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

Abstract

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.

Towards Sustainable Cities: Utilizing Floating Car Data to Support Location-Based Road Network Performance Measurements

“…Processing FCD to measure RNP is challenging as traffic data can be considered big data due to its complexity and heterogeneity [78,93,94]. However, navigation service providers can produce the needed data efficiently [84].…”

“…For a case-specific analysis, Alho A.R. et al [78] find that declared data regarding bases might not be as accurate as inferred data, suggesting the identification of central network nodes via algorithms instead of relying on survey data to determine meaningful points of origin. Referring to Saedi et al [63] our approach does not report RNP across the complete road network but well-defined partitions.…”

A Review of Big Data in Road Freight Transport Modeling–Gaps and Potentials

“…Romano Alho et al (2019) demonstrated the importance of carefully selecting and disclosing the methods for data processing by illustrating that the predictions of tours, tour types, and tour chains are highly dependent on assumptions. They implement high-resolution tour-chain algorithms for a large-scale GPS-based survey and use stop-level data from a driver survey conducted from 2017 to 2019 in Singapore.…”

Forecasting Delivery Pattern through Floating Car Data: Empirical Evidence

“…These can support the development of models for simulating freight movements [26,31,32]. In order to identify the vehicle freight activities from GPS data (e.g., stops), algorithms have to be developed [33,34]. Vehicle stop identification is a key aspect faced when analyzing the data, that can be very hard when intermediate short stops (e.g., waiting at traffic lights, congestion) are detected [33,[35][36][37].…”

“…In this topic, Holguín-Veras et al [34] proposed a procedure based on changes in speed and acceleration to identify the stops of freight vehicles, while Alho et al [33] compared some algorithms developed with the aim of identifying the tour stops. Some methods ( [38,39]) suggest extracting and evaluating vehicle stops by considering engine ignition, others are based on speed ( [40,41]).…”