.12.019

Cited by 46 publications

(25 citation statements)

References 7 publications

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“…The identification of stops is a two-step process using a custom developed method (Zhao et al 2015) which includes DBSCAN (Ester et al 1996), a clustering algorithm. First, a stop detection algorithm is applied, and then we aggregate raw stop records over the vehicle tracking period.…”

Section: Stop Identificationmentioning

confidence: 99%

Exploring Algorithms for Revealing Freight Vehicle Tours, Tour-Types, and Tour-Chain-Types from GPS Vehicle Traces and Stop Activity Data

Alho

Sakai

Chua

et al. 2019

J. Big Data Anal. Transp.

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Freight vehicle tours and tour-chains are essential elements of state-the-art agent-based urban freight simulations as well as key units to analyse freight vehicle demand. GPS traces are typically used to extract vehicle tours and tour-chains and became available in a large scale to, for example, fleet management firms. While methods to process this data with the objective of analysing and modelling tour-based freight vehicle operations have been proposed, they were not fully explored with regard to the implication of underlying assumptions. In this context, we test different algorithms of stop-to-tour assignment, tour-type and tour-chain identification, aiming to expose their implications. Specifically, we compare the traditional stopto-tour assignment algorithm using the location of a "base" as the start/end point of tours, against other algorithms using stop activities or payload capacity usage. Furthermore, we explore high-resolution tour-type/chain identification algorithms, considering stop types and recurrence of visits. For tour-chain identification, we explore two algorithms: one defines the day-level tour-chain-type based on the predominant tour-type identified for the period of 1 day and another defines the tourchain-type based on the average number of stops per tour by stop type. For a demonstration purpose, we apply the methods to data from a large-scale GPS-based survey conducted during 2017-2019 in Singapore. We compare the algorithms in an assessment of freight vehicle operations day-to-day pattern homogeneity. Our analysis demonstrates that the predictions of tours, tourtypes, and tour-chain-types are highly dependent on the assumptions used, underlining the importance of carefully selecting and disclosing the methods for data processing. Finally, the exploration of day-to-day pattern homogeneity reveals operational differences across vehicle types and industries.

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Section: Stop Identificationmentioning

confidence: 99%

Exploring Algorithms for Revealing Freight Vehicle Tours, Tour-Types, and Tour-Chain-Types from GPS Vehicle Traces and Stop Activity Data

Alho

Sakai

Chua

et al. 2019

J. Big Data Anal. Transp.

Self Cite

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show abstract

“…Hence, although the IMU is sampled at a lower frequency, the framework is able to detect the trips and the transfers in between the trips effectively. A prior study on stop detection from smartphone-based travel surveys that also includes GSM trajectories and 3-axis accelerometer signals sampled at a lower frequency demonstrates the efficacy of such sampling strategy [65]. The sampling rate is sufficient for trip or transfer detection, which are phenomena of significantly coarser temporal granularity.…”

Section: Discussionmentioning

confidence: 99%

Automated Urban Travel Interpretation: A Bottom-up Approach for Trajectory Segmentation

Das

Winter

2016

Sensors

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Understanding travel behavior is critical for an effective urban planning as well as for enabling various context-aware service provisions to support mobility as a service (MaaS). Both applications rely on the sensor traces generated by travellers’ smartphones. These traces can be used to interpret travel modes, both for generating automated travel diaries as well as for real-time travel mode detection. Current approaches segment a trajectory by certain criteria, e.g., drop in speed. However, these criteria are heuristic, and, thus, existing approaches are subjective and involve significant vagueness and uncertainty in activity transitions in space and time. Also, segmentation approaches are not suited for real time interpretation of open-ended segments, and cannot cope with the frequent gaps in the location traces. In order to address all these challenges a novel, state based bottom-up approach is proposed. This approach assumes a fixed atomic segment of a homogeneous state, instead of an event-based segment, and a progressive iteration until a new state is found. The research investigates how an atomic state-based approach can be developed in such a way that can work in real time, near-real time and offline mode and in different environmental conditions with their varying quality of sensor traces. The results show the proposed bottom-up model outperforms the existing event-based segmentation models in terms of adaptivity, flexibility, accuracy and richness in information delivery pertinent to automated travel behavior interpretation.

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“…One example is the future mobility sensing (FMS) survey platform developed by the Singapore‐MIT Alliance for Research and Technology (Zhao, Ghorpade, Pereira, Zegras, & Ben‐Akiva, ). Combining raw GPS data and contextual information, the survey platform uses machine‐learning algorithms to infer the truck's stops and activities (Teo et al., ; Zhao et al., ). The inferred information is then presented to the truck driver for validation through an online interface (Figure ).…”

Section: Truck Driver Activity Surveysmentioning

confidence: 99%

Using spatially explicit commodity flow and truck activity data to map urban material flows

Yeow

Cheah

2019

J of Industrial Ecology

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To analyze and promote resource efficiency in urban areas, it is important to characterize urban metabolism and particularly, material flows. Material flow analysis (MFA) offers a means to capture the dynamism of cities and their activities. Urban‐scale MFAs have been conducted in many cities, usually employing variants of the Eurostat methodology. However, current methodologies generally reduce the study area into a “black box,” masking details of the complex processes within the city's metabolism. Therefore, besides the aggregated stocks and flows of materials, the movement of materials—often embedded in goods or commodities—should also be highlighted. Understanding the movement and dispersion of goods and commodities can allow for more detailed analysis of material flows. We highlight the potential benefits of using high‐resolution urban commodity flows in the context of understanding material resource use and opportunities for conservation. Through the use of geographic information systems and visualizations, we analyze two spatially explicit datasets: (1) commodity flow data in the United States, and (2) Global Positioning System‐based commercial vehicle (truck) driver activity data in Singapore. In the age of “big data,” we bring advancements in freight data collection to the field of urban metabolism, uncovering the secondary sourcing of materials that would otherwise have been masked in typical MFA studies. This brings us closer to a consumption‐based, finer‐resolution approach to MFA, which more effectively captures human activities and its impact on urban environments.

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Stop Detection in Smartphone-based Travel Surveys

Cited by 46 publications

References 7 publications

Exploring Algorithms for Revealing Freight Vehicle Tours, Tour-Types, and Tour-Chain-Types from GPS Vehicle Traces and Stop Activity Data

Exploring Algorithms for Revealing Freight Vehicle Tours, Tour-Types, and Tour-Chain-Types from GPS Vehicle Traces and Stop Activity Data

Automated Urban Travel Interpretation: A Bottom-up Approach for Trajectory Segmentation

Using spatially explicit commodity flow and truck activity data to map urban material flows

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